diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100644 index 0000000..3a7e5af --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,13 @@ +version: "2" + +build: + os: "ubuntu-22.04" + tools: + python: "3.10" + +python: + install: + - requirements: docs/requirements.txt + +sphinx: + configuration: docs/source/conf.py \ No newline at end of file diff --git a/README.md b/README.md index 65e2b22..3b10270 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,6 @@ -# intro-to-pytorch +# Introduction to Pytorch This repo is designed as a comprehensive starting point for those new to PyTorch and deep learning. It provides hands-on tutorials and examples to help you get acquainted with the core concepts and features of PyTorch, one of the most popular open-source machine learning libraries. + +**Documentation**: https://intro-to-pytorch.readthedocs.io/en/latest/index.html + +**Australian Research Environment (ARE)**: https://handson-with-gadi.readthedocs.io/en/latest/tutorial/login.html diff --git a/data/pima-indians-diabetes.data.csv b/data/pima-indians-diabetes.data.csv new file mode 100644 index 0000000..9c3e9b8 --- /dev/null +++ b/data/pima-indians-diabetes.data.csv @@ -0,0 +1,768 @@ +6,148,72,35,0,33.6,0.627,50,1 +1,85,66,29,0,26.6,0.351,31,0 +8,183,64,0,0,23.3,0.672,32,1 +1,89,66,23,94,28.1,0.167,21,0 +0,137,40,35,168,43.1,2.288,33,1 +5,116,74,0,0,25.6,0.201,30,0 +3,78,50,32,88,31.0,0.248,26,1 +10,115,0,0,0,35.3,0.134,29,0 +2,197,70,45,543,30.5,0.158,53,1 +8,125,96,0,0,0.0,0.232,54,1 +4,110,92,0,0,37.6,0.191,30,0 +10,168,74,0,0,38.0,0.537,34,1 +10,139,80,0,0,27.1,1.441,57,0 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a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 0000000..c26c3da --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1,5 @@ +sphinx==7.1.2 +sphinx-rtd-theme==1.3.0rc1 +#sphinxcontrib-pseudocode==0.7.0 +#sphinxcontrib-jsmath==1.0.1 +#sphinxcontrib-plantuml==0.30 diff --git a/docs/source/conf.py b/docs/source/conf.py new file mode 100644 index 0000000..0c05c0b --- /dev/null +++ b/docs/source/conf.py @@ -0,0 +1,40 @@ +# Configuration file for the Sphinx documentation builder. + +# -- Project information + +project = 'Introduction to Neural Networks and PyTorch' +copyright = '2024, National Computational Infrastructure' +author = 'Joseph John' + +release = '0.1' +version = '0.1.0' + +# -- General configuration + +extensions = [ + 'sphinx.ext.duration', + 'sphinx.ext.doctest', + 'sphinx.ext.autodoc', + 'sphinx.ext.autosummary', + 'sphinx.ext.intersphinx', + 'sphinx.ext.mathjax', + #'sphinx.ext.graphviz', + #'sphinxcontrib.pseudocode', + #'sphinxcontrib.plantuml', +] + +intersphinx_mapping = { + 'python': ('https://docs.python.org/3/', None), + 'sphinx': ('https://www.sphinx-doc.org/en/master/', None), +} +intersphinx_disabled_domains = ['std'] + +templates_path = ['_templates'] + +# -- Options for HTML output + +html_theme = 'sphinx_rtd_theme' + +# -- Options for EPUB output +epub_show_urls = 'footnote' + diff --git a/docs/source/figs/.DS_Store b/docs/source/figs/.DS_Store new file mode 100644 index 0000000..c4ef5c5 Binary files /dev/null and b/docs/source/figs/.DS_Store differ diff --git a/docs/source/figs/2layer_NN b/docs/source/figs/2layer_NN new file mode 100644 index 0000000..859c853 --- /dev/null +++ b/docs/source/figs/2layer_NN @@ -0,0 +1,121 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/source/figs/2layer_NN.drawio 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/dev/null and b/docs/source/figs/neuron.drawio.png differ diff --git a/docs/source/figs/pinning.png b/docs/source/figs/pinning.png new file mode 100644 index 0000000..db84c2e Binary files /dev/null and b/docs/source/figs/pinning.png differ diff --git a/docs/source/index.rst b/docs/source/index.rst new file mode 100644 index 0000000..1b879ae --- /dev/null +++ b/docs/source/index.rst @@ -0,0 +1,24 @@ +Introduction to Neural Networks and PyTorch +=========================================== + +This workshop provides an introduction to Neural Networks Using Pytorch. + +.. note:: + + This project is under active development. + +Contents +-------- + +.. toctree:: + + prerequisite + outcomes + modules + packages + tutorial + references + + + + diff --git a/docs/source/modules.rst b/docs/source/modules.rst new file mode 100644 index 0000000..d2b84c7 --- /dev/null +++ b/docs/source/modules.rst @@ -0,0 +1,34 @@ +Modules +======= + +.. note:: + 1. python3/3.11.0 + 2. cuda/12.3.2 + +Modules are how we manage software in most HPC machines. We can see all the available modules using the command + +.. code-block:: console + :linenos: + + module avail + +If we want load a module *python3/3.11.0* we can use the command + +.. code-block:: console + :linenos: + + module load python3/3.11.0 + +If we want to unload the same module use the command + +.. code-block:: console + :linenos: + + module unload python3/3.11.0 + +We can unload all the modules using the command + +.. code-block:: console + :linenos: + + module purge \ No newline at end of file diff --git a/docs/source/outcomes.rst b/docs/source/outcomes.rst new file mode 100644 index 0000000..f3ae1fe --- /dev/null +++ b/docs/source/outcomes.rst @@ -0,0 +1,12 @@ +Learning Outcomes +================= + +.. note:: + #. Learn the theoretical aspects of Neural Networks. + #. Understand how tensors work in PyTorch. + #. Learn how to build a Neural Network in Pytorch. + +In this workshop, you will learn the basics of PyTorch, including tensor operations, dynamic computation graphs, and neural network modules. +You will also gain practical experience in building and training neural networks using PyTorch. + + diff --git a/docs/source/packages.rst b/docs/source/packages.rst new file mode 100644 index 0000000..b09f447 --- /dev/null +++ b/docs/source/packages.rst @@ -0,0 +1,60 @@ +Python Virtual Environment +========================== + +.. note:: + + #. torch + #. torchvision + #. torchaudio + #. NumPy + #. Matplotlib + #. jupyterlab + #. pandas + + +In this workshop, we will use a Python virtual environment to manage all the required Python packages. A Python virtual environment is an isolated +workspace that allows you to manage project-specific dependencies without affecting the global Python installation or other projects. By creating a +virtual environment, you can install and manage libraries and packages independently, ensuring that each project has its own set of dependencies and +avoiding version conflicts. This isolation helps maintain consistent and reproducible development environments. + +We've already set up the Python virtual environment for this workshop, so you *don't need* to install one separately. However, the following +commands will guide you on how to create one if necessary. + +To get started with Python virtual environment load the Python module you want to use. In this workshop, we will be using *python3/3.11.0*. + +.. code-block:: console + :linenos: + + module load python3/3.11.0 cuda/12.3.2 + +Create the Python virtual environment. + +.. code-block:: console + :linenos: + + python3 -m venv my_env + +Activate the Python virtual environment. + +.. code-block:: console + :linenos: + + source my_env/bin/activate + +Install all the required Python packages. + +.. code-block:: console + :linenos: + + python3 -m pip install python-papi numpy codetiming numba mpi4py + python3 -m pip install torch torchvision torchaudio + python3 -m pip install jupyterlab + python3 -m pip install matplotlib + +You can deactivate the virtual environment once you are done with it. + +.. code-block:: console + :linenos: + + deactivate + \ No newline at end of file diff --git a/docs/source/prerequisite.rst b/docs/source/prerequisite.rst new file mode 100644 index 0000000..34b2c25 --- /dev/null +++ b/docs/source/prerequisite.rst @@ -0,0 +1,35 @@ +Prerequisite +============ + +.. note:: + #. Experience with Python. + #. Experience with Jupyter notebooks. + #. Experience with bash or similar Unix shells. + + It's also beneficial to have a solid understanding of *matrix operations* and *differential calculus*. + While this is not required for programming, it is essential for the theoretical aspects of neural + networks. + + This workshop assumes that you have experience coding in Python and are familiar with using Unix shell. + If you are using a Windows machine please make sure you have a shell that supports SSH. Windows users can either use + `PowerShell `_ or the `Windows Subsystem for Linux `_ + +.. important:: + For a smooth setup on the supercomputer system, please register for an NCI account if you don't have one: + + #. Go to https://my.nci.org.au/mancini + #. Click on the "Sign up" button to start your registration form. + #. Complete all steps in the registration form. (Note: you must provide a current email address from your institution or place of work, not a personal email.) + #. Select the option to join project vp91 at Step 3 of the form. + #. Click "Finish" on the final page of the form to complete your registration request. + + or + + If you already have an account + #. Login to your NCI account https://my.nci.org.au/mancini + #. Select Project and Groups on the left-side menu + #. Select Find project or group on the top tab and search vp91 to apply for membership + + +.. warning:: + Project vp91 is temporary for training purposes only. Data in vp91 will be cleaned in one week time after the training. \ No newline at end of file diff --git a/docs/source/references.rst b/docs/source/references.rst new file mode 100644 index 0000000..8f8a88a --- /dev/null +++ b/docs/source/references.rst @@ -0,0 +1,14 @@ +Reference +========= +1. https://pytorch.org/tutorials/intermediate/ddp_series_multinode.html +2. https://www.run.ai/guides/multi-gpu/pytorch-multi-gpu-4-techniques-explained +3. https://medium.com/codex/a-comprehensive-tutorial-to-pytorch-distributeddataparallel-1f4b42bb1b51 +4. https://www.coursera.org/learn/neural-networks-deep-learning + + +Contributers +************ + +1. `Joseph John, Staff Scientist, NCI `_ + +*ChatGPT has been utilized to enhance and generate texts in this document*. \ No newline at end of file diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst new file mode 100644 index 0000000..f7e8eb8 --- /dev/null +++ b/docs/source/tutorial.rst @@ -0,0 +1,65 @@ +Tutorial +======== + +In this tutorial, we'll be using the Gadi HPC machine at NCI. A Python virtual environment +will be provided for you during the session. + +.. list-table:: + :widths: 20 20 20 20 + :header-rows: 1 + + * - Topics + - Material + - Exercises + - Duration + * - What is a Neural Network? + - 60 minutes + - + - 60 minutes + * - Tensors + - 20 minutes + - 10 minutes + - 30 minutes + * - Loading a Dataset + - 30 minutes + - 10 minutes + - 40 minutes + * - Building a Neural Network + - 30 minutes + - 30 minutes + - 60 minutes + * - Training on the GPU + - 20 minutes + - 15 minutes + - 35 minutes + * - Training on Multiple GPUs (DataParallel) + - 20 minutes + - 10 minutes + - 30 minutes + * - Training on Multiple GPUs (DistributedDataParallelism) + - 20 minutes + - 10 minutes + - 30 minutes + * - Training on Multiple Nodes + - 30 minutes + - 10 minutes + - 40 minutes + + +.. toctree:: + + tutorial/getting_started + tutorial/what_is_NN.rst + tutorial/tensor.rst + tutorial/dataloader.rst + tutorial/building_NN.rst + tutorial/gpu_NN.rst + tutorial/dataparallel.rst + tutorial/distributed_dataparallel.rst + tutorial/multi_node.rst + + + + + + diff --git a/docs/source/tutorial/basics.rst b/docs/source/tutorial/basics.rst new file mode 100644 index 0000000..dbe5623 --- /dev/null +++ b/docs/source/tutorial/basics.rst @@ -0,0 +1,118 @@ +Basics of Parallelism +-------------------- + +.. admonition:: Overview + :class: Overview + + * **Tutorial:** 15 min + * **Exercises:** 15 min + + **Objectives:** + #. Learn about the difference between threads and process + #. Learn how to synchronize between threads. + #. Learn how to synchronize between processes. + + +Process +******** + +A process is an instance of a program in execution. A process is responsible for executing a program's +instructions and providing the environment in which the program operates (such as memory and I/O devices). + +#. **Program Code**: The instructions of the program that the process is executing. +#. **Process Stack**: Contains temporary data such as method/function parameters, return addresses, and local variables. +#. **Heap**: A region of memory used for dynamic memory allocation during the process's execution. +#. **Data Section**: Contains global and static variables used by the process. +#. **Process Control Block (PCB)**: A data structure maintained by the operating system that holds information about the process, including its state, program counter, CPU registers, and memory management information. + +We can launch multiple process and the same time and the processes are isolated from each other. +Each process manages its own resources, including memory and CPU time and one application can +have more than one process. Process communicates between each other using Inter-Process Communication (IPC) +mechanisms. + +The OS can manage multiple process at the same time and the OS can switch the process executing in a CPU. +This involves saving the current state of the process that is being paused (the "old" process) and restoring +the state of the process that is being resumed (the "new" process). The state of a process is +captured in its *Process Control Block (PCB)*. This idea of switching between processes is called +*Context Switching*. + +Threads +******* + +A thread is the smallest unit of execution within a process. A process can contain multiple threads that +share the same resources but execute independently. While each process is isolated from processes, threads +within the same process share the same memory space and resources but they execute independently. + +*Concurrency* refers to the ability to run multiple threads simultaneously. Threads can be managed by +the operating system to run on different CPU cores, doing different computations, thereby +improving performance. If two concurrent threads (or processes) can be run simultaneously we can say +they are *parallel*. + +Challenges with Threads +*********************** + +#. **Synchronization**: As threads share resources, they need mechanisms to synchronize access to prevent conflicts and ensure data consistency. Common synchronization tools include mutexes, semaphores, and locks. +#. **Deadlock**: A situation where two or more threads are waiting indefinitely for resources held by each other, leading to a standstill. +#. **Race Conditions**: Occur when the outcome depends on the unpredictable timing of thread execution, potentially causing inconsistent results. + + +Synchronization in programming is the coordination of concurrent threads or processes to ensure they operate +correctly when accessing shared resources. It prevents issues such as race conditions and data corruption by +managing access to shared resources, ensuring that only one thread or process can modify the resource at a time. +Synchronization mechanisms, like locks, semaphores, and mutexes, help maintain consistency and order in a +multithreaded or multiprocess environment. + +The Room and the Key: An analogy +********************************* + +**The Room**: Think of a room that represents a shared resource or a critical section of code in a program. +This room can only be used by one person at a time to ensure that things don't get messed up. + +**The Lock**: The lock is like a physical lock that controls access to the room. Only one person can hold the +key of the lock at any given time. + +**Entering the Room**: When a person (a thread) wants to use the room (access the shared resource), +they need to get the key (acquire the lock). If no one else is using the room, the person can take the key, +enter the room, and use it as needed. + +**Occupied Room**: If someone is already inside the room and using it, other people who want to use the room +must wait outside. They cannot enter until the current occupant leaves and returns the key. + +**Exiting the Room**: Once the person is done using the room, they leave and return the key (release the lock). +This allows another person to take the key and use the room. + +**Preventing Conflicts**: The lock ensures that only one person is in the room at any time. This prevents +conflicts or issues that might arise if multiple people were trying to use the room simultaneously. + +Exercise +********* + +1. What occurs when locks aren't used?? + +.. code-block:: console + :linenos: + + module load python3/3.11.0 + python3 threads.py + +2. How do threads differ from processes? + +.. code-block:: console + :linenos: + + module load python3/3.11.0 + python3 process.py + + + +.. admonition:: Key Points + :class: hint + + #. Processes are isolated with separate memory spaces, while threads share the same memory space within a process. + #. Processes have higher creation and management overhead due to separate resources and memory, whereas threads are lighter and cheaper to manage. + #. Threads can communicate easily and efficiently since they share memory, while processes require more complex and resource-intensive Inter-Process Communication (IPC) mechanisms. + #. Locks can be used for synchronization. + + + + diff --git a/docs/source/tutorial/building_NN.rst b/docs/source/tutorial/building_NN.rst new file mode 100644 index 0000000..9362113 --- /dev/null +++ b/docs/source/tutorial/building_NN.rst @@ -0,0 +1,157 @@ +Building a Neural Network +========================= + +.. admonition:: Overview + :class: Overview + + * **Tutorial:** 30 min + * **Exercises:** 30 min + + **Objectives:** + #. Learn how to implement a neural network in PyTorch. + #. Learn the differ modules that go into building a neural network in PyTorch. + +Dataset +******* +We will use the Pima Indians Diabetes dataset for the demonstration. The Pima Indians Diabetes dataset is a popular dataset in the field of machine learning +and statistics, particularly for those working on classification problems. + +#. **Source**: The dataset was created by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and is available in the UCI Machine Learning Repository. +#. **Purpose**: The dataset is used to predict the onset of diabetes within five years based on diagnostic measures. +#. **Features**: The dataset contains 768 samples, each with 8 features. + +The features are: + +#. Pregnancies: Number of times pregnant. +#. Glucose: Plasma glucose concentration (mg/dL) a 2 hours in an oral glucose tolerance test. +#. Blood Pressure: Diastolic blood pressure (mm Hg) at the time of screening. +#. Skin Thickness: Triceps skinfold thickness (mm) measured at the back of the upper arm. +#. Insulin: 2-Hour serum insulin (mu U/ml). +#. BMI: Body mass index. +#. Diabetes Pedigree Function: A function that scores likelihood of diabetes based on family history. +#. Age: Age of the individual (years). + +**Outcome**: Whether or not the individual has diabetes (1 for positive, 0 for negative). + +Defining the Model +******************* + +When designing the model, we have to keep the following points in mind: + +#. The input features in the input layer must match the input features in the dataset. +#. A high number of layers can increase computation time, while too few layers may result in poor predictions. +#. Each layer should be followed by an activation function. + +In this example, we will use a 3-layer neural network: + +#. The input layer expects 8 features. +#. The first hidden layer has 12 neurons, followed by a ReLU activation function. +#. The second hidden layer has 8 neurons, followed by another ReLU activation function. +#. The output layer has one neuron, followed by a sigmoid activation function. + +The **sigmoid function** outputs values between 0 and 1, which is exactly what we need. + +Sequential vs. Class-Based Models +*********************************** + +In PyTorch, neural networks can be defined using different approaches, and two common ones are the `Sequential` model and the `class-based model`. + +The `Sequential` model is a simple, linear stack of layers where each layer has a single input and output. It is useful for straightforward feedforward +networks where layers are applied in a sequential order. + +**Characteristics:** + +#. **Ease of Use:** It is easier to use for simple architectures where layers are applied in a linear fashion. +#. **Defined Using:** `torch.nn.Sequential`. + +**Limitations:** + +#. **Flexibility:** Limited flexibility for more complex architectures (e.g., networks with multiple inputs/outputs, shared layers, or non-sequential data flow). +#. **Custom Behavior:** Difficult to implement custom forward passes or dynamic architectures. + + +The `class-based`` model allows you to define a network by subclassing `torch.nn.Module`. This approach provides greater flexibility and control, making it +suitable for complex models and custom behaviors. + +**Characteristics:** + +#. **Flexibility:** Offers full control over the network architecture, including complex data flows, multiple inputs/outputs, and custom forward methods. +#. **Defined Using:** Subclass of `torch.nn.Module`. + + +**Advantages:** + +#. **Custom Forward Pass:** You can define complex forward passes and control data flow through the network +#. **Dynamic Behavior:** Allows for dynamic computations, such as conditional layers or operations. + + +Choosing between the two depends on the complexity of the network you need to build and your specific requirements for flexibility and control. + +Loss function +************* + +Each model needs a loss function. In this case we will use the Binary Cross-Entropy (BCE) Loss. It Measures the performance of a classification model whose +output is a probability value between 0 and 1. It calculates the difference between the predicted probabilities and the actual binary labels (0 or 1) and +penalizes the model more when the predictions are further from the true labels. + +.. math:: + + BCELoss(y', y) = −[ylog(y')+(1 − y)log(1 − y')] + +Where, y' is the predicted output and y is the actual otput. + +Optmizer +********* + +Optimizer's main role is to update the model's parameters based on the gradients computed during backpropagation. + +1. **Parameter Updates**: Optimizers adjust the weights and biases of the neural network to reduce the loss. This involves applying algorithms that modify +the parameters to minimize the difference between the predicted outputs and the actual targets. + +2. **Learning Rate Management**: Most optimizers include mechanisms to adjust the learning rate, either statically or dynamically, to control how large +the parameter updates are. + +In this example we use an optimizer called Adaptive Moment Estimation (Adam). This computes an adaptive learning rates for each parameter by considering +both the mean and the variance of the gradients. + +Training the Model +******************* + +Training a neural network involves epochs and batches, which define how data is fed to the model: + +#. **Epoch:** A full pass through the entire training dataset. +#. **Batch:** A subset of samples processed at a time, with gradient descent performed after each batch. + +In practice, the dataset is divided into batches, and each batch is processed sequentially in a training loop. Completing all batches constitutes one epoch. +The process is repeated for multiple epochs to refine the model. + +Batch size is constrained by system memory (GPU memory), and computational demands scale with batch size. More epochs and batches lead to better model +performance but increase training time. The optimal number of epochs and batch size is often determined through experimentation. + +1. **optimizer.zero_grad()**: During training, gradients accumulate by default in PyTorch. This means that if you don't clear them, gradients from multiple +backward passes (from different batches) will be added together, which can lead to incorrect updates to the model parameters. By calling optimizer.zero_grad(), +you ensure that gradients from previous steps are reset to zero, preventing them from affecting the current update. + +2. **loss.backward()**: Calculates the gradients of the loss with respect to each parameter of the model. This is done using backpropagation, a key algorithm +for training neural networks. + +3. **optimizer.step()**: Used to update the model's parameters based on the gradients computed during during the backward pass (loss.backward()). + +Model Evaluation +****************** + +Ideally, we should split the data into separate training and testing datasets, or use a distinct dataset for evaluation. For simplicity, we are testing the +model on the same data used for training. + + +.. admonition:: Exercise + :class: todo + + Try the notebook *building_NN.ipynb*. + +.. admonition:: Key Points + :class: hint + + #. PyTorch offers *Sequential* models for simple linear stacks and *Class-based* models for complex, customizable architectures. + #. Training involves epochs and batches with functions like `optimizer.zero_grad()`, `loss.backward()`, and `optimizer.step()` + #. Ideally, data should be split into training and testing sets. \ No newline at end of file diff --git a/docs/source/tutorial/cnn.rst b/docs/source/tutorial/cnn.rst new file mode 100644 index 0000000..4669009 --- /dev/null +++ b/docs/source/tutorial/cnn.rst @@ -0,0 +1,11 @@ +What is a Convolutional Neural Network (CNN)? +================================================== + +.. admonition:: Overview + :class: Overview + + * **Tutorial:** 45 min + * **Exercises:** 0 min + + **Objectives:** + #. Learn the different parts of a convolutional neural network. \ No newline at end of file diff --git a/docs/source/tutorial/dataloader.rst b/docs/source/tutorial/dataloader.rst new file mode 100644 index 0000000..09f4ef3 --- /dev/null +++ b/docs/source/tutorial/dataloader.rst @@ -0,0 +1,251 @@ +Loading a Dataset in PyTorch +============================= + +.. admonition:: Overview + :class: Overview + + * **Tutorial:** 15 min + * **Exercises:** 15 min + + **Objectives:** + #. Learn how to use pre-loaded data in PyTorch. + #. Learn how to use custom data in PyTorch. + #. Learn how to use custom dataloader in PyTorch. + +PyTorch offers two data primitives—`torch.utils.data.DataLoader` and `torch.utils.data.Dataset`— which facilitate the use of both pre-loaded datasets and custom data. +Dataset is an abstract class that represents a dataset. It defines how the data should be accessed and loaded, allowing users to specify how to retrieve +individual data points. DataLoader wraps around a Dataset and provides iterable functionality, handling batching, shuffling, and loading data in +parallel using multiprocessing. + +.. list-table:: Differences Between Dataset and DataLoader + :header-rows: 1 + + * - Feature + - Dataset + - DataLoader + * - Purpose + - Defines how individual data samples are loaded. + - Provides batch loading and efficient data iteration. + * - Customizable + - Users implement custom loading logic (e.g., loading images, preprocessing). + - Handles batching, shuffling, and parallel data loading. + * - Methods + - Requires ``__len__()`` and ``__getitem__()`` methods. + - Takes a Dataset as input and provides data batches. + * - Functionality + - Accesses individual data points (samples). + - Loads data in batches and supports multiprocessing. + * - Parallelization + - Not parallelized (loads one item at a time). + - Supports parallel data loading (``num_workers``). + + +Pre-loaded Datasets +******************** + +The `Fashion-MNIST` dataset is an example of a pre-loaded curated dataset. It can be loaded using the following parameters: + +- `root` specifies the path where the training or test data is stored. +- `train` indicates whether to load the training or test dataset. +- `download=True` will download the data from the internet if it's not available at the specified `root`. +- `transform` and `target_transform` define the transformations applied to the features and labels, respectively. + +Load the training data: + +.. code-block:: python + :linenos: + + training_data = datasets.FashionMNIST( + root="data", # root directory of data + train=True, # load training dataset + download=True, # download the data if unvailable at the `root` + transform=ToTensor() # transformations applied to the features and labels + ) + +Load the testing data: + +.. code-block:: python + :linenos: + + training_data = datasets.FashionMNIST( + root="data", # root directory of data + train=False, # load testing dataset + download=True, # download the data if unvailable at the `root` + transform=ToTensor() # transformations applied to the features and labels + ) + +Custom Dataset +*************** + +What if working with a custom dataset? To illustrate this, we will download a dataset and set it up for +use in PyTorch training. + +.. admonition:: Explanation + :class: attention + + The data used for this demonstration is relatively *clean*. In a practical use case, significant + time will likely be spent on cleaning and preparing the data. + +The data: + + #. There are **3 classes**: pizza, steak, and sushi. + #. The data is split into *train* and *test* datasets. + #. Both *train* and *test* datasets are further organized into 3 directories, each corresponding to one of the classes. + +.. admonition:: Explanation + :class: attention + + In practice, it is our responsibility to divide the data into training and testing sets and + further categorize it into different classes. + +Transformation on the data +************************************** + +Transform functions in the PyTorch library simplify the application of various data enhancement/manipulation techniques +to your input data. These functions enable you to apply multiple changes simultaneously. + + +.. code-block:: python + :linenos: + + data_transform = transforms.Compose([ + transforms.Resize(size=(64, 64)), # Resize the images to 64x64 + transforms.RandomHorizontalFlip(p=0.5), # Horizontally flip image with a 0.5 probability + transforms.ToTensor() # convert to tensor of shape (C x H x W) in the range [0.0, 1.0] + ]) + +.. admonition:: Explanation + :class: attention + + A Tensor Image is a tensor with a shape of (C, H, W), where C represents the number of channels, + and H and W denote the image's height and width. Typically, an image consists of three color + channels: red, green, and blue (RGB). + + **Note**: PyTorch uses the [C, H, W] format by default, while Matplotlib uses [H, W, C]. + +Loading Image Data Using ImageFolder +*********************************** + +`ImageFolder` is a generic data loader where images are expected to be organized into separate directories, +each corresponding to a different class. + +.. code-block:: python + :linenos: + + train_data = datasets.ImageFolder(root=train_dir, # root of the train images + transform=data_transform, # transforms to perform on each image + target_transform=None # transforms to perform on labels (eg: 1-hot encoding) + ) + + test_data = datasets.ImageFolder(root=test_dir, # root of the test images + transform=data_transform # transforms to perform on each image + ) + + +DataLoader +********** + +In PyTorch, `DataLoader` is a built-in class that offers an efficient and flexible method for loading +data into a model for training or inference. It is especially beneficial for managing large datasets that +may not fit into memory and for carrying out data augmentation and preprocessing. +Data loader combines a dataset and a sampler, and provides an iterable over the given dataset. + + +.. code-block:: python + :linenos: + + from torch.utils.data import DataLoader + + train_dataloader = DataLoader(dataset=train_data, # dataset from which to load the data + batch_size=8, # samples per batch to load + num_workers=1, # subprocesses to use for data loading + shuffle=True) # reshuffled the data at every epoch + + test_dataloader = DataLoader(dataset=test_data, # dataset from which to load the data + batch_size=8, # samples per batch to load + num_workers=1, # subprocesses to use for data loading + shuffle=False) # don't shuffle testing data + +.. admonition:: Explanation + :class: attention + + Each tensor will be of size [8, 3, 64, 64] -> [batch_size, channels, height, width]. + + +Writing a custom DataLoader +**************************** + +The DataLoader works in conjunction with a Dataset class that defines how to access and preprocess data. + +1. Initialization (`__init__``): Loads the dataset from a file (e.g., CSV) or another source. Performs any necessary preprocessing, such as normalization or +feature extraction. + +2. Length (`__len__``): Returns the number of samples in the dataset, which helps the DataLoader know how many batches to create. + +3. Item Retrieval (`__getitem__``): Retrieves a sample from the dataset given an index. This method is called by the DataLoader to get individual data points +for batching. + +We will use the Pima Indians Diabetes dataset for the demonstration. The Pima Indians Diabetes dataset is a popular dataset in the field of machine learning +and statistics, particularly for those working on classification problems. + +#. **Source**: The dataset was created by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and is available in the UCI Machine Learning Repository. +#. **Purpose**: The dataset is used to predict the onset of diabetes within five years based on diagnostic measures. +#. **Features**: The dataset contains 768 samples, each with 8 features. + +The features are: + +#. Pregnancies: Number of times pregnant. +#. Glucose: Plasma glucose concentration (mg/dL) a 2 hours in an oral glucose tolerance test. +#. Blood Pressure: Diastolic blood pressure (mm Hg) at the time of screening. +#. Skin Thickness: Triceps skinfold thickness (mm) measured at the back of the upper arm. +#. Insulin: 2-Hour serum insulin (mu U/ml). +#. BMI: Body mass index. +#. Diabetes Pedigree Function: A function that scores likelihood of diabetes based on family history. +#. Age: Age of the individual (years). + +**Outcome**: Whether or not the individual has diabetes (1 for positive, 0 for negative). + +.. code-block:: python + :linenos: + + column_names = [ 'Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness','Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'] + + class PimaDataset(Dataset): + + def __init__(self, csv_file): + # Load the CSV file without header and assign column names + self.data = pd.read_csv(csv_file, header=None, names=column_names) + self.features = self.data.drop('Outcome', axis=1).values + self.labels = self.data['Outcome'].values + + # Convert to PyTorch tensors + self.features_tensor = torch.tensor(self.features, dtype=torch.float32) + self.labels_tensor = torch.tensor(self.labels, dtype=torch.long) + + # Calculate mean and std + self.mean = self.features_tensor.mean(dim=0) + self.std = self.features_tensor.std(dim=0) + + # Normalize the features + self.features_tensor = (self.features_tensor - self.mean) / self.std + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + feature = self.features_tensor[idx] + label = self.labels_tensor[idx] + return feature, label + + +.. admonition:: Exercise + :class: todo + + Try the notebook *dataloader.ipynb*. + +.. admonition:: Key Points + :class: hint + + #. PyTorch provides pre-loaded datasets that can be used directly. + #. Custom datasets can also be utilized in PyTorch. + #. We can create custom dataloaders in PyTorch. \ No newline at end of file diff --git a/docs/source/tutorial/dataparallel.rst b/docs/source/tutorial/dataparallel.rst new file mode 100644 index 0000000..9c6a848 --- /dev/null +++ b/docs/source/tutorial/dataparallel.rst @@ -0,0 +1,86 @@ +Multi-GPU Training using Data Parallelism +========================================= + +.. admonition:: Overview + :class: Overview + + * **Tutorial:** 15 min + * **Exercises:** 10 min + + **Objectives:** + #. Learn how to use multiple GPUs in training using data parallelism. + +By default, PyTorch will use only one GPU. However, you can easily leverage multiple GPUs by running your model in parallel using `DataParallel`. + +DataParallel +************* + +Whenever you have multiple GPUs, you can wrap your model with `nn.DataParallel`. Then, you can move your model to the GPUs using `model.to(device)`. + +.. code-block:: python + :linenos: + + if torch.cuda.device_count() > 1: + class_model = nn.DataParallel(class_model) + class_model.to(device) + +Then we can use the model as usual and pytorch will distribute the data across multiple GPUs. + + +Detailed Working +***************** + +`nn.DataParallel` splits the input data across the available GPUs, performing computations in parallel, and then aggregating the results. + +1. **Splitting the Input Data** + +- **Batch Splitting**: `nn.DataParallel` splits each mini-batch of data into smaller chunks, with each chunk sent to a different GPU. + +- **Replication**: The model is replicated on each GPU, ensuring that each GPU has a copy of the model. + +2. **Parallel Computation** + +- **Forward Pass**: Each GPU performs a forward pass on its respective chunk of the data. Since the model is replicated on each GPU, the computations are done independently for each chunk. + +- **Backward Pass**: During backpropagation, gradients are computed separately on each GPU. + +3. **Aggregation of Results** + +- **Concatenation of Outputs**: After the forward pass, `nn.DataParallel` gathers the outputs from all GPUs and concatenates them along the batch dimension. This is necessary to maintain the correct order of the outputs. + +- **Gradient Aggregation**: During backpropagation, `nn.DataParallel` aggregates the gradients from each GPU. It does this by summing the gradients computed by each GPU, which are then used to update the model parameters. + +4. **Synchronizing Parameters** + +- **Parameter Updates**: After gradients are aggregated, the model parameters are updated on the primary GPU. The updated parameters are then synchronized and broadcasted to all other GPUs. + + +Limitations +*********** + +`nn.DataParallel` in PyTorch allows you to distribute data across multiple GPUs for parallel processing, but it has some limitations: + +#. **Single-process bottleneck**: nn.DataParallel uses a single process that sends data to each GPU, collects the results, and aggregates them. This can become a bottleneck, especially with a large number of GPUs. +#. **Limited scalability**: As the number of GPUs increases, the performance gains from nn.DataParallel diminish due to the overhead of distributing data and collecting results. +#. **Less efficient memory usage**: nn.DataParallel replicates the entire model on each GPU, which can lead to inefficient memory usage, especially with large models. +#. **Inflexible device placement**: nn.DataParallel requires all GPUs to be on the same machine. It doesn't support distributed training across multiple nodes + + +When using nn.DataParallel in PyTorch, `class_model.parameters()).device` often returns `cuda:0`, even if multiple GPUs are used. This happens because +nn.DataParallel replicates the model across multiple GPUs but keeps the original model's parameters on the primary GPU (cuda:0). The `nn.DataParallel` wrapper +itself does not move parameters to different GPUs; it only distributes the input data to the GPUs and then aggregates the results. The underlying parameters of the model are still located on the primary device. +It's always a good idea to use `nvidia-smi` to check that the GPU utilization is as expected. + + +.. admonition:: Exercise + :class: todo + + Try the notebook *multi_GPU.ipynb*. + + +.. admonition:: Key Points + :class: hint + + #. We can use `nn.DataParallel` to utilize multiple GPUs for training. + #. The training is limited to a single node and cannot span across multiple nodes. + diff --git a/docs/source/tutorial/distributed_dataparallel.rst b/docs/source/tutorial/distributed_dataparallel.rst new file mode 100644 index 0000000..0fb7188 --- /dev/null +++ b/docs/source/tutorial/distributed_dataparallel.rst @@ -0,0 +1,125 @@ +Distributed Data Parallelism +============================= + +.. admonition:: Overview + :class: Overview + + * **Tutorial:** 20 min + * **Exercises:** 10 min + + **Objectives:** + #. Learn how to use multiple GPUs in training using distributed data parallelism. + #. Train the model using PBS a job script. + + +Components of a distributed data parallel model: + +- **Master Node:** The primary GPU responsible for synchronization, model replication, loading models, and logging. +- **Process Group:** When training or testing a model across K GPUs, these K processes form a group. +- **Rank:** Each process within the process group is identified by a rank, ranging from 0 to K-1 (similar to MPI). +- **World Size:** The total number of processes in the group, which equals the number of GPUs (similar to MPI). + +Advantage over DataParallel +**************************** + +- **Scalability:** DataParallel operates as a single-process, multi-threaded approach and only works on a single machine, whereas, DistributedDataParallel (DDP) uses a multi-process approach and supports both single- and multi-machine training. DataParallel is often slower than DDP, even on a single machine, due to *GIL* contention across threads, the overhead of replicating the model per iteration, and the extra steps involved in scattering inputs and gathering outputs. + +- **Model Parallelism:** If your model is too large to fit on a single GPU, you need to use model parallelism to distribute it across multiple GPUs. DistributedDataParallel supports model parallelism, while DataParallel does not. When combining DDP with model parallelism, each DDP process utilizes model parallelism, and all processes together perform data parallelism. + + +Process Group +************* + +In DistributedDataParallel (DDP), a *Process Group* is a collection of processes that can communicate with each other during distributed training. + +.. code-block:: python + :linenos: + + import torch.distributed as dist + + def setup(rank, world_size): + os.environ['MASTER_ADDR'] = 'localhost' + os.environ['MASTER_PORT'] = '12355' + dist.init_process_group("nccl", rank=rank, world_size=world_size) + +.. admonition:: Explanation + :class: attention + + Here, `nccl` is the backend that determines how communication between processes is handled. Other common backends are `gloo`, a CPU-based backend, and `mpi` + the where MPI (Message Passing Interface) based backend. + +Splitting the Dataloader +************************ + +To split the data across multiple GPUs we use `DistributedSampler`. + +.. code-block:: python + :linenos: + + def prepare(rank, world_size, batch_size=32, pin_memory=False, num_workers=0): + dataset = PimaDataset(datapath) + sampler = DistributedSampler(dataset, num_replicas=world_size, rank=rank, shuffle=False, drop_last=False) + + dataloader = DataLoader(dataset, batch_size=batch_size, pin_memory=pin_memory, num_workers=num_workers, drop_last=False, shuffle=False, sampler=sampler) + + return dataloader + + +.. admonition:: Explanation + :class: attention + + - `num_replicas` - Is typically the number of processes in the distributed training job. + - `rank` - Each process is assigned a rank which ensures that each process only accesses the data corresponding to its rank. + - `drop_last` - When working with datasets in distributed training, it is common for the total number of samples in the dataset to not be perfectly divisible by the product of the batch size and the number of replicas. When `drop_last` is set to *True*, the last batch that is not full will be dropped. + +and a distributed `DataLoader`. + +.. admonition:: Explanation + :class: attention + + - `num_workers` - Number of subprocesses to use for data loading. + - `pin_memory` - Pinned (or Page-locked) memory is a region of host memory that is "locked" in physical RAM and cannot be paged out to disk by the operating system. This ensures that the memory remains in RAM and is directly accessible for operations like data transfer between the CPU and GPU. Page-locking excessive amounts of memory with cudaMallocHost() may degrade system performance, since it reduces the amount of memory available to the system for paging. As a result, this function is best used sparingly to allocate staging areas for data exchange between host and device. + + .. image:: ../figs/pinning.png + + + +Wrapping a Model in DDP +********************** + +DistributedDataParallel (DDP) is a PyTorch wrapper that helps to parallelize training across multiple GPUs and minimizes communication overhead and +synchronizes gradients automatically. + + +.. code-block:: python + :linenos: + + model_ddp = DDP(model, device_ids=[rank], output_device=rank, find_unused_parameters=True) + +.. admonition:: Explanation + :class: attention + + - `model`: The neural network (`torch.nn.Module`) that you want to train. Before wrapping it with DDP, it should be placed on the appropriate device (GPU) using model.to(device). + - `device-ids`: Specifies the GPU device(s) to which this process's model should be mapped. The rank typically corresponds to the index of the current process within the distributed setup, and in a single-node setup with multiple GPUs, rank is often the GPU ID. For example, if rank=0, it means this process will use GPU 0. + - `output_device` : Specifies the device where the output of the model should be stored. + - `find_unused_parameters` : DDP assumes all model parameters are used in every forward pass, and it synchronizes their gradients accordingly. Setting `find_unused_parameters=True`` ensures that DDP will only synchronize the gradients of parameters that are actually used, preventing errors and unnecessary communication overhead. + + +.. admonition:: Exercise + :class: todo + + 1. Examine the program *src/distributed_data_parallel.py*. What the changes from data_parallel.ipynb? + 2. Examine the job script *job_scripts/distributed_data_parallel.pbs*. + 3. Run the program using the job script *job_scripts/distributed_data_parallel.pbs*. + + .. code-block:: console + :linenos: + + cd job_scripts + qsub distributed_data_parallel.pbs + + +.. admonition:: Key Points + :class: hint + + #. We can use distributed data parallelism to use multiple GPUs on the same node. \ No newline at end of file diff --git a/docs/source/tutorial/getting_started.rst b/docs/source/tutorial/getting_started.rst new file mode 100644 index 0000000..f90ffc0 --- /dev/null +++ b/docs/source/tutorial/getting_started.rst @@ -0,0 +1,47 @@ +Getting Started +=============== + +To access the Gadi system, follow these steps: + +1. **SSH into the Gadi system**: + + .. code-block:: console + :linenos: + + ssh -XY @gadi.nci.org.au + + + Alternatively, you can use the Gadi terminal option at `ARE `_. + +2. **Change to the project directory**: + + .. code-block:: console + :linenos: + + cd /scratch/vp91 + + +3. **Create and navigate to a directory with your username**: + + .. code-block:: console + :linenos: + + mkdir -p $USER + cd $USER + + +4. **Clone the repository**: + + .. code-block:: console + :linenos: + + git clone https://github.com/NCI900-Training-Organisation/intro-to-pytorch.git + cd intro-to-pytorch.git + + + +In the repository: + +- The `python/src` directory contains all the Python code. +- The `python/job_scripts` directory includes all the PBS job scripts. +- The `python/job_scripts/sample_outputs` directory holds the sample outputs. diff --git a/docs/source/tutorial/gpu_NN.rst b/docs/source/tutorial/gpu_NN.rst new file mode 100644 index 0000000..5f55346 --- /dev/null +++ b/docs/source/tutorial/gpu_NN.rst @@ -0,0 +1,81 @@ +Training on a GPU +================= + +.. admonition:: Overview + :class: Overview + + * **Tutorial:** 15 min + * **Exercises:** 15 min + + **Objectives:** + #. Learn how to traing the model on a GPU. + #. Learn how to save a model. + #. Learn how to load a saved model. + +Set the default device +********************** + +We can set a default device when building a model, ensuring that all operations occur on this device. If available, we can set the GPU as the default device. + +.. code-block:: python + :linenos: + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +Saving and Loading a Model +****************************** + +We can save the model in a specific path in the syste. + +.. code-block:: python + :linenos: + + modelpath = os.path.expandvars('/home/$USER/class_model') + torch.save(class_model.state_dict(), modelpath) + +This saved model can be loaded when needed. During loading, you can directly specify the device using the `map_location` parameter or move the model to the +desired device afterward using the `.to()` function. + +.. code-block:: python + :linenos: + + class_model.load_state_dict(torch.load(modelpath, map_location=device, weights_only=True)) + class_model.to(device) + +Training on the GPU +******************* + +When training, both the model and all the data it operates on should be on the same device. + +.. code-block:: python + :linenos: + + n_epochs = 100 + batch_size = 10 + + for epoch in range(n_epochs): + for i in range(0, len(X_tensor), batch_size): + Xbatch = X_tensor[i:i+batch_size].to(device) # move the tensor to GPU + + y_pred = class_model(Xbatch) + + ybatch = y_tensor[i:i+batch_size].to(device) # move the tensor to GPU + + loss = loss_fn(y_pred, ybatch) + optimizer.zero_grad() + loss.backward() + optimizer.step() + +.. admonition:: Exercise + :class: todo + + Try the notebook *GPU_NN.ipynb*. + + +.. admonition:: Key Points + :class: hint + + #. We can set a default device in PyTorch. + #. During training, ensure that both the model and the data it operates on are on the same device. + + diff --git a/docs/source/tutorial/multi_node.rst b/docs/source/tutorial/multi_node.rst new file mode 100644 index 0000000..c7dbc9f --- /dev/null +++ b/docs/source/tutorial/multi_node.rst @@ -0,0 +1,178 @@ +Multi-Node Parallelism +======================= + +.. admonition:: Overview + :class: Overview + + * **Tutorial:** 20 min + * **Exercises:** 10 min + + **Objectives:** + #. Learn how to use multiple GPUs, in multiple nodes using Torchrun. + + +To run the provided code on multiple nodes using torchrun (previously torch.distributed.launch), we need to make a few modifications to the +single node code: + +- **Environment Variables for Multi-Node Training**: Set environment variables like MASTER_ADDR, MASTER_PORT, WORLD_SIZE, and RANK using command-line arguments when launching the script with torchrun. +- **Modifications to the setup function**: The setup function should be updated to handle the environment variables for multi-node training. +- **main function**: Remove the use of mp.spawn and instead rely on torchrun to handle the spawning of processes across nodes. + +In PyTorch distributed parallelism, **global rank** and **local rank** are key concepts for managing processes across multiple nodes and +GPUs. The global rank uniquely identifies each process in the entire distributed setup, ranging from `0` to `world_size - 1`, where +`world_size` is the total number of processes across all nodes. The local rank, on the other hand, identifies each process within a +specific node, typically corresponding to a particular GPU on that node. The global rank is crucial for tasks that require a unique +process identity across the system, while the local rank is used for GPU assignment within a node. These ranks are essential for +ensuring that each process operates correctly within the distributed environment. + +.. image:: ../figs/global_local.png + + +PBS Script +********** + +As Gadi uses the PBS job scheduler we can use it to run the training on multiple nodes. Here we are requesting 2 nodes, each with 4 GPUs. + +.. code-block:: console + :linenos: + + #!/bin/bash + + #PBS -P vp91 + #PBS -q gpuvolta + + #PBS -l ncpus=96 + #PBS -l ngpus=8 + #PBS -l mem=10GB + #PBS -l walltime=00:20:00 + + #PBS -N multinode + + module load python3/3.11.0 + module load cuda/12.3.2 + + . /scratch/vp91/Training-Venv/pytorch/bin/activate + + # Set variables + if [[ $PBS_NCPUS -ge $PBS_NCI_NCPUS_PER_NODE ]] + then + NNODES=$((PBS_NCPUS / PBS_NCI_NCPUS_PER_NODE)) + else + NNODES=1 + fi + + PROC_PER_NODE=$((PBS_NGPUS / NNODES)) + + MASTER_ADDR=$(cat $PBS_NODEFILE | head -n 1) + + # Launch script + LAUNCH_SCRIPT=/scratch/vp91/jxj900/intro-to-pytorch/job_scripts/multinode_torchrun.sh + + # Set execute permission + chmod u+x ${LAUNCH_SCRIPT} + + # Run PyTorch application + for inode in $(seq 1 $PBS_NCI_NCPUS_PER_NODE $PBS_NCPUS); do + echo $inode + pbsdsh -n $inode ${LAUNCH_SCRIPT} ${NNODES} ${PROC_PER_NODE} ${MASTER_ADDR} & + done + + wait + +.. admonition:: Explanation + :class: attention + + `MASTER_ADDR`: The IP address or hostname of the master node, which is typically the first node allocated by PBS. + `PROC_PER_NODE`: The number of GPUs per node. + `NNODES`: The total number of nodes. + +Here, `pbsdsh` launches the `multinode_torchrun.sh` script simultaneously on all nodes. The `multinode_torchrun.sh` script contains the following: + +.. code-block:: console + :linenos: + + #!