Fix S3 artifact store memory leak and other improvements

Describe changes

This PR fixes a memory leak was observed while using the ZenML dashboard to view the logs of a pipeline or the visualization of an artifact that logged through an S3 Artifact Store linked to an AWS Service Connector.

How to reproduce

configure an AWS Service Connector that uses the IAM role authentication method.
configure an S3 artifact store and link it to an S3 bucket through the service connector
configure a stack with the S3 artifact store and the default orchestrator
run a pipeline, preferably one that has rich visualizations, like the evidently integration test example (you also need the evidently data validator in the stack for that)
visualize the artifacts or the logs of the pipeline in the dashboard. The memory usage will keep going up.

NOTE: even with this patch applied, the memory usage may still go up until it reaches a stable value. This depends largely on the number of threads that the server is allowed to create (default: 40). It is recommended to configure the number of threads to something low like 2 or 3 (by using the ZENML_SERVER_THREAD_POOL_SIZE environment variable when running the server) to reach that memory usage plateau faster.

Details

The investigation lead to the following findings:

the s3fs library used in the S3 Artifact Store caches all its S3FileSystem client instances permanently, based on the constructor input arguments. In a more dynamic and long-lived environment like the ZenML server, this means that a new S3FileSystem is created (and never de-allocated) for every new set of credentials. Given that the AWS Service Connector generates temporary credentials, the number of cached S3FileSystem clients slowly grows with every invocation of the pipeline logs or artifact visualization endpoint until the server runs out of memory.

Solution: S3FileSystem instance caching is a class-level setting. This PR disables instance caching by sub-classing S3FileSystem and using the sub-class in the ZenML S3 Artifact Store implementation:

class ZenMLS3Filesystem(s3fs.S3FileSystem):

    cachable = False

with the caching out of the picture, a second bug was revealed: S3FileSystem client instances were incorrectly de-allocated by the garbage collector, leading to additional memleaks. The exact root cause of this was not clearly identified even though it was fixed, but a likely explanation is included here.

s3fs is using asyncio at its core, but also provides support for synchronous calls by maintaining its own asyncio event loop thread and converting sync calls in asyncio tasks. s3fs also uses aiobotocore instead of botocore for that same reason and the aiobotocore clients are implemented using asynchronous context managers (more details on this below).

Normally, an async botocore client is used like this, where the client is created and deallocated in the same code block:

    session = aiobotocore.session.AioSession(**self.kwargs)
    async with self.session.create_client("s3", config=self.conf) as client:
        # do work with client

However, in the case of a class like S3FileSystem, where the client needs to be reused across different calls happening at different times, the aiobotocore client context manager needs to be entered during initialization and exited during cleanup:

async def initialize_client(self):
    self.session = aiobotocore.session.AioSession(**self.kwargs)
    client = self.session.create_client(
                "s3", config=self.conf
    )
    self._s3 = await s3creator.__aenter__()

async def cleanup_client(self):
    await self._s3.__aexit__(None, None, None)

The problem comes from when and how the cleanup is called: it needs to run as an asyncio task and therefore it needs to be invoked at a time when an asyncio event loop still exists i.e. before the garbage collector deallocates the event loop resources. The cleanup might also need to be run using the same event loop as the initialization - which is the assumed source for the second memleak: he way s3fs was doing de-initialization was buggy in that it was not using the same event loop as the one used for initialization.

Side-changes

Some more changes are included that help make the ZenML server leaner and more efficient concerning memory usage:

add a cleanup method to the stack component base class to be called in the ZenML server to proactively deallocate the client when done working with it instead of waiting for the garbage collector
stop registering the Artifact Store instances as I/O filesystems in the ZenML server. We don't use this feature in the ZenML server (we access artifacts through the artifact store directly) and it just keeps an artifact store instance constantly loaded in memory
stop verifying service connectors again when they are used to generate credentials for their clients (stack components). This speeds up the artifact and logs visualization a bit.

Pre-requisites

Please ensure you have done the following:

[x] 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.
[x] 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

[x] 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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zenml-io / zenml