Avoid using tensorflow-estimator 2.7

[harupy]

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

What changes are proposed in this pull request?

It appears tensorflow-estimator 2.7 (released on 2021/10/31) broke several tests:

https://github.com/mlflow/mlflow/runs/4054183145?check_suite_focus=true#step:8:15715

      For more details on warm-start configuration, see
      `tf.estimator.WarmStartSettings`.
    
      @compatibility(eager)
      Calling methods of `Estimator` will work while eager execution is enabled.
      However, the `model_fn` and `input_fn` is not executed eagerly, `Estimator`
      will switch to graph mode before calling all user-provided functions (incl.
      hooks), so their code has to be compatible with graph mode execution. Note
      that `input_fn` code using `tf.data` generally works in both graph and eager
      modes.
      @end_compatibility
E     AttributeError: module 'tensorflow.tools.docs.doc_controls' has no attribute 'inheritable_header'

Similar issues have been reported in other repositories:

https://github.com/search?q=inheritable_header&type=issues

How is this patch tested?

NA

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.

(Details in 1-2 sentences. You can just refer to another PR with a description if this PR is part of a larger change.)

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

[harupy]

tensorflow/tensorflow#52883 (comment) says:

Looks the problem is in wrong dependency in tensorflow 2.6.0. It has tensorflow-estimator~=2.6, while it shall be tensorflow-estimator~=2.6.0.
Similar thing will happen to keras, when it is not in rc phase anymore, as the same dependency (~=2.6) is set there

[WeichenXu123]

LGTM, thanks!