/bin/bash + + # Load shell environment variables + source ~/.bashrc + + module load python3/3.11.0 + module load cuda/12.3.2 + + . /scratch/vp91/Training-Venv/pytorch/bin/activate + + # Application script + APPLICATION_SCRIPT=/scratch/vp91/jxj900/intro-to-pytorch/src/multinode_torchrun.py + + # Set execute permission + chmod u+x ${APPLICATION_SCRIPT} + + # Run PyTorch application + torchrun --nnodes=${1} --nproc_per_node=${2} --rdzv_id=100 --rdzv_backend=c10d --rdzv_endpoint=${3}:29400 ${APPLICATION_SCRIPT} + + +Where `torchrun` will launch the training program `distributed_data_parallel.py` on each node and +use all the 4 GPUs on each node. + + + +.. admonition:: Explanation + :class: attention + + The rendezvous backend in PyTorch is a key component of the distributed training setup. It is + responsible for coordinating the initialization of multiple processes that may be running across different + nodes in a distributed system. This process is crucial for ensuring that all distributed processes are aware + of each other and can start training in a synchronized manner. + + - `rdzv_backend`: The backend used for the rendezvous process (c10d is default for PyTorch). + - `rdzv_endpoint`: The network address of the rendezvous server, combining `MASTER_ADDR` and `MASTER_PORT`. + +Alternative Options +******************** + +Alternatively, if you can SSH into the individual nodes, you can proceed with the following steps. + +On the first node (rank 0): + +.. code-block:: console + :linenos: + + torchrun --nnodes=2 --nproc_per_node=4 --node_rank=0 --master_addr="" --master_port=12355 /scratch/vp91/$USER/intro-to-pytorch/src/multinode_torchrun.py + +On the second node (rank 1): + + +.. code-block:: console + :linenos: + + torchrun --nnodes=2 --nproc_per_node=4 --node_rank=1 --master_addr="" --master_port=12355 /scratch/vp91/$USER/intro-to-pytorch/src/multinode_torchrun.py + +Of course, this becomes be a very difficult task if you have large number of Nodes. + +.. admonition:: Explanation + :class: attention + + If you have a `SLURM scheduler `_, things are a bit easier since the *srun* command can launch the Torchrun directly + from the job script, on all nodes, eliminating the need for an additional shell script. + + +.. admonition:: Exercise + :class: todo + + 1. Examine the program *src/ multinode_torchrun.py*. What are the changes from *src/distributed_data_parallel.py*? + 2. Examine the job script *job_scripts/multinode_torchrun.pbs*. Can you simplify it? + 3. Examine the job script *job_scripts/multinode_torchrun.sh*. Can you simplify it? + 4. Run the program using the job script *job_scripts/multinode_torchrun.pbs*. + + .. code-block:: console + :linenos: + + cd job_scripts + qsub multinode_torchrun.pbs + + +.. admonition:: Key Points + :class: hint + + #. We can use Torchrun to use multiple GPUs in multiple nodes. + #. We can use PBS script to launch multi-node trainings. \ No newline at end of file diff --git a/docs/source/tutorial/tensor.rst b/docs/source/tutorial/tensor.rst new file mode 100644 index 0000000..e2aa910 --- /dev/null +++ b/docs/source/tutorial/tensor.rst @@ -0,0 +1,247 @@ +Tensors in PyTorch +=================== + +.. admonition:: Overview + :class: Overview + + * **Tutorial:** 20 min + * **Exercises:** 15 min + + **Objectives:** + #. Learn about tensors. + #. Learn the differences between a tensor and NumPy array. + #. Learn how to move tensors to GPUs. + + +Tensors are specialized data structures used in PyTorch to represent model inputs, outputs, and parameters. While they are conceptually similar to +arrays and matrices, they offer additional features such as support for hardware accelerators like GPUs and +automatic differentiation. + +Creating a Tensor +***************** + +A tensor can be created in multiple ways: + +1. Directly from data + +.. code-block:: python + :linenos: + + data = [[1, 2],[3, 4]] + x_tensor= torch.tensor(data) + +2. From NumPy + +.. code-block:: python + :linenos: + + x_np = np.array(data) + x_tensor = torch.from_numpy(x_np) + +3. From another Tensor + +.. code-block:: python + :linenos: + + x_tensor = torch.ones_like(x_tensor) + y_tensor = torch.rand_like(x_tensor, dtype=torch.float) + + +.. admonition:: Explanation + :class: attention + + **torch.rand_like()** returns a tensor with the same size as input that but filled with random numbers + from the interval [0,1). + + +Operations on Tensors +********************* + +Tensors can perform almost all operations a NumPy array can perform + +1. indexing and slicing + +.. code-block:: python + :linenos: + + x_tensor = torch.ones(4, 4) + print(f"First row: {x_tensor[0]}") + print(f"First column: {x_tensor[:, 0]}") + print(f"Last column: {x_tensor[..., -1]}") + x_tensor[:,1] = 0 + print(x_tensor) + +2. Concatenate multiple tensors + +.. code-block:: python + :linenos: + + y_tensor = torch.cat([x_tensor, x_tensor, x_tensor], dim=1) + print(y_tensor) + + +3. Arithmetic Operations + +.. code-block:: python + :linenos: + + x_tensor = torch.ones(4, 4) + + # Transpose + x_T_tensor = x_tensor.T + + # Matrix Multiplication + y1_tensor = x_tensor @ x_tensor.T + y2_tensor = x_tensor.matmul(x_tensor.T) + + y3_tensor = torch.rand_like(y1_tensor) + torch.matmul(x_tensor, x_tensor.T, out=y3_tensor) + + + # Element-wise multiplication + z1_tensor = x_tensor * x_tensor + z2_tensor = x_tensor.mul(x_tensor) + + z3_tensor = torch.rand_like(x_tensor) + torch.mul(x_tensor, x_tensor, out=z3_tensor) + +3. In-place Operations + +.. code-block:: python + :linenos: + + x_tensor = torch.ones(4, 4) + + # Transpose + x_tensor.t_() + + # Copy + y_tensor = torch.rand_like(x_tensor) + x_tensor.copy_(y_tensor) + +NumPy and Tensor +**************** + +Tensors on the **CPU** and NumPy arrays can share memory locations, so modifying one will also affect +the other. + +.. code-block:: python + :linenos: + + x_tensor = torch.ones(5) + x_np = x_tensor.numpy() # tensor to numpy + print(f"t: {x_tensor}") + print(f"n: {x_np}") + + x_tensor.add_(1) + + print(f"t: {x_tensor}") + print(f"n: {x_np}") + + y_np = np.ones(5) + z_np = np.zeros(5) + y_tensor = torch.from_numpy(y_np) # numpy to tensor + + np.add(y_np, 1, out=z_np) + + np.add(y_np, 1, out=n) + + print(f"t: {x_tensor}") + print(f"n: {x_np}") + + +Moving Tensor to GPU +********************* + +It's always wise to check for GPU availability before performing any GPU operations. If a GPU is available, +we can move our tensor to it. + +.. code-block:: python + :linenos: + + x_tensor_gpu = x_tensor.to("cuda") + +A better approach is to set the default device before starting any computations. + +.. code-block:: python + :linenos: + + device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") + y_tensor_gpu = y_tensor.to(device) + +This way, your code will work regardless of whether a GPU is available or not. + +Tensor Attributes +***************** + +.. code-block:: python + :linenos: + + print(f"Shape of tensor: {y_tensor.shape}") + print(f"Datatype of tensor: {y_tensor.dtype}") + print(f"Device tensor is stored on: {y_tensor.device}") + + +*Automatic differentiation* is a key feature that distinguishes tensors from NumPy arrays. This capability +is particularly useful in neural networks, where model weights are adjusted during backpropagation based +on the gradient of the loss function with respect to each parameter. Tensors support automatic gradient +computation for any computational graph. For example, consider the computational graph of a one-layer +neural network: + + +.. image:: ../figs/loss.png + +In this context, **w** and **b** are the parameters that need to be optimized. Therefore, we compute +the gradients of the loss function with respect to these variables. + +.. math:: + + z = x * w + b + + g1 = \frac{\partial loss}{\partial w} + + g2 = \frac{\partial loss}{\partial b} + +Tensors make this process quite straightforward: + +.. code-block:: python + :linenos: + + x_tensor = torch.ones(5) # input tensor + y_tensor = torch.zeros(3) # expected output + + w_tensor = torch.randn(5, 3, requires_grad=True) + b_tensor = torch.randn(3, requires_grad=True) + + z_tensor = torch.matmul(x_tensor, w_tensor) + b_tensor + + loss_tensor = torch.nn.functional.binary_cross_entropy_with_logits(z_tensor, y_tensor) + loss_tensor.backward() + + print(w_tensor.grad) + print(b_tensor.grad) + + +When you perform operations in PyTorch involving tensors that have **requires_grad=True**, PyTorch builds a computational graph in the background. +This graph records the operations performed on the tensors, allowing for automatic differentiation during backpropagation. +When you calculate *z_tensor = torch.matmul(x_tensor, w_tensor) + b_tensor*, PyTorch tracks the entire sequence of operations. +Because w_tensor and b_tensor have requires_grad=True, PyTorch knows that these tensors are part of the computational graph. +Every operation (such as torch.matmul and addition) creates nodes in this graph, linking the output z_tensor back to the inputs w_tensor and b_tensor. + + + +.. admonition:: Exercise + :class: todo + + Try the notebook *tensors.ipynb*. + +.. admonition:: Key Points + :class: hint + + #. Tensors in PyTorch can be created using various methods. + #. Moving tensors to GPUs can be done in a device-agnostic manner. + #. Automatic differentiation is straightforward with tensors in PyTorch. + + + + diff --git a/docs/source/tutorial/what_is_NN.rst b/docs/source/tutorial/what_is_NN.rst new file mode 100644 index 0000000..7603077 --- /dev/null +++ b/docs/source/tutorial/what_is_NN.rst @@ -0,0 +1,554 @@ +What is a Neural Network? +========================= + +.. admonition:: Overview + :class: Overview + + * **Tutorial:** 45 min + * **Exercises:** 0 min + + **Objectives:** + #. Learn the different parts of a neural network + +Neural Networks (NN) are computational models inspired by the human brain, designed to recognize patterns and make data-based decisions. +They consist of interconnected layers of nodes, or "neurons," which process and transform input information. Through training, neural networks +learn to improve their accuracy in tasks like image recognition, language processing, and more. + +Neuron +****** + +In the context of a neural network, a neuron is a fundamental unit that processes inputs to produce an +output. Let's break down its role and functionality step by step: + +1. **Input features**: These are the individual measurable properties or characteristics of the data that are fed into the network. Features can be any numerical data - +for example if we use image as the input, the input features will be the pixel values of the image. + +1. **Weights**: Input features are each associated with a weight, which is a numerical value that adjusts +the importance of the corresponding input feature. + +2. **Calculating the Weighted Sum**: For each neuron, you first multiply each input feature by its corresponding weight. Then, you sum up all these weighted inputs. +This sum represents the combined influence of all the input feature on the neuron. + +3. **Adding Bias**: To this weighted sum, you add a bias term. The bias is another adjustable parameter that helps the neuron model more complex patterns +by shifting the activation function's input. + +4. **Activation Function**: Finally, you apply an activation function to the resulting value (the weighted sum plus bias). The activation function introduces +non-linearity into the neuron's output, which allows the network to learn and represent more complex patterns and relationships. + + +.. image:: ../figs/neuron.drawio.png + +In summary, a neuron in a neural network processes its inputs through a series of multiplication, summation, bias addition, and activation +function application—to produce an output value. This output is then used in further computations within the network or as the final prediction, depending +on the network's structure. + + + +Activation Function +******************** + +Activation functions are crucial components of neural networks, performing several key roles that +influence the network's ability to learn and make predictions. + +Role of Activation Functions: + +1. **Produce Outputs of Neurons**: After computing the weighted sum of inputs and adding the bias, an activation function is applied to + this value. + +2. **Update Weights and Biases During Training**: Activation functions play a role in updating weights and biases during the *training process*. When the + network is trained using methods like *backpropagation*, the *gradient* of the *loss function* with respect to the weights and biases is calculated. + The gradient depends on the *derivative* of the activation function, which helps adjust weights and biases to minimize the *error*. Therefore, the choice of + activation function affects how effectively the network learns. + +Characteristics of Activation Functions: + +1. **Scale and Normalize Outputs**: + Activation functions often scale and normalize the neuron's output. +2. **Introduce Non-Linearity**: + Activation functions introduce non-linearity into the network. Without non-linearity, even a multi-layer network would behave like a single-layer network, + as linear combinations of linear functions are still linear. Non-linearity allows the network to learn and model complex patterns + and relationships in the data. + +3. **Define Range of Outputs**: It defines the minumun and maximum value of the network output. For instance, The sigmoid activation function outputs values + between 0 and 1. It is defined as :math:`\sigma(x) = \frac{1}{1 + e^{-x}}`. + +4. **Simple Derivatives**: + Most activation functions have simple derivatives, which makes them computationally efficient during the training process. + The derivative of the **ReLU** (Rectified Linear Unit) function, which is :math:`\max(0, x)`. + +Neural Network - A Network of Neurons +************************************** + +A neural network is a complex system of interconnected neurons organized into layers. Each layer's output serves as the input for the next layer, +creating a stack of neurons that processes data in stages. Mathematically this can be boiled down to a sophisticated function that maps inputs to outputs +through numerous parameters. Training involves adjusting these parameters to improve the network's performance and accuracy. + +.. image:: ../figs/layers.png + +A neural network consists of three types of layers: input, hidden, and output. The input layer receives and holds raw data, with each neuron representing a +feature of the data. Hidden layers process this data by applying weights, biases, and activation functions to extract and learn complex patterns. These layers +transform the data and pass it to the next layer in the network. The output layer produces the final prediction or classification result based on the +processed information from the hidden layers. Each layer plays a crucial role in enabling the network to learn from and make accurate predictions on the data. + +.. admonition:: Explanation + :class: attention + + Matrix X represents the input matrix, where each column vector corresponds to an input sample. So if the matrix has the dimensions :math:`n \times m` + *n* will be the number of featues in each input sample and *m* will the total number of samples (also called training data). + + .. math:: + + X = \begin{bmatrix} + x_{1}^{(1)} & x_{1}^{(2)} & x_{1}^{(3)} & .... & x_{1}^{(m)} \\ + x_{2}^{(1)} & x_{2}^{(2)} & x_{3}^{(3)} & .... & x_{2}^{(m)} \\ + x_{3}^{(1)} & x_{3}^{(2)} & x_{3}^{(3)} & .... & x_{3}^{(m)} \\ + . & . & . & .... & . \\ + . & . & . & .... & . \\ + x_{n}^{(1)} & x_{n}^{(2)} & x_{n}^{(3)} & .... & x_{n}^{(m)} + \end{bmatrix} + + :math:`X^{(1)}` will represent the entire vector n x 1 vector representing first data sample while + :math:`x_{3}^{(1)}` will represent the third feature in first data sample. + + The figure below illustrates a 2-layer neural network where a single data sample (with 3 featues) is provided as input. + The input layer is not counted as one of the layers. + + .. image:: ../figs/2layer_NN.drawio.png + + Each hidden layer produces activations: in this example, layer 1 has 3 activations, while layer 2 has only one activation. + + .. math:: + a^{[1]} = \begin{bmatrix} + a_{1}^{[1]} \\ + a_{2}^{[1]} \\ + a_{3}^{[1]} + \end{bmatrix} + + .. math:: + a^{[2]} = a_{1}^{[2]} + + The number in square brackets represents the layer number, while the subscript denotes the neuron's index within that layer. + + Each neuron in every layer computes the *Z* value for each input sample and then calculates the activation value for that sample. + + The figure illustrates this process with an example of the first neuron in layer 1 processing the first input sample. + + .. image:: ../figs/activation.drawio.png + + In this situation if :math:`W_{1}^{[1]}` is + + .. math:: + + W_{1}^{[1]} = \begin{bmatrix} + 10.0 \\ + 20.0 \\ + 30.0 + \end{bmatrix} + + then :math:`W_{1}^{[1]T}` becomes + + .. math:: + W_{1}^{[1]} = \begin{bmatrix} + 10.0 & 20.0 & 30.0 + \end{bmatrix} + + and if :math:`X^{(1)}` is + + .. math:: + + X^{(1)} = \begin{bmatrix} + 0.3 \\ + 0.2 \\ + 0.5 + \end{bmatrix} + + and if bias b = 10 + + we can calculate :math:`Z_{1}^{[1](1)}` as + + .. math:: + + Z_{1}^{[1](1)} = \begin{bmatrix} + 10.0 & 20.0 & 30.0 + \end{bmatrix} \times \begin{bmatrix} + 0.3 \\ + 0.2 \\ + 0.5 + \end{bmatrix} + 10 \\ + + = (10.0* 0.3) + (20.0 * 0.2) + (30.0 * 0.5) + 10 = 32 + + Now if we apply the ReLU activation fuction :math:`max(0, x)` we get the activation as :math:`a_{1}^{[1](1)}` + + .. math:: + max(0, 32) = 32 + + + Similarly, we can calculate the activations for all the neorons in layer 1 for the input sample :math:`X^{(1)}` + + + .. math:: + + a_{1}^{[1](1)} = f(Z_{1}^{[1](1)}) = W_{1}^{[1]T} \times X^{(1)} + b_{1}^{[1]} + + .. math:: + + a_{2}^{[1](1)} = f(Z_{2}^{[1](1)}) = W_{2}^{[1]T} \times X^{(1)} + b_{2}^{[1]} + + .. math:: + + a_{3}^{[1](1)} = f(Z_{3}^{[1](1)}) = W_{3}^{[1]T} \times X^{(1)} + b_{3}^{[1]} + + Where :math:`W_{1}^{[1]T}, W_{2}^{[1]T}, W_{3}^{[1]T}` are transpose of vectors of size :math:`(3 \times 1)`. + + The above example demonstrates how this process works for a single neuron within a layer. In practice we can stack the weights of all neuron in a layer + into a matrix. + + .. math:: + + W = \begin{bmatrix} + ------ W_{1}^{[1]T} ------- \\ + ------ W_{2}^{[1]T} ------- \\ + ------ W_{3}^{[1]T} ------- + \end{bmatrix} + + Similarly we can stack the bias of different neuron in a layer + + .. math:: + + B = \begin{bmatrix} + b_{1}^{[1]} \\ + b_{2}^{[1]} \\ + b_{3}^{[1]} + \end{bmatrix} + + and the operation + + .. math:: + + Z = W^{T} \times X + B + + corresponds to the calculations + + .. math:: + + Z^{[1](1)} = \begin{bmatrix} + Z_{1}^{[1](1)} \\ + Z_{2}^{[1](1)} \\ + Z_{3}^{[1](1)} + \end{bmatrix} + + .. math:: + + = \begin{bmatrix} + W_{1}^{[1]T} \times X^{(1)} + b_{1}^{[1]} \\ + W_{2}^{[1]T} \times X^{(1)} + b_{2}^{[1]} \\ + W_{3}^{[1]T} \times X^{(1)} + b_{3}^{[1]} + \end{bmatrix} + + + + and finally we apply the activation function to the above matrix + + .. math:: + + a^{[1](1)} = \begin{bmatrix} + f(Z_{1}^{[1](1)}) \\ + f(Z_{2}^{[1](1)}) \\ + f(Z_{3}^{[1](1)}) + \end{bmatrix} + + .. math:: + + = \begin{bmatrix} + a_{1}^{[1](1)} \\ + a_{2}^{[1](1)} \\ + a_{3}^{[2](1)} + \end{bmatrix} + + + + + The above example illustrates how a single input sample is processed by a layer with 3 neurons. For *m* input samples and *a* neurons, we can compute the + complete activation of the first layer for all samples as follows: + + .. math:: + + a^{[1]} = \begin{bmatrix} + a_{1}^{(1)} & a_{1}^{(2)} & .... & a_{1}^{(m)} \\ + a_{2}^{(1)} & a_{2}^{(2)} & .... & a_{2}^{(m)} \\ + a_{3}^{(1)} & a_{3}^{(2)} & .... & a_{3}^{(m)} \\ + . & . & .... & . \\ + . & . & .... & . \\ + a_{a}^{(1)} & a_{a}^{(2)} & .... & a_{a}^{(m)} \\ + \end{bmatrix} + +This will involve a GEneral Matrix multiplication (GEMM) operation :math:`W^{[1]T} \times X` where :math:`X` will be the entire input sample +represented as a matrix of dimensions :math:`n \times m` (where *n* is the number of features in an input sample and *m* is the number of input samples. +In the above example *n* is 3). :math:`W^{[1]T}` will be a matrix of dimensions :math:`a \times m` (where *a* is the number of input neurons in that layer +and *m* is the number input samples. The above example *a* is 3). This will result in the output of the first layer represented as the matrix :math:`a^{[1]}` +and it will have the dimensions :math:`a \times n`. In matrix :math:`a^{[1]}`, the horizontal axis represents the training samples, while the vertical axis +represents the neurons in a layer. + +The matrix :math:`a^{[1]}` holds the value of :math:`a` neurons applied to :math:`m` input samples. This matrix then forms the input to the next layer in the neural network. + +We typically initialize the weights of each neuron randomly, although methods like **Xavier Initialization**, **He Initialization**, or +**Orthogonal Initialization** are commonly used to improve training efficiency. + + + +Loss Function and Cost Functions +******************************** + +During training, for each batch of input samples, calculations are +propagated through the network in a process called the **forward pass**. After each forward pass, the weights of the network are updated using +the **backpropagation** algorithm, which adjusts the weights based on the gradients to minimize error. However, in practice weight updates do not happen after +every individual sample; instead, they occur after each batch of data, depending on the **batch size** used. An **epoch** refers to a full pass +through the entire training dataset, where the network processes all data samples, performing forward passes and backpropagation for each batch. + +1. The **loss function** (also known as the error function or objective function) measures the error or difference between +the predicted output of the neural network and the actual target values for a single training example. +In this tutorial loss function will be denoted as :math:`L(y', y)` where :math:`y'` is the predicted output while :math:`y` is the actual output. + +2. The **cost function** is the average or aggregate of the loss function computed over the entire training dataset. It provides a measure of the +overall performance of the model across all examples. In this tutorial loss function will be denoted as :math:`J(W, b)` where :math:`w` is weight +and :math:`b` is biases in the NN. + + +The network performs the following steps to calculate the cost: + +1. Inputs the data. +2. Executes a forward pass to generate the network's output. +3. Computes the error in the output using the loss function. + +In the example of the 2-layer neural network we discussed earlier, the loss calculation would look like this: + +.. math:: + + Z^{[1]} = W^{[1]T} \times X + b^{[1]} \rightarrow a^{[1]} = f(Z^{[1]}) \rightarrow Z^{[2]} = W^{[2]T} \times X + b^{[2]} \rightarrow a^{[2]} = f(Z^{[2]}) \rightarrow L(a^{[2]}, y) + +Where the loss :math:`L(y', y)` is + +.. math:: + + L(y', y) = y' - y = a^{[2]} - y + +Since errors can be both positive and negative, we want to ensure they don't cancel each other out. +Therefore, in the cost function :math:`J(W, b)` we typically use the square of the error or the absolute value to avoid this issue. + + +.. admonition:: Explanation + :class: attention + + Mean Squared Error (MSE) is a common cost function. + + .. math:: + + J(W, b) = \frac{1}{2} \times \sum_{n=1}^{m} (y_{train} - y_{network})^{2} + + +Gradient Descent +**************** + +After computing the cost, we can adjust the weights and biases to minimize the cost in the next epoch. This is done using an optimization algorithm like +gradient descent. The goal is to iteratively update the values of W (weights) and b (biases) in the direction that reduces the cost function :math:`J(W, b)`. + +In gradient descent, we compute the gradient of the cost function with respect to the weights and biases, which tells us the direction of the steepest +increase in the cost. We then adjust the weights and biases by moving in the opposite direction of this gradient to minimize the cost. The update rule +is as follows: + +.. math:: + + w := w - \alpha \times \frac{\partial J(W, b)}{\partial w} \\ + b := w - \alpha \times \frac{\partial J(W, b)}{\partial b} + +until we find the optimal values for *w* and *b* that yield the minimum value for :math:`J(W, b)`. Here :math:`\alpha` is the learning rate. + +.. image:: ../figs/gradient-descent.png +.. image:: ../figs/gradient.png + +When selecting a cost function for a neural network, we typically choose a **convex function** because it ensures that there is only a single global +optimal value, rather than multiple local minima. A **convex function** has the property that any line segment between two points on the function +lies above or on the graph, meaning it has a **single valley shape**. This guarantees that when we minimize the cost, we are moving toward the global +minimum, rather than getting stuck in a local minimum. To find this optimal value, we continuously update the model parameters, such as the weights and +biases, using optimization techniques like gradient descent. This process moves us steadily toward the minimum point of the cost function. + + + +.. admonition:: Explanation + :class: attention + + The derivatives give you the slope (the direction in which we need to move the parameter values) of the loss function and eventually it moves to the local optimum. + + Suppose we have a function + + .. math:: + + J = 3 \times v + + Then the derivative of j with respect to v is + + .. math:: + + \frac{\partial J}{\partial v} = 3 + + What this means is that if *v* changes by a small value :math:`\delta`, J changes by :math:`3 \times \delta`. For example + + .. math:: + v = 2 \rightarrow J = 6 + v = 2.001 \rightarrow j = 6.003 + + In this example when v changes by 0.001 J changes by .003 (:math: `6.003 - 6`). + +But how does this approach help when the cost function :math:`J` involves weights and biases across multiple layers in the neural network, rather than just +a single layer? So we are not dealing with :math:`J(W, b)` but instead :math:`J(W^{[1]}, W^{[1]}, ...., W^{[L]}, b^{[1]}, b^{[2]},...., b^{[L]})`. +That is where we use the conscept of **Computational graphs**. + + +How does this approach help when the cost function :math:`J` involves weights and biases across multiple layers in the neural network, rather than just +a single layer? In this case, we are dealing with a more complex function, :math:`J(W, b)` but instead :math:`J(W^{[1]}, W^{[1]}, ...., W^{[L]}, b^{[1]}, b^{[2]},...., b^{[L]})` +where :math:`L` represents the number of layers in the network. + +This complexity is addressed using the concept of **computational graphs**. A **computational graph** is a directed acyclic graph where each node +represents an operation (like addition or multiplication) or a variable (such as weights, biases, or activations), and the edges represent the flow of +data between operations. + + +.. admonition:: Explanation + :class: attention + + + Suppose we have a set of computations as follows: + + .. math:: + + J(a, b, c) = 3 \times (a + b \times c) \\ + + We can rewrite this as: + + .. math:: + + u = b \times c + + .. math:: + + v = a + u + + .. math:: + + J = 3 \times v + + We can reprsent this computation as a directed graph where the nodes represent operations and edges represent the flow of data between these operations. + + .. image:: ../figs/comp_graph.drawio.png + + Then, by traversing the computational graph from right to left, we can determine how changes in parameters in one node affect the cost + function :math:`J(a, b c)`. + + If we change the value of *v* how much would the value of *J* change? + + .. math:: + + \frac{\partial J}{\partial v} = 3 \; \rightarrow eq(1) + + How does the change in *a* change the value of *J* (chain rule)? + + .. math:: + + \frac{\partial J}{\partial a} = \frac{\partial J}{\partial v} \times \frac{\partial v}{\partial a} \\ + \frac{\partial J}{\partial v} = 3 \; (from \: eq(1)) \\ + \frac{\partial v}{\partial a} = 1 \\ + + \frac{\partial J}{\partial a} = 3 \times 1 = 3 \; \rightarrow eq(2) + + How does the change in *u* change the value of *J* (chain rule)? + + .. math:: + + \frac{\partial J}{\partial u} = \frac{\partial J}{\partial v} \times \frac{\partial v}{\partial u} \\ + \frac{\partial J}{\partial v} = 3 \; (from \: eq(1)) \\ + \frac{\partial v}{\partial a} = 1 \\ + + \frac{\partial J}{\partial u} = 3 \times 1 = 3 \; \rightarrow eq(3) + + How does the change in *b* change the value of *J* (chain rule)? + + .. math:: + + \frac{\partial J}{\partial b} = \frac{\partial J}{\partial u} \times \frac{\partial u}{\partial b} \\ + \frac{\partial J}{\partial u} = 3 \; (from \: eq(3)) \\ + \frac{\partial u}{\partial b} = c \\ + + \frac{\partial J}{\partial u} = 3 \times c = 3c + + + How does the change in *c* change the value of *J* (chain rule)? + + .. math:: + + \frac{\partial J}{\partial c} = \frac{\partial J}{\partial u} \times \frac{\partial u}{\partial c} \\ + \frac{\partial J}{\partial u} = 3 \; (from \: eq(3)) \\ + \frac{\partial u}{\partial c} = b \\ + + \frac{\partial J}{\partial u} = 3 \times b = 3b + + As seen from above when computing a derivative it is easier to move from the right to left following the computation graph. + + + +Backpropagation +*************** + +Based on the cost function, we may need to either excite (increase the influence) or inhibit (decrease the influence) certain neurons. To achieve this, +each layer indirectly affects the weights and biases of the preceding layer using the same computational graph concept we discussed earlier. This process +is known as backpropagation. + +So, how does backpropagation connect with computational graphs? Let's examine a brief (and incomplete) Python code snippet that demonstrates how to +update the final hidden layer using the cost function from the output layer. + +.. code-block:: python + :linenos: + + # forward pass from the last hidden layer to the output layer + for i in range (1, m): + Zi = gemm(W, X[:i]) + b # matrix multiplication followed by addition + ai = f(Zi) # f() is the activation function + + l = L(ai, yi) # L() is the loss function + J+ = l # accumulate the loss for each input sample + + # average over m input samples + J = J / m + + # Backpropagation from the output layer to the last hidden layer + # assuming we have just two neurons in the layer + dW1 += slope_W1(J, W1) # find the slope (derivative) of the cost function wrt W1 + dW2 += slope_W2(J, W2) # find the slope (derivative) of the cost function wrt W2 + db1 += slope_b1(J, b1) # find the slope (derivative) of the cost function wrt db1 + db2 += slope_b2(J, b2) # find the slope (derivative) of the cost function wrt db2 + + # update the weights and biases + W1 = W1 - alpha * dW1 # alpha is the learning rate + W2 = W2 - alpha * dW2 + b1 = b1 - alpha * db1 + b2 = b2 - alpha * db2 + + +Where :math:`dW1 = \frac{\partial J}{\partial W_{1}}`, :math:`dW2 = \frac{\partial J}{\partial W_{2}}`, :math:`db1 = \frac{\partial J}{\partial b_{1}}` and :math:`db2 = \frac{\partial J}{\partial b_{2}}`. In practice, we will replace the for loop with a vectorized implementation to improve efficiency. + +Convergence +*************** + +Finally, we stop the training when the network converges. In the context of neural networks, convergence refers to the point where the training process +stabilizes, and the performance metrics (such as the cost function) cease to improve significantly or become consistent. + + + + + +.. admonition:: Key Points + :class: hint + + #. At its core, a neural network performs general matrix-matrix operations (GEMM). + #. After each epoch, weights are adjusted to recalibrate the network. + #. The more data you have, the more effective this recalibration becomes (brute force approach). \ No newline at end of file diff --git a/job_scripts/bkp_multinode_torchrun.pbs b/job_scripts/bkp_multinode_torchrun.pbs new file mode 100644 index 0000000..cad8293 --- /dev/null +++ b/job_scripts/bkp_multinode_torchrun.pbs @@ -0,0 +1,42 @@ +#!