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CONTRIBUTING.md
```bash docker run -it --rm -v $PWD:/tmp -w /tmp tensorflow/build:2.15-python3.10 ``` Once you have the packages installed, you can run a specific unit test in bazel by doing as follows: ```bash export flags="--config=opt -k" ``` If the tests are to be run on the GPU, add CUDA paths to LD_LIBRARY_PATH and add the `cuda` option flag ```bash
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tensorflow/c/eager/c_api_experimental.h
// devices, so executing tf.functions which contain operations placed on the // custom devices will fail. // // `device_name` must not name an existing physical or custom device. It must // follow the format: // // /job:<name>/replica:<replica>/task:<task>/device:<type>:<device_num> // // If the device is successfully registered, `status` is set to TF_OK. Otherwise
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RELEASE.md
import os;os.environ["TF_USE_LEGACY_KERAS"]=1. Please note that this will set it for all packages in your Python runtime program * 1. Change import of keras from tensorflow as follows * import tensorflow.keras as keras and import keras to import tf_keras as keras * **Apple Silicon users:** If you previously installed TensorFlow using
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tensorflow/c/eager/tape.h
// - target: dy in dx/dy // - source: dx in dx/dy // - tensor: one of the many inputs or outputs of an operation // // Below here we do the gradient algorithm. It works as follows: // // First we filter the tape to just the subset of operations we want to // differentiate. In the process of doing so we count how many times each Tensor
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ci/official/utilities/setup.sh
# functions (except for tfrun); use dedicated scripts instead and reference them # specifically. Use your best judgment to keep the scripts in this directory # lean and easy to follow. When in doubt, remember that for CI scripts, "keep it # simple" is MUCH more important than "don't repeat yourself." # -e: abort script if one command fails # -u: error if undefined variable used # -x: log all commands
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