Skip to content

Releases: pytorch/serve

TorchServe v0.1.1 Release Notes (Experimental)

09 Jun 22:04
@mycpuorg mycpuorg
v0.1.1
1286634
This commit was created on GitHub.com and signed with GitHub’s verified signature. The key has expired.
GPG key ID: 4AEE18F83AFDEB23
Expired
Learn about vigilant mode.
Compare
Choose a tag to compare
TorchServe v0.1.1 Release Notes (Experimental)

This is the release of TorchServe v0.1.1

Highlights:

  • HuggingFace BERT Example - Support for HuggingFace Models demonstrated with examples under examples/ directory.
  • Waveglow Example - Support for Nvidia Waveglow model demonstrated with examples under examples/ directory.
  • Model Zoo - Model Zoo with model archives created from popular pre-trained models from PyTorch Model Zoo
  • AWS Cloud Formation Support - Support added for spinning up TorchServe Model Server on an EC2 instance via the convenience of AWS Cloud Formation Template.
  • Snakeviz Profiler - Support for Profiling TorchServe Python execution via snakevize profiler for detailed execution time reporting.
  • Docker improvements - Docker image size optimization, detailed docs for running docker.
  • Regression Test Suite - Detailed Regression Test Suite to allow comprehensive tests for all supported REST APIs. Automating this test helps faster regression detection.
  • Detailed Unit Test Reporting - Detailed breakdown of Unit Test Reports from gradle build system.
  • Installation Process Streamlining - Easier user onboarding with detailed documentation for installation
  • Documentation Clean up - Refactored documentation with clear instructions
  • GPU Device Assignment - Object Detection Model now correctly runs on multiple GPU devices
  • Model Store Clean-up - Clean up Model store of all artifacts for a deleted model

Other PRs since v0.1.0

Bug Fixes:

  • Fixes Incorrect Version number reporting #360
  • Validation for correct port range 0-65535 #304
  • Gradle build failures for new Gradle version-6.4 #352
  • Standardize "Model version not found." response for all applicable Api's with Http status code 404. #282
  • The --model-store should point to a user-relative directory. #248
  • Corrected query parameter name in OpenApi description for registration api. #328
  • psutil install de-duplication #329
  • Maven tests should output only errors and not info / stack traces #326
  • Fixed installation issues for Python VirtualEnv #341

Documentation

  • Using GPU in Docker #205

Others

  • Github Issue templates #273

Platform Support

Ubuntu 16.04, Ubuntu 18.04, MacOS 10.14+

Getting Started with TorchServe

Additionally, you can get started at pytorch.org/serve with installation instructions, tutorials and docs.
Lastly, if you have questions, please drop it into the PyTorch discussion forums using the ‘deployment’ tag or file an issue on GitHub with a way to reproduce.

Assets 10

TorchServe v0.1.0

21 Apr 17:58
@mycpuorg mycpuorg
v0.1.0
f6c2d2c
This commit was created on GitHub.com and signed with GitHub’s verified signature. The key has expired.
GPG key ID: 4AEE18F83AFDEB23
Expired
Learn about vigilant mode.
Compare
Choose a tag to compare
TorchServe v0.1.0

TorchServe (Experimental) v0.1.0 Release Notes

This is the first release of TorchServe (Experimental), a new open-source model serving framework under the PyTorch project (RFC #27610).

Highlights

  • Clean APIs - Support for an Inference API for predictions and a Management API for managing the model server.

  • Secure Deployment - Includes HTTPS support for secure deployment.

  • Robust model management capabilities - Allows full configuration of models, versions, and individual worker threads via command line interface, config file, or run-time API.

  • Model archival - Provides tooling to perform a ‘model archive’, a process of packaging a model, parameters, and supporting files into a single, persistent artifact. Using a simple command-line interface, you can package and export in a single ‘.mar’ file that contains everything you need for serving a PyTorch model. This `.mar’ file can be shared and reused. Learn more here.

  • Built-in model handlers - Support for model handlers covering the most common use-cases (image classification, object detection, text classification, image segmentation). TorchServe also supports custom handlers

  • Logging and Metrics - Support for robust logging and real-time metrics to monitor inference service and endpoints, performance, resource utilization, and errors. You can also generate custom logs and define custom metrics.

  • Model Management - Support for management of multiple models or multiple versions of the same model at the same time. You can use model versions to roll back to earlier versions or route traffic to different versions for A/B testing.

  • Prebuilt Images - Ready to go Dockerfiles and Docker images for deploying TorchServe on CPU and NVIDIA GPU based environments. The latest Dockerfiles and images can be found here.

Platform Support

      - Ubuntu 16.04, Ubuntu 18.04, MacOS 10.14+

Known Issues

  • The default object detection handler only works on cuda:0 device on GPU machines #104
  • For torchtext based models, the sentencepiece dependency fails for MacOS with python 3.8 #232

Getting Started with TorchServe

  • Additionally, you can get started at pytorch.org/serve with installation instructions, tutorials and docs.
  • Lastly, if you have questions, please drop it into the PyTorch discussion forums using the ‘deployment’ tag or file an issue on GitHub with a way to reproduce.
Assets 2