Avoid using torchmetrics 0.6.0

[harupy]

Signed-off-by: harupy hkawamura0130@gmail.com

What changes are proposed in this pull request?

It appears that torchmetrics 0.6.0 (released on 2021/10/29) introduced breaking changes and broke our example tests:

https://github.com/mlflow/mlflow/runs/4058853498?check_suite_focus=true#step:7:751

Traceback (most recent call last):
  File "iterative_prune_mnist.py", line 15, in <module>
    from mnist import (
  File "/tmp/pytest-of-runner/pytest-0/test_mlflow_run_example_pytorc4/pytorch/IterativePruning/mnist.py", line 15, in <module>
    from pytorch_lightning.metrics.functional import accuracy
  File "/usr/share/miniconda/envs/mlflow-4be2c887adb73537631be3a364e2cad20927ca38/lib/python3.8/site-packages/pytorch_lightning/metrics/functional/__init__.py", line 31, in <module>
    from pytorch_lightning.metrics.functional.r2score import r2score  # noqa: F401
  File "/usr/share/miniconda/envs/mlflow-4be2c887adb73537631be3a364e2cad20927ca38/lib/python3.8/site-packages/pytorch_lightning/metrics/functional/r2score.py", line 16, in <module>
    from torchmetrics.functional import r2score as _r2score
ImportError: cannot import name 'r2score' from 'torchmetrics.functional' (/usr/share/miniconda/envs/mlflow-4be2c887adb73537631be3a364e2cad20927ca38/lib/python3.8/site-packages/torchmetrics/functional/__init__.py)

How is this patch tested?

(Details)

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.

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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

[WeichenXu123]

LGTM, thanks!