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ci/official/wheel_test/README.md
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 `_requirements_lock` files will be generated by the presubmit job. A detailed
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tensorflow/c/eager/c_api_experimental_test.cc
server_def_0, tensorflow::Env::Default(), &worker_server1) .ok()); ASSERT_TRUE(worker_server1->Start().ok()); // 5a. Update `ctx_0` with updated `server_def_0`. { server_def_0.set_task_index(0); string serialized_update = server_def_0.SerializeAsString(); TF_Status* status = TF_NewStatus();
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tensorflow/c/eager/c_api_test.cc
server_def_0, tensorflow::Env::Default(), &worker_server1) .ok()); ASSERT_TRUE(worker_server1->Start().ok()); // 5a. Update `ctx_0` with updated `server_def_0`. { server_def_0.set_task_index(0); string serialized_update = server_def_0.SerializeAsString(); TF_Status* status = TF_NewStatus();
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ci/official/requirements_updater/updater.sh
# ============================================================================== # script to run pip-compile for each requirement. # if there is a change in requirements.in then all lock files will be updated # accordingly # All commands run relative to this directory cd "$(dirname "${BASH_SOURCE[0]}")" mv BUILD.bazel BUILD SUPPORTED_VERSIONS=("3_9" "3_10" "3_11" "3_12")
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tensorflow/c/eager/abstract_operation.h
virtual const string& Name() const = 0; // Returns the operation's device name. // // The value returned may be different from the one set by SetDeviceName, but // it will be compatible with it: the name will be updated by device placement // logic to refer to the specific device chosen. // // Example: If one calls `op->SetDeviceName("/device:GPU")`, the value // returned by DeviceName should be "/device:GPU:*" until a particular GPU is
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ci/official/wheel_test/update_requirements.sh
# ============================================================================== # script to run pip-compile for keras, tensorboard, estimator deps. # if there is a change in requirements.in then all lock files will be updated # accordingly. # -e: abort script if one command fails # -u: error if undefined variable used # -o pipefail: entire command fails if pipe fails. watch out for yes | ... # -o history: record shell history
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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
build --distinct_host_configuration=false # Disable clang extension that rejects type definitions within offsetof. # This was added in clang-16 by https://reviews.llvm.org/D133574. # Can be removed once upb is updated, since a type definition is used within # offset of in the current version of ubp. # See https://github.com/protocolbuffers/upb/blob/9effcbcb27f0a665f9f345030188c0b291e32482/upb/upb.c#L183. build --copt=-Wno-gnu-offsetof-extensions
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ci/official/requirements_updater/README.md
`release_updater.sh` 5) Run the `updater.sh` shell script. \ If the base requirements file hasn't yet been updated to account for the new Python version, which will require different versions for at least some dependencies, it will need to be updated now, for the script to run successfully. 6) A new `requirements_lock_3_11.txt` file should appear under the root of the
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tensorflow/c/eager/immediate_execution_tensor_handle.cc
} Status ImmediateExecutionTensorHandle::SummarizeValue( std::string& summary) const { Status status; AbstractTensorPtr resolved( // TODO(allenl): Resolve should be const, and the caches that get updated // marked mutable. const_cast<ImmediateExecutionTensorHandle*>(this)->Resolve(&status)); if (!status.ok()) { return status; } summary = resolved->SummarizeValue(); return absl::OkStatus();
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.github/ISSUE_TEMPLATE/tflite-converter-issue.md
``` (You can paste links or attach files by dragging & dropping them below) - Provide links to your updated versions of the above two colab notebooks. - Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model. ``` #### Option B: Paste your code here or provide a link to a custom end-to-end colab ```
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