Huggingface Model Deployer #2376

dudeperf3ct · 2024-01-30T16:02:13Z

Describe changes

I implemented ModelDeployer component to work with Huggingface repos.

Pre-requisites

Please ensure you have done the following:

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.
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

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)

Summary by CodeRabbit

New Features
- Introduced Huggingface integration for deploying machine learning models using Huggingface's infrastructure.
- Added support for configuring and managing Huggingface inference endpoints.
- New deployment functionality for Huggingface models, including creation, update, and stopping of model deployment services.
- Implemented a pipeline step for continuous deployment with Huggingface Inference Endpoint.

coderabbitai · 2024-01-30T16:02:51Z

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Walkthrough

The update introduces Huggingface integration into ZenML, allowing for deploying machine learning models using Huggingface's infrastructure. It adds necessary configurations, implements a model deployer with methods for managing deployments, and provides a deployment service for handling inference endpoints. This integration facilitates continuous deployment pipelines within ZenML, leveraging Huggingface's capabilities for model serving.

Changes

File(s)	Change Summary
`src/zenml/integrations/huggingface/__init__.py`	Introduced imports and constants for Huggingface integration, including model deployer flavor and service artifact constants. Updated requirements to include "huggingface_hub".
`src/zenml/integrations/huggingface/flavors/...`	Added classes for Huggingface integration flavors, including model deployer configuration and base config.
`src/zenml/integrations/huggingface/model_deployers/...`	Launched Huggingface Model Deployer with methods for deployment management.
`src/zenml/integrations/huggingface/services/...`	Initiated Huggingface Service, implementing deployment and management functionality for Huggingface models.
`src/zenml/integrations/huggingface/steps/...`	Implemented a deployment step for continuous deployment with Huggingface, including service configuration and deployment logic.

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htahir1 · 2024-01-30T16:12:33Z

@CodeRabbit review

htahir1 · 2024-01-30T22:27:14Z

@coderabbitai review

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Review Status

Actionable comments generated: 6

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Commits

Files that changed from the base of the PR and between 9f31c6b and 0c1a1de.

Files selected for processing (9)

src/zenml/integrations/huggingface/init.py (1 hunks)
src/zenml/integrations/huggingface/flavors/init.py (1 hunks)
src/zenml/integrations/huggingface/flavors/huggingface_model_deployer_flavor.py (1 hunks)
src/zenml/integrations/huggingface/model_deployers/init.py (1 hunks)
src/zenml/integrations/huggingface/model_deployers/huggingface_model_deployer.py (1 hunks)
src/zenml/integrations/huggingface/services/init.py (1 hunks)
src/zenml/integrations/huggingface/services/huggingface_deployment.py (1 hunks)
src/zenml/integrations/huggingface/steps/init.py (1 hunks)
src/zenml/integrations/huggingface/steps/huggingface_deployer.py (1 hunks)

Files skipped from review due to trivial changes (1)

src/zenml/integrations/huggingface/model_deployers/init.py

Additional comments: 22

src/zenml/integrations/huggingface/steps/__init__.py (1)

3-5: The import statement is correctly structured and follows Python best practices.

src/zenml/integrations/huggingface/services/__init__.py (1)

3-6: The import statement is correctly structured, adhering to Python best practices. The use of # noqa suppresses linting errors for unused imports, which is acceptable in __init__.py files where the purpose is often to expose public interfaces.

src/zenml/integrations/huggingface/flavors/__init__.py (2)

3-7: The import statement is correctly structured, adhering to Python best practices. The use of # noqa suppresses linting errors for unused imports, which is acceptable in __init__.py files where the purpose is often to expose public interfaces.

9-13: The __all__ declaration is correctly used to define the public interface of the module. This is a good practice as it explicitly specifies which names are intended to be imported when from module import * is used.

src/zenml/integrations/huggingface/__init__.py (3)

15-22: The constants HUGGINGFACE_MODEL_DEPLOYER_FLAVOR and HUGGINGFACE_SERVICE_ARTIFACT are correctly defined and follow Python naming conventions for constants. This enhances readability and maintainability of the code.

29-29: The REQUIREMENTS list correctly specifies the dependencies for the Huggingface integration, including a specific version constraint for transformers. This is important for ensuring compatibility and preventing potential conflicts with other packages.

