added documentation

Author: SubhadityaMukherjee

.github/workflows/ci.yml

@@ -0,0 +1,38 @@
+name: ci 
+on:
+  push:
+    branches:
+      - master 
+      - main
+      - simple_dataloaders
+permissions:
+  contents: write
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Configure Git Credentials
+        run: |
+          git config user.name github-actions[bot]
+          git config user.email 41898282+github-actions[bot]@users.noreply.github.com
+      - uses: actions/setup-python@v5
+        with:
+          python-version: 3.x
+      - run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV 
+      - uses: actions/cache@v4
+        with:
+          key: mkdocs-material-${{ env.cache_id }}
+          path: .cache
+          restore-keys: |
+            mkdocs-material-
+      - run: pip install mkdocs-material 
+      - run: pip install mkdocs-material-extensions
+      - run: pip install mkdocs-jupyter
+      - run: pip install mkdocs-redirects
+      - run: pip install mkdocs-autorefs
+      - run: pip install mkdocs-awesome-pages-plugin
+      - run: pip install mkdocstrings
+      - run: pip install mkdocstrings-python
+      - run: pip install mknotebooks
+      - run: mkdocs gh-deploy --force

README.md

@@ -11,23 +11,58 @@ PyPi link https://pypi.org/project/openml-pytorch/
 #### Usage
 Import openML libraries
 ```python
-import openml
-import openml_pytorch
-import openml_pytorch.layers
+import torch.nn
+import torch.optim
+
 import openml_pytorch.config
+import openml
+import logging
+
+from openml_pytorch.trainer import OpenMLTrainerModule
+from openml_pytorch.trainer import OpenMLDataModule
+from torchvision.transforms import Compose, Resize, ToPILImage, ToTensor, Lambda
+import torchvision
+from openml_pytorch.trainer import convert_to_rgb
 
 ```
-Create a torch model
+Create a pytorch model and get a task from openML
 ```python
-model = torch.nn.Sequential(
-    processing_net,
-    features_net,
-    results_net
+model = torchvision.models.efficientnet_b0(num_classes=200)
+# Download the OpenML task for tiniest imagenet
+task = openml.tasks.get_task(362127)
+```
+Download the task from openML and define Data and Trainer configuration
+```python
+transform = Compose(
+    [
+        ToPILImage(),  # Convert tensor to PIL Image to ensure PIL Image operations can be applied.
+        Lambda(
+            convert_to_rgb
+        ),  # Convert PIL Image to RGB if it's not already.
+        Resize(
+            (64, 64)
+        ),  # Resize the image.
+        ToTensor(),  # Convert the PIL Image back to a tensor.
+    ]
+)
+data_module = OpenMLDataModule(
+    type_of_data="image",
+    file_dir="datasets",
+    filename_col="image_path",
+    target_mode="categorical",
+    target_column="Class_encoded",
+    batch_size = 64,
+    transform=transform
+)
+trainer = OpenMLTrainerModule(
+    data_module=data_module,
+    verbose = True,
+    epoch_count = 1,
 )
+openml_pytorch.config.trainer = trainer
 ```
-Download the task from openML and run the model on task.
+Run the model on the task
 ```python
-task = openml.tasks.get_task(3573)
 run = openml.runs.run_model_on_task(model, task, avoid_duplicate_runs=False)
 run.publish()
 print('URL for run: %s/run/%d' % (openml.config.server, run.run_id))

docs/API reference/Callbacks.md

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+# Callbacks
+Callbacks module contains classes and functions for handling callback functions during an event-driven process. This makes it easier to customize the behavior of the training loop and add additional functionality to the training process without modifying the core code.
+
+To use a callback, create a class that inherits from the Callback class and implement the necessary methods. Callbacks can be used to perform actions at different stages of the training process, such as at the beginning or end of an epoch, batch, or fitting process. Then pass the callback object to the Trainer.
+
+::: callbacks

docs/API reference/Custom Datasets.md

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+# Custom Datasets
+This module contains custom dataset classes for handling image and tabular data from OpenML in PyTorch. To add support for new data types, new classes can be added to this module.
+
+::: custom_datasets

docs/API reference/Metrics.md

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+# Metrics
+This module provides utility functions for evaluating model performance and activation functions.
+It includes functions to compute the accuracy, top-k accuracy of model predictions, and the sigmoid function.
+
+::: metrics

docs/API reference/OpenML Connection.md

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+# OpenML Connection
+
+This module defines the Pytorch wrapper for OpenML-python.
+
+::: extension

docs/API reference/Trainer.md

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+# Trainer
+
+This module provides classes and methods to facilitate the configuration, data handling, training, and evaluation of machine learning models using PyTorch and OpenML datasets. The functionalities include:
+- Generation of default configurations for models.
+- Handling of image and tabular data.
+- Training and evaluating machine learning models.
+- Exporting trained models to ONNX format.
+- Managing data transformations and loaders.
+
+::: trainer