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ci/official/containers/linux_arm64/Dockerfile
RUN mkdir -p /tf/venv RUN chown -R buildslave:buildslave /tf RUN dpkg -i /patchelf/patchelf_0.14.3-1_arm64.deb # All lines past this point are reset when $CACHEBUSTER is set. We need this # for Python specifically because we install some nightly packages which are # likely to change daily. ARG CACHEBUSTER=0 RUN echo $CACHEBUSTER # Setup build and environment COPY devel.usertools /usertools
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ci/official/containers/linux_arm64/devel.usertools/code_check_full.bats
//tensorflow/core/tfrt //tensorflow/python/kernel_tests/signal //tensorflow/examples //tensorflow/tools/android //tensorflow/python/eager/benchmarks EOF # grep patterns for files and targets which don't need to be in the pip # package, ever. cat > $BATS_TEST_TMPDIR/ignore_these_deps <<EOF benchmark _test$ _test.py$ _test_cpu$ _test_cpu.py$ _test_gpu$ _test_gpu.py$ _test_lib$
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tensorflow/c/eager/c_api.h
TF_CAPI_EXPORT extern void TFE_ContextExportRunMetadata(TFE_Context* ctx, TF_Buffer* buf, TF_Status* status); // Some TF ops need a step container to be set to limit the lifetime of some // resources (mostly TensorArray and Stack, used in while loop gradients in // graph mode). Calling this on a context tells it to start a step.
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Apr 27 21:07:00 GMT 2023 - 22.8K bytes - Viewed (1) -
CONTRIBUTING.md
* If you are an individual writing original source code and you're sure you own the intellectual property, then you'll need to sign an [individual CLA](https://code.google.com/legal/individual-cla-v1.0.html). * If you work for a company that wants to allow you to contribute your work, then you'll need to sign a [corporate CLA](https://code.google.com/legal/corporate-cla-v1.0.html).
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ci/official/containers/linux_arm64/builder.patchelf/build_patchelf.sh
# See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== # Need a newer version of patchelf as the installed version is buggy in 20.04 # so get patchelf source from 22.04 ie 'jammy' and build it to avoid dependency # problems that would occur with a binary package mkdir -p /patchelf
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ci/official/containers/linux_arm64/devel.usertools/rename_and_verify_wheels.sh
set -euxo pipefail for wheel in /tf/pkg/*.whl; do echo "Checking and renaming $wheel..." time python3 -m auditwheel repair --plat manylinux2014_aarch64 "$wheel" --wheel-dir /tf/pkg 2>&1 | tee check.txt # We don't need the original wheel if it was renamed new_wheel=$(grep --extended-regexp --only-matching '/tf/pkg/\S+.whl' check.txt) if [[ "$new_wheel" != "$wheel" ]]; then rm "$wheel" wheel="$new_wheel" fi
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tensorflow/c/experimental/grappler/grappler.h
typedef enum TF_TriState { TF_TriState_Default = 0, TF_TriState_Off, TF_TriState_On, } TF_TriState; // TF_GrapplerItem represents a combination of a graph, one of more fetch nodes, // and potentially a set of nodes to feed. typedef struct TF_GrapplerItem TF_GrapplerItem; // Flags indicating whether existing optimizers should be turned off. // It's optional for plugin to set functions to return true/false. If not
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ci/official/README.md
# destroys some temporary bazel data and causes longer builds. # # You will need the NVIDIA Container Toolkit for GPU testing: # https://github.com/NVIDIA/nvidia-container-toolkit # # Note: if you interrupt a bazel command on docker (ctrl-c), you # will need to run `docker exec tf pkill bazel` to quit bazel. # # Note: new files created from the container are owned by "root".
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tensorflow/c/eager/immediate_execution_operation.h
virtual ImmediateExecutionContext* GetContext() const = 0; // Following two methods are used to support custom device. // Return true if the inputs contain custom device tensor handle. It means // that the argument need to be handled by a custom device. virtual bool HasCustomDeviceInput() const = 0; virtual const tensorflow::OpDef* OpDef() const = 0; virtual Status InputLength(const char* input_name, int* length) = 0;
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.github/bot_config.yml
* It has an added advantage since you can you easily switch to different hardware accelerators (cpu, gpu, tpu) as per the task. * All you need is a good internet connection and you are all set. * Try to build TF from sources by changing CPU optimization flags. *Please let us know if this helps.* windows_comment: >
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