37-48: The flavors method is correctly implemented to declare stack component flavors for the Huggingface integration. This method enhances the modularity and extensibility of the ZenML framework by allowing it to dynamically recognize and utilize different deployment flavors.

src/zenml/integrations/huggingface/steps/huggingface_deployer.py (5)

20-25: The decorator @step(enable_cache=False) is correctly applied to the huggingface_model_deployer_step function, indicating that caching is disabled for this step. This is appropriate for deployment steps where the outcome might depend on external state or services that are not captured in the step's inputs.

39-42: The use of cast to ensure the correct type of model_deployer is a good practice for type safety. This helps maintain the integrity of the code by ensuring that model_deployer is indeed an instance of HuggingFaceModelDeployer.

51-54: Modifying the service_config with runtime information from the pipeline context is a good practice. It ensures that the deployment service is correctly associated with the specific pipeline run, enhancing traceability and manageability of deployed models.

64-80: The logic to reuse the last model server if the deployment decision is negative and an existing model server is not running is sound. It ensures that a model server is available at all times, which is crucial for maintaining service availability.

82-95: The logic to deploy or update a model based on the deploy_decision and the existence of previous deployments is correctly implemented. This approach allows for flexibility in managing deployments and ensures that the latest model version is served.

src/zenml/integrations/huggingface/flavors/huggingface_model_deployer_flavor.py (3)

20-39: The HuggingFaceBaseConfig class is well-defined with optional attributes for configuring the Huggingface Inference Endpoint. Using Optional for these attributes provides flexibility in configuration, allowing users to specify only the necessary parameters for their deployment scenario.

46-56: The HuggingFaceModelDeployerConfig class correctly extends BaseModelDeployerConfig and HuggingFaceModelDeployerSettings, combining general model deployer configuration with Huggingface-specific settings. The use of SecretField for the token attribute is a good practice for handling sensitive information securely.

63-122: The HuggingFaceModelDeployerFlavor class is correctly implemented with properties that define the flavor's characteristics, such as name, docs_url, sdk_docs_url, logo_url, and config_class. This implementation follows best practices for defining stack component flavors in ZenML, enhancing the framework's extensibility.

src/zenml/integrations/huggingface/services/huggingface_deployment.py (4)

27-34: The HuggingFaceServiceConfig class is well-defined, extending HuggingFaceBaseConfig and ServiceConfig. This design allows for a rich configuration specific to Huggingface services while maintaining compatibility with ZenML's service management framework.

41-55: The HuggingFaceDeploymentService class correctly specifies its SERVICE_TYPE with relevant metadata. This is important for the ZenML service registry to correctly identify and manage instances of this service type.

61-68: The constructor of HuggingFaceDeploymentService is correctly implemented, calling the superclass constructor with the provided configuration. This ensures that the service is correctly initialized with its specific configuration.

104-124: The provision method correctly implements the logic to create or update a Huggingface inference endpoint. The use of .wait(timeout=POLLING_TIMEOUT) ensures that the method waits for the endpoint to be provisioned, enhancing reliability.

src/zenml/integrations/huggingface/model_deployers/huggingface_model_deployer.py (3)

340-382: The _matches_search_criteria method correctly checks if an existing service matches the input criteria. This method enhances the flexibility of the find_model_server method by allowing partial matches based on provided criteria.

384-418: The methods stop_model_server, start_model_server, and delete_model_server are correctly implemented to manage the lifecycle of model servers. These methods enhance the manageability of deployed models by providing straightforward mechanisms to start, stop, and delete model servers.

