Generic batch processing framework for managing the orchestration, dispatch, fault tolerance, and monitoring of arbitrary work items against many endpoints. Extensible via dependency injection.
Generic batch processing framework for managing the orchestration, dispatch, fault tolerance, and monitoring of arbitrary work items against many endpoints. Extensible via dependency injection. Worker endpoints can be local, remote, containers, cloud APIs, different processes, or even just different listener sockets in the same process.
Includes examples against Azure Cognitive Service containers for ML eval workloads.
The framework can be built on via template method pattern and dependency injection. One simply needs to provide concrete implementation for the following types:
WorkItemRequest: Encapsulates all the details needed by the WorkItemProcessor to process a work item.
WorkItemResult: Representation of the outcome of an attempt to process a WorkItemRequest.
WorkItemProcessor: Provides implementation on how to process a WorkItemRequest against an endpoint.
BatchRequest: Represents a batch of work items to do. Produces a collection of WorkItemRequests.
BatchConfig: Details needed for a BatchRequest to produce the collection of WorkItemRequests.
BatchRunSummarizer: Implements a near-real-time status updater based on WorkItemResults as the batch progresses.
EndpointStatusChecker: Specifies how to determine whether an endpoint is healthy and ready to take on work from a WorkItemProcessor.
The Speech Batch Kit is currently our prime example for consuming the framework.
The batchkit package is available as an ordinary pypi package. See versions here: https://pypi.org/project/batchkit
This project is developed for and consumed in Linux environments. Consumers also use WSL2, and other POSIX platforms may be compatible but are untested. For development and deployment outside of a container, we recommend using a Python virtual environment to install the requirements.txt. The Speech Batch Kit example builds a container.
This project uses both unit tests run-tests and stress tests run-stress-tests for functional verification.
There are currently 3 artifacts:
The pypi library of the batchkit framework as a library.
The pypi library of the batchkit-examples-speechsdk.
Docker container image for speech-batch-kit.
The Speech Batch Kit (batchkit_examples/speech_sdk) uses the framework to produce a tool that can be used for transcription of very large numbers of audio files against Azure Cognitive Service Speech containers or cloud endpoints.
For introduction, see the Azure Cognitive Services page.
For detailed information, see the Speech Batch Kit's README.
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.
When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repositories using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
FAQs
Generic batch processing framework for managing the orchestration, dispatch, fault tolerance, and monitoring of arbitrary work items against many endpoints. Extensible via dependency injection.
We found that batchkit demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
PyPI confirms no security flaws were exploited in the Ultralytics supply chain attack and highlights improvements for safer package publishing.
Security News
Research
The Socket Research Team breaks down a malicious wrapper package that uses obfuscation to harvest credentials and exfiltrate sensitive data.
Research
Security News
Attackers used a malicious npm package typosquatting a popular ESLint plugin to steal sensitive data, execute commands, and exploit developer systems.
