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ci/official/wheel_test/README.md
2. Creates a `requirements_wheel_test.in` file and specifies the path to the actual TensorFlow wheel. 3. Creates a `requirements_lock_<python_version>.txt` file. 4. Updates the `requirements_lock_<python_version>.txt` file using a Bazel command. 5. Moves the updated `requirements_lock_<python_version>.txt` file to the `../wheel_test/` directory. ### How it Works in the Presubmit Job
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README.md
[community](https://www.tensorflow.org/community) resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications. TensorFlow was originally developed by researchers and engineers working within the Machine Intelligence team at Google Brain to conduct research in machine learning and neural networks. However, the framework is versatile enough to be used in other areas as well.
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ci/official/requirements_updater/README.md
as a rough example of the steps below. All the files referenced below are located in the same directory as this README, unless indicated otherwise. 1) Add the new version to the `VERSIONS` variable inside `tensorflow/tools/toolchains/python/python_repo.bzl`. \ While this isn't necessary for running the updater, it is required for actually using the new version with Tensorflow.
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ci/official/README.md
# GPU version of TensorFlow for Python 3.12, using the public TF bazel cache and # a local build cache: TFCI=py312,linux_x86_cuda,public_cache,disk_cache ci/official/wheel.sh # First, set your TFCI variable to choose the environment settings. # TFCI is a comma-separated list of filenames from the envs directory, which # are all settings for the scripts. TF's CI jobs are all made of a combination
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