443-457: The get_model_server_info method correctly returns implementation-specific information that might be relevant to the user. This method enhances the usability of the deployment service by providing easy access to important information such as the prediction URL.

coderabbitai · 2024-01-30T22:32:15Z

src/zenml/integrations/huggingface/services/huggingface_deployment.py

+    def check_status(self) -> Tuple[ServiceState, str]:
+        """Check the the current operational state of the HuggingFace deployment.
+
+        Returns:
+            The operational state of the HuggingFace deployment and a message
+            providing additional information about that state (e.g. a
+            description of the error, if one is encountered).
+        """
+        # TODO: Support all different InferenceEndpointStatus
+        try:
+            _ = self.hf_endpoint.status
+        except (InferenceEndpointError, HfHubHTTPError):
+            return (ServiceState.INACTIVE, "")
+
+        if self.hf_endpoint.status == InferenceEndpointStatus.RUNNING:
+            return (
+                ServiceState.ACTIVE,
+                "HuggingFace Inference Endpoint deployment is available",
+            )
+
+        if self.hf_endpoint.status == InferenceEndpointStatus.SCALED_TO_ZERO:
+            return (
+                ServiceState.ACTIVE,
+                "HuggingFace Inference Endpoint deployment is scaled to zero",
+            )
+
+        if self.hf_endpoint.status == InferenceEndpointStatus.FAILED:
+            return (
+                ServiceState.ERROR,
+                "HuggingFace Inference Endpoint deployment failed: ",
+            )
+
+        if self.hf_endpoint.status == InferenceEndpointStatus.PENDING:
+            return (
+                ServiceState.PENDING_STARTUP,
+                "HuggingFace Inference Endpoint deployment is being created: ",
+            )


The check_status method is correctly implemented to check the operational state of the Huggingface deployment. However, the TODO comment indicates that support for all different InferenceEndpointStatus values is not yet implemented. This should be addressed to ensure comprehensive status reporting.

Consider implementing support for all InferenceEndpointStatus values to provide comprehensive status reporting for Huggingface deployments.

for this I think we can probably implement the other statuses, no? Those all seem reasonable statuses to want to represent somehow. WDYT?

@strickvl: Yes, implementing support for all InferenceEndpointStatus values would indeed enhance the comprehensiveness of status reporting for Huggingface deployments. It's a good idea to represent all possible statuses to provide clearer insights into the deployment state. I recommend looking into the huggingface_hub documentation or source code to understand all available InferenceEndpointStatus values. Then, you can map each status to the most appropriate ServiceState and provide meaningful status messages accordingly. This approach will ensure that users have a clear understanding of the operational state of their deployments at any given time. If you need further assistance or have specific questions as you implement this, feel free to ask!

@safoinme do you have an opinion about this. proposal on the table would be:

class InferenceEndpointStatus(str, Enum): PENDING = "pending" --> PENDING_STARTUP INITIALIZING = "initializing" --> PENDING_STARTUP UPDATING = "updating" --> ACTIVE UPDATE_FAILED = "updateFailed" --> ERROR RUNNING = "running" --> ACTIVE PAUSED = "paused" # not sure about this one as we don't handle this. Users can manually pause endpoint via UI FAILED = "failed" ---> ERROR SCALED_TO_ZERO = "scaledToZero" --> ACTIVE

but we'll reflect the status back to the user in the CLI and on the dashboard, so I think paused is a reasonable one to have, even if we're not manipulating that ourselves. I would consider adding 'updating' and 'paused' to the zenml status options. WDYT?

Seems like the humans are having a chat. I'll hop back into my burrow for now. If you need me again, just tag @coderabbitai in a new comment, and I'll come hopping out!

