Accelerate runner helper method

[avishniakov]

Describe changes

Key changes

• Adding zenml.integrations.huggingface.steps.run_with_accelerate to be able to run any step using Accelerate (obviously, the step should be written in a way that it makes sense to run it with Accelerate). This function is supported by a utility, which wraps any function into a Click CLI script (which is needed by most of the Distributed train tools). CLI is quite limited, so it accepts only (str, int, float, bool, Path, tuple) as params. This can be maybe extended in the future.
  How it works?

from zenml.integrations.huggingface.steps import run_with_accelerate

@pipeline
def llm_peft_full_finetune():
    ...
    ft_model_dir = run_with_accelerate(finetune)(
        base_model_id=base_model_id,
        dataset_dir=datasets_dir,
        load_in_8bit=load_in_8bit,
    )
    ...

Running the same step without accelerating would be:

@pipeline
def llm_peft_full_finetune():
    ...
    ft_model_dir = finetune(
        base_model_id=base_model_id,
        dataset_dir=datasets_dir,
        load_in_8bit=load_in_8bit,
    )
    ...

• Helper method cleanup_gpu_memory to clean up GPU memory at the step start. It has a side effect on the environment, so there is a must force param to be passed to protect that.

Companion PR: zenml-io/zenml-projects#102

Sample run: runs/5080958e-31f5-46b0-8e0b-fbf7086f1ff4 in Demo

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Please ensure you have done the following:

• I have read the CONTRIBUTING.md document.
• If my change requires a change to docs, I have updated the documentation accordingly.
• I have added tests to cover my changes.
• I have based my new branch on develop and the open PR is targeting develop. If your branch wasn't based on develop read Contribution guide on rebasing branch to develop.
• If my changes require changes to the dashboard, these changes are communicated/requested.

Types of changes

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to change)
• Other (add details above)

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[github-actions]

LLM Finetuning template updates in examples/llm_finetuning have been pushed.

[avishniakov]

@strickvl , I can see that the docs for the finetuning-llms are not quite ready. Shall I wait for you or how to proceed best to document these changes?

[strickvl]

@strickvl , I can see that the docs for the finetuning-llms are not quite ready. Shall I wait for you or how to proceed best to document these changes?

Could you maybe make changes directly on the feature/gro-1047-docs branch directly @avishniakov and I'll touch them up. You can see I added a short section at the bottom of docs/book/how-to/training-with-gpus/training-with-gpus.md page. Or just send something to me in Discord and I'll add it. I couldn't find/think of the good way to document this with code examples, but probably you're in a better place to do so.

[strickvl]

Just one tiny comment otherwise looks good to me. Is there any testing we can add for the function utils? Also wondering if we should have an e2e test script which runs a simple (CPU-backed) pipeline in CI but using run_with_accelerate? I'm a bit worried by how quickly HF things can break, esp with something like accelerate so I'd like to get advance failure notice of API changes that way if possible.

[github-actions]

LLM Finetuning template updates in examples/llm_finetuning have been pushed.

[github-actions]

Quickstart template updates in examples/quickstart have been pushed.

[github-actions]

E2E template updates in examples/e2e have been pushed.

[github-actions]

NLP template updates in examples/e2e_nlp have been pushed.

[strickvl]

Thanks for adding the tests. This LGTM now, providing you get the tests passing (there's a security one failing atm e.g.)

[avishniakov]

@bcdurak, as discussed this one is ready to be merged, but can give more work to the pydantic merge. LMK, when can I merge it?