/bin/bash + +#PBS -P vp91 +#PBS -q gpuvolta + +#PBS -l ncpus=96 +#PBS -l ngpus=8 +#PBS -l mem=10GB +#PBS -l walltime=00:05:00 + +#PBS -N multinode + +module load python3/3.11.0 +module load cuda/12.3.2 +module load nccl/2.19.4 + +. /scratch/vp91/Training-Venv/pytorch/bin/activate + +python3 /scratch/vp91/$USER/intro-to-pytorch/src/distributed_data_parallel.py + +# Get the list of allocated nodes +NODES=$(cat $PBS_NODEFILE | uniq) +NODE_ARR=($NODES) + + + +# Define the master node (usually the first node in the list) +MASTER_ADDR=${NODE_ARR[0]} +MASTER_PORT=12355 # Set an appropriate port for communication + +NNODES=2 +NPROC_PER_NODE=4 +WORLD_SIZE=$(($NNODES * $NPROC_PER_NODE)) + +# Rendezvous backend and endpoint +RDZV_BACKEND="c10d" +RDZV_ENDPOINT="${MASTER_ADDR}:${MASTER_PORT}" +RDZV_ID="100" + +torchrun --nnodes=$NNODES --nproc_per_node=$NPROC_PER_NODE \ + --rdzv_backend=$RDZV_BACKEND --rdzv_endpoint=$RDZV_ENDPOINT --rdzv_id=$RDZV_ID \ + /scratch/vp91/$USER/intro-to-pytorch/src/multinode_torchrun.py \ No newline at end of file diff --git a/job_scripts/distributed_data_parallel.pbs b/job_scripts/distributed_data_parallel.pbs new file mode 100644 index 0000000..2bdf2d1 --- /dev/null +++ b/job_scripts/distributed_data_parallel.pbs @@ -0,0 +1,18 @@ +#!/bin/bash + +#PBS -P vp91 +#PBS -q gpuvolta + +#PBS -l ncpus=24 +#PBS -l ngpus=2 +#PBS -l mem=10GB +#PBS -l walltime=00:05:00 + +#PBS -N distributed_data_parallel + +module load python3/3.11.0 +module load cuda/12.3.2 + +. /scratch/vp91/Training-Venv/pytorch/bin/activate + +python3 /scratch/vp91/$USER/intro-to-pytorch/src/distributed_data_parallel.py \ No newline at end of file diff --git a/job_scripts/multinode_torchrun.pbs b/job_scripts/multinode_torchrun.pbs new file mode 100644 index 0000000..fee78ab --- /dev/null +++ b/job_scripts/multinode_torchrun.pbs @@ -0,0 +1,42 @@ +#!/bin/bash + +#PBS -P vp91 +#PBS -q gpuvolta + +#PBS -l ncpus=96 +#PBS -l ngpus=8 +#PBS -l mem=10GB +#PBS -l walltime=00:20:00 + +#PBS -N multinode + +module load python3/3.11.0 +module load cuda/12.3.2 + +. /scratch/vp91/Training-Venv/pytorch/bin/activate + +# Set variables +if [[ $PBS_NCPUS -ge $PBS_NCI_NCPUS_PER_NODE ]] +then + NNODES=$((PBS_NCPUS / PBS_NCI_NCPUS_PER_NODE)) +else + NNODES=1 +fi + +PROC_PER_NODE=$((PBS_NGPUS / NNODES)) + +MASTER_ADDR=$(cat $PBS_NODEFILE | head -n 1) + +# Launch script +LAUNCH_SCRIPT=/scratch/vp91/jxj900/intro-to-pytorch/job_scripts/multinode_torchrun.sh + +# Set execute permission +chmod u+x ${LAUNCH_SCRIPT} + +# Run PyTorch application +for inode in $(seq 1 $PBS_NCI_NCPUS_PER_NODE $PBS_NCPUS); do + echo $inode + pbsdsh -n $inode ${LAUNCH_SCRIPT} ${NNODES} ${PROC_PER_NODE} ${MASTER_ADDR} & +done + +wait \ No newline at end of file diff --git a/job_scripts/multinode_torchrun.sh b/job_scripts/multinode_torchrun.sh new file mode 100755 index 0000000..dee1fec --- /dev/null +++ b/job_scripts/multinode_torchrun.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +# Load shell environment variables +source ~/.bashrc + +module load python3/3.11.0 +module load cuda/12.3.2 + +. /scratch/vp91/Training-Venv/pytorch/bin/activate + +# Application script +APPLICATION_SCRIPT=/scratch/vp91/jxj900/intro-to-pytorch/src/multinode_torchrun.py + +# Set execute permission +chmod u+x ${APPLICATION_SCRIPT} + +# Run PyTorch application +torchrun --nnodes=${1} --nproc_per_node=${2} --rdzv_id=100 --rdzv_backend=c10d --rdzv_endpoint=${3}:29400 ${APPLICATION_SCRIPT} diff --git a/job_scripts/test.pbs b/job_scripts/test.pbs new file mode 100644 index 0000000..4706964 --- /dev/null +++ b/job_scripts/test.pbs @@ -0,0 +1,42 @@ +#!/bin/bash + +#PBS -P vp91 +#PBS -q normal + +#PBS -l ncpus=96 +#PBS -l mem=10GB +#PBS -l walltime=00:05:00 + +#PBS -N multinode + +module load python3/3.11.0 +module load cuda/12.3.2 +module load nccl/2.19.4 + +. /scratch/vp91/Training-Venv/pytorch/bin/activate + +which python + +# Get the list of allocated nodes +NODES=$(cat $PBS_NODEFILE | uniq) +echo $NODES + +NODE_ARR=($NODES) +echo $NODE_ARR + +# Define the master node (usually the first node in the list) +MASTER_ADDR=${NODE_ARR[0]} +MASTER_PORT=12355 # Set an appropriate port for communication +echo $MASTER_ADDR + +NNODES=2 +NPROC_PER_NODE=4 +WORLD_SIZE=$(($NNODES * $NPROC_PER_NODE)) + +echo $WORLD_SIZE + +echo $PBS_NODEID + +# Rendezvous backend and endpoint +RDZV_BACKEND="c10d" +RDZV_ENDPOINT="${MASTER_ADDR}:${MASTER_PORT}" \ No newline at end of file diff --git a/notebooks/.ipynb_checkpoints/GPU_NN-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/GPU_NN-checkpoint.ipynb new file mode 100644 index 0000000..3a0b249 --- /dev/null +++ b/notebooks/.ipynb_checkpoints/GPU_NN-checkpoint.ipynb @@ -0,0 +1,315 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1cdb801e-e281-476f-b3e9-e470785d3ad9", + "metadata": {}, + "source": [ + "### Neural Networks" + ] + }, + { + "cell_type": "markdown", + "id": "96127ef4-bb03-492f-81b9-672f74c20b5c", + "metadata": {}, + "source": [ + "Neural networks are computational models inspired by the human brain, designed to recognize patterns and\n", + "make decisions based on data. They consist of interconnected layers of nodes, or \"neurons,\" which process\n", + "and transform input information. Through training, neural networks learn to improve their accuracy in tasks like image recognition, language processing, and more.Neural networks comprise of layers that perform operations on data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "604d5312-0b33-4162-b5f4-551c21732550", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49a40db4-da7b-4d24-b707-a39b79d2440e", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "# The jupyter notebook is launched from your $HOME directory.\n", + "# Change the working directory to the workshop directory\n", + "# which was created in your username directory under /scratch/vp91\n", + "os.chdir(os.path.expandvars(\"/scratch/vp91/$USER/\"))" + ] + }, + { + "cell_type": "markdown", + "id": "4098c4ec-368b-4802-9800-fc4c4b7479ba", + "metadata": {}, + "source": [ + "#### Set Device\n", + "Se the default device as the GPU if it exists" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b51be64-542f-401c-ae73-00da2bbd6471", + "metadata": {}, + "outputs": [], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "print(device)" + ] + }, + { + "cell_type": "markdown", + "id": "b32c13d2-bee6-436c-b838-2c8e04a24ec6", + "metadata": {}, + "source": [ + "### Curate the dataset\n", + "Load the dataset, split into features (X) and output (y) variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "222a7a99-2723-486d-9a1e-58d2792c84e8", + "metadata": {}, + "outputs": [], + "source": [ + "datapath = os.path.expandvars('/scratch/vp91/$USER/intro-to-pytorch/data/pima-indians-diabetes.data.csv')\n", + "\n", + "dataset = np.loadtxt(datapath, delimiter=',')\n", + "X = dataset[:,0:8] \n", + "y = dataset[:,8]\n", + "\n", + "X_tensor = torch.tensor(X, dtype=torch.float32)\n", + "y_tensor = torch.tensor(y, dtype=torch.float32).reshape(-1, 1)" + ] + }, + { + "cell_type": "markdown", + "id": "abc70a8b-2c37-4c09-bd7c-717d556cb39c", + "metadata": {}, + "source": [ + "### Defining the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "263d5838-320d-4dad-ac59-e2d95ada7873", + "metadata": {}, + "outputs": [], + "source": [ + "class PimaClassifier(nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.hidden1 = nn.Linear(8, 12)\n", + " self.act1 = nn.ReLU()\n", + " self.hidden2 = nn.Linear(12, 8)\n", + " self.act2 = nn.ReLU()\n", + " self.output = nn.Linear(8, 1)\n", + " self.act_output = nn.Sigmoid()\n", + " \n", + " def forward(self, x):\n", + " x = self.act1(self.hidden1(x))\n", + " x = self.act2(self.hidden2(x))\n", + " x = self.act_output(self.output(x))\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "289c55b3-f54b-4a79-ba58-5788237aabb9", + "metadata": {}, + "outputs": [], + "source": [ + "class_model = PimaClassifier()\n", + "print(class_model)" + ] + }, + { + "cell_type": "markdown", + "id": "09ec5647-aded-4179-89c0-0c5d44b0c6db", + "metadata": {}, + "source": [ + "#### Save the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5a58574-4ce7-495c-be0b-d22694a6ed7a", + "metadata": {}, + "outputs": [], + "source": [ + "modelpath = os.path.expandvars('/scratch/vp91/$USER/class_model')\n", + "print(modelpath)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b2f9c1c9-4d45-447d-ac23-40593759da3c", + "metadata": {}, + "outputs": [], + "source": [ + "torch.save(class_model.state_dict(), modelpath)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65839c60-6d5f-4540-aabe-fbd1914692d8", + "metadata": {}, + "outputs": [], + "source": [ + "!ls /scratch/vp91/$USER/class_model" + ] + }, + { + "cell_type": "markdown", + "id": "a7fcdf90-1db4-4c6e-9f28-1bc8e7a4bfd0", + "metadata": {}, + "source": [ + "#### Load the model on the GPU" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d70bfdf8-9619-4448-ad45-cb2277d937ea", + "metadata": {}, + "outputs": [], + "source": [ + "class_model.load_state_dict(torch.load(modelpath, map_location=device, weights_only=True))\n", + "class_model.to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "593672e5-4e14-473d-80f9-2ed00c127729", + "metadata": {}, + "outputs": [], + "source": [ + "loss_fn = nn.BCELoss()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "518bf03c-0594-494e-bdb3-a41421ac53a0", + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = optim.Adam(class_model.parameters(), lr=0.001)" + ] + }, + { + "cell_type": "markdown", + "id": "8608326d-27a3-4a5b-b485-e9af74b0f2e8", + "metadata": {}, + "source": [ + "#### Training the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad6b09b5-9e9b-4376-ad89-d1ff8b4791eb", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "n_epochs = 100\n", + "batch_size = 10\n", + " \n", + "for epoch in range(n_epochs):\n", + " for i in range(0, len(X_tensor), batch_size):\n", + " Xbatch = X_tensor[i:i+batch_size].to(device) # move the tensor to GPU\n", + "\n", + " y_pred = class_model(Xbatch)\n", + " \n", + " ybatch = y_tensor[i:i+batch_size].to(device) # move the tensor to GPU\n", + " \n", + " loss = loss_fn(y_pred, ybatch)\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + " \n", + " print(f'Finished epoch {epoch}, latest loss {loss}')" + ] + }, + { + "cell_type": "markdown", + "id": "04b239a9-7e1c-42ba-8b54-58c79d26986b", + "metadata": {}, + "source": [ + "#### Evaluate the Model\n", + "\n", + "Currently, we are testing the model on the training dataset. Ideally, we should split the data into separate training and testing datasets, or use a distinct dataset for evaluation. For simplicity, we are testing the model on the same data used for training.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89e28fe2-90c5-4cd4-bd37-30ebe9183772", + "metadata": {}, + "outputs": [], + "source": [ + "with torch.no_grad():\n", + " y_pred = class_model(X_tensor.to(device))\n", + " \n", + "accuracy = (y_pred.round().to(device) == y_tensor.to(device)).float().mean()\n", + "print(f\"Accuracy {accuracy}\")" + ] + }, + { + "cell_type": "markdown", + "id": "e07a44bb-25c6-4f67-8a34-514d7eadbbaf", + "metadata": {}, + "source": [ + "### Exercise\n", + "\n", + "1. **What is the time difference in training**? Compare it with the previous training." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84664807-8163-46dd-b037-1b3f73d8cbd9", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/.ipynb_checkpoints/building_NN-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/building_NN-checkpoint.ipynb new file mode 100644 index 0000000..20e626c --- /dev/null +++ b/notebooks/.ipynb_checkpoints/building_NN-checkpoint.ipynb @@ -0,0 +1,426 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1cdb801e-e281-476f-b3e9-e470785d3ad9", + "metadata": {}, + "source": [ + "### Neural Networks" + ] + }, + { + "cell_type": "markdown", + "id": "96127ef4-bb03-492f-81b9-672f74c20b5c", + "metadata": {}, + "source": [ + "Neural networks are computational models inspired by the human brain, designed to recognize patterns and\n", + "make decisions based on data. They consist of interconnected layers of nodes, or \"neurons,\" which process\n", + "and transform input information. Through training, neural networks learn to improve their accuracy in tasks like image recognition, language processing, and more.Neural networks comprise of layers that perform operations on data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49a40db4-da7b-4d24-b707-a39b79d2440e", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "# The jupyter notebook is launched from your $HOME directory.\n", + "# Change the working directory to the workshop directory\n", + "# which was created in your username directory under /scratch/vp91\n", + "os.chdir(os.path.expandvars(\"/scratch/vp91/$USER/\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "604d5312-0b33-4162-b5f4-551c21732550", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim" + ] + }, + { + "cell_type": "markdown", + "id": "7f1baab1-a7b6-429e-afa3-822e61da46ad", + "metadata": {}, + "source": [ + "### Dataset\n", + "The Pima Indians Diabetes dataset is a popular dataset in the field of machine learning and statistics, particularly for those working on classification problems. \n", + "\n", + "Dataset Overview:\n", + "**Source**: The dataset was created by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and is available in the UCI Machine Learning Repository.\n", + "**Purpose**: The dataset is used to predict the onset of diabetes within five years based on diagnostic measures.\n", + "**Features**: The dataset contains 768 samples, each with 8 features. \n", + "\n", + "The features are:\n", + "\n", + "1. Pregnancies: Number of times pregnant.\n", + "2. Glucose: Plasma glucose concentration (mg/dL) a 2 hours in an oral glucose tolerance test.\n", + "3. Blood Pressure: Diastolic blood pressure (mm Hg) at the time of screening.\n", + "4. Skin Thickness: Triceps skinfold thickness (mm) measured at the back of the upper arm.\n", + "5. Insulin: 2-Hour serum insulin (mu U/ml).\n", + "6. BMI: Body mass index (weight in kg/(height in m)^2).\n", + "7. Diabetes Pedigree Function: A function that scores likelihood of diabetes based on family history.\n", + "8. Age: Age of the individual (years).\n", + "\n", + "**Outcome**: Whether or not the individual has diabetes (1 for positive, 0 for negative)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d4b1b9b-bf50-4867-8345-43a7106a25da", + "metadata": {}, + "outputs": [], + "source": [ + "!head /scratch/vp91/$USER/intro-to-pytorch/data/pima-indians-diabetes.data.csv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee303492-97bf-4274-9a14-04c1c116f6c8", + "metadata": {}, + "outputs": [], + "source": [ + "datapath = os.path.expandvars('/scratch/vp91/$USER/intro-to-pytorch/data/pima-indians-diabetes.data.csv')\n", + "print(datapath)" + ] + }, + { + "cell_type": "markdown", + "id": "b32c13d2-bee6-436c-b838-2c8e04a24ec6", + "metadata": {}, + "source": [ + "### Curate the dataset\n", + "Load the dataset, split into features (X) and output (y) variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "222a7a99-2723-486d-9a1e-58d2792c84e8", + "metadata": {}, + "outputs": [], + "source": [ + "dataset = np.loadtxt(datapath, delimiter=',')\n", + "X = dataset[:,0:8] \n", + "y = dataset[:,8]" + ] + }, + { + "cell_type": "markdown", + "id": "4c2a20b9-0c73-4995-b772-0e773cc03c8b", + "metadata": {}, + "source": [ + "### Convert the data to tensors" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3c45e8f-894e-46d3-84c1-25fbca333f81", + "metadata": {}, + "outputs": [], + "source": [ + "X_tensor = torch.tensor(X, dtype=torch.float32)\n", + "y_tensor = torch.tensor(y, dtype=torch.float32).reshape(-1, 1)" + ] + }, + { + "cell_type": "markdown", + "id": "abc70a8b-2c37-4c09-bd7c-717d556cb39c", + "metadata": {}, + "source": [ + "### Defining the Model\n", + "\n", + "When designing the model, keep the following points in mind:\n", + "\n", + "1. The input features in the input layer must match the input features in the dataset (`X_tensor`).\n", + "2. A high number of layers can increase computation time, while too few layers may result in poor predictions.\n", + "3. Each layer should be followed by an activation function.\n", + "\n", + "In this example, we will use a 3-layer neural network:\n", + "\n", + "1. The input layer expects 8 features.\n", + "2. The first hidden layer has 12 neurons, followed by a ReLU activation function.\n", + "3. The second hidden layer has 8 neurons, followed by another ReLU activation function.\n", + "4. The output layer has one neuron, followed by a sigmoid activation function.\n", + "\n", + "The sigmoid function outputs values between 0 and 1, which is exactly what we need." + ] + }, + { + "cell_type": "markdown", + "id": "20d8051c-32ee-45c1-b797-58c0e68bbcfb", + "metadata": {}, + "source": [ + "\n", + "In PyTorch, neural networks can be defined using different approaches, and two common ones are the Sequential model and the class-based model." + ] + }, + { + "cell_type": "markdown", + "id": "a81a5b5b-d3bc-434f-9b92-a8571d7599f5", + "metadata": {}, + "source": [ + "#### Sequential model\n", + "\n", + "* The Sequential model is a simple, linear stack of layers where each layer has a single input and output. It is useful for straightforward feedforward networks where layers are applied in a sequential order.\n", + "* It is easier to use for simple architectures where layers are applied in a linear fashion.\n", + "* Defined Using: *torch.nn.Sequential*." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6cb10442-640a-4ded-a81f-6c2607a86bed", + "metadata": {}, + "outputs": [], + "source": [ + "seq_model = nn.Sequential(\n", + " nn.Linear(8, 12),\n", + " nn.ReLU(),\n", + " nn.Linear(12, 8),\n", + " nn.ReLU(),\n", + " nn.Linear(8, 1),\n", + " nn.Sigmoid()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c8647eb7-799b-42e5-b42d-be699b5e5a3e", + "metadata": {}, + "outputs": [], + "source": [ + "print(seq_model)" + ] + }, + { + "cell_type": "markdown", + "id": "36a8225d-4528-4a2f-a23e-9285d4ab5c8e", + "metadata": {}, + "source": [ + "### Class-Based Model\n", + "\n", + "The class-based model allows you to define a network by subclassing torch.nn.Module. This approach provides greater flexibility and control, making it suitable for complex models and custom behaviors.\n", + "\n", + "* Offers full control over the network architecture, including complex data flows, multiple inputs/outputs, and custom forward methods.\n", + "* Custom Forward Pass: You can define complex forward passes and control data flow through the network.\n", + "* Dynamic Behavior: Allows for dynamic computations, such as conditional layers or operations.\n", + "* Defined Using: Subclass of torch.nn.Module" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "263d5838-320d-4dad-ac59-e2d95ada7873", + "metadata": {}, + "outputs": [], + "source": [ + "class PimaClassifier(nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.hidden1 = nn.Linear(8, 12)\n", + " self.act1 = nn.ReLU()\n", + " self.hidden2 = nn.Linear(12, 8)\n", + " self.act2 = nn.ReLU()\n", + " self.output = nn.Linear(8, 1)\n", + " self.act_output = nn.Sigmoid()\n", + " \n", + " def forward(self, x):\n", + " x = self.act1(self.hidden1(x))\n", + " x = self.act2(self.hidden2(x))\n", + " x = self.act_output(self.output(x))\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "289c55b3-f54b-4a79-ba58-5788237aabb9", + "metadata": {}, + "outputs": [], + "source": [ + "class_model = PimaClassifier()\n", + "print(class_model)" + ] + }, + { + "cell_type": "markdown", + "id": "2e889fd5-49a7-4509-a2fc-ef7d5f8c722f", + "metadata": {}, + "source": [ + "### Define the loss function\n", + "Binary Cross-Entropy (BCE) Loss: Measures the performance of a classification model whose output is a probability value between 0 and 1. It calculates the difference between the predicted probabilities and the actual binary labels (0 or 1) and penalizes the model more when the predictions are further from the true labels.\n", + "\n", + "BCELoss(y', y)=−[ylog(y')+(1−y)log(1−y')]\n", + "\n", + "Where, y' is the predicted output and y is the actual otput." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "593672e5-4e14-473d-80f9-2ed00c127729", + "metadata": {}, + "outputs": [], + "source": [ + "loss_fn = nn.BCELoss()" + ] + }, + { + "cell_type": "markdown", + "id": "76aa2eab-7897-439e-9c20-08eb523ec7d6", + "metadata": {}, + "source": [ + "### Optimizer\n", + "\n", + "Optimizer's main role is to update the model's parameters based on the gradients computed during backpropagation.\n", + "\n", + "1. **Parameter Updates**: Optimizers adjust the weights and biases of the neural network to reduce the loss. This involves applying algorithms that modify the parameters to minimize the difference between the predicted outputs and the actual targets.\n", + "2. **Learning Rate Management**: Most optimizers include mechanisms to adjust the learning rate, either statically or dynamically, to control how large the parameter updates are.\n", + "\n", + "In this example we use an optimizer called Adaptive Moment Estimation (Adam). This computes an adaptive learning rates for each parameter by considering both the mean and the variance of the gradients." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "518bf03c-0594-494e-bdb3-a41421ac53a0", + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = optim.Adam(class_model.parameters(), lr=0.001)" + ] + }, + { + "cell_type": "markdown", + "id": "8608326d-27a3-4a5b-b485-e9af74b0f2e8", + "metadata": {}, + "source": [ + "#### Training the Model\n", + "\n", + "Training a neural network involves epochs and batches, which define how data is fed to the model:\n", + "\n", + "- **Epoch:** A full pass through the entire training dataset.\n", + "- **Batch:** A subset of samples processed at a time, with gradient descent performed after each batch.\n", + "\n", + "In practice, the dataset is divided into batches, and each batch is processed sequentially in a training loop. Completing all batches constitutes one epoch. The process is repeated for multiple epochs to refine the model.\n", + "\n", + "Batch size is constrained by system memory (GPU memory), and computational demands scale with batch size. More epochs and batches lead to better model performance but increase training time. The optimal number of epochs and batch size is often determined through experimentation." + ] + }, + { + "cell_type": "markdown", + "id": "6ea1f2ef-0b63-435c-a93f-329aa9ae6228", + "metadata": {}, + "source": [ + "#### Purpose of optimizer.zero_grad(), loss.backward(), optimizer.step()\n", + "\n", + "**optimizer.zero_grad()**: During training, gradients accumulate by default in PyTorch. This means that if you don’t clear them, gradients from multiple backward passes (from different batches) will be added together, which can lead to incorrect updates to the model parameters.\n", + "By calling optimizer.zero_grad(), you ensure that gradients from previous steps are reset to zero, preventing them from affecting the current update.\n", + "\n", + "**loss.backward()**: Calculates the gradients of the loss with respect to each parameter of the model. This is done using backpropagation, a key algorithm for training neural networks.\n", + "\n", + "**optimizer.step()**: Used to update the model's parameters based on the gradients computed during during the backward pass (**loss.backward()**)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad6b09b5-9e9b-4376-ad89-d1ff8b4791eb", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "n_epochs = 100\n", + "batch_size = 10\n", + " \n", + "for epoch in range(n_epochs):\n", + " for i in range(0, len(X_tensor), batch_size):\n", + " Xbatch = X_tensor[i:i+batch_size]\n", + " y_pred = class_model(Xbatch)\n", + " ybatch = y_tensor[i:i+batch_size]\n", + " loss = loss_fn(y_pred, ybatch)\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + " print(f'Finished epoch {epoch}, latest loss {loss}')" + ] + }, + { + "cell_type": "markdown", + "id": "04b239a9-7e1c-42ba-8b54-58c79d26986b", + "metadata": {}, + "source": [ + "# Evaluate the Model\n", + "\n", + "Currently, we are testing the model on the training dataset. Ideally, we should split the data into separate training and testing datasets, or use a distinct dataset for evaluation. For simplicity, we are testing the model on the same data used for training.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89e28fe2-90c5-4cd4-bd37-30ebe9183772", + "metadata": {}, + "outputs": [], + "source": [ + "with torch.no_grad():\n", + " y_pred = class_model(X_tensor)\n", + " \n", + "accuracy = (y_pred.round() == y_tensor).float().mean()\n", + "print(f\"Accuracy {accuracy}\")" + ] + }, + { + "cell_type": "markdown", + "id": "e07a44bb-25c6-4f67-8a34-514d7eadbbaf", + "metadata": {}, + "source": [ + "### Exercise\n", + "\n", + "1. **Increase the number of layers in the neural network.** Observe any changes in accuracy.\n", + "2. **Change the optimizer from Adam to [Stochastic Gradient Descent (SGD)](https://pytorch.org/docs/stable/generated/torch.optim.SGD.html).** Evaluate how this affects the loss calculation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84664807-8163-46dd-b037-1b3f73d8cbd9", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/.ipynb_checkpoints/data_parallel-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/data_parallel-checkpoint.ipynb new file mode 100644 index 0000000..e90d4e5 --- /dev/null +++ b/notebooks/.ipynb_checkpoints/data_parallel-checkpoint.ipynb @@ -0,0 +1,348 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1cdb801e-e281-476f-b3e9-e470785d3ad9", + "metadata": {}, + "source": [ + "### Using Multiple GPUs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "604d5312-0b33-4162-b5f4-551c21732550", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "from torch.utils.data import Dataset, DataLoader\n", + "import pandas as pd\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49a40db4-da7b-4d24-b707-a39b79d2440e", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "# The jupyter notebook is launched from your $HOME directory.