Add warning in MLflow pytorch docs to include signature

[mehtayogita]

What changes are proposed in this pull request?

Updates MLFlow pytorch documentation to add warning suggesting to add signature while logging model to avoid float precision errors.

How is this patch tested?

Building docs locally and verifying the change.

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.

Updates the MLFlow pytorch documentation.

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

[mehtayogita]

autoformat

[mehtayogita]

autoformat

[mehtayogita]

autoformat

[ankit-db]

Good start - a few notes

[ankit-db]

I would maybe re-frame this a bit and say:

For models without signatures, the MLflow Model Server relies on the default inferred data type from NumPy. However, PyTorch often expects different defaults, particularly when parsing floats.

[mehtayogita]

Updated as suggested.

[ankit-db]

One minor nit, but looks great otherwise!

[andreakress]

Suggested change

	correct data type so that the MLflow model server can correctly provide valid input
	correct data type so that the MLflow model server correctly provides valid input

[ankit-db]

Just to clarify @andreakress - one thing I think would be good to emphasize is that by logging a signature, the user is making it possible for the model server to provide valid input. Correctly inferring the correct data types without the signature is an impossible problem. Maybe just me, but, in your suggested phrasing, it kind of feels like we're saying that there's a bug where it won't provide it correctly.

[andreakress]

WDYT of something like this:

If the model is logged without a signature, the MLflow Model Server relies on the default inferred data type from NumPy. However, PyTorch often expects different defaults, particularly when parsing floats. You must include the signature to ensure that the model is logged with the correct data type so that the MLflow model server can correctly provide valid input.

[ankit-db]

Sounds good!

[ankit-db]

Thanks for helping iterate on this one!

[andreakress]

Approved with one suggestion.