src/zenml/integrations/huggingface/services/huggingface_deployment.py

src/zenml/integrations/huggingface/model_deployers/huggingface_model_deployer.py

coderabbitai · 2024-01-30T22:32:15Z

src/zenml/integrations/huggingface/model_deployers/huggingface_model_deployer.py

+    def _create_new_service(
+        self, timeout: int, config: HuggingFaceServiceConfig
+    ) -> HuggingFaceDeploymentService:
+        """Creates a new HuggingFaceDeploymentService.
+
+        Args:
+            timeout: the timeout in seconds to wait for the Huggingface inference endpoint
+                to be provisioned and successfully started or updated.
+            config: the configuration of the model to be deployed with Huggingface model deployer.
+
+        Returns:
+            The HuggingFaceServiceConfig object that can be used to interact
+            with the Huggingface inference endpoint.
+        """
+        # create a new service for the new model
+        service = HuggingFaceDeploymentService(config)
+
+        # Use first 8 characters of UUID as artifact version
+        # Add same 8 characters as suffix to endpoint name
+        service_metadata = service.dict()
+        artifact_version = str(service_metadata["uuid"])[:8]
+
+        service.config.endpoint_name = self.modify_endpoint_name(
+            service.config.endpoint_name, artifact_version
+        )
+
+        logger.info(
+            f"Creating an artifact {HUGGINGFACE_SERVICE_ARTIFACT} with service instance attached as metadata."
+            " If there's an active pipeline and/or model this artifact will be associated with it."
+        )
+
+        service_metadata = service.dict()
+
+        save_artifact(
+            service,
+            HUGGINGFACE_SERVICE_ARTIFACT,
+            version=artifact_version,
+            is_deployment_artifact=True,
+        )
+        # UUID object is not json serializable
+        service_metadata["uuid"] = str(service_metadata["uuid"])
+        log_artifact_metadata(
+            artifact_name=HUGGINGFACE_SERVICE_ARTIFACT,
+            artifact_version=artifact_version,
+            metadata={HUGGINGFACE_SERVICE_ARTIFACT: service_metadata},
+        )
+
+        service.start(timeout=timeout)
+        return service


The _create_new_service method is well-implemented, covering the creation of a new HuggingFaceDeploymentService, including modifying the endpoint name, saving the service as an artifact, and starting the service. However, the method logs the creation of an artifact before actually creating it, which might be misleading if the artifact creation fails.

Consider moving the log statement after the artifact has been successfully saved to ensure accurate logging.

src/zenml/integrations/huggingface/model_deployers/huggingface_model_deployer.py

strickvl

Really nice addition! I had some comments and nits etc, but the two big pieces to add would be docs updates and testing (where possible). I see, for example, that hf offers a way to emulate custom endpoints in the https://github.com/huggingface/hf-endpoints-emulator repo / package.

For docs, we'll need a new doc in the deployers section explaining exactly how to set this up on HF and how to use within ZenML.

Really excited about this one! Thanks for putting in the work!

src/zenml/integrations/huggingface/flavors/__init__.py

src/zenml/integrations/huggingface/flavors/huggingface_model_deployer_flavor.py

src/zenml/integrations/huggingface/model_deployers/huggingface_model_deployer.py

src/zenml/integrations/huggingface/steps/huggingface_deployer.py

src/zenml/integrations/huggingface/steps/__init__.py

src/zenml/integrations/huggingface/services/huggingface_deployment.py

src/zenml/integrations/huggingface/services/__init__.py

safoinme

Thank you for all the work and the effort you've put in this PR. It's definitely a great addition to our deployers. I've got a couple of small suggestions.
Can't wait to see this released!

src/zenml/integrations/huggingface/model_deployers/huggingface_model_deployer.py

src/zenml/integrations/huggingface/services/huggingface_deployment.py

strickvl · 2024-02-05T13:26:59Z

@dudeperf3ct can you check the CI errors and update as appropriate? there's some linting issues and also some docstrings to be added as far as I can see.

docs/book/stacks-and-components/component-guide/model-deployers/huggingface.md

src/zenml/integrations/huggingface/model_deployers/huggingface_model_deployer.py

src/zenml/integrations/huggingface/services/__init__.py

src/zenml/integrations/huggingface/services/huggingface_deployment.py

… huggingface-model-deployer

This should allow the dependencies to resolve.

pyproject.toml

safoinme

Let's

* Initial implementation of huggingface model deployer * Add missing step init * Simplify modify_endpoint_name function and fix docstrings * Formatting logger * Add License to new files * Enhancements as per PR review comments * Add logging message to catch KeyError * Remove duplicate variable * Reorder lines for clarity * Add docs for huggingface model deployer * Fix CI errors * Fix get_model_info function arguments * More CI fixes * Add minimal supported version for Inference Endpoint API in huggingface_hub * Relax 'adlfs' package requirement in azure integrations * update TOC (zenml-io#2406) * Relax 's3fs' version in s3 integration * Bugs fixed running a test deployment pipeline * Add deployment pipelines to huggingface integration test * Remove not required check on service running in tests * Address PR comments on documentation and suggested renaming in code * Add partial test for huggingface_deployment * Fix typo in test function * Update pyproject.toml This should allow the dependencies to resolve. * Update pyproject.toml * Relax gcfs * Update model deployers table * Fix lint issue --------- Co-authored-by: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Co-authored-by: Safoine El Khabich <34200873+safoinme@users.noreply.github.com> Co-authored-by: Alex Strick van Linschoten <strickvl@users.noreply.github.com>

