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Label Noise, Schedulers and Generalization

This is a repository for my semester project (Research in Computer Science: 263-0600-00L) under the Data Analytics group at ETH Zürich in the fall semester of 2021. For a detailed explanation of the idea, the approach and the findings, please check the project report.

Advisors

  • Antonio Orvieto
  • Jonas Kohler

Setup

Poetry is used for conveniently installing and managing dependencies. pre-commit is used for managing hooks that run before each commit, to ensure code quality and run some basic tests.

  1. [Optional] Create and activate a virtual environment with Python >= 3.8.5.

  2. Install Poetry globally (recommended), or in a virtual environment. Please refer to Poetry's installation guide for recommended installation options.

    You can use pip to install it:

    pip install poetry

  3. Install all dependencies, including extra dependencies for development, with Poetry:

    poetry install

    To avoid installing development dependencies, run:

    poetry install --no-dev

    If you didn't create and activate a virtual environment in step 1, Poetry creates one for you and installs all dependencies there. To use this virtual environment, run:

    poetry shell

  4. Install pre-commit hooks:

    pre-commit install

NOTE: You need to be inside the virtual environment where you installed the above dependencies every time you commit. However, this is not required if you have installed pre-commit globally.

Tasks

The optimizers are tested on multiple tasks. Each task involves training a certain model in a certain manner (supervised, unsupervised, etc.) on a certain dataset. Every task is given a task ID, which is used when running scripts.

The list of tasks implemented, along with their IDs, are:

Task Task ID Description
CIFAR-10 cifar A ResNet-18 on the CIFAR10 dataset.

Scripts

All scripts use argparse to parse commandline arguments. Each Python script takes the task ID as a positional argument. To view the list of all positional and optional arguments for a script script.py, run:

./script.py --help

Hyper-Parameters

Configuration

Hyper-parameters can be specified through YAML configs. For example, to specify a batch size of 32 and a learning rate of 0.001, use the following config:

lr: 0.001
batch_size: 32

You can store configs in a directory named configs located in the root of this repository. It has an entry in the .gitignore file so that custom configs aren't picked up by git.

The available hyper-parameters, their documentation and default values are specified in the Config class in the file src/config.py.

NOTE: You do not need to mention every single hyper-parameter in a config. In such a case, the missing ones will use their default values.

Tuning

Support for tuning hyper-parameters for the optimizers is available in the training script. It has the -m or the --mode flag to set the mode of operation. This has the following values:

  • train: This simply trains a model. This is the default mode.
  • tune: This tunes the hyper-parameters using Hyperopt.

Thus, to tune hyper-parameters for models on a certain task, run the training script as follows:

./train.py --mode tune

Logs

Logs are stored with certain directory structures. For training, this is:

project root
|_ root log directory
   |_ experiment name
      |_ timestamped run directory

For tuning, this is:

project root
|_ root log directory
   |_ experiment name
      |_ timestamped tuning run directory
         |_ training run 0 directory
         |_ training run 1 directory
         ...

The timestamp uses the ISO 8601 convention along with the local timezone. The root log directory can be specified with the --log-dir argument. By default, this is logs.

The sub-directory for each training run will contain:

  • The latest checkpoint of the trained model, within the checkpoints sub-directory
  • Training logs, as a file with the prefix events.out.tfevents.
  • The hyper-parameter config (including defaults), as a YAML file named hparams.yaml

The sub-directory for a tuning run will contain:

  • Sub-directories for each training run
  • The best hyper-parameter config (including defaults), as a YAML file named best-hparams.yaml

Miscellaneous Features

Multi-GPU Training

For choosing which GPUs to train on, use the -g or the --num-gpus flag when running a script as follows:

./script.py --num-gpus 3

This selects three available GPUs for training. By default, only one GPU is chosen.

Mixed Precision Training

This implementation supports mixed-precision training, which is enabled by default. To manually set the floating-point precision, use the -p or the --precision flag when running a script as follows:

./script.py --precision 32

Note that mixed-precision training will only provide significant speed-ups if your GPUs have special support for mixed-precision compute.