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ci/official/wheel_test/README.md
[requirements updater readme](https://github.com/tensorflow/tensorflow/blob/master/ci/official/requirements_updater/README.md) ### Prerequisites for Local Testing To run tests locally, follow these steps: 1. Navigate to the relevant directory: ``` cd ci/official/wheel_test ``` 2. Run a script for creating requirements file: ```
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ci/official/README.md
## How to Test Your Changes to TensorFlow You may check how your changes will affect TensorFlow by: 1. Creating a PR and observing the presubmit test results 2. Running the CI scripts locally, as explained below 3. **Google employees only**: Google employees can use an internal-only tool called "MLCI" that makes testing more convenient: it can execute any full CI job
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CONTRIBUTING.md
[TensorFlow Builds](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/ci_build) for details. #### Running doctest for testable docstring There are two ways to test the code in the docstring locally: 1. If you are only changing the docstring of a class/function/method, then you can test it by passing that file's path to [tf_doctest.py](https://www.tensorflow.org/code/tensorflow/tools/docs/tf_doctest.py).
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tensorflow/c/eager/c_api_experimental.h
// EagerService to maintain consistency between client and worker. The // context_id is initialized with a dummy value and is later set when the worker // is initialized (either locally or remotely). The context_id can change during // the process lifetime although this should cause the worker to be // reinitialized (e.g. cleared caches) as well. TF_CAPI_EXPORT extern uint64_t TFE_GetContextId(TFE_Context* ctx);
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.bazelrc
# leading to build failures. To resolve the issue, the following line is added # to make sure all Genrule targets are excuted locally. build:rbe_cross_compile_base_xla --config=rbe_cross_compile_base build:rbe_cross_compile_base_xla --strategy=Genrule=standalone # Due to the above strategy, all Genrule commands are executed locally, but the # following actions invoke tools (E.g `flatc`, `llvm-tblgen`, etc.) that are
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configure.py
if not is_linux(): raise ValueError('Currently NCCL is only supported on Linux platform.') if 'TF_NCCL_VERSION' in environ_cp: return ask_nccl_version = ( 'Please specify the locally installed NCCL version you want to use. ' '[Leave empty to use http://github.com/nvidia/nccl]: ') tf_nccl_version = get_from_env_or_user_or_default(environ_cp,
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ci/official/utilities/setup.sh
# $TFCI_OUTPUT_DIR/script.log exec > >(tee "$TFCI_OUTPUT_DIR/script.log") 2>&1 # Setup tfrun, a helper function for executing steps that can either be run # locally or run under Docker. setup_docker.sh, below, redefines it as "docker # exec". # Important: "tfrun foo | bar" is "( tfrun foo ) | bar", not "tfrun (foo | bar)". # Therefore, "tfrun" commands cannot include pipes -- which is
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