Fix log_artifact for large model in HDFS

[hitchhicker]

Use HadoopFileSystem upload API can address the problem of log_artifact when the model is larger than 2GB.

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Components

• area/artifacts: Artifact stores and artifact logging
• area/build: Build and test infrastructure for MLflow
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[github-actions]

@hitchhicker Thanks for the contribution! The DCO check failed. Please sign off your commits by following the instructions here: https://github.com/mlflow/mlflow/runs/6266347573. See https://github.com/mlflow/mlflow/blob/master/CONTRIBUTING.rst#sign-your-work for more details.

[dbczumar]

Nit: Can we use open() as a context manager to make sure that the file is closed after read? Can we also make that change to the other line below?

Suggested change

	hdfs.upload(destination, open(local_file, "rb"))
	with open(local_file, "rb") as f:
	hdfs.upload(destination, f)

[hitchhicker]

Thanks for you review. Very good advice ! I just added it.

I think the last failure might be due to the fact that it is not closed correctly. I don't have such failure when I run the unit tests on Linux though.

[dbczumar]

Awesome! Looks like that addressed it :)

[dbczumar]

@hitchhicker Thanks so much for filing this PR! It looks great! Just left a very tiny suggestion & am happy to merge once it's addressed. I've confirmed that the HDFS upload API is available in older pyarrow versions (e.g. 1.0 - https://arrow.apache.org/docs/1.0/python/generated/pyarrow.HadoopFileSystem.upload.html), so it should be safe to make this change.