pyfunc.spark_udf: conda env dir / cache dir isolation and NFS optimization

[WeichenXu123]

What changes are proposed in this pull request?

For pyfunc.spark_udf:

1. Make conda env dir / cache dir isolated for different python processes.
2. NFS optimization: Create conda env once in driver side (write env dir into NFS mounted directory), and executor side directly read the env set up in driver side.

How is this patch tested?

Manually:

1. NFS enabled: https://e2-dogfood.staging.cloud.databricks.com/?o=6051921418418893#notebook/2674920562695789/command/2674920562695795

2. NFS disabled: https://e2-dogfood.staging.cloud.databricks.com/?o=6051921418418893#notebook/2674920562695847/command/2674920562695848

Does this PR change the documentation?

• No. You can skip the rest of this section.
• Yes. Make sure the changed pages / sections render correctly by following the steps below.

1. Check the status of the ci/circleci: build_doc check. If it's successful, proceed to the
   next step, otherwise fix it.
2. Click Details on the right to open the job page of CircleCI.
3. Click the Artifacts tab.
4. Click docs/build/html/index.html.
5. Find the changed pages / sections and make sure they render correctly.

Release Notes

Is this a user-facing change?

• No. You can skip the rest of this section.
• Yes. Give a description of this change to be included in the release notes for MLflow users.

For pyfunc.spark_udf:

Make conda env dir / cache dir isolated for different python processes.
NFS optimization: Create conda env once in driver side (write env dir into NFS mounted directory), and executor side directly read the env set up in driver side.

What component(s), interfaces, languages, and integrations does this PR affect?

Components

• area/artifacts: Artifact stores and artifact logging
• area/build: Build and test infrastructure for MLflow
• area/docs: MLflow documentation pages
• area/examples: Example code
• area/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registry
• area/models: MLmodel format, model serialization/deserialization, flavors
• area/projects: MLproject format, project running backends
• area/scoring: MLflow Model server, model deployment tools, Spark UDFs
• area/server-infra: MLflow Tracking server backend
• area/tracking: Tracking Service, tracking client APIs, autologging

Interface

• area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev server
• area/docker: Docker use across MLflow's components, such as MLflow Projects and MLflow Models
• area/sqlalchemy: Use of SQLAlchemy in the Tracking Service or Model Registry
• area/windows: Windows support

Language

• language/r: R APIs and clients
• language/java: Java APIs and clients
• language/new: Proposals for new client languages

Integrations

• integrations/azure: Azure and Azure ML integrations
• integrations/sagemaker: SageMaker integrations
• integrations/databricks: Databricks integrations

How should the PR be classified in the release notes? Choose one:

• rn/breaking-change - The PR will be mentioned in the "Breaking Changes" section
• rn/none - No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" section
• rn/feature - A new user-facing feature worth mentioning in the release notes
• rn/bug-fix - A user-facing bug fix worth mentioning in the release notes
• rn/documentation - A user-facing documentation change worth mentioning in the release notes

[dbczumar]

@WeichenXu123 Looks great! Tried it out on Databricks & it seemed to work well! Left a few small comments.

[dbczumar]

If the user forks a new process after running this method, _CONDA_ENV_ROOT_DIR will still be defined and the same cache dir will be reused. Shall we handle this case?

[WeichenXu123]

Good question... This might be hard to address.
But what's the case user need to fork a process ? Is it a common case ?

[dbczumar]

Could be! Can't we store and check the process id? https://docs.python.org/3/library/os.html#os.getpid. If the pid doesn't match the one used to configure _CONDA_ENV_ROOT_DIR the first time, can we overwrite it?

[WeichenXu123]

Addressed! See cache_return_value_per_process decorator and it is applied on get_or_create_nfs_tmp_dir, get_or_create_tmp_dir, _get_or_create_env_root_dir

[harupy]

LGTM once the remaining comments are addressed.