Accelerate scaler

[avishniakov]

Describe changes

This PR introduces the concept of Scalers, which can be passed into the steps' definitions to allow step run on some parallelization engine. Scalers included here:

• AccelerateScaler (a major goal of this PR): allows to run a step function via accelerate run... without any effort for rewriting the step (except the fact, that it should properly handle accelerate by itself, like save in the main process, etc.)
• AggregateScaler - this is more of a demo of what we can do for other use cases. This scaler just parallelizes the step logic locally and later aggregates back the results using the chosen aggregate function.

Now I would like to gather some early feedback from you.

Examples

from zenml import step
from zenml.integrations.accelerate import AccelerateScaler

@step(scaler=AccelerateScaler(num_processes=42))
def training_step(some_param: int, ...):
    # your training code is below
    ...

from zenml import step, pipeline
from zenml.scalers import AggregateScaler

@step(scaler=AggregateScaler(parameters={"a":[1,2,3],"b":[4,5,6]}, agg_function="sum"))
def training_step_with_sum_aggregation(a:int = None, b:int = None, c:int = 2)->int:
    # your code is below
    return a+b+c

@pipeline
def pipeline_with_aggregate_scaler():
    training_step_with_sum_aggregation(c=3)
# actual step output would be (1+4+3)+(2+5+3)+(3+6+3) = 30,
# where last "+3" comes from constant `c` parameter

To be done still in this PR:

• Tests
• Docs
• Make accelerate a proper integration

How this works in the wild: https://github.com/zenml-io/zenml-projects/pull/102

Pre-requisites

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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[avishniakov]

@htahir1 not putting you to reviewers, but you might have what to add to this story.

[htahir1]

Wow what a great idea. I havnt look to deep but what is the difference between this and step operators?

[avishniakov]

Wow what a great idea. I havnt look to deep but what is the difference between this and step operators?

Thx, IMO, going forward one can define a step which, for example, takes some slicing parameters and inside the step, some intense data processing is happening and instead of running it with step operator k8s or other, I say scale it using step operator k8s or other. This is not yet implemented for sure or shaped well, but this is how I see this: a mixture of specific libs and the reuse of some step operators to give a performance boost in parallelisable operations. Does that answer?

[htahir1]

The only reservation I have is that there are simply too many concepts in ZenML and this is a new one that I haven't really understood yet

[avishniakov]

The only reservation I have is that there are simply too many concepts in ZenML and this is a new one that I haven't really understood yet

That's very valid, based on the feedback we saw. Do you feel that saying AccelerateStepOperator is a better option here? On the other hand, it shall be still backed by VertexStepOperator or another GPU compute, which makes things really complicated to implement.

[avishniakov]

Closed in favor of #2746