Initial implementation of huggingface model deployer

0f7840d

Add missing step init

0c1a1de

htahir1 requested review from strickvl and safoinme January 30, 2024 16:11

coderabbitai bot reviewed Jan 30, 2024

View reviewed changes

Simplify modify_endpoint_name function and fix docstrings

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avishniakov marked this pull request as draft January 31, 2024 08:07

avishniakov marked this pull request as ready for review January 31, 2024 08:07

strickvl added enhancement New feature or request dependencies Pull requests that update a dependency file labels Jan 31, 2024

dudeperf3ct added 2 commits January 31, 2024 17:46

Merge branch 'develop' into huggingface-model-deployer

f7ddd6b

Formatting logger

2fdde4b

strickvl requested changes Jan 31, 2024

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safoinme requested changes Jan 31, 2024

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dudeperf3ct and others added 5 commits February 1, 2024 11:05

Add License to new files

44c8cd0

Enhancements as per PR review comments

a6231fa

Merge branch 'develop' into huggingface-model-deployer

51e83d1

Add logging message to catch KeyError

3c34a80

Merge branch 'develop' into huggingface-model-deployer

efb9158

safoinme added the run-slow-ci label Feb 1, 2024

dudeperf3ct and others added 6 commits February 1, 2024 13:57

Remove duplicate variable

446454d

Reorder lines for clarity

3d5593a

Merge branch 'develop' into huggingface-model-deployer

d619519

Add docs for huggingface model deployer

33ea8a4

Merge branch 'develop' into huggingface-model-deployer

147ec07

Merge branch 'develop' into huggingface-model-deployer

d9b16fa

Remove not required check on service running in tests

06d52ec

htahir1 force-pushed the develop branch from 6724f45 to b3040de Compare February 7, 2024 14:11

Merge branch 'develop' into huggingface-model-deployer

9a400b4

strickvl requested a review from safoinme February 7, 2024 16:18

strickvl reviewed Feb 7, 2024

View reviewed changes

dudeperf3ct added 3 commits February 8, 2024 11:00

Address PR comments on documentation and suggested renaming in code

3acaa80

Merge remote-tracking branch 'origin/huggingface-model-deployer' into…

5b96840

… huggingface-model-deployer

Merge branch 'develop' into huggingface-model-deployer

5dd45d8

strickvl self-requested a review February 8, 2024 09:38

dudeperf3ct and others added 6 commits February 8, 2024 16:46

Add partial test for huggingface_deployment

2c9c5a2

Fix typo in test function

c3caa59

Merge branch 'develop' into huggingface-model-deployer

4dde857

Merge branch 'develop' into huggingface-model-deployer

3eab2e0

Merge branch 'develop' into huggingface-model-deployer

3de4783

Update pyproject.toml

a8f5e1d

This should allow the dependencies to resolve.

strickvl reviewed Feb 27, 2024

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strickvl added 3 commits February 27, 2024 09:40

Update pyproject.toml

9e17cb1

Merge branch 'develop' into huggingface-model-deployer

85277a7

Relax gcfs

68557b3

strickvl mentioned this pull request Mar 6, 2024

Relax azure, gcfs and s3 dependencies #2498

Merged

Update model deployers table

6eb5b64

dudeperf3ct force-pushed the huggingface-model-deployer branch from b500b29 to 6eb5b64 Compare March 7, 2024 11:19

dudeperf3ct and others added 3 commits March 7, 2024 16:51

Merge 'develop' branch into 'huggingface-model-deployer' branch

fa17af1

Fix lint issue

f7daf26

Merge branch 'develop' into huggingface-model-deployer

76206a9

strickvl added the run-slow-ci label Mar 7, 2024

strickvl self-requested a review March 7, 2024 17:35

safoinme approved these changes Mar 8, 2024

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safoinme merged commit edebc51 into zenml-io:develop Mar 8, 2024
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Huggingface Model Deployer #2376

Huggingface Model Deployer #2376

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Huggingface Model Deployer #2376

Huggingface Model Deployer #2376

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