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[Good First Issue][Keras 3 OpenVINO Backend]: Support numpy.diag operation #29115
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Thank you for looking into this issue! Please let us know if you have any questions or require any help. |
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Thanks for being interested in this issue. It looks like this ticket is already assigned to a contributor. Please communicate with the assigned contributor to confirm the status of the issue. |
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Thanks for being interested in this issue. It looks like this ticket is already assigned to a contributor. Please communicate with the assigned contributor to confirm the status of the issue. |
Hi @srinjoydutta03 , I saw that you're currently working on the OpenVINO backend support for numpy.diag (#20910), and I’m very interested in contributing to OpenVINO as part of my GSoC journey. I noticed that you've already submitted a PR for this issue. Is there anything pending that I could help with, such as additional testing, documentation, or reviewing? If you've encountered any challenges, I’d love to assist in any way possible. |
Hey @leoheim, appreciate the help. But, I'm almost done with the PR, awaiting a review. Maybe we can collaborate in a future task :) |
Hello @rkazants , I’m interested in taking on this task as part of the GSoC pre-requisite. Could you please assign it to me? |
bump @rkazants |
Context
🚀**A great opportunity to contribute to two popular AI projects with just one PR:: Keras 3 and OpenVINO.**🚀
Keras 3 enables seamless switching between supported backends—PyTorch, TensorFlow, and JAX—for both training and inference of traditional models and LLMs/GenAI pipelines. Since Keras 3.8.0, we've introduced a preview version of the OpenVINO backend (for inference only), allowing developers to leverage OpenVINO for model predictions directly within Keras 3 workflows. Activating the OpenVINO backend requires just one line of code to run inference on Keras 3-trained models. Here’s an example for a BERT model from Keras Hub:
Currently, the OpenVINO backend lacks support for some operations. Our goal is to resolve this gap and to optimize it for inference on Intel devices—including CPUs, integrated GPUs, discrete GPUs, and NPUs—by supporting as many models as possible while delivering optimal performance. We aim to make the OpenVINO backend the No. 1 choice for model inference within the Keras 3 workflow.
What needs to be done?
Steps to Contribute
pip install -r requirements-openvino.txt
. The requirements file can be found in the root directory of cloned repositoryProvide decomposition in Python for numpy.diag using OpenVINO opset
Include tests by removing line corresponding to the implemented operation in excluded_concrete_tests.txt file.
Make sure that tests are passing
pytest.ini
file and place it in the root directory of the clone repository. This file should contain the following content:pytest-c ./pytest.ini ./keras/src/ops/numpy_test.py
Below is an exemplar PR of how support for numpy.amax has been implemented.
Example Pull Requests
Resources
Contact points
@rkazants
Ticket
No response
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