\n", + "# Change the working directory to the workshop directory\n", + "# which was created in your username directory under /scratch/vp91\n", + "os.chdir(os.path.expandvars(\"/scratch/vp91/$USER/\"))" + ] + }, + { + "cell_type": "markdown", + "id": "4098c4ec-368b-4802-9800-fc4c4b7479ba", + "metadata": {}, + "source": [ + "#### Set Device\n", + "Se the default device as the GPU if it exists" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b51be64-542f-401c-ae73-00da2bbd6471", + "metadata": {}, + "outputs": [], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "print(device)" + ] + }, + { + "cell_type": "markdown", + "id": "2d29976e-56b5-46ce-8743-5480524bbca1", + "metadata": {}, + "source": [ + "### Dataloader" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea28a5d7-0d69-47c9-ba24-995f02168856", + "metadata": {}, + "outputs": [], + "source": [ + "datapath = os.path.expandvars('/scratch/vp91/$USER/intro-to-pytorch/data/pima-indians-diabetes.data.csv')\n", + "\n", + "# Define the custom Dataset class\n", + "column_names = [\n", + " 'Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness',\n", + " 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'\n", + "]\n", + "\n", + "# Define the custom Dataset class\n", + "class PimaDataset(Dataset):\n", + " def __init__(self, csv_file):\n", + " # Load the CSV file without header and assign column names\n", + " self.data = pd.read_csv(csv_file, header=None, names=column_names)\n", + " self.features = self.data.drop('Outcome', axis=1).values\n", + " self.labels = self.data['Outcome'].values\n", + " \n", + " # Convert to PyTorch tensors\n", + " self.features_tensor = torch.tensor(self.features, dtype=torch.float32)\n", + " self.labels_tensor = torch.tensor(self.labels, dtype=torch.long)\n", + " \n", + " # Calculate mean and std\n", + " self.mean = self.features_tensor.mean(dim=0)\n", + " self.std = self.features_tensor.std(dim=0)\n", + " \n", + " # Normalize the features\n", + " self.features_tensor = (self.features_tensor - self.mean) / self.std\n", + "\n", + " def __len__(self):\n", + " return len(self.data)\n", + "\n", + " def __getitem__(self, idx):\n", + " feature = self.features_tensor[idx]\n", + " label = self.labels_tensor[idx]\n", + " return feature, label" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "110beb4c-c07b-4a1b-b2d4-ce7795af31a4", + "metadata": {}, + "outputs": [], + "source": [ + "dataset = PimaDataset(datapath)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26d7f973-cf59-4709-a0c2-a158100449e9", + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 32\n", + "data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)" + ] + }, + { + "cell_type": "markdown", + "id": "abc70a8b-2c37-4c09-bd7c-717d556cb39c", + "metadata": {}, + "source": [ + "### Defining the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "263d5838-320d-4dad-ac59-e2d95ada7873", + "metadata": {}, + "outputs": [], + "source": [ + "class PimaClassifier(nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.hidden1 = nn.Linear(8, 12)\n", + " self.act1 = nn.ReLU()\n", + " self.hidden2 = nn.Linear(12, 8)\n", + " self.act2 = nn.ReLU()\n", + " self.output = nn.Linear(8, 1)\n", + " self.act_output = nn.Sigmoid()\n", + " \n", + " def forward(self, x):\n", + " x = self.act1(self.hidden1(x))\n", + " x = self.act2(self.hidden2(x))\n", + " x = self.act_output(self.output(x))\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "289c55b3-f54b-4a79-ba58-5788237aabb9", + "metadata": {}, + "outputs": [], + "source": [ + "class_model = PimaClassifier()\n", + "print(class_model)" + ] + }, + { + "cell_type": "markdown", + "id": "09ec5647-aded-4179-89c0-0c5d44b0c6db", + "metadata": {}, + "source": [ + "#### Data Parallelism\n", + "Pytorch will only use one GPU by default. You can easily run your operations on multiple GPUs by making your model run parallelly using `nn.DataParallel`. \n", + "\n", + "Check for multiple GPUs and if multiple GPUs are available, wrap the model with `nn.DataParallel`. Finally, move the model to the GPUs using `model.to(device)`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0dc9a702-4b3a-423f-80d3-79d1e3d9e11f", + "metadata": {}, + "outputs": [], + "source": [ + "print(torch.cuda.device_count())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5a58574-4ce7-495c-be0b-d22694a6ed7a", + "metadata": {}, + "outputs": [], + "source": [ + "if torch.cuda.device_count() > 1:\n", + " class_model = nn.DataParallel(class_model)\n", + " print(f\"Using {torch.cuda.device_count()} GPUs: {', '.join([torch.cuda.get_device_name(i) for i in range(torch.cuda.device_count())])}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d70bfdf8-9619-4448-ad45-cb2277d937ea", + "metadata": {}, + "outputs": [], + "source": [ + "class_model.to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "593672e5-4e14-473d-80f9-2ed00c127729", + "metadata": {}, + "outputs": [], + "source": [ + "loss_fn = nn.BCELoss()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "518bf03c-0594-494e-bdb3-a41421ac53a0", + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = optim.Adam(class_model.parameters(), lr=0.001)" + ] + }, + { + "cell_type": "markdown", + "id": "8608326d-27a3-4a5b-b485-e9af74b0f2e8", + "metadata": {}, + "source": [ + "#### Training the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "67278570-0838-40a9-9410-851c51a46b95", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "04b239a9-7e1c-42ba-8b54-58c79d26986b", + "metadata": {}, + "source": [ + "DataParallel splits your data automatically and sends job orders to multiple models on several GPUs. After each model finishes their job, DataParallel collects and merges the results before returning it to you." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84664807-8163-46dd-b037-1b3f73d8cbd9", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "n_epochs = 100\n", + "batch_size = 10\n", + " \n", + "for epoch in range(n_epochs):\n", + " running_loss = 0.0\n", + " for batch_features, batch_labels in data_loader:\n", + " batch_features = batch_features.to(device)\n", + " batch_labels = batch_labels.to(device)\n", + "\n", + " optimizer.zero_grad()\n", + " \n", + " outputs = class_model(batch_features)\n", + " \n", + " batch_labels = batch_labels.unsqueeze(1).float()\n", + " loss = loss_fn(outputs, batch_labels)\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " running_loss += loss.item() * batch_features.size(0)\n", + " \n", + " epoch_loss = running_loss / len(dataset)\n", + " print(f'Epoch {epoch+1}/{n_epochs}, Loss: {epoch_loss:.4f}')\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32e7a7d0-666f-482a-b250-34a3f1199240", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aedca9ef-fa2d-41e8-8847-c7e9fbcf498c", + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"Model is on device: {next(class_model.parameters()).device}\")\n", + "if isinstance(class_model, nn.DataParallel):\n", + " print(f\"DataParallel devices: {class_model.device_ids}\")" + ] + }, + { + "cell_type": "markdown", + "id": "e07a44bb-25c6-4f67-8a34-514d7eadbbaf", + "metadata": {}, + "source": [ + "### Exercise\n", + "\n", + "1. **What is the time difference in training**? Compare it with the previous training (change epoch to 100)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/.ipynb_checkpoints/dataloader-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/dataloader-checkpoint.ipynb new file mode 100644 index 0000000..e21222e --- /dev/null +++ b/notebooks/.ipynb_checkpoints/dataloader-checkpoint.ipynb @@ -0,0 +1,577 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "b670ae8e-1350-4be1-8575-df9267fdfae7", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "from torch.utils.data import Dataset\n", + "from torchvision import datasets\n", + "from torchvision.transforms import ToTensor\n", + "from torch.utils.data import Dataset, DataLoader\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import requests\n", + "import zipfile\n", + "from pathlib import Path\n", + "\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f82a4673-e5e9-4f5f-b7e6-112a8fa1e47d", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "# The jupyter notebook is launched from your $HOME directory.\n", + "# Change the working directory to the workshop directory\n", + "# which was created in your username directory under /scratch/vp91\n", + "os.chdir(os.path.expandvars(\"/scratch/vp91/$USER/\"))" + ] + }, + { + "cell_type": "markdown", + "id": "775f4111-d1e2-4cd1-bd2d-a7a1bbb3d21d", + "metadata": {}, + "source": [ + "PyTorch offers two data primitives—`torch.utils.data.DataLoader` and `torch.utils.data.Dataset`— which \n", + "facilitate the use of both pre-loaded datasets and custom data.\n", + "\n", + "The `Fashion-MNIST` dataset is an example of a pre-loaded curated dataset. It can be loaded using the following parameters:\n", + "\n", + "- `root` specifies the path where the training or test data is stored.\n", + "- `train` indicates whether to load the training or test dataset.\n", + "- `download=True` will download the data from the internet if it's not available at the specified `root`.\n", + "- `transform` and `target_transform` define the transformations applied to the features and labels, respectively.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "749f1295-2191-40e3-9f7f-8c34589d1a7d", + "metadata": {}, + "outputs": [], + "source": [ + "training_data = datasets.FashionMNIST(\n", + " root=\"data\",\n", + " train=True,\n", + " download=True,\n", + " transform=ToTensor()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b818ed5f-c845-4fca-9015-99196f3b937d", + "metadata": {}, + "outputs": [], + "source": [ + "test_data = datasets.FashionMNIST(\n", + " root=\"data\",\n", + " train=False,\n", + " download=True,\n", + " transform=ToTensor()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aef48041-ad83-4813-924d-22404d691286", + "metadata": {}, + "outputs": [], + "source": [ + "!ls data/FashionMNIST/raw/" + ] + }, + { + "cell_type": "markdown", + "id": "80984294-a299-4da7-802d-9184706a5f2a", + "metadata": {}, + "source": [ + "#### Visualizing a sample of the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9f52a941-cd0e-4665-9e6c-182ffc33c5b5", + "metadata": {}, + "outputs": [], + "source": [ + "labels_map = {\n", + " 0: \"T-Shirt\",\n", + " 1: \"Trouser\",\n", + " 2: \"Pullover\",\n", + " 3: \"Dress\",\n", + " 4: \"Coat\",\n", + " 5: \"Sandal\",\n", + " 6: \"Shirt\",\n", + " 7: \"Sneaker\",\n", + " 8: \"Bag\",\n", + " 9: \"Ankle Boot\",\n", + "}\n", + "figure = plt.figure(figsize=(8, 8))\n", + "cols, rows = 3, 3\n", + "for i in range(1, cols * rows + 1):\n", + " sample_idx = torch.randint(len(training_data), size=(1,)).item()\n", + " img, label = training_data[sample_idx]\n", + " figure.add_subplot(rows, cols, i)\n", + " plt.title(labels_map[label])\n", + " plt.axis(\"off\")\n", + " plt.imshow(img.squeeze(), cmap=\"gray\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7d1d3c91-ba90-469d-9d8b-ab18a7768b84", + "metadata": {}, + "source": [ + "### Custom dataset" + ] + }, + { + "cell_type": "markdown", + "id": "57f0bdd5-5521-421f-a56c-532764d123af", + "metadata": {}, + "source": [ + "What if working with a custom dataset? To illustrate this, we will download a dataset and set it up for\n", + "use in PyTorch training. The data used for this demonstration is relatively *clean*. In a practical use case, significant time will likely be spent on cleaning and preparing the data." + ] + }, + { + "cell_type": "markdown", + "id": "727a8d18-c149-4ae7-8791-d301fa83e579", + "metadata": {}, + "source": [ + "The data:\n", + "\n", + "1. There are **3 classes**: pizza, steak, and sushi.\n", + "2. The data is split into *train* and *test* datasets.\n", + "3. Both *train* and *test* datasets are further organized into 3 directories, each corresponding to one of the classes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "88c9b050-a64a-45a6-ac46-06b9a0632431", + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "import zipfile\n", + "from pathlib import Path\n", + "\n", + "# Setup path to data folder\n", + "data_root = Path(\"custom_data/\")\n", + "image_path = data_root / \"pizza_steak_sushi\"\n", + "\n", + "# If the image data doesn't exist, download it and curate it. \n", + "if not image_path.is_dir():\n", + " image_path.mkdir(parents=True, exist_ok=True)\n", + " \n", + " # Download pizza, steak, sushi data\n", + " url = \"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip\"\n", + " with open(data_root / \"pizza_steak_sushi.zip\", \"wb\") as f:\n", + " request = requests.get(url)\n", + " f.write(request.content)\n", + "\n", + " with zipfile.ZipFile(data_root / \"pizza_steak_sushi.zip\", \"r\") as zip_ref:\n", + " zip_ref.extractall(image_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9675e0c5-cffc-4420-a419-eaf3e2198fb3", + "metadata": {}, + "outputs": [], + "source": [ + "!ls custom_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b2d51954-2b02-42bb-90e3-121c105e3c7c", + "metadata": {}, + "outputs": [], + "source": [ + "!ls custom_data/pizza_steak_sushi" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0a8043a6-7ea8-44e7-908d-e0921c9930db", + "metadata": {}, + "outputs": [], + "source": [ + "!ls custom_data/pizza_steak_sushi/train" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1425fa36-3c2e-4597-9b78-65516835ac83", + "metadata": {}, + "outputs": [], + "source": [ + "!ls custom_data/pizza_steak_sushi/test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b27f580f-988a-425f-9b3b-e65a23742500", + "metadata": {}, + "outputs": [], + "source": [ + "!ls custom_data/pizza_steak_sushi/train/pizza" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "696d0b71-2825-48ed-b84a-cf9519e296f4", + "metadata": {}, + "outputs": [], + "source": [ + "from PIL import Image\n", + "img = Image.open(\"custom_data/pizza_steak_sushi/train/pizza/928670.jpg\")\n", + "img" + ] + }, + { + "cell_type": "markdown", + "id": "4577dd6b-c581-4dc5-996a-92b7c3a07436", + "metadata": {}, + "source": [ + "#### Setup train and testing paths" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49b85b56-2ace-4a01-b361-b00ef2b28f9e", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "train_dir = image_path / \"train\"\n", + "test_dir = image_path / \"test\"\n", + "\n", + "train_dir, test_dir" + ] + }, + { + "cell_type": "markdown", + "id": "ba0bbe2b-edcd-4f07-b715-e42c6d33dc1c", + "metadata": {}, + "source": [ + "#### Transformation on the data\n", + "\n", + "\n", + "Transform functions in the PyTorch library simplify the application of various data enhancement/manipulation techniques \n", + "to your input data. These functions enable you to apply multiple changes simultaneously." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6ce41688-a909-477c-94ef-010ea6724445", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "from torch.utils.data import DataLoader\n", + "from torchvision import datasets, transforms" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c42cbfdb-a5f3-4e07-8beb-e151835902fb", + "metadata": {}, + "outputs": [], + "source": [ + "# Write transform for image\n", + "data_transform = transforms.Compose([\n", + " # Resize the images to 64x64\n", + " transforms.Resize(size=(64, 64)),\n", + " # Flip the images randomly on the horizontal\n", + " transforms.RandomHorizontalFlip(p=0.5), # p = probability of flip, 0.5 = 50% chance\n", + " # Turn the image into a torch.Tensor\n", + " transforms.ToTensor() # this also converts all pixel values from 0 to 255 to be between 0.0 and 1.0 \n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e53af9e2-b0b9-40ff-9526-a4239600dc3f", + "metadata": {}, + "outputs": [], + "source": [ + "with Image.open(\"custom_data/pizza_steak_sushi/train/pizza/928670.jpg\") as f:\n", + " fig, ax = plt.subplots(1, 2)\n", + " ax[0].imshow(f) \n", + " ax[0].set_title(f\"Original \\nSize: {f.size}\")\n", + " ax[0].axis(\"off\")\n", + "\n", + " transformed_image = data_transform(f).permute(1, 2, 0) \n", + " ax[1].imshow(transformed_image) \n", + " ax[1].set_title(f\"Transformed \\nSize: {transformed_image.shape}\")\n", + " ax[1].axis(\"off\")" + ] + }, + { + "cell_type": "markdown", + "id": "af651bd5-8afa-4c8c-9661-03683f91be8d", + "metadata": {}, + "source": [ + "#### Loading Image Data Using ImageFolder\n", + "\n", + "`ImageFolder` is a generic data loader where images are expected to be organized into separate directories,\n", + "each corresponding to a different class." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "245ada1a-e053-4905-aa44-39ef9814fde8", + "metadata": {}, + "outputs": [], + "source": [ + "# Use ImageFolder to create dataset(s)\n", + "from torchvision import datasets\n", + "train_data = datasets.ImageFolder(root=train_dir, # target folder of images\n", + " transform=data_transform, # transforms to perform on data (images)\n", + " target_transform=None) # transforms to perform on labels (if necessary)\n", + "\n", + "test_data = datasets.ImageFolder(root=test_dir, \n", + " transform=data_transform)\n", + "\n", + "print(f\"Train data:\\n{train_data}\\nTest data:\\n{test_data}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c7ad54a-a8f2-4963-86c3-3150cb68cae3", + "metadata": {}, + "outputs": [], + "source": [ + "# Get class names as a list\n", + "class_names = train_data.classes\n", + "class_names" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59f14ace-5c56-4a18-b24e-67c34321a2ec", + "metadata": {}, + "outputs": [], + "source": [ + "# Can also get class names as a dict\n", + "class_dict = train_data.class_to_idx\n", + "class_dict" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ede23b28-601c-45ba-a544-ca4d828045ba", + "metadata": {}, + "outputs": [], + "source": [ + "# Check the lengths\n", + "len(train_data), len(test_data)" + ] + }, + { + "cell_type": "markdown", + "id": "8c8d91e6-c12d-4fb1-ab75-c95f1309c995", + "metadata": {}, + "source": [ + "#### DataLoader\n", + "\n", + "\n", + "In PyTorch, `DataLoader` is a built-in class that offers an efficient and flexible method for loading \n", + "data into a model for training or inference. It is especially beneficial for managing large datasets that \n", + "may not fit into memory and for carrying out data augmentation and preprocessing. \n", + "Data loader combines a dataset and a sampler, and provides an iterable over the given dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4e7b6e1-7ec9-411e-9d3d-422d4d6f8bc9", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "69ed025b-c14c-4130-97f6-5d00a3757880", + "metadata": {}, + "outputs": [], + "source": [ + "# Turn train and test Datasets into DataLoaders\n", + "from torch.utils.data import DataLoader\n", + "train_dataloader = DataLoader(dataset=train_data, \n", + " batch_size=8, # how many samples per batch?\n", + " num_workers=1, # how many subprocesses to use for data loading? (higher = more)\n", + " shuffle=True) # shuffle the data?\n", + "\n", + "test_dataloader = DataLoader(dataset=test_data, \n", + " batch_size=8, \n", + " num_workers=1, \n", + " shuffle=False) # don't usually need to shuffle testing data\n", + "\n", + "train_dataloader, test_dataloader" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59008e99-69c0-4a5b-ae9a-419273c07841", + "metadata": {}, + "outputs": [], + "source": [ + "img, label = next(iter(train_dataloader))\n", + "\n", + "print(f\"Image shape: {img.shape} -> [batch_size, color_channels, height, width]\")\n", + "print(f\"Label shape: {label.shape}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e3c2b18-2162-4d8e-beb9-991093854c57", + "metadata": {}, + "outputs": [], + "source": [ + "type(img)" + ] + }, + { + "cell_type": "markdown", + "id": "496fb7e6-2727-4ba8-bb2c-6b8460b9565f", + "metadata": {}, + "source": [ + "#### Custom DataLoader" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0887d989-49c3-4012-910f-e011340b0059", + "metadata": {}, + "outputs": [], + "source": [ + "datapath = os.path.expandvars('/scratch/vp91/$USER/intro-to-pytorch/data/pima-indians-diabetes.data.csv')\n", + "\n", + "# Define the custom Dataset class\n", + "column_names = [\n", + " 'Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness',\n", + " 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'\n", + "]\n", + "\n", + "# Define the custom Dataset class\n", + "class PimaDataset(Dataset):\n", + " def __init__(self, csv_file):\n", + " # Load the CSV file without header and assign column names\n", + " self.data = pd.read_csv(csv_file, header=None, names=column_names)\n", + " self.features = self.data.drop('Outcome', axis=1).values\n", + " self.labels = self.data['Outcome'].values\n", + " \n", + " # Convert to PyTorch tensors\n", + " self.features_tensor = torch.tensor(self.features, dtype=torch.float32)\n", + " self.labels_tensor = torch.tensor(self.labels, dtype=torch.long)\n", + " \n", + " # Calculate mean and std\n", + " self.mean = self.features_tensor.mean(dim=0)\n", + " self.std = self.features_tensor.std(dim=0)\n", + " \n", + " # Normalize the features\n", + " self.features_tensor = (self.features_tensor - self.mean) / self.std\n", + "\n", + " def __len__(self):\n", + " return len(self.data)\n", + "\n", + " def __getitem__(self, idx):\n", + " feature = self.features_tensor[idx]\n", + " label = self.labels_tensor[idx]\n", + " return feature, label" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c088ccc3-85a1-4a5d-ac9b-7699ff7e91fa", + "metadata": {}, + "outputs": [], + "source": [ + "dataset = PimaDataset(datapath)\n", + "batch_size = 32\n", + "data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87e8a8d6-f0bc-4618-ad3e-dd3b1a5ca8c8", + "metadata": {}, + "outputs": [], + "source": [ + "features, outcomes = next(iter(data_loader))\n", + "\n", + "print(f\"Image shape: {features.shape} -> [batch_size, inputs_features]\")\n", + "print(f\"Label shape: {outcomes.shape}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0f401ab-c3cd-400f-8c97-b3c94146ac56", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/.ipynb_checkpoints/distributed_data_parallel-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/distributed_data_parallel-checkpoint.ipynb new file mode 100644 index 0000000..23a6c4e --- /dev/null +++ b/notebooks/.ipynb_checkpoints/distributed_data_parallel-checkpoint.ipynb @@ -0,0 +1,305 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1cdb801e-e281-476f-b3e9-e470785d3ad9", + "metadata": {}, + "source": [ + "### Using Multiple GPUs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "604d5312-0b33-4162-b5f4-551c21732550", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import os\n", + "import pandas as pd\n", + "\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "import torch.distributed as dist\n", + "import torch.multiprocessing as mp\n", + "from torch.utils.data import Dataset, DataLoader\n", + "from torch.utils.data.distributed import DistributedSampler\n", + "from torch.nn.parallel import DistributedDataParallel as DDP" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49a40db4-da7b-4d24-b707-a39b79d2440e", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "# The jupyter notebook is launched from your $HOME directory.\n", + "# Change the working directory to the workshop directory\n", + "# which was created in your username directory under /scratch/vp91\n", + "os.chdir(os.path.expandvars(\"/scratch/vp91/$USER/\"))" + ] + }, + { + "cell_type": "markdown", + "id": "4098c4ec-368b-4802-9800-fc4c4b7479ba", + "metadata": {}, + "source": [ + "#### Set Device\n", + "Se the default device as the GPU if it exists" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b51be64-542f-401c-ae73-00da2bbd6471", + "metadata": {}, + "outputs": [], + "source": [ + "nb_gpus = 2\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "datapath = os.path.expandvars('/scratch/vp91/$USER/intro-to-pytorch/data/pima-indians-diabetes.data.csv')\n", + "\n", + "# Define the custom Dataset class\n", + "column_names = [\n", + " 'Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness',\n", + " 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "f944a147-3f5e-4c42-b142-850d04458270", + "metadata": {}, + "source": [ + "### Process Groups" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "50696138-0c0d-4a0b-aa80-ed73cff87fd2", + "metadata": {}, + "outputs": [], + "source": [ + "def setup(rank, world_size):\n", + " os.environ['MASTER_ADDR'] = 'localhost'\n", + " os.environ['MASTER_PORT'] = '12355'\n", + " dist.init_process_group(\"nccl\", rank=rank, world_size=world_size)\n", + " \n", + "def cleanup():\n", + " dist.destroy_process_group()" + ] + }, + { + "cell_type": "markdown", + "id": "2d29976e-56b5-46ce-8743-5480524bbca1", + "metadata": {}, + "source": [ + "### Dataloader" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea28a5d7-0d69-47c9-ba24-995f02168856", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the custom Dataset class\n", + "class PimaDataset(Dataset):\n", + " def __init__(self, csv_file):\n", + " # Load the CSV file without header and assign column names\n", + " self.data = pd.read_csv(csv_file, header=None, names=column_names)\n", + " self.features = self.data.drop('Outcome', axis=1).values\n", + " self.labels = self.data['Outcome'].values\n", + " \n", + " # Convert to PyTorch tensors\n", + " self.features_tensor = torch.tensor(self.features, dtype=torch.float32)\n", + " self.labels_tensor = torch.tensor(self.labels, dtype=torch.long)\n", + " \n", + " # Calculate mean and std\n", + " self.mean = self.features_tensor.mean(dim=0)\n", + " self.std = self.features_tensor.std(dim=0)\n", + " \n", + " # Normalize the features\n", + " self.features_tensor = (self.features_tensor - self.mean) / self.std\n", + "\n", + " def __len__(self):\n", + " return len(self.data)\n", + "\n", + " def __getitem__(self, idx):\n", + " feature = self.features_tensor[idx]\n", + " label = self.labels_tensor[idx]\n", + " return feature, label" + ] + }, + { + "cell_type": "markdown", + "id": "ec4ebf96-12bc-4520-bbcb-690e5edebac9", + "metadata": {}, + "source": [ + "### Split the dataloader" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49ca23e3-f8d9-4818-93e4-fa9304792335", + "metadata": {}, + "outputs": [], + "source": [ + "def prepare(rank, world_size, batch_size=32, pin_memory=False, num_workers=0):\n", + " dataset = PimaDataset(datapath)\n", + " sampler = DistributedSampler(dataset, num_replicas=world_size, rank=rank, shuffle=False, drop_last=False)\n", + " \n", + " dataloader = DataLoader(dataset, batch_size=batch_size, pin_memory=pin_memory, num_workers=num_workers, drop_last=False, shuffle=False, sampler=sampler)\n", + " \n", + " return dataloader" + ] + }, + { + "cell_type": "markdown", + "id": "abc70a8b-2c37-4c09-bd7c-717d556cb39c", + "metadata": {}, + "source": [ + "### Defining the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "263d5838-320d-4dad-ac59-e2d95ada7873", + "metadata": {}, + "outputs": [], + "source": [ + "class PimaClassifier(nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.hidden1 = nn.Linear(8, 12)\n", + " self.act1 = nn.ReLU()\n", + " self.hidden2 = nn.Linear(12, 8)\n", + " self.act2 = nn.ReLU()\n", + " self.output = nn.Linear(8, 1)\n", + " self.act_output = nn.Sigmoid()\n", + " \n", + " def forward(self, x):\n", + " x = self.act1(self.hidden1(x))\n", + " x = self.act2(self.hidden2(x))\n", + " x = self.act_output(self.output(x))\n", + " return x" + ] + }, + { + "cell_type": "markdown", + "id": "09ec5647-aded-4179-89c0-0c5d44b0c6db", + "metadata": {}, + "source": [ + "#### Wrap model in DDP\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0dc9a702-4b3a-423f-80d3-79d1e3d9e11f", + "metadata": {}, + "outputs": [], + "source": [ + "def main(rank, world_size):\n", + "\n", + " # setup the process groups\n", + " setup(rank, world_size)\n", + " # prepare the dataloader\n", + " dataloader = prepare(rank, world_size)\n", + " \n", + " # instantiate the model(it's your own model) and move it to the right device\n", + " model = PimaClassifier().to(rank)\n", + " \n", + " # wrap the model with DDP\n", + " # device_ids tell DDP where is your model\n", + " # output_device tells DDP where to output, in our case, it is rank\n", + " # find_unused_parameters=True instructs DDP to find unused output of the forward() function of any module in the model\n", + " model = DDP(model, device_ids=[rank], output_device=rank, find_unused_parameters=True)\n", + "\n", + " loss_fn = nn.BCELoss()\n", + " optimizer = optim.Adam(model.parameters(), lr=0.001)\n", + "\n", + " n_epochs = 100\n", + " for epoch in range(n_epochs):\n", + "\n", + " # if we are using DistributedSampler, we have to tell it which epoch this is\n", + " dataloader.sampler.set_epoch(epoch)\n", + "\n", + " for batch_features, batch_labels in dataloader:\n", + " batch_features = batch_features.to(rank)\n", + " batch_labels = batch_labels.to(rank)\n", + "\n", + " optimizer.zero_grad()\n", + " \n", + " outputs = model(batch_features)\n", + " \n", + " batch_labels = batch_labels.unsqueeze(1).float()\n", + " loss = loss_fn(outputs, batch_labels)\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " cleanup()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d70bfdf8-9619-4448-ad45-cb2277d937ea", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "593672e5-4e14-473d-80f9-2ed00c127729", + "metadata": {}, + "outputs": [], + "source": [ + "if __name__ == '__main__':\n", + "\n", + " world_size = nb_gpus \n", + " mp.spawn(main, args=(world_size,), nprocs=world_size)" + ] + }, + { + "cell_type": "markdown", + "id": "e07a44bb-25c6-4f67-8a34-514d7eadbbaf", + "metadata": {}, + "source": [ + "### Exercise\n", + "\n", + "1. **What is the time difference in training**? Compare it with the previous training (change epoch to 100)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/.ipynb_checkpoints/multi_GPU-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/multi_GPU-checkpoint.ipynb new file mode 100644 index 0000000..e90d4e5 --- /dev/null +++ b/notebooks/.ipynb_checkpoints/multi_GPU-checkpoint.ipynb @@ -0,0 +1,348 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1cdb801e-e281-476f-b3e9-e470785d3ad9", + "metadata": {}, + "source": [ + "### Using Multiple GPUs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "604d5312-0b33-4162-b5f4-551c21732550", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "from torch.utils.data import Dataset, DataLoader\n", + "import pandas as pd\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49a40db4-da7b-4d24-b707-a39b79d2440e", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "# The jupyter notebook is launched from your $HOME directory.