tractorun 0.53.0

Run distributed training in TractoAI

🚜 Tractorun

tractorun is a powerful tool for distributed ML operations on the Tracto.ai platform. It helps manage and run workflows across multiple nodes with minimal changes in the user's code.

Besides machine learning, tractorun can also run arbitrary gang operations on the Tracto.ai

Core features

• Simple distributed training setup on JAX and PyTorch with minimal code changes
• Convenient ways to run and configure: CLI, YAML config, and Python SDK
• Integration with the Tracto.ai platform

Getting started

To use these examples, you'll need a Tracto account. If you don't have one yet, please sign up at tracto.ai.

Install tractorun into your python3 environment:

pip install --upgrade tractorun

Configure the client to work with your cluster:

mkdir ~/.yt
cat <<EOF > ~/.yt
"proxy"={
  "url"="$YT_PROXY";
};
"token"="$YT_TOKEN";
EOF

Please put your actual Tracto.ai cluster address to $YT_PROXY and your token to $YT_TOKEN.

How to try

Run an example script:

tractorun \
    --yt-path "//tmp/$USER/tractorun_getting_started" \
    --bind-local './examples/pytorch/lightning_mnist_ddp_script/lightning_mnist_ddp_script.py:/lightning_mnist_ddp_script.py' \
    --bind-local-lib ./tractorun \
    --docker-image ghcr.io/tractoai/tractorun-examples-runtime:2025-01-15-20-21-21 \
    python3 /lightning_mnist_ddp_script.py

How to run

CLI

tractorun --help

or with yaml config

tractorun --run-config-path config.yaml

You can find a relevant examples:

• CLI arguments example.
• YAML config example.

Python SDK

SDK is convenient to use from Jupyter notebooks for development purposes.

You can find a relevant example in the repository.

WARNING: the local environment should be equal to the remote docker image on the TractoAI platform to use SDK.

• This requirement is met in Jupyter Notebook on the Tracto.ai platform.
• For local use, it is recommended to run the code locally in the same container as specified in the docker_image parameter in tractorun

How to adapt code for tractorun

CLI

1. Wrap all training/inference code to a function.
2. Initiate environment and Toolbox by from tractorun.run.prepare_and_get_toolbox

An example of adapting the mnist training from the PyTorch repository: https://github.com/tractoai/tractorun/tree/main/examples/adoptation/mnist_simple/cli

SDK

1. Wrap all training/inference code to a function with a toolbox: tractorun.toolbox.Toolbox parameter.
2. Run this function by tractorun.run.run.

An example of adapting the mnist training from the PyTorch repository: https://github.com/tractoai/tractorun/tree/main/examples/adoptation/mnist_simple/sdk

Features

Toolbox

tractorun.toolbox.Toolbox provides extra integrations with the Tracto.ai platform:

• Preconfigured client by toolbox.yt_client
• Basic checkpoints by toolbox.checkpoint_manager
• Control over the operation description in the UI by toolbox.description_manager
• Access to coordination information by toolbox.coordinator

Backends

Backends configure tractorun to work with a specific ML framework.

Tractorun supports multiple backends:

• Tractorch for PyTorch
  • examples
• Tractorax for Jax
  • examples
• Generic
  • non-specialized backend, can be used as a basis for other backends

Options and settings

Options reference page provides an overview of all available options for tractorun, explaining their purpose and usage. Options can be defined by:

• CLI parameters
• yaml config
• python options

Development

Install local environment

1. Install pyenv
2. Create and activate new env pyenv virtualenv 3.10 tractorun && pyenv activate tractorun
3. Install all dependencies: pip install ."[all]

Build new image for tests

./run_build.sh generic
./run_build.sh tractorch_tests
./run_build.sh tractorax_tests
./run_build.sh tensorproxy_tests

and update images in ./run_tests and tests/utils.py

Build and push a new image for examples

./run_build.sh examples_runtime --push

and update the image in ./examples/run_example

Update current image tag for tests and examples

./run_update_tag.sh new_tag

Run tests

To run tests on local YT run pytest

./run_tests.sh all . -s

To run tests on remote cluster

./run_tests.sh general . -s
./run_tests.sh tensorproxy . -s

It is possible to provide extra pytest options

./run_tests.sh generic test_sidecars.py
./run_tests.sh generic test_sidecars.py::test_sidecar_run

Build and upload

1. Run Create release
2. Run Build and upload to external pypi. Specify the latest tag from the list to upload the latest version.