Registry / aws / sagemaker-schema-inference-artifacts

sagemaker-schema-inference-artifacts

library0.0.5pypypiunverified

sagemaker-schema-inference-artifacts is an open-source Python library designed to provide sample inputs and outputs for Hugging Face tasks within the Amazon SageMaker ecosystem. It is currently at version 0.0.5 and appears to have a low release cadence based on its history. The library's primary purpose, as described, is to facilitate the handling of data schemas for inference with Hugging Face models on SageMaker.

aws ai-ml data

pip install sagemaker-schema-inference-artifacts

github ↗package ↗

INSTALL

IMPORT

SIG · SAGEMAKER-SCHEMA-I

sagemaker-schema-inference-artifacts

awspythonv0.0.5

harness data pending

Install & Compatibility

Where this runs

No compatibility data collected yet for this library.

Code

Verified usage

Due to a lack of public documentation or detailed usage examples specific to `sagemaker-schema-inference-artifacts`, a runnable quickstart cannot be provided at this time. The library's stated purpose implies its use in defining and handling input/output schemas for Hugging Face models deployed on SageMaker, likely integrating with the broader SageMaker Python SDK.

# No specific quickstart code found in public documentation or examples for sagemaker-schema-inference-artifacts. # The library's purpose is described as providing sample inputs/outputs for Hugging Face tasks # within SageMaker inference, implying it would be used in conjunction with other SageMaker SDK # and Hugging Face libraries. Specific classes or functions are not publicly documented. # Example placeholder (conceptual, not runnable without specific API knowledge): # from sagemaker_schema_inference_artifacts import HuggingFaceInputSchema, HuggingFaceOutputSchema # # def get_inference_payload(task_type: str, data: dict): # schema = HuggingFaceInputSchema.for_task(task_type) # return schema.validate_and_serialize(data) # # def parse_inference_result(task_type: str, raw_output: str): # schema = HuggingFaceOutputSchema.for_task(task_type) # return schema.deserialize_and_validate(raw_output) # # print("No direct runnable quickstart available without further library specifics.")

Debug

Known issues

gotchaThe official GitHub repository URL provided on PyPI (https://github.com/aws/sagemaker-schema-inference-artifacts/) currently returns a 404. This makes finding detailed documentation, examples, or contributing to the project challenging.

fix

Users should search for alternative documentation sources or community discussions if available. The library's functionality might be tightly coupled with internal AWS SageMaker implementations not fully exposed or documented externally.

affects: All (0.0.1 - 0.0.5)

gotchaSpecific classes, functions, and their usage patterns are not readily available in public search results or a dedicated documentation site. This makes it difficult to understand how to correctly import and utilize the library's features.

fix

Inspect the installed package's source code (e.g., using `dir()` and `help()` on the module after installation) to infer available components, or consult AWS support channels for guidance if using it within an AWS-managed context.

affects: All (0.0.1 - 0.0.5)

gotchaThe library's description on PyPI is generic ('Open source library for Hugging Face Task Sample Inputs and Outputs') and lacks specifics about supported Hugging Face tasks, model types, or how it integrates with SageMaker's inference endpoints.

fix

Assume a close integration with specific SageMaker and Hugging Face components, and validate its applicability for particular use cases through experimentation or by seeking direct clarification from AWS if possible.

affects: All (0.0.1 - 0.0.5)

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Version history

0.0.5latest on PyPI · released Apr 8, 2024

Audit

Dependencies

No dependency data recorded yet.

Agent activity

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