\n", + "# Change the working directory to the workshop directory\n", + "# which was created in your username directory under /scratch/vp91\n", + "os.chdir(os.path.expandvars(\"/scratch/vp91/$USER/\"))" + ] + }, + { + "cell_type": "markdown", + "id": "4098c4ec-368b-4802-9800-fc4c4b7479ba", + "metadata": {}, + "source": [ + "#### Set Device\n", + "Se the default device as the GPU if it exists" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b51be64-542f-401c-ae73-00da2bbd6471", + "metadata": {}, + "outputs": [], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "print(device)" + ] + }, + { + "cell_type": "markdown", + "id": "2d29976e-56b5-46ce-8743-5480524bbca1", + "metadata": {}, + "source": [ + "### Dataloader" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea28a5d7-0d69-47c9-ba24-995f02168856", + "metadata": {}, + "outputs": [], + "source": [ + "datapath = os.path.expandvars('/scratch/vp91/$USER/intro-to-pytorch/data/pima-indians-diabetes.data.csv')\n", + "\n", + "# Define the custom Dataset class\n", + "column_names = [\n", + " 'Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness',\n", + " 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'\n", + "]\n", + "\n", + "# Define the custom Dataset class\n", + "class PimaDataset(Dataset):\n", + " def __init__(self, csv_file):\n", + " # Load the CSV file without header and assign column names\n", + " self.data = pd.read_csv(csv_file, header=None, names=column_names)\n", + " self.features = self.data.drop('Outcome', axis=1).values\n", + " self.labels = self.data['Outcome'].values\n", + " \n", + " # Convert to PyTorch tensors\n", + " self.features_tensor = torch.tensor(self.features, dtype=torch.float32)\n", + " self.labels_tensor = torch.tensor(self.labels, dtype=torch.long)\n", + " \n", + " # Calculate mean and std\n", + " self.mean = self.features_tensor.mean(dim=0)\n", + " self.std = self.features_tensor.std(dim=0)\n", + " \n", + " # Normalize the features\n", + " self.features_tensor = (self.features_tensor - self.mean) / self.std\n", + "\n", + " def __len__(self):\n", + " return len(self.data)\n", + "\n", + " def __getitem__(self, idx):\n", + " feature = self.features_tensor[idx]\n", + " label = self.labels_tensor[idx]\n", + " return feature, label" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "110beb4c-c07b-4a1b-b2d4-ce7795af31a4", + "metadata": {}, + "outputs": [], + "source": [ + "dataset = PimaDataset(datapath)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26d7f973-cf59-4709-a0c2-a158100449e9", + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 32\n", + "data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)" + ] + }, + { + "cell_type": "markdown", + "id": "abc70a8b-2c37-4c09-bd7c-717d556cb39c", + "metadata": {}, + "source": [ + "### Defining the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "263d5838-320d-4dad-ac59-e2d95ada7873", + "metadata": {}, + "outputs": [], + "source": [ + "class PimaClassifier(nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.hidden1 = nn.Linear(8, 12)\n", + " self.act1 = nn.ReLU()\n", + " self.hidden2 = nn.Linear(12, 8)\n", + " self.act2 = nn.ReLU()\n", + " self.output = nn.Linear(8, 1)\n", + " self.act_output = nn.Sigmoid()\n", + " \n", + " def forward(self, x):\n", + " x = self.act1(self.hidden1(x))\n", + " x = self.act2(self.hidden2(x))\n", + " x = self.act_output(self.output(x))\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "289c55b3-f54b-4a79-ba58-5788237aabb9", + "metadata": {}, + "outputs": [], + "source": [ + "class_model = PimaClassifier()\n", + "print(class_model)" + ] + }, + { + "cell_type": "markdown", + "id": "09ec5647-aded-4179-89c0-0c5d44b0c6db", + "metadata": {}, + "source": [ + "#### Data Parallelism\n", + "Pytorch will only use one GPU by default. You can easily run your operations on multiple GPUs by making your model run parallelly using `nn.DataParallel`. \n", + "\n", + "Check for multiple GPUs and if multiple GPUs are available, wrap the model with `nn.DataParallel`. Finally, move the model to the GPUs using `model.to(device)`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0dc9a702-4b3a-423f-80d3-79d1e3d9e11f", + "metadata": {}, + "outputs": [], + "source": [ + "print(torch.cuda.device_count())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5a58574-4ce7-495c-be0b-d22694a6ed7a", + "metadata": {}, + "outputs": [], + "source": [ + "if torch.cuda.device_count() > 1:\n", + " class_model = nn.DataParallel(class_model)\n", + " print(f\"Using {torch.cuda.device_count()} GPUs: {', '.join([torch.cuda.get_device_name(i) for i in range(torch.cuda.device_count())])}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d70bfdf8-9619-4448-ad45-cb2277d937ea", + "metadata": {}, + "outputs": [], + "source": [ + "class_model.to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "593672e5-4e14-473d-80f9-2ed00c127729", + "metadata": {}, + "outputs": [], + "source": [ + "loss_fn = nn.BCELoss()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "518bf03c-0594-494e-bdb3-a41421ac53a0", + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = optim.Adam(class_model.parameters(), lr=0.001)" + ] + }, + { + "cell_type": "markdown", + "id": "8608326d-27a3-4a5b-b485-e9af74b0f2e8", + "metadata": {}, + "source": [ + "#### Training the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "67278570-0838-40a9-9410-851c51a46b95", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "04b239a9-7e1c-42ba-8b54-58c79d26986b", + "metadata": {}, + "source": [ + "DataParallel splits your data automatically and sends job orders to multiple models on several GPUs. After each model finishes their job, DataParallel collects and merges the results before returning it to you." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84664807-8163-46dd-b037-1b3f73d8cbd9", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "n_epochs = 100\n", + "batch_size = 10\n", + " \n", + "for epoch in range(n_epochs):\n", + " running_loss = 0.0\n", + " for batch_features, batch_labels in data_loader:\n", + " batch_features = batch_features.to(device)\n", + " batch_labels = batch_labels.to(device)\n", + "\n", + " optimizer.zero_grad()\n", + " \n", + " outputs = class_model(batch_features)\n", + " \n", + " batch_labels = batch_labels.unsqueeze(1).float()\n", + " loss = loss_fn(outputs, batch_labels)\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " running_loss += loss.item() * batch_features.size(0)\n", + " \n", + " epoch_loss = running_loss / len(dataset)\n", + " print(f'Epoch {epoch+1}/{n_epochs}, Loss: {epoch_loss:.4f}')\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32e7a7d0-666f-482a-b250-34a3f1199240", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aedca9ef-fa2d-41e8-8847-c7e9fbcf498c", + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"Model is on device: {next(class_model.parameters()).device}\")\n", + "if isinstance(class_model, nn.DataParallel):\n", + " print(f\"DataParallel devices: {class_model.device_ids}\")" + ] + }, + { + "cell_type": "markdown", + "id": "e07a44bb-25c6-4f67-8a34-514d7eadbbaf", + "metadata": {}, + "source": [ + "### Exercise\n", + "\n", + "1. **What is the time difference in training**? Compare it with the previous training (change epoch to 100)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/.ipynb_checkpoints/tensors-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/tensors-checkpoint.ipynb new file mode 100644 index 0000000..cbd5bf4 --- /dev/null +++ b/notebooks/.ipynb_checkpoints/tensors-checkpoint.ipynb @@ -0,0 +1,375 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "205ea8ba-4865-4511-8a54-14fcd4b22ed0", + "metadata": {}, + "source": [ + "### Tensors in PyTorch\n", + "\n", + "Tensors are specialized data structures used in PyTorch to represent model inputs, outputs, and parameters. While they are conceptually similar to arrays and matrices, they offer additional features such as support for hardware accelerators like GPUs and automatic differentiation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "244c9ced-e83c-4c24-a992-f216dfa34456", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "# The jupyter notebook is launched from your $HOME directory.\n", + "# Change the working directory to the workshop directory\n", + "# which was created in your username directory under /scratch/vp91\n", + "os.chdir(os.path.expandvars(\"/scratch/vp91/$USER/\"))" + ] + }, + { + "cell_type": "markdown", + "id": "c20aaadc-e2a0-4a89-9703-b091578b4dc0", + "metadata": {}, + "source": [ + "### Creating a Tensor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "816961f2-0932-47d1-923f-d9743ec8c062", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "id": "383b7ba2-6d5d-4380-9f36-fb62f6ce1d8f", + "metadata": {}, + "source": [ + "##### 1. Directly from data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2137307c-2fa0-4953-92e3-fcb24408ff77", + "metadata": {}, + "outputs": [], + "source": [ + "data = [[1, 2],[3, 4]]\n", + "x_tensor= torch.tensor(data)" + ] + }, + { + "cell_type": "markdown", + "id": "cba5f6e9-f0fc-4cee-b48d-660e6267541d", + "metadata": {}, + "source": [ + "##### 2. From NumPy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48cd31ef-a810-4aaa-a314-d5f5705e7be4", + "metadata": {}, + "outputs": [], + "source": [ + "x_np = np.array(data)\n", + "x_tensor = torch.from_numpy(x_np)" + ] + }, + { + "cell_type": "markdown", + "id": "0d365755-871b-4380-8e36-972799542b5e", + "metadata": {}, + "source": [ + "##### 3. From another Tensor\n", + "\n", + "**torch.rand_like()** returns a tensor with the same size as input that but filled with random numbers from the interval [0,1)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62f40f87-637b-4e58-be66-8fe8f8d4b84b", + "metadata": {}, + "outputs": [], + "source": [ + "x_tensor = torch.ones_like(x_tensor)\n", + "y_tensor = torch.rand_like(x_tensor, dtype=torch.float) " + ] + }, + { + "cell_type": "markdown", + "id": "84e9c4b6-6b85-430d-a793-93521879f671", + "metadata": {}, + "source": [ + "### Operations on Tensors" + ] + }, + { + "cell_type": "markdown", + "id": "c59bbc8d-00d1-48cc-bc2c-4443c8ccec31", + "metadata": {}, + "source": [ + "#### 1. indexing and slicing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d3e7c194-ecea-4ef9-af39-3f563daddc3c", + "metadata": {}, + "outputs": [], + "source": [ + "x_tensor = torch.ones(4, 4)\n", + "print(f\"First row: {x_tensor[0]}\")\n", + "print(f\"First column: {x_tensor[:, 0]}\")\n", + "print(f\"Last column: {x_tensor[..., -1]}\")\n", + "x_tensor[:,1] = 0\n", + "print(x_tensor)" + ] + }, + { + "cell_type": "markdown", + "id": "bbe77b93-12ab-475f-81fc-c5a00db24621", + "metadata": {}, + "source": [ + "#### 2. Concatenate multiple tensors" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b00e821-b480-4f7e-8788-1e983ed1693b", + "metadata": {}, + "outputs": [], + "source": [ + "y_tensor = torch.cat([x_tensor, x_tensor, x_tensor], dim=1)\n", + "print(y_tensor)" + ] + }, + { + "cell_type": "markdown", + "id": "77389fec-07d3-4473-a036-af0b9cd39986", + "metadata": {}, + "source": [ + "#### 3. Arithmetic Operations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d518086-f064-486e-9e28-29ea15ce7779", + "metadata": {}, + "outputs": [], + "source": [ + "x_tensor = torch.ones(4, 4)\n", + "\n", + "# Transpose\n", + "x_T_tensor = x_tensor.T\n", + "\n", + "# Matrix Multiplication\n", + "y1_tensor = x_tensor @ x_tensor.T\n", + "y2_tensor = x_tensor.matmul(x_tensor.T)\n", + "\n", + "y3_tensor = torch.rand_like(y1_tensor)\n", + "torch.matmul(x_tensor, x_tensor.T, out=y3_tensor)\n", + "\n", + "\n", + "# Element-wise multiplication\n", + "z1_tensor = x_tensor * x_tensor\n", + "z2_tensor = x_tensor.mul(x_tensor)\n", + "\n", + "z3_tensor = torch.rand_like(x_tensor)\n", + "torch.mul(x_tensor, x_tensor, out=z3_tensor)" + ] + }, + { + "cell_type": "markdown", + "id": "6ce833cc-6ff6-4016-96bb-60b70812d584", + "metadata": {}, + "source": [ + "##### 3. In-place Operations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21c046eb-43ad-4259-a2b6-55da191d22db", + "metadata": {}, + "outputs": [], + "source": [ + "x_tensor = torch.ones(4, 4)\n", + "\n", + "# Transpose\n", + "x_tensor.t_()\n", + "\n", + "# Copy\n", + "y_tensor = torch.rand_like(x_tensor)\n", + "x_tensor.copy_(y_tensor)" + ] + }, + { + "cell_type": "markdown", + "id": "d73522a1-db18-4fe0-9c07-f27d39f4a992", + "metadata": {}, + "source": [ + "### NumPy and Tensor\n", + "Tensors on the **CPU** and NumPy arrays can share memory locations, so modifying one will also affect \n", + "the other." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1eee82b4-8f71-4e19-b27e-c47add1714e3", + "metadata": {}, + "outputs": [], + "source": [ + "x_tensor = torch.ones(5) \n", + "x_np = x_tensor.numpy() # tensor to numpy\n", + "print(f\"t: {x_tensor}\")\n", + "print(f\"n: {x_np}\")\n", + "\n", + "x_tensor.add_(1)\n", + "\n", + "print(f\"t: {x_tensor}\")\n", + "print(f\"n: {x_np}\")\n", + "\n", + "y_np = np.ones(5)\n", + "z_np = np.zeros(5)\n", + "y_tensor = torch.from_numpy(y_np) # numpy to tensor\n", + "\n", + "np.add(y_np, 1, out=z_np)\n", + "\n", + "print(f\"t: {x_tensor}\")\n", + "print(f\"n: {x_np}\")" + ] + }, + { + "cell_type": "markdown", + "id": "43bca47c-de5b-423d-a3d3-153fdaa76bd9", + "metadata": {}, + "source": [ + "### Moving Tensor to GPU\n", + "It's always wise to check for GPU availability before performing any GPU operations. If a GPU is available, we can move our tensor to it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2199f283-5bbd-4534-b10b-1ed259d56f31", + "metadata": {}, + "outputs": [], + "source": [ + "x_tensor_gpu = x_tensor.to(\"cuda\")" + ] + }, + { + "cell_type": "markdown", + "id": "663e4316-0e47-4528-bfc0-873b695d3e23", + "metadata": {}, + "source": [ + "A better approach is to set the default device before starting any computations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a9aa52b7-b24d-4686-8798-6ec9582b19f9", + "metadata": {}, + "outputs": [], + "source": [ + "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n", + "y_tensor_gpu = y_tensor.to(device)" + ] + }, + { + "cell_type": "markdown", + "id": "55d565e8-074b-4e10-872f-c6051c38c20b", + "metadata": {}, + "source": [ + "### Tensor attributes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "552841ee-fa9d-450e-8ab5-16d7d1d41008", + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"Shape of tensor: {y_tensor.shape}\")\n", + "print(f\"Datatype of tensor: {y_tensor.dtype}\")\n", + "print(f\"Device tensor is stored on: {y_tensor.device}\")" + ] + }, + { + "cell_type": "markdown", + "id": "6e90c428-525b-47f2-9dbd-8e017f56e813", + "metadata": {}, + "source": [ + "*Automatic differentiation* is a key feature that distinguishes tensors from NumPy arrays. This capability\n", + "is particularly useful in neural networks, where model weights are adjusted during backpropagation based \n", + "on the gradient of the loss function with respect to each parameter. Tensors support automatic gradient \n", + "computation for any computational graph. For example, consider the computational graph of a one-layer \n", + "neural network:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c93bab5-f9bd-439b-a334-cbe482c379ad", + "metadata": {}, + "outputs": [], + "source": [ + "x_tensor = torch.ones(5) # input tensor\n", + "y_tensor = torch.zeros(3) # expected output\n", + "\n", + "w_tensor = torch.randn(5, 3, requires_grad=True)\n", + "b_tensor = torch.randn(3, requires_grad=True)\n", + "\n", + "z_tensor = torch.matmul(x_tensor, w_tensor) + b_tensor\n", + "\n", + "loss_tensor = torch.nn.functional.binary_cross_entropy_with_logits(z_tensor, y_tensor)\n", + "loss_tensor.backward()\n", + "\n", + "print(w_tensor.grad)\n", + "print(b_tensor.grad)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2cd58b4d-d46d-4095-a21e-38179685590b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/GPU_NN.ipynb b/notebooks/GPU_NN.ipynb new file mode 100644 index 0000000..a912a11 --- /dev/null +++ b/notebooks/GPU_NN.ipynb @@ -0,0 +1,305 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1cdb801e-e281-476f-b3e9-e470785d3ad9", + "metadata": {}, + "source": [ + "### Training on GPUs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "604d5312-0b33-4162-b5f4-551c21732550", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49a40db4-da7b-4d24-b707-a39b79d2440e", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "# The jupyter notebook is launched from your $HOME directory.\n", + "# Change the working directory to the workshop directory\n", + "# which was created in your username directory under /scratch/vp91\n", + "os.chdir(os.path.expandvars(\"/scratch/vp91/$USER/\"))" + ] + }, + { + "cell_type": "markdown", + "id": "4098c4ec-368b-4802-9800-fc4c4b7479ba", + "metadata": {}, + "source": [ + "#### Set Device\n", + "Se the default device as the GPU if it exists" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b51be64-542f-401c-ae73-00da2bbd6471", + "metadata": {}, + "outputs": [], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "print(device)" + ] + }, + { + "cell_type": "markdown", + "id": "b32c13d2-bee6-436c-b838-2c8e04a24ec6", + "metadata": {}, + "source": [ + "### Curate the dataset\n", + "Load the dataset, split into features (X) and output (y) variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "222a7a99-2723-486d-9a1e-58d2792c84e8", + "metadata": {}, + "outputs": [], + "source": [ + "datapath = os.path.expandvars('/scratch/vp91/$USER/intro-to-pytorch/data/pima-indians-diabetes.data.csv')\n", + "\n", + "dataset = np.loadtxt(datapath, delimiter=',')\n", + "X = dataset[:,0:8] \n", + "y = dataset[:,8]\n", + "\n", + "X_tensor = torch.tensor(X, dtype=torch.float32)\n", + "y_tensor = torch.tensor(y, dtype=torch.float32).reshape(-1, 1)" + ] + }, + { + "cell_type": "markdown", + "id": "abc70a8b-2c37-4c09-bd7c-717d556cb39c", + "metadata": {}, + "source": [ + "### Defining the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "263d5838-320d-4dad-ac59-e2d95ada7873", + "metadata": {}, + "outputs": [], + "source": [ + "class PimaClassifier(nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.hidden1 = nn.Linear(8, 12)\n", + " self.act1 = nn.ReLU()\n", + " self.hidden2 = nn.Linear(12, 8)\n", + " self.act2 = nn.ReLU()\n", + " self.output = nn.Linear(8, 1)\n", + " self.act_output = nn.Sigmoid()\n", + " \n", + " def forward(self, x):\n", + " x = self.act1(self.hidden1(x))\n", + " x = self.act2(self.hidden2(x))\n", + " x = self.act_output(self.output(x))\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "289c55b3-f54b-4a79-ba58-5788237aabb9", + "metadata": {}, + "outputs": [], + "source": [ + "class_model = PimaClassifier()\n", + "print(class_model)" + ] + }, + { + "cell_type": "markdown", + "id": "09ec5647-aded-4179-89c0-0c5d44b0c6db", + "metadata": {}, + "source": [ + "#### Save the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5a58574-4ce7-495c-be0b-d22694a6ed7a", + "metadata": {}, + "outputs": [], + "source": [ + "modelpath = os.path.expandvars('/scratch/vp91/$USER/class_model')\n", + "print(modelpath)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b2f9c1c9-4d45-447d-ac23-40593759da3c", + "metadata": {}, + "outputs": [], + "source": [ + "torch.save(class_model.state_dict(), modelpath)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65839c60-6d5f-4540-aabe-fbd1914692d8", + "metadata": {}, + "outputs": [], + "source": [ + "!ls /scratch/vp91/$USER/class_model" + ] + }, + { + "cell_type": "markdown", + "id": "a7fcdf90-1db4-4c6e-9f28-1bc8e7a4bfd0", + "metadata": {}, + "source": [ + "#### Load the model on the GPU" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d70bfdf8-9619-4448-ad45-cb2277d937ea", + "metadata": {}, + "outputs": [], + "source": [ + "class_model.load_state_dict(torch.load(modelpath, map_location=device, weights_only=True))\n", + "class_model.to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "593672e5-4e14-473d-80f9-2ed00c127729", + "metadata": {}, + "outputs": [], + "source": [ + "loss_fn = nn.BCELoss()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "518bf03c-0594-494e-bdb3-a41421ac53a0", + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = optim.Adam(class_model.parameters(), lr=0.001)" + ] + }, + { + "cell_type": "markdown", + "id": "8608326d-27a3-4a5b-b485-e9af74b0f2e8", + "metadata": {}, + "source": [ + "#### Training the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad6b09b5-9e9b-4376-ad89-d1ff8b4791eb", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "n_epochs = 100\n", + "batch_size = 10\n", + " \n", + "for epoch in range(n_epochs):\n", + " for i in range(0, len(X_tensor), batch_size):\n", + " Xbatch = X_tensor[i:i+batch_size].to(device) # move the tensor to GPU\n", + "\n", + " y_pred = class_model(Xbatch)\n", + " \n", + " ybatch = y_tensor[i:i+batch_size].to(device) # move the tensor to GPU\n", + " \n", + " loss = loss_fn(y_pred, ybatch)\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + " \n", + " print(f'Finished epoch {epoch}, latest loss {loss}')" + ] + }, + { + "cell_type": "markdown", + "id": "04b239a9-7e1c-42ba-8b54-58c79d26986b", + "metadata": {}, + "source": [ + "#### Evaluate the Model\n", + "\n", + "Currently, we are testing the model on the training dataset. Ideally, we should split the data into separate training and testing datasets, or use a distinct dataset for evaluation. For simplicity, we are testing the model on the same data used for training.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89e28fe2-90c5-4cd4-bd37-30ebe9183772", + "metadata": {}, + "outputs": [], + "source": [ + "with torch.no_grad():\n", + " y_pred = class_model(X_tensor.to(device))\n", + " \n", + "accuracy = (y_pred.round().to(device) == y_tensor.to(device)).float().mean()\n", + "print(f\"Accuracy {accuracy}\")" + ] + }, + { + "cell_type": "markdown", + "id": "e07a44bb-25c6-4f67-8a34-514d7eadbbaf", + "metadata": {}, + "source": [ + "### Exercise\n", + "\n", + "1. **What is the time difference in training**? Compare it with the previous training." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84664807-8163-46dd-b037-1b3f73d8cbd9", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/building_NN.ipynb b/notebooks/building_NN.ipynb new file mode 100644 index 0000000..20e626c --- /dev/null +++ b/notebooks/building_NN.ipynb @@ -0,0 +1,426 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1cdb801e-e281-476f-b3e9-e470785d3ad9", + "metadata": {}, + "source": [ + "### Neural Networks" + ] + }, + { + "cell_type": "markdown", + "id": "96127ef4-bb03-492f-81b9-672f74c20b5c", + "metadata": {}, + "source": [ + "Neural networks are computational models inspired by the human brain, designed to recognize patterns and\n", + "make decisions based on data. They consist of interconnected layers of nodes, or \"neurons,\" which process\n", + "and transform input information. Through training, neural networks learn to improve their accuracy in tasks like image recognition, language processing, and more.Neural networks comprise of layers that perform operations on data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49a40db4-da7b-4d24-b707-a39b79d2440e", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "# The jupyter notebook is launched from your $HOME directory.\n", + "# Change the working directory to the workshop directory\n", + "# which was created in your username directory under /scratch/vp91\n", + "os.chdir(os.path.expandvars(\"/scratch/vp91/$USER/\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "604d5312-0b33-4162-b5f4-551c21732550", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim" + ] + }, + { + "cell_type": "markdown", + "id": "7f1baab1-a7b6-429e-afa3-822e61da46ad", + "metadata": {}, + "source": [ + "### Dataset\n", + "The Pima Indians Diabetes dataset is a popular dataset in the field of machine learning and statistics, particularly for those working on classification problems. \n", + "\n", + "Dataset Overview:\n", + "**Source**: The dataset was created by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and is available in the UCI Machine Learning Repository.\n", + "**Purpose**: The dataset is used to predict the onset of diabetes within five years based on diagnostic measures.\n", + "**Features**: The dataset contains 768 samples, each with 8 features. \n", + "\n", + "The features are:\n", + "\n", + "1. Pregnancies: Number of times pregnant.\n", + "2. Glucose: Plasma glucose concentration (mg/dL) a 2 hours in an oral glucose tolerance test.\n", + "3. Blood Pressure: Diastolic blood pressure (mm Hg) at the time of screening.\n", + "4. Skin Thickness: Triceps skinfold thickness (mm) measured at the back of the upper arm.\n", + "5. Insulin: 2-Hour serum insulin (mu U/ml).\n", + "6. BMI: Body mass index (weight in kg/(height in m)^2).\n", + "7. Diabetes Pedigree Function: A function that scores likelihood of diabetes based on family history.\n", + "8. Age: Age of the individual (years).\n", + "\n", + "**Outcome**: Whether or not the individual has diabetes (1 for positive, 0 for negative)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d4b1b9b-bf50-4867-8345-43a7106a25da", + "metadata": {}, + "outputs": [], + "source": [ + "!head /scratch/vp91/$USER/intro-to-pytorch/data/pima-indians-diabetes.data.csv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee303492-97bf-4274-9a14-04c1c116f6c8", + "metadata": {}, + "outputs": [], + "source": [ + "datapath = os.path.expandvars('/scratch/vp91/$USER/intro-to-pytorch/data/pima-indians-diabetes.data.csv')\n", + "print(datapath)" + ] + }, + { + "cell_type": "markdown", + "id": "b32c13d2-bee6-436c-b838-2c8e04a24ec6", + "metadata": {}, + "source": [ + "### Curate the dataset\n", + "Load the dataset, split into features (X) and output (y) variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "222a7a99-2723-486d-9a1e-58d2792c84e8", + "metadata": {}, + "outputs": [], + "source": [ + "dataset = np.loadtxt(datapath, delimiter=',')\n", + "X = dataset[:,0:8] \n", + "y = dataset[:,8]" + ] + }, + { + "cell_type": "markdown", + "id": "4c2a20b9-0c73-4995-b772-0e773cc03c8b", + "metadata": {}, + "source": [ + "### Convert the data to tensors" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3c45e8f-894e-46d3-84c1-25fbca333f81", + "metadata": {}, + "outputs": [], + "source": [ + "X_tensor = torch.tensor(X, dtype=torch.float32)\n", + "y_tensor = torch.tensor(y, dtype=torch.float32).reshape(-1, 1)" + ] + }, + { + "cell_type": "markdown", + "id": "abc70a8b-2c37-4c09-bd7c-717d556cb39c", + "metadata": {}, + "source": [ + "### Defining the Model\n", + "\n", + "When designing the model, keep the following points in mind:\n", + "\n", + "1. The input features in the input layer must match the input features in the dataset (`X_tensor`).\n", + "2. A high number of layers can increase computation time, while too few layers may result in poor predictions.\n", + "3. Each layer should be followed by an activation function.\n", + "\n", + "In this example, we will use a 3-layer neural network:\n", + "\n", + "1. The input layer expects 8 features.\n", + "2. The first hidden layer has 12 neurons, followed by a ReLU activation function.\n", + "3. The second hidden layer has 8 neurons, followed by another ReLU activation function.\n", + "4. The output layer has one neuron, followed by a sigmoid activation function.\n", + "\n", + "The sigmoid function outputs values between 0 and 1, which is exactly what we need." + ] + }, + { + "cell_type": "markdown", + "id": "20d8051c-32ee-45c1-b797-58c0e68bbcfb", + "metadata": {}, + "source": [ + "\n", + "In PyTorch, neural networks can be defined using different approaches, and two common ones are the Sequential model and the class-based model." + ] + }, + { + "cell_type": "markdown", + "id": "a81a5b5b-d3bc-434f-9b92-a8571d7599f5", + "metadata": {}, + "source": [ + "#### Sequential model\n", + "\n", + "* The Sequential model is a simple, linear stack of layers where each layer has a single input and output. It is useful for straightforward feedforward networks where layers are applied in a sequential order.\n", + "* It is easier to use for simple architectures where layers are applied in a linear fashion.\n", + "* Defined Using: *torch.nn.Sequential*." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6cb10442-640a-4ded-a81f-6c2607a86bed", + "metadata": {}, + "outputs": [], + "source": [ + "seq_model = nn.Sequential(\n", + " nn.Linear(8, 12),\n", + " nn.ReLU(),\n", + " nn.Linear(12, 8),\n", + " nn.ReLU(),\n", + " nn.Linear(8, 1),\n", + " nn.Sigmoid()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c8647eb7-799b-42e5-b42d-be699b5e5a3e", + "metadata": {}, + "outputs": [], + "source": [ + "print(seq_model)" + ] + }, + { + "cell_type": "markdown", + "id": "36a8225d-4528-4a2f-a23e-9285d4ab5c8e", + "metadata": {}, + "source": [ + "### Class-Based Model\n", + "\n", + "The class-based model allows you to define a network by subclassing torch.nn.Module. This approach provides greater flexibility and control, making it suitable for complex models and custom behaviors.