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@paulguerrie paulguerrie released this 07 Feb 17:22
· 3082 commits to main since this release
v0.9.9
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🚀 Added

Roboflow workflows 🤖

A new way to create ML pipelines without writing code. Declare the sequence of models and intermediate processing steps using JSON config and execute using inference container (or Hosted Roboflow platform). No Python code needed! 🤯 Just watch our feature preview

workflows_feature_preview.mp4

Want to experiment more?

pip install inference-cli

inference server start --dev

Hit http://127.0.0.1:9001 in your browser, then click Jump Into an Inference Enabled Notebook → button and open the notebook named workflows.ipynb:

We encourage to acknowledge our documentation 📖 to reveal full potential of Roboflow workflows.

This feature is still under heavy development. Your feedback is needed to make it better!

Take inference to the cloud with one command 🚀

Yes, you got it right. inference-cli package now provides set of inference cloud commands to deploy required infrastructure without effort.

Just:

pip install inference-cli

And depended on your needs use:

inference cloud deploy --provider aws --compute-type gpu
# or
inference cloud deploy --provider gcp --compute-type cpu

With example posted here, we are just scratching the surface - visit our docs 📖 where more examples are presented.

🔥 YOLO-NAS is coming!

  • We plan to onboard YOLO-NAS to the Roboflow platform. In this release we are introducing foundation work to make that happen. Stay tuned!

supervision 🤝 inference

We've extended capabilities of inference infer command of inference-cli package. Now it is capable to run inference against images, directories of images and videos, visualise predictions using supervision and save them in the location of choice.

What does it take to get your predictions?

pip install inference-cli

# start the server
inference server start 

# run inference
inference infer -i {PATH_TO_VIDEO} -m coco/3 -c bounding_boxes_tracing -o {OUTPUT_DIRECTORY} -D

There are plenty of configuration options that can alter the visualisation. You can use predefined configs (example: -c bounding_boxes_tracing) or create your own. See our docs 📖 to discover all options.

🌱 Changed

  • ❗ breaking: Pydantic 2: Inference now depends on pydantic>=2.
  • ❗ breaking: Default values of parameters (like confidence, iou_threshold etc.) that were set for newer parts of inference (including inference HTTP container endpoints) were aligned with more reasonable defaults that hosted Roboflow platform uses. That is going to make the experience of inference usage consistent with Roboflow platform. This, however, will alter the behaviour of package for clients that do not specify their own values of parameters while making predictions. Summary: confidence is from now on defaulted to 0.4 and iou_threshold to 0.3. We encourage clients using self-hosted containers to evaluate results on their end. Changes to be inspected here.
  • API calls to HTTP endpoints with Roboflow models now accept disable_active_learning flag that prevents Active Learning being active for specific request
  • Documentation 📖 was refreshed. Redesign is supposed to make the content easier to comprehend. We would love to have some feedback 🙏

🔨 Fixed

  • ❗ breaking: Fixed the issue #260 with bug introduced in version v0.9.3 causing classification models with 10 and more classes to assign wrong class name to predictions (despite maintaining good class ids) - clients relying on class name instead on class_id of predictions were affected.
  • ❗ breaking: Typo coglvm -> cogvlm in inference-sdk HTTP client method name prompt_cogvlm(...)

Full Changelog: v0.9.8...v0.9.9

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