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.github/workflows/create_issue.js
owner, repo, assignees: assignee_logins, title: `Issue created for Rollback of PR #${pr_number}: ${pr_title}`, body: `Merged PR #${pr_number} is rolled back in ${rollback_commit}. Please follow up with the reviewer and close this issue once its resolved.` }); return `Issue created: ${resp.data.number} with Title: ${resp.data.title}`;
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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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README.md
[Stack Overflow](https://stackoverflow.com/questions/tagged/tensorflow).** The TensorFlow project strives to abide by generally accepted best practices in open-source software development. ## Patching guidelines Follow these steps to patch a specific version of TensorFlow, for example, to apply fixes to bugs or security vulnerabilities: * Clone the TensorFlow repo and switch to the corresponding branch for your
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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/containers/linux_arm64/jax.requirements.txt
# JAX requirements, passed into container by defining the ARG # REQUIREMENTS_FILE=jax.requirements.txt setuptools wheel cloudpickle colorama>=0.4.4 matplotlib pillow>=9.1.0 rich absl-py portpicker six opt-einsum auditwheel typing_extensions importlib_metadata>=4.6 numpy==1.26.0;python_version=="3.12" numpy==1.23.4;python_version=="3.11" numpy==1.22.4;python_version<"3.11" scipy==1.11.2;python_version=="3.12"
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tensorflow/c/eager/parallel_device/parallel_device_test.cc
TFE_NewContextOptions(), TFE_DeleteContextOptions); std::unique_ptr<TF_Buffer, decltype(&TF_DeleteBuffer)> config( TF_CreateConfig( /*enable_xla_compilation=*/false, /*gpu_memory_allow_growth=*/true, /*num_cpu_devices=*/2), TF_DeleteBuffer); TFE_ContextOptionsSetConfig(opts.get(), config->data, config->length, status.get());
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ci/official/containers/linux_arm64/build.sh
export DOCKER_BUILDKIT=1 for target in jax tf; do IMAGE="gcr.io/tensorflow-sigs/build-arm64:$target-$TAG" docker pull "$IMAGE" || true # Due to some flakiness of resources pulled in the build, allow the docker # command to reattempt build a few times in the case of failure (b/302558736) set +e for i in $(seq 1 5) do docker build \ --build-arg REQUIREMENTS_FILE=jax.requirements.txt \
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tensorflow/c/eager/parallel_device/parallel_device_lib.h
// `StartExecute`s will deadlock). // // If `is_async=false` (constructor argument), `cancellation_manager` must // live until `Join` finishes. If `is_async=true` it must live until `Join` is // followed by `TFE_ContextAsyncWait` to clear pending operations. It will be // used to cancel all other operations if any fails. // // Set step_id to configure the step id used for rendezvous creation. step id
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tensorflow/c/c_api.h
// Similar to TF_GraphToFunction but allows specifying control outputs of the // function. // // The arguments of TF_GraphToFunction have the same meaning, but the new // arguments are as follows: // // ncontrol_outputs: Number of control outputs of the function. // control_outputs: vector of TF_Operation objects to be marked as control
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tensorflow/c/eager/parallel_device/parallel_device.cc
if (TF_GetCode(status) != TF_OK) return nullptr; if (parallel_tensor->num_tensors() == 1) { // Copy-off for single-device tensors is allowed to make debugging dynamic // control flow easier. return TFE_TensorHandleCopySharingTensor(parallel_tensor->tensor(0), status); } else { TF_SetStatus( status, TF_UNIMPLEMENTED,
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