\n", + "\n", + "* Offers full control over the network architecture, including complex data flows, multiple inputs/outputs, and custom forward methods.\n", + "* Custom Forward Pass: You can define complex forward passes and control data flow through the network.\n", + "* Dynamic Behavior: Allows for dynamic computations, such as conditional layers or operations.\n", + "* Defined Using: Subclass of torch.nn.Module" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "263d5838-320d-4dad-ac59-e2d95ada7873", + "metadata": {}, + "outputs": [], + "source": [ + "class PimaClassifier(nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.hidden1 = nn.Linear(8, 12)\n", + " self.act1 = nn.ReLU()\n", + " self.hidden2 = nn.Linear(12, 8)\n", + " self.act2 = nn.ReLU()\n", + " self.output = nn.Linear(8, 1)\n", + " self.act_output = nn.Sigmoid()\n", + " \n", + " def forward(self, x):\n", + " x = self.act1(self.hidden1(x))\n", + " x = self.act2(self.hidden2(x))\n", + " x = self.act_output(self.output(x))\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "289c55b3-f54b-4a79-ba58-5788237aabb9", + "metadata": {}, + "outputs": [], + "source": [ + "class_model = PimaClassifier()\n", + "print(class_model)" + ] + }, + { + "cell_type": "markdown", + "id": "2e889fd5-49a7-4509-a2fc-ef7d5f8c722f", + "metadata": {}, + "source": [ + "### Define the loss function\n", + "Binary Cross-Entropy (BCE) Loss: Measures the performance of a classification model whose output is a probability value between 0 and 1. It calculates the difference between the predicted probabilities and the actual binary labels (0 or 1) and penalizes the model more when the predictions are further from the true labels.\n", + "\n", + "BCELoss(y', y)=−[ylog(y')+(1−y)log(1−y')]\n", + "\n", + "Where, y' is the predicted output and y is the actual otput." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "593672e5-4e14-473d-80f9-2ed00c127729", + "metadata": {}, + "outputs": [], + "source": [ + "loss_fn = nn.BCELoss()" + ] + }, + { + "cell_type": "markdown", + "id": "76aa2eab-7897-439e-9c20-08eb523ec7d6", + "metadata": {}, + "source": [ + "### Optimizer\n", + "\n", + "Optimizer's main role is to update the model's parameters based on the gradients computed during backpropagation.\n", + "\n", + "1. **Parameter Updates**: Optimizers adjust the weights and biases of the neural network to reduce the loss. This involves applying algorithms that modify the parameters to minimize the difference between the predicted outputs and the actual targets.\n", + "2. **Learning Rate Management**: Most optimizers include mechanisms to adjust the learning rate, either statically or dynamically, to control how large the parameter updates are.\n", + "\n", + "In this example we use an optimizer called Adaptive Moment Estimation (Adam). This computes an adaptive learning rates for each parameter by considering both the mean and the variance of the gradients." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "518bf03c-0594-494e-bdb3-a41421ac53a0", + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = optim.Adam(class_model.parameters(), lr=0.001)" + ] + }, + { + "cell_type": "markdown", + "id": "8608326d-27a3-4a5b-b485-e9af74b0f2e8", + "metadata": {}, + "source": [ + "#### Training the Model\n", + "\n", + "Training a neural network involves epochs and batches, which define how data is fed to the model:\n", + "\n", + "- **Epoch:** A full pass through the entire training dataset.\n", + "- **Batch:** A subset of samples processed at a time, with gradient descent performed after each batch.\n", + "\n", + "In practice, the dataset is divided into batches, and each batch is processed sequentially in a training loop. Completing all batches constitutes one epoch. The process is repeated for multiple epochs to refine the model.\n", + "\n", + "Batch size is constrained by system memory (GPU memory), and computational demands scale with batch size. More epochs and batches lead to better model performance but increase training time. The optimal number of epochs and batch size is often determined through experimentation." + ] + }, + { + "cell_type": "markdown", + "id": "6ea1f2ef-0b63-435c-a93f-329aa9ae6228", + "metadata": {}, + "source": [ + "#### Purpose of optimizer.zero_grad(), loss.backward(), optimizer.step()\n", + "\n", + "**optimizer.zero_grad()**: During training, gradients accumulate by default in PyTorch. This means that if you don’t clear them, gradients from multiple backward passes (from different batches) will be added together, which can lead to incorrect updates to the model parameters.\n", + "By calling optimizer.zero_grad(), you ensure that gradients from previous steps are reset to zero, preventing them from affecting the current update.\n", + "\n", + "**loss.backward()**: Calculates the gradients of the loss with respect to each parameter of the model. This is done using backpropagation, a key algorithm for training neural networks.\n", + "\n", + "**optimizer.step()**: Used to update the model's parameters based on the gradients computed during during the backward pass (**loss.backward()**)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad6b09b5-9e9b-4376-ad89-d1ff8b4791eb", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "n_epochs = 100\n", + "batch_size = 10\n", + " \n", + "for epoch in range(n_epochs):\n", + " for i in range(0, len(X_tensor), batch_size):\n", + " Xbatch = X_tensor[i:i+batch_size]\n", + " y_pred = class_model(Xbatch)\n", + " ybatch = y_tensor[i:i+batch_size]\n", + " loss = loss_fn(y_pred, ybatch)\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + " print(f'Finished epoch {epoch}, latest loss {loss}')" + ] + }, + { + "cell_type": "markdown", + "id": "04b239a9-7e1c-42ba-8b54-58c79d26986b", + "metadata": {}, + "source": [ + "# Evaluate the Model\n", + "\n", + "Currently, we are testing the model on the training dataset. Ideally, we should split the data into separate training and testing datasets, or use a distinct dataset for evaluation. For simplicity, we are testing the model on the same data used for training.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89e28fe2-90c5-4cd4-bd37-30ebe9183772", + "metadata": {}, + "outputs": [], + "source": [ + "with torch.no_grad():\n", + " y_pred = class_model(X_tensor)\n", + " \n", + "accuracy = (y_pred.round() == y_tensor).float().mean()\n", + "print(f\"Accuracy {accuracy}\")" + ] + }, + { + "cell_type": "markdown", + "id": "e07a44bb-25c6-4f67-8a34-514d7eadbbaf", + "metadata": {}, + "source": [ + "### Exercise\n", + "\n", + "1. **Increase the number of layers in the neural network.** Observe any changes in accuracy.\n", + "2. **Change the optimizer from Adam to [Stochastic Gradient Descent (SGD)](https://pytorch.org/docs/stable/generated/torch.optim.SGD.html).** Evaluate how this affects the loss calculation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84664807-8163-46dd-b037-1b3f73d8cbd9", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/data_parallel.ipynb b/notebooks/data_parallel.ipynb new file mode 100644 index 0000000..e90d4e5 --- /dev/null +++ b/notebooks/data_parallel.ipynb @@ -0,0 +1,348 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1cdb801e-e281-476f-b3e9-e470785d3ad9", + "metadata": {}, + "source": [ + "### Using Multiple GPUs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "604d5312-0b33-4162-b5f4-551c21732550", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "from torch.utils.data import Dataset, DataLoader\n", + "import pandas as pd\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49a40db4-da7b-4d24-b707-a39b79d2440e", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "# The jupyter notebook is launched from your $HOME directory.\n", + "# Change the working directory to the workshop directory\n", + "# which was created in your username directory under /scratch/vp91\n", + "os.chdir(os.path.expandvars(\"/scratch/vp91/$USER/\"))" + ] + }, + { + "cell_type": "markdown", + "id": "4098c4ec-368b-4802-9800-fc4c4b7479ba", + "metadata": {}, + "source": [ + "#### Set Device\n", + "Se the default device as the GPU if it exists" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b51be64-542f-401c-ae73-00da2bbd6471", + "metadata": {}, + "outputs": [], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "print(device)" + ] + }, + { + "cell_type": "markdown", + "id": "2d29976e-56b5-46ce-8743-5480524bbca1", + "metadata": {}, + "source": [ + "### Dataloader" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea28a5d7-0d69-47c9-ba24-995f02168856", + "metadata": {}, + "outputs": [], + "source": [ + "datapath = os.path.expandvars('/scratch/vp91/$USER/intro-to-pytorch/data/pima-indians-diabetes.data.csv')\n", + "\n", + "# Define the custom Dataset class\n", + "column_names = [\n", + " 'Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness',\n", + " 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'\n", + "]\n", + "\n", + "# Define the custom Dataset class\n", + "class PimaDataset(Dataset):\n", + " def __init__(self, csv_file):\n", + " # Load the CSV file without header and assign column names\n", + " self.data = pd.read_csv(csv_file, header=None, names=column_names)\n", + " self.features = self.data.drop('Outcome', axis=1).values\n", + " self.labels = self.data['Outcome'].values\n", + " \n", + " # Convert to PyTorch tensors\n", + " self.features_tensor = torch.tensor(self.features, dtype=torch.float32)\n", + " self.labels_tensor = torch.tensor(self.labels, dtype=torch.long)\n", + " \n", + " # Calculate mean and std\n", + " self.mean = self.features_tensor.mean(dim=0)\n", + " self.std = self.features_tensor.std(dim=0)\n", + " \n", + " # Normalize the features\n", + " self.features_tensor = (self.features_tensor - self.mean) / self.std\n", + "\n", + " def __len__(self):\n", + " return len(self.data)\n", + "\n", + " def __getitem__(self, idx):\n", + " feature = self.features_tensor[idx]\n", + " label = self.labels_tensor[idx]\n", + " return feature, label" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "110beb4c-c07b-4a1b-b2d4-ce7795af31a4", + "metadata": {}, + "outputs": [], + "source": [ + "dataset = PimaDataset(datapath)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26d7f973-cf59-4709-a0c2-a158100449e9", + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 32\n", + "data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)" + ] + }, + { + "cell_type": "markdown", + "id": "abc70a8b-2c37-4c09-bd7c-717d556cb39c", + "metadata": {}, + "source": [ + "### Defining the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "263d5838-320d-4dad-ac59-e2d95ada7873", + "metadata": {}, + "outputs": [], + "source": [ + "class PimaClassifier(nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.hidden1 = nn.Linear(8, 12)\n", + " self.act1 = nn.ReLU()\n", + " self.hidden2 = nn.Linear(12, 8)\n", + " self.act2 = nn.ReLU()\n", + " self.output = nn.Linear(8, 1)\n", + " self.act_output = nn.Sigmoid()\n", + " \n", + " def forward(self, x):\n", + " x = self.act1(self.hidden1(x))\n", + " x = self.act2(self.hidden2(x))\n", + " x = self.act_output(self.output(x))\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "289c55b3-f54b-4a79-ba58-5788237aabb9", + "metadata": {}, + "outputs": [], + "source": [ + "class_model = PimaClassifier()\n", + "print(class_model)" + ] + }, + { + "cell_type": "markdown", + "id": "09ec5647-aded-4179-89c0-0c5d44b0c6db", + "metadata": {}, + "source": [ + "#### Data Parallelism\n", + "Pytorch will only use one GPU by default. You can easily run your operations on multiple GPUs by making your model run parallelly using `nn.DataParallel`. \n", + "\n", + "Check for multiple GPUs and if multiple GPUs are available, wrap the model with `nn.DataParallel`. Finally, move the model to the GPUs using `model.to(device)`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0dc9a702-4b3a-423f-80d3-79d1e3d9e11f", + "metadata": {}, + "outputs": [], + "source": [ + "print(torch.cuda.device_count())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5a58574-4ce7-495c-be0b-d22694a6ed7a", + "metadata": {}, + "outputs": [], + "source": [ + "if torch.cuda.device_count() > 1:\n", + " class_model = nn.DataParallel(class_model)\n", + " print(f\"Using {torch.cuda.device_count()} GPUs: {', '.join([torch.cuda.get_device_name(i) for i in range(torch.cuda.device_count())])}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d70bfdf8-9619-4448-ad45-cb2277d937ea", + "metadata": {}, + "outputs": [], + "source": [ + "class_model.to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "593672e5-4e14-473d-80f9-2ed00c127729", + "metadata": {}, + "outputs": [], + "source": [ + "loss_fn = nn.BCELoss()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "518bf03c-0594-494e-bdb3-a41421ac53a0", + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = optim.Adam(class_model.parameters(), lr=0.001)" + ] + }, + { + "cell_type": "markdown", + "id": "8608326d-27a3-4a5b-b485-e9af74b0f2e8", + "metadata": {}, + "source": [ + "#### Training the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "67278570-0838-40a9-9410-851c51a46b95", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "04b239a9-7e1c-42ba-8b54-58c79d26986b", + "metadata": {}, + "source": [ + "DataParallel splits your data automatically and sends job orders to multiple models on several GPUs. After each model finishes their job, DataParallel collects and merges the results before returning it to you." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84664807-8163-46dd-b037-1b3f73d8cbd9", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "n_epochs = 100\n", + "batch_size = 10\n", + " \n", + "for epoch in range(n_epochs):\n", + " running_loss = 0.0\n", + " for batch_features, batch_labels in data_loader:\n", + " batch_features = batch_features.to(device)\n", + " batch_labels = batch_labels.to(device)\n", + "\n", + " optimizer.zero_grad()\n", + " \n", + " outputs = class_model(batch_features)\n", + " \n", + " batch_labels = batch_labels.unsqueeze(1).float()\n", + " loss = loss_fn(outputs, batch_labels)\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " running_loss += loss.item() * batch_features.size(0)\n", + " \n", + " epoch_loss = running_loss / len(dataset)\n", + " print(f'Epoch {epoch+1}/{n_epochs}, Loss: {epoch_loss:.4f}')\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32e7a7d0-666f-482a-b250-34a3f1199240", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aedca9ef-fa2d-41e8-8847-c7e9fbcf498c", + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"Model is on device: {next(class_model.parameters()).device}\")\n", + "if isinstance(class_model, nn.DataParallel):\n", + " print(f\"DataParallel devices: {class_model.device_ids}\")" + ] + }, + { + "cell_type": "markdown", + "id": "e07a44bb-25c6-4f67-8a34-514d7eadbbaf", + "metadata": {}, + "source": [ + "### Exercise\n", + "\n", + "1. **What is the time difference in training**? Compare it with the previous training (change epoch to 100)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/dataloader.ipynb b/notebooks/dataloader.ipynb new file mode 100644 index 0000000..c889a63 --- /dev/null +++ b/notebooks/dataloader.ipynb @@ -0,0 +1,779 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "b670ae8e-1350-4be1-8575-df9267fdfae7", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "from torch.utils.data import Dataset\n", + "from torchvision import datasets\n", + "from torchvision.transforms import ToTensor\n", + "from torch.utils.data import Dataset, DataLoader\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import requests\n", + "import zipfile\n", + "from pathlib import Path\n", + "\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f82a4673-e5e9-4f5f-b7e6-112a8fa1e47d", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "# The jupyter notebook is launched from your $HOME directory.\n", + "# Change the working directory to the workshop directory\n", + "# which was created in your username directory under /scratch/vp91\n", + "os.chdir(os.path.expandvars(\"/scratch/vp91/$USER/\"))" + ] + }, + { + "cell_type": "markdown", + "id": "775f4111-d1e2-4cd1-bd2d-a7a1bbb3d21d", + "metadata": {}, + "source": [ + "PyTorch offers two data primitives—`torch.utils.data.DataLoader` and `torch.utils.data.Dataset`— which \n", + "facilitate the use of both pre-loaded datasets and custom data.\n", + "\n", + "The `Fashion-MNIST` dataset is an example of a pre-loaded curated dataset. It can be loaded using the following parameters:\n", + "\n", + "- `root` specifies the path where the training or test data is stored.\n", + "- `train` indicates whether to load the training or test dataset.\n", + "- `download=True` will download the data from the internet if it's not available at the specified `root`.\n", + "- `transform` and `target_transform` define the transformations applied to the features and labels, respectively.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "749f1295-2191-40e3-9f7f-8c34589d1a7d", + "metadata": {}, + "outputs": [], + "source": [ + "training_data = datasets.FashionMNIST(\n", + " root=\"data\",\n", + " train=True,\n", + " download=True,\n", + " transform=ToTensor()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b818ed5f-c845-4fca-9015-99196f3b937d", + "metadata": {}, + "outputs": [], + "source": [ + "test_data = datasets.FashionMNIST(\n", + " root=\"data\",\n", + " train=False,\n", + " download=True,\n", + " transform=ToTensor()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "aef48041-ad83-4813-924d-22404d691286", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "t10k-images-idx3-ubyte\t train-images-idx3-ubyte\n", + "t10k-images-idx3-ubyte.gz train-images-idx3-ubyte.gz\n", + "t10k-labels-idx1-ubyte\t train-labels-idx1-ubyte\n", + "t10k-labels-idx1-ubyte.gz train-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "!ls data/FashionMNIST/raw/" + ] + }, + { + "cell_type": "markdown", + "id": "80984294-a299-4da7-802d-9184706a5f2a", + "metadata": {}, + "source": [ + "#### Visualizing a sample of the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9f52a941-cd0e-4665-9e6c-182ffc33c5b5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "labels_map = {\n", + " 0: \"T-Shirt\",\n", + " 1: \"Trouser\",\n", + " 2: \"Pullover\",\n", + " 3: \"Dress\",\n", + " 4: \"Coat\",\n", + " 5: \"Sandal\",\n", + " 6: \"Shirt\",\n", + " 7: \"Sneaker\",\n", + " 8: \"Bag\",\n", + " 9: \"Ankle Boot\",\n", + "}\n", + "figure = plt.figure(figsize=(8, 8))\n", + "cols, rows = 3, 3\n", + "for i in range(1, cols * rows + 1):\n", + " sample_idx = torch.randint(len(training_data), size=(1,)).item()\n", + " img, label = training_data[sample_idx]\n", + " figure.add_subplot(rows, cols, i)\n", + " plt.title(labels_map[label])\n", + " plt.axis(\"off\")\n", + " plt.imshow(img.squeeze(), cmap=\"gray\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7d1d3c91-ba90-469d-9d8b-ab18a7768b84", + "metadata": {}, + "source": [ + "### Custom dataset" + ] + }, + { + "cell_type": "markdown", + "id": "57f0bdd5-5521-421f-a56c-532764d123af", + "metadata": {}, + "source": [ + "What if working with a custom dataset? To illustrate this, we will download a dataset and set it up for\n", + "use in PyTorch training. The data used for this demonstration is relatively *clean*. In a practical use case, significant time will likely be spent on cleaning and preparing the data." + ] + }, + { + "cell_type": "markdown", + "id": "727a8d18-c149-4ae7-8791-d301fa83e579", + "metadata": {}, + "source": [ + "The data:\n", + "\n", + "1. There are **3 classes**: pizza, steak, and sushi.\n", + "2. The data is split into *train* and *test* datasets.\n", + "3. Both *train* and *test* datasets are further organized into 3 directories, each corresponding to one of the classes." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "88c9b050-a64a-45a6-ac46-06b9a0632431", + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "import zipfile\n", + "from pathlib import Path\n", + "\n", + "# Setup path to data folder\n", + "data_root = Path(\"custom_data/\")\n", + "image_path = data_root / \"pizza_steak_sushi\"\n", + "\n", + "# If the image data doesn't exist, download it and curate it. \n", + "if not image_path.is_dir():\n", + " image_path.mkdir(parents=True, exist_ok=True)\n", + " \n", + " # Download pizza, steak, sushi data\n", + " url = \"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip\"\n", + " with open(data_root / \"pizza_steak_sushi.zip\", \"wb\") as f:\n", + " request = requests.get(url)\n", + " f.write(request.content)\n", + "\n", + " with zipfile.ZipFile(data_root / \"pizza_steak_sushi.zip\", \"r\") as zip_ref:\n", + " zip_ref.extractall(image_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9675e0c5-cffc-4420-a419-eaf3e2198fb3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pizza_steak_sushi pizza_steak_sushi.zip\n" + ] + } + ], + "source": [ + "!ls custom_data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b2d51954-2b02-42bb-90e3-121c105e3c7c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "test train\n" + ] + } + ], + "source": [ + "!ls custom_data/pizza_steak_sushi" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0a8043a6-7ea8-44e7-908d-e0921c9930db", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pizza steak sushi\n" + ] + } + ], + "source": [ + "!ls custom_data/pizza_steak_sushi/train" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1425fa36-3c2e-4597-9b78-65516835ac83", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pizza steak sushi\n" + ] + } + ], + "source": [ + "!ls custom_data/pizza_steak_sushi/test" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "b27f580f-988a-425f-9b3b-e65a23742500", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1008844.jpg 1654444.jpg 2291093.jpg 2785084.jpg 320570.jpg\t 5764.jpg\n", + "1033251.jpg 1660415.jpg 2330965.jpg 2800325.jpg 3269634.jpg 618348.jpg\n", + "1044789.jpg 1899785.jpg 2382016.jpg 2811032.jpg 3281494.jpg 667309.jpg\n", + "1089334.jpg 1947572.jpg 2426686.jpg 2821048.jpg 3338774.jpg 68684.jpg\n", + "1105700.jpg 1968947.jpg 2428085.jpg 2885050.jpg 3441394.jpg 702165.jpg\n", + "12301.jpg 2026009.jpg 244505.jpg 2885796.jpg 3505182.jpg 715169.jpg\n", + "1285298.jpg 2121603.jpg 2451169.jpg 2924941.jpg 3530210.jpg 739735.jpg\n", + "138855.jpg 2154394.jpg 2493954.jpg 29417.jpg 3589437.jpg 741883.jpg\n", + "1412034.jpg 218711.jpg 2569760.jpg 2992084.jpg 3699992.jpg 764429.jpg\n", + "1524655.jpg 2190018.jpg 2576168.jpg 300869.jpg 3821701.jpg 765799.jpg\n", + "1572608.jpg 220190.jpg 2687575.jpg 3018077.jpg 38349.jpg\t 786995.jpg\n", + "1633289.jpg 2228322.jpg 2702825.jpg 3109486.jpg 3860002.jpg 853441.jpg\n", + "1649276.jpg 2285942.jpg 2760984.jpg 3196721.jpg 393658.jpg\t 928670.jpg\n" + ] + } + ], + "source": [ + "!ls custom_data/pizza_steak_sushi/train/pizza" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "696d0b71-2825-48ed-b84a-cf9519e296f4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", 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", + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from PIL import Image\n", + "img = Image.open(\"custom_data/pizza_steak_sushi/train/pizza/928670.jpg\")\n", + "img" + ] + }, + { + "cell_type": "markdown", + "id": "4577dd6b-c581-4dc5-996a-92b7c3a07436", + "metadata": {}, + "source": [ + "#### Setup train and testing paths" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "49b85b56-2ace-4a01-b361-b00ef2b28f9e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(PosixPath('custom_data/pizza_steak_sushi/train'),\n", + " PosixPath('custom_data/pizza_steak_sushi/test'))" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "train_dir = image_path / \"train\"\n", + "test_dir = image_path / \"test\"\n", + "\n", + "train_dir, test_dir" + ] + }, + { + "cell_type": "markdown", + "id": "ba0bbe2b-edcd-4f07-b715-e42c6d33dc1c", + "metadata": {}, + "source": [ + "#### Transformation on the data\n", + "\n", + "\n", + "Transform functions in the PyTorch library simplify the application of various data enhancement/manipulation techniques \n", + "to your input data. These functions enable you to apply multiple changes simultaneously." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "6ce41688-a909-477c-94ef-010ea6724445", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "from torch.utils.data import DataLoader\n", + "from torchvision import datasets, transforms" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "c42cbfdb-a5f3-4e07-8beb-e151835902fb", + "metadata": {}, + "outputs": [], + "source": [ + "# Write transform for image\n", + "data_transform = transforms.Compose([\n", + " # Resize the images to 64x64\n", + " transforms.Resize(size=(64, 64)),\n", + " # Flip the images randomly on the horizontal\n", + " transforms.RandomHorizontalFlip(p=0.5), # p = probability of flip, 0.5 = 50% chance\n", + " # Turn the image into a torch.Tensor\n", + " transforms.ToTensor() # this also converts all pixel values from 0 to 255 to be between 0.0 and 1.0 \n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "e53af9e2-b0b9-40ff-9526-a4239600dc3f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with Image.open(\"custom_data/pizza_steak_sushi/train/pizza/928670.jpg\") as f:\n", + " fig, ax = plt.subplots(1, 2)\n", + " ax[0].imshow(f) \n", + " ax[0].set_title(f\"Original \\nSize: {f.size}\")\n", + " ax[0].axis(\"off\")\n", + "\n", + " transformed_image = data_transform(f).permute(1, 2, 0) \n", + " ax[1].imshow(transformed_image) \n", + " ax[1].set_title(f\"Transformed \\nSize: {transformed_image.shape}\")\n", + " ax[1].axis(\"off\")" + ] + }, + { + "cell_type": "markdown", + "id": "af651bd5-8afa-4c8c-9661-03683f91be8d", + "metadata": {}, + "source": [ + "#### Loading Image Data Using ImageFolder\n", + "\n", + "`ImageFolder` is a generic data loader where images are expected to be organized into separate directories,\n", + "each corresponding to a different class." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "245ada1a-e053-4905-aa44-39ef9814fde8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train data:\n", + "Dataset ImageFolder\n", + " Number of datapoints: 225\n", + " Root location: custom_data/pizza_steak_sushi/train\n", + " StandardTransform\n", + "Transform: Compose(\n", + " Resize(size=(64, 64), interpolation=bilinear, max_size=None, antialias=True)\n", + " RandomHorizontalFlip(p=0.5)\n", + " ToTensor()\n", + " )\n", + "Test data:\n", + "Dataset ImageFolder\n", + " Number of datapoints: 75\n", + " Root location: custom_data/pizza_steak_sushi/test\n", + " StandardTransform\n", + "Transform: Compose(\n", + " Resize(size=(64, 64), interpolation=bilinear, max_size=None, antialias=True)\n", + " RandomHorizontalFlip(p=0.5)\n", + " ToTensor()\n", + " )\n" + ] + } + ], + "source": [ + "# Use ImageFolder to create dataset(s)\n", + "from torchvision import datasets\n", + "train_data = datasets.ImageFolder(root=train_dir, # target folder of images\n", + " transform=data_transform, # transforms to perform on data (images)\n", + " target_transform=None) # transforms to perform on labels (if necessary)\n", + "\n", + "test_data = datasets.ImageFolder(root=test_dir, \n", + " transform=data_transform)\n", + "\n", + "print(f\"Train data:\\n{train_data}\\nTest data:\\n{test_data}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "2c7ad54a-a8f2-4963-86c3-3150cb68cae3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['pizza', 'steak', 'sushi']" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get class names as a list\n", + "class_names = train_data.classes\n", + "class_names" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "59f14ace-5c56-4a18-b24e-67c34321a2ec", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'pizza': 0, 'steak': 1, 'sushi': 2}" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Can also get class names as a dict\n", + "class_dict = train_data.class_to_idx\n", + "class_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "ede23b28-601c-45ba-a544-ca4d828045ba", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(225, 75)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check the lengths\n", + "len(train_data), len(test_data)" + ] + }, + { + "cell_type": "markdown", + "id": "8c8d91e6-c12d-4fb1-ab75-c95f1309c995", + "metadata": {}, + "source": [ + "#### DataLoader\n", + "\n", + "\n", + "In PyTorch, `DataLoader` is a built-in class that offers an efficient and flexible method for loading \n", + "data into a model for training or inference. It is especially beneficial for managing large datasets that \n", + "may not fit into memory and for carrying out data augmentation and preprocessing. \n", + "Data loader combines a dataset and a sampler, and provides an iterable over the given dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4e7b6e1-7ec9-411e-9d3d-422d4d6f8bc9", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "69ed025b-c14c-4130-97f6-5d00a3757880", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(,\n", + " )" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Turn train and test Datasets into DataLoaders\n", + "from torch.utils.data import DataLoader\n", + "train_dataloader = DataLoader(dataset=train_data, \n", + " batch_size=8, # how many samples per batch?\n", + " num_workers=1, # how many subprocesses to use for data loading? (higher = more)\n", + " shuffle=True) # shuffle the data?\n", + "\n", + "test_dataloader = DataLoader(dataset=test_data, \n", + " batch_size=8, \n", + " num_workers=1, \n", + " shuffle=False) # don't usually need to shuffle testing data\n", + "\n", + "train_dataloader, test_dataloader" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "59008e99-69c0-4a5b-ae9a-419273c07841", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Image shape: torch.Size([8, 3, 64, 64]) -> [batch_size, color_channels, height, width]\n", + "Label shape: torch.Size([8])\n" + ] + } + ], + "source": [ + "img, label = next(iter(train_dataloader))\n", + "\n", + "print(f\"Image shape: {img.shape} -> [batch_size, color_channels, height, width]\")\n", + "print(f\"Label shape: {label.shape}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "1e3c2b18-2162-4d8e-beb9-991093854c57", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Tensor" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(img)" + ] + }, + { + "cell_type": "markdown", + "id": "496fb7e6-2727-4ba8-bb2c-6b8460b9565f", + "metadata": {}, + "source": [ + "#### Custom DataLoader" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "0887d989-49c3-4012-910f-e011340b0059", + "metadata": {}, + "outputs": [], + "source": [ + "datapath = os.path.expandvars('/scratch/vp91/$USER/intro-to-pytorch/data/pima-indians-diabetes.data.csv')\n", + "\n", + "# Define the custom Dataset class\n", + "column_names = [\n", + " 'Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness',\n", + " 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'\n", + "]\n", + "\n", + "# Define the custom Dataset class\n", + "class PimaDataset(Dataset):\n", + " def __init__(self, csv_file):\n", + " # Load the CSV file without header and assign column names\n", + " self.data = pd.read_csv(csv_file, header=None, names=column_names)\n", + " self.features = self.data.drop('Outcome', axis=1).values\n", + " self.labels = self.data['Outcome'].values\n", + " \n", + " # Convert to PyTorch tensors\n", + " self.features_tensor = torch.tensor(self.features, dtype=torch.float32)\n", + " self.labels_tensor = torch.tensor(self.labels, dtype=torch.long)\n", + " \n", + " # Calculate mean and std\n", + " self.mean = self.features_tensor.mean(dim=0)\n", + " self.std = self.features_tensor.std(dim=0)\n", + " \n", + " # Normalize the features\n", + " self.features_tensor = (self.features_tensor - self.mean) / self.std\n", + "\n", + " def __len__(self):\n", + " return len(self.data)\n", + "\n", + " def __getitem__(self, idx):\n", + " feature = self.features_tensor[idx]\n", + " label = self.labels_tensor[idx]\n", + " return feature, label" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c088ccc3-85a1-4a5d-ac9b-7699ff7e91fa", + "metadata": {}, + "outputs": [], + "source": [ + "dataset = PimaDataset(datapath)\n", + "batch_size = 32\n", + "data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87e8a8d6-f0bc-4618-ad3e-dd3b1a5ca8c8", + "metadata": {}, + "outputs": [], + "source": [ + "features, outcomes = next(iter(data_loader))\n", + "\n", + "print(f\"Image shape: {features.shape} -> [batch_size, inputs_features]\")\n", + "print(f\"Label shape: {outcomes.shape}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0f401ab-c3cd-400f-8c97-b3c94146ac56", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/distributed_data_parallel.ipynb b/notebooks/distributed_data_parallel.ipynb new file mode 100644 index 0000000..23a6c4e --- /dev/null +++ b/notebooks/distributed_data_parallel.ipynb @@ -0,0 +1,305 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1cdb801e-e281-476f-b3e9-e470785d3ad9", + "metadata": {}, + "source": [ + "### Using Multiple GPUs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "604d5312-0b33-4162-b5f4-551c21732550", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import os\n", + "import pandas as pd\n", + "\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "import torch.distributed as dist\n", + "import torch.multiprocessing as mp\n", + "from torch.utils.data import Dataset, DataLoader\n", + "from torch.utils.data.distributed import DistributedSampler\n", + "from torch.nn.parallel import DistributedDataParallel as DDP" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49a40db4-da7b-4d24-b707-a39b79d2440e", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "# The jupyter notebook is launched from your $HOME directory.\n", + "# Change the working directory to the workshop directory\n", + "# which was created in your username directory under /scratch/vp91\n", + "os.chdir(os.path.expandvars(\"/scratch/vp91/$USER/\"))" + ] + }, + { + "cell_type": "markdown", + "id": "4098c4ec-368b-4802-9800-fc4c4b7479ba", + "metadata": {}, + "source": [ + "#### Set Device\n", + "Se the default device as the GPU if it exists" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b51be64-542f-401c-ae73-00da2bbd6471", + "metadata": {}, + "outputs": [], + "source": [ + "nb_gpus = 2\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "datapath = os.path.expandvars('/scratch/vp91/$USER/intro-to-pytorch/data/pima-indians-diabetes.data.csv')\n", + "\n", + "# Define the custom Dataset class\n", + "column_names = [\n", + " 'Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness',\n", + " 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "f944a147-3f5e-4c42-b142-850d04458270", + "metadata": {}, + "source": [ + "### Process Groups" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "50696138-0c0d-4a0b-aa80-ed73cff87fd2", + "metadata": {}, + "outputs": [], + "source": [ + "def setup(rank, world_size):\n", + " os.environ['MASTER_ADDR'] = 'localhost'\n", + " os.environ['MASTER_PORT'] = '12355'\n", + " dist.init_process_group(\"nccl\", rank=rank, world_size=world_size)\n", + " \n", + "def cleanup():\n", + " dist.destroy_process_group()" + ] + }, + { + "cell_type": "markdown", + "id": "2d29976e-56b5-46ce-8743-5480524bbca1", + "metadata": {}, + "source": [ + "### Dataloader" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea28a5d7-0d69-47c9-ba24-995f02168856", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the custom Dataset class\n", + "class PimaDataset(Dataset):\n", + " def __init__(self, csv_file):\n", + " # Load the CSV file without header and assign column names\n", + " self.data = pd.read_csv(csv_file, header=None, names=column_names)\n", + " self.features = self.data.drop('Outcome', axis=1).values\n", + " self.labels = self.data['Outcome'].values\n", + " \n", + " # Convert to PyTorch tensors\n", + " self.features_tensor = torch.tensor(self.features, dtype=torch.float32)\n", + " self.labels_tensor = torch.tensor(self.labels, dtype=torch.long)\n", + " \n", + " # Calculate mean and std\n", + " self.mean = self.features_tensor.mean(dim=0)\n", + " self.std = self.features_tensor.std(dim=0)\n", + " \n", + " # Normalize the features\n", + " self.features_tensor = (self.features_tensor - self.mean) / self.std\n", + "\n", + " def __len__(self):\n", + " return len(self.data)\n", + "\n", + " def __getitem__(self, idx):\n", + " feature = self.features_tensor[idx]\n", + " label = self.labels_tensor[idx]\n", + " return feature, label" + ] + }, + { + "cell_type": "markdown", + "id": "ec4ebf96-12bc-4520-bbcb-690e5edebac9", + "metadata": {}, + "source": [ + "### Split the dataloader" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49ca23e3-f8d9-4818-93e4-fa9304792335", + "metadata": {}, + "outputs": [], + "source": [ + "def prepare(rank, world_size, batch_size=32, pin_memory=False, num_workers=0):\n", + " dataset = PimaDataset(datapath)\n", + " sampler = DistributedSampler(dataset, num_replicas=world_size, rank=rank, shuffle=False, drop_last=False)\n", + " \n", + " dataloader = DataLoader(dataset, batch_size=batch_size, pin_memory=pin_memory, num_workers=num_workers, drop_last=False, shuffle=False, sampler=sampler)\n", + " \n", + " return dataloader" + ] + }, + { + "cell_type": "markdown", + "id": "abc70a8b-2c37-4c09-bd7c-717d556cb39c", + "metadata": {}, + "source": [ + "### Defining the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "263d5838-320d-4dad-ac59-e2d95ada7873", + "metadata": {}, + "outputs": [], + "source": [ + "class PimaClassifier(nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.hidden1 = nn.Linear(8, 12)\n", + " self.act1 = nn.ReLU()\n", + " self.hidden2 = nn.Linear(12, 8)\n", + " self.act2 = nn.ReLU()\n", + " self.output = nn.Linear(8, 1)\n", + " self.act_output = nn.Sigmoid()\n", + " \n", + " def forward(self, x):\n", + " x = self.act1(self.hidden1(x))\n", + " x = self.act2(self.hidden2(x))\n", + " x = self.act_output(self.output(x))\n", + " return x" + ] + }, + { + "cell_type": "markdown", + "id": "09ec5647-aded-4179-89c0-0c5d44b0c6db", + "metadata": {}, + "source": [ + "#### Wrap model in DDP\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0dc9a702-4b3a-423f-80d3-79d1e3d9e11f", + "metadata": {}, + "outputs": [], + "source": [ + "def main(rank, world_size):\n", + "\n", + " # setup the process groups\n", + " setup(rank, world_size)\n", + " # prepare the dataloader\n", + " dataloader = prepare(rank, world_size)\n", + " \n", + " # instantiate the model(it's your own model) and move it to the right device\n", + " model = PimaClassifier().to(rank)\n", + " \n", + " # wrap the model with DDP\n", + " # device_ids tell DDP where is your model\n", + " # output_device tells DDP where to output, in our case, it is rank\n", + " # find_unused_parameters=True instructs DDP to find unused output of the forward() function of any module in the model\n", + " model = DDP(model, device_ids=[rank], output_device=rank, find_unused_parameters=True)\n", + "\n", + " loss_fn = nn.BCELoss()\n", + " optimizer = optim.Adam(model.parameters(), lr=0.001)\n", + "\n", + " n_epochs = 100\n", + " for epoch in range(n_epochs):\n", + "\n", + " # if we are using DistributedSampler, we have to tell it which epoch this is\n", + " dataloader.sampler.set_epoch(epoch)\n", + "\n", + " for batch_features, batch_labels in dataloader:\n", + " batch_features = batch_features.to(rank)\n", + " batch_labels = batch_labels.to(rank)\n", + "\n", + " optimizer.zero_grad()\n", + " \n", + " outputs = model(batch_features)\n", + " \n", + " batch_labels = batch_labels.unsqueeze(1).float()\n", + " loss = loss_fn(outputs, batch_labels)\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " cleanup()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d70bfdf8-9619-4448-ad45-cb2277d937ea", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "593672e5-4e14-473d-80f9-2ed00c127729", + "metadata": {}, + "outputs": [], + "source": [ + "if __name__ == '__main__':\n", + "\n", + " world_size = nb_gpus \n", + " mp.spawn(main, args=(world_size,), nprocs=world_size)" + ] + }, + { + "cell_type": "markdown", + "id": "e07a44bb-25c6-4f67-8a34-514d7eadbbaf", + "metadata": {}, + "source": [ + "### Exercise\n", + "\n", + "1. **What is the time difference in training**? Compare it with the previous training (change epoch to 100)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/tensors.ipynb b/notebooks/tensors.ipynb new file mode 100644 index 0000000..cbd5bf4 --- /dev/null +++ b/notebooks/tensors.ipynb @@ -0,0 +1,375 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "205ea8ba-4865-4511-8a54-14fcd4b22ed0", + "metadata": {}, + "source": [ + "### Tensors in PyTorch\n", + "\n", + "Tensors are specialized data structures used in PyTorch to represent model inputs, outputs, and parameters. While they are conceptually similar to arrays and matrices, they offer additional features such as support for hardware accelerators like GPUs and automatic differentiation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "244c9ced-e83c-4c24-a992-f216dfa34456", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "# The jupyter notebook is launched from your $HOME directory.\n", + "# Change the working directory to the workshop directory\n", + "# which was created in your username directory under /scratch/vp91\n", + "os.chdir(os.path.expandvars(\"/scratch/vp91/$USER/\"))" + ] + }, + { + "cell_type": "markdown", + "id": "c20aaadc-e2a0-4a89-9703-b091578b4dc0", + "metadata": {}, + "source": [ + "### Creating a Tensor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "816961f2-0932-47d1-923f-d9743ec8c062", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "id": "383b7ba2-6d5d-4380-9f36-fb62f6ce1d8f", + "metadata": {}, + "source": [ + "##### 1. Directly from data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2137307c-2fa0-4953-92e3-fcb24408ff77", + "metadata": {}, + "outputs": [], + "source": [ + "data = [[1, 2],[3, 4]]\n", + "x_tensor= torch.tensor(data)" + ] + }, + { + "cell_type": "markdown", + "id": "cba5f6e9-f0fc-4cee-b48d-660e6267541d", + "metadata": {}, + "source": [ + "##### 2. From NumPy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48cd31ef-a810-4aaa-a314-d5f5705e7be4", + "metadata": {}, + "outputs": [], + "source": [ + "x_np = np.array(data)\n", + "x_tensor = torch.from_numpy(x_np)" + ] + }, + { + "cell_type": "markdown", + "id": "0d365755-871b-4380-8e36-972799542b5e", + "metadata": {}, + "source": [ + "##### 3. From another Tensor\n", + "\n", + "**torch.rand_like()** returns a tensor with the same size as input that but filled with random numbers from the interval [0,1)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62f40f87-637b-4e58-be66-8fe8f8d4b84b", + "metadata": {}, + "outputs": [], + "source": [ + "x_tensor = torch.ones_like(x_tensor)\n", + "y_tensor = torch.rand_like(x_tensor, dtype=torch.float) " + ] + }, + { + "cell_type": "markdown", + "id": "84e9c4b6-6b85-430d-a793-93521879f671", + "metadata": {}, + "source": [ + "### Operations on Tensors" + ] + }, + { + "cell_type": "markdown", + "id": "c59bbc8d-00d1-48cc-bc2c-4443c8ccec31", + "metadata": {}, + "source": [ + "#### 1. indexing and slicing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d3e7c194-ecea-4ef9-af39-3f563daddc3c", + "metadata": {}, + "outputs": [], + "source": [ + "x_tensor = torch.ones(4, 4)\n", + "print(f\"First row: {x_tensor[0]}\")\n", + "print(f\"First column: {x_tensor[:, 0]}\")\n", + "print(f\"Last column: {x_tensor[..., -1]}\")\n", + "x_tensor[:,1] = 0\n", + "print(x_tensor)" + ] + }, + { + "cell_type": "markdown", + "id": "bbe77b93-12ab-475f-81fc-c5a00db24621", + "metadata": {}, + "source": [ + "#### 2. Concatenate multiple tensors" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b00e821-b480-4f7e-8788-1e983ed1693b", + "metadata": {}, + "outputs": [], + "source": [ + "y_tensor = torch.cat([x_tensor, x_tensor, x_tensor], dim=1)\n", + "print(y_tensor)" + ] + }, + { + "cell_type": "markdown", + "id": "77389fec-07d3-4473-a036-af0b9cd39986", + "metadata": {}, + "source": [ + "#### 3. Arithmetic Operations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d518086-f064-486e-9e28-29ea15ce7779", + "metadata": {}, + "outputs": [], + "source": [ + "x_tensor = torch.ones(4, 4)\n", + "\n", + "# Transpose\n", + "x_T_tensor = x_tensor.T\n", + "\n", + "# Matrix Multiplication\n", + "y1_tensor = x_tensor @ x_tensor.T\n", + "y2_tensor = x_tensor.matmul(x_tensor.T)\n", + "\n", + "y3_tensor = torch.rand_like(y1_tensor)\n", + "torch.matmul(x_tensor, x_tensor.T, out=y3_tensor)\n", + "\n", + "\n", + "# Element-wise multiplication\n", + "z1_tensor = x_tensor * x_tensor\n", + "z2_tensor = x_tensor.mul(x_tensor)\n", + "\n", + "z3_tensor = torch.rand_like(x_tensor)\n", + "torch.mul(x_tensor, x_tensor, out=z3_tensor)" + ] + }, + { + "cell_type": "markdown", + "id": "6ce833cc-6ff6-4016-96bb-60b70812d584", + "metadata": {}, + "source": [ + "##### 3. In-place Operations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21c046eb-43ad-4259-a2b6-55da191d22db", + "metadata": {}, + "outputs": [], + "source": [ + "x_tensor = torch.ones(4, 4)\n", + "\n", + "# Transpose\n", + "x_tensor.t_()\n", + "\n", + "# Copy\n", + "y_tensor = torch.rand_like(x_tensor)\n", + "x_tensor.copy_(y_tensor)" + ] + }, + { + "cell_type": "markdown", + "id": "d73522a1-db18-4fe0-9c07-f27d39f4a992", + "metadata": {}, + "source": [ + "### NumPy and Tensor\n", + "Tensors on the **CPU** and NumPy arrays can share memory locations, so modifying one will also affect \n", + "the other." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1eee82b4-8f71-4e19-b27e-c47add1714e3", + "metadata": {}, + "outputs": [], + "source": [ + "x_tensor = torch.ones(5) \n", + "x_np = x_tensor.numpy() # tensor to numpy\n", + "print(f\"t: {x_tensor}\")\n", + "print(f\"n: {x_np}\")\n", + "\n", + "x_tensor.add_(1)\n", + "\n", + "print(f\"t: {x_tensor}\")\n", + "print(f\"n: {x_np}\")\n", + "\n", + "y_np = np.ones(5)\n", + "z_np = np.zeros(5)\n", + "y_tensor = torch.from_numpy(y_np) # numpy to tensor\n", + "\n", + "np.add(y_np, 1, out=z_np)\n", + "\n", + "print(f\"t: {x_tensor}\")\n", + "print(f\"n: {x_np}\")" + ] + }, + { + "cell_type": "markdown", + "id": "43bca47c-de5b-423d-a3d3-153fdaa76bd9", + "metadata": {}, + "source": [ + "### Moving Tensor to GPU\n", + "It's always wise to check for GPU availability before performing any GPU operations. If a GPU is available, we can move our tensor to it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2199f283-5bbd-4534-b10b-1ed259d56f31", + "metadata": {}, + "outputs": [], + "source": [ + "x_tensor_gpu = x_tensor.to(\"cuda\")" + ] + }, + { + "cell_type": "markdown", + "id": "663e4316-0e47-4528-bfc0-873b695d3e23", + "metadata": {}, + "source": [ + "A better approach is to set the default device before starting any computations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a9aa52b7-b24d-4686-8798-6ec9582b19f9", + "metadata": {}, + "outputs": [], + "source": [ + "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n", + "y_tensor_gpu = y_tensor.to(device)" + ] + }, + { + "cell_type": "markdown", + "id": "55d565e8-074b-4e10-872f-c6051c38c20b", + "metadata": {}, + "source": [ + "### Tensor attributes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "552841ee-fa9d-450e-8ab5-16d7d1d41008", + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"Shape of tensor: {y_tensor.shape}\")\n", + "print(f\"Datatype of tensor: {y_tensor.dtype}\")\n", + "print(f\"Device tensor is stored on: {y_tensor.device}\")" + ] + }, + { + "cell_type": "markdown", + "id": "6e90c428-525b-47f2-9dbd-8e017f56e813", + "metadata": {}, + "source": [ + "*Automatic differentiation* is a key feature that distinguishes tensors from NumPy arrays. This capability\n", + "is particularly useful in neural networks, where model weights are adjusted during backpropagation based \n", + "on the gradient of the loss function with respect to each parameter. Tensors support automatic gradient \n", + "computation for any computational graph. For example, consider the computational graph of a one-layer \n", + "neural network:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c93bab5-f9bd-439b-a334-cbe482c379ad", + "metadata": {}, + "outputs": [], + "source": [ + "x_tensor = torch.ones(5) # input tensor\n", + "y_tensor = torch.zeros(3) # expected output\n", + "\n", + "w_tensor = torch.randn(5, 3, requires_grad=True)\n", + "b_tensor = torch.randn(3, requires_grad=True)\n", + "\n", + "z_tensor = torch.matmul(x_tensor, w_tensor) + b_tensor\n", + "\n", + "loss_tensor = torch.nn.functional.binary_cross_entropy_with_logits(z_tensor, y_tensor)\n", + "loss_tensor.backward()\n", + "\n", + "print(w_tensor.grad)\n", + "print(b_tensor.grad)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2cd58b4d-d46d-4095-a21e-38179685590b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/distributed_data_parallel.py b/src/distributed_data_parallel.py new file mode 100755 index 0000000..1261438 --- /dev/null +++ b/src/distributed_data_parallel.py @@ -0,0 +1,136 @@ +import numpy as np +import os +import pandas as pd + +import torch +import torch.nn as nn +import torch.optim as optim +import torch.distributed as dist +import torch.multiprocessing as mp +from torch.utils.data import Dataset, DataLoader +from torch.utils.data.distributed import DistributedSampler +from torch.nn.parallel import DistributedDataParallel as DDP + + + +nb_gpus = 2 +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +datapath = os.path.expandvars('/scratch/vp91/$USER/intro-to-pytorch/data/pima-indians-diabetes.data.csv') + + +# Define the custom Dataset class +column_names = [ + 'Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', + 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome' +] + +# Define the custom Dataset class +class PimaDataset(Dataset): + def __init__(self, csv_file): + # Load the CSV file without header and assign column names + self.data = pd.read_csv(csv_file, header=None, names=column_names) + self.features = self.data.drop('Outcome', axis=1).values + self.labels = self.data['Outcome'].values + + # Convert to PyTorch tensors + self.features_tensor = torch.tensor(self.features, dtype=torch.float32) + self.labels_tensor = torch.tensor(self.labels, dtype=torch.long) + + # Calculate mean and std + self.mean = self.features_tensor.mean(dim=0) + self.std = self.features_tensor.std(dim=0) + + # Normalize the features + self.features_tensor = (self.features_tensor - self.mean) / self.std + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + feature = self.features_tensor[idx] + label = self.labels_tensor[idx] + return feature, label + +def setup(rank, world_size): + os.environ['MASTER_ADDR'] = 'localhost' + os.environ['MASTER_PORT'] = '12355' + dist.init_process_group("nccl", rank=rank, world_size=world_size) + +def cleanup(): + dist.destroy_process_group() + +def prepare(rank, world_size, batch_size=32, pin_memory=False, num_workers=0): + dataset = PimaDataset(datapath) + sampler = DistributedSampler(dataset, num_replicas=world_size, rank=rank, shuffle=False, drop_last=False) + + dataloader = DataLoader(dataset, batch_size=batch_size, pin_memory=pin_memory, num_workers=num_workers, drop_last=False, shuffle=False, sampler=sampler) + + return dataloader + +class PimaClassifier(nn.Module): + def __init__(self): + super().__init__() + self.hidden1 = nn.Linear(8, 12) + self.act1 = nn.ReLU() + self.hidden2 = nn.Linear(12, 8) + self.act2 = nn.ReLU() + self.output = nn.Linear(8, 1) + self.act_output = nn.Sigmoid() + + def forward(self, x): + x = self.act1(self.hidden1(x)) + x = self.act2(self.hidden2(x)) + x = self.act_output(self.output(x)) + return x + + +def main(rank, world_size): + + # setup the process groups + setup(rank, world_size) + # prepare the dataloader + dataloader = prepare(rank, world_size) + + # instantiate the model(it's your own model) and move it to the right device + model = PimaClassifier().to(rank) + + # wrap the model with DDP + # device_ids tell DDP where is your model + # output_device tells DDP where to output, in our case, it is rank + # find_unused_parameters=True instructs DDP to find unused output of the forward() function of any module in the model + model = DDP(model, device_ids=[rank], output_device=rank, find_unused_parameters=True) + + loss_fn = nn.BCELoss() + optimizer = optim.Adam(model.parameters(), lr=0.001) + + n_epochs = 100 + for epoch in range(n_epochs): + + # if we are using DistributedSampler, we have to tell it which epoch this is + dataloader.sampler.set_epoch(epoch) + + for batch_features, batch_labels in dataloader: + batch_features = batch_features.to(rank) + batch_labels = batch_labels.to(rank) + + optimizer.zero_grad() + + outputs = model(batch_features) + + batch_labels = batch_labels.unsqueeze(1).float() + loss = loss_fn(outputs, batch_labels) + loss.backward() + optimizer.step() + + cleanup() + + + + + +if __name__ == '__main__': + + world_size = nb_gpus + mp.spawn(main, args=(world_size,), nprocs=world_size) + + diff --git a/src/multinode_torchrun.py b/src/multinode_torchrun.py new file mode 100755 index 0000000..4bd9738 --- /dev/null +++ b/src/multinode_torchrun.py @@ -0,0 +1,143 @@ +import numpy as np +import os +import pandas as pd + +import torch +import torch.nn as nn +import torch.optim as optim +import torch.distributed as dist +import torch.multiprocessing as mp +from torch.utils.data import Dataset, DataLoader +from torch.utils.data.distributed import DistributedSampler +from torch.nn.parallel import DistributedDataParallel as DDP + + + +nb_gpus = 2 +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +datapath = os.path.expandvars('/scratch/vp91/jxj900/intro-to-pytorch/data/pima-indians-diabetes.data.csv') + + +# Define the custom Dataset class +column_names = [ + 'Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', + 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome' +] + +# Define the custom Dataset class +class PimaDataset(Dataset): + def __init__(self, csv_file): + # Load the CSV file without header and assign column names + self.data = pd.read_csv(csv_file, header=None, names=column_names) + self.features = self.data.drop('Outcome', axis=1).values + self.labels = self.data['Outcome'].values + + # Convert to PyTorch tensors + self.features_tensor = torch.tensor(self.features, dtype=torch.float32) + self.labels_tensor = torch.tensor(self.labels, dtype=torch.long) + + # Calculate mean and std + self.mean = self.features_tensor.mean(dim=0) + self.std = self.features_tensor.std(dim=0) + + # Normalize the features + self.features_tensor = (self.features_tensor - self.mean) / self.std + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + feature = self.features_tensor[idx] + label = self.labels_tensor[idx] + return feature, label + +def setup(): + #rank = int(os.environ['RANK']) + #world_size = int(os.environ['WORLD_SIZE']) + #dist.init_process_group("nccl", rank=rank, world_size=world_size) + dist.init_process_group("nccl") + #torch.cuda.set_device(rank) + torch.cuda.set_device(int(os.environ["LOCAL_RANK"])) + + +def cleanup(): + dist.destroy_process_group() + +def prepare(rank, world_size, batch_size=32, pin_memory=False, num_workers=0): + dataset = PimaDataset(datapath) + sampler = DistributedSampler(dataset, num_replicas=world_size, rank=rank, shuffle=False, drop_last=False) + + dataloader = DataLoader(dataset, batch_size=batch_size, pin_memory=pin_memory, num_workers=num_workers, drop_last=False, shuffle=False, sampler=sampler) + + return dataloader + +class PimaClassifier(nn.Module): + def __init__(self): + super().__init__() + self.hidden1 = nn.Linear(8, 12) + self.act1 = nn.ReLU() + self.hidden2 = nn.Linear(12, 8) + self.act2 = nn.ReLU() + self.output = nn.Linear(8, 1) + self.act_output = nn.Sigmoid() + + def forward(self, x): + x = self.act1(self.hidden1(x)) + x = self.act2(self.hidden2(x)) + x = self.act_output(self.output(x)) + return x + + +def main(): + + # setup the process groups + setup() + + gpu_id = int(os.environ['LOCAL_RANK']) + rank = int(os.environ['RANK']) + world_size = int(os.environ['WORLD_SIZE']) + + # prepare the dataloader + dataloader = prepare(rank, world_size) + + # instantiate the model(it's your own model) and move it to the right device + model = PimaClassifier().to(gpu_id) + + # wrap the model with DDP + # device_ids tell DDP where is your model + # output_device tells DDP where to output, in our case, it is rank + # find_unused_parameters=True instructs DDP to find unused output of the forward() function of any module in the model + model = DDP(model, device_ids=[gpu_id], output_device=rank, find_unused_parameters=True) + + loss_fn = nn.BCELoss() + optimizer = optim.Adam(model.parameters(), lr=0.001) + + n_epochs = 100 + for epoch in range(n_epochs): + + # if we are using DistributedSampler, we have to tell it which epoch this is + dataloader.sampler.set_epoch(epoch) + + for batch_features, batch_labels in dataloader: + batch_features = batch_features.to(gpu_id) + batch_labels = batch_labels.to(gpu_id) + + optimizer.zero_grad() + + outputs = model(batch_features) + + batch_labels = batch_labels.unsqueeze(1).float() + loss = loss_fn(outputs, batch_labels) + loss.backward() + optimizer.step() + + cleanup() + + + + + +if __name__ == '__main__': + main() + +