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ci/official/envs/rbe
# This env is only valid when RBE is available on the selected platform, since # it enables a derived --config setting. If RBE is not available (i.e. there # is no --config setting), bazel would fail and quit. This script does a quick # check This script checks for such errors early if ! grep "rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX" .bazelrc; then cat <<EOF
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tensorflow/c/experimental/gradients/nn_grad_test.cc
immediate_execution_ctx_.reset(ctx_raw); } // Computing numerical gradients with TensorFloat-32 is numerically // unstable. Some forward pass tests also fail with TensorFloat-32 due to // low tolerances enable_tensor_float_32_execution(false); } AbstractContextPtr immediate_execution_ctx_; GradientRegistry registry_; Status status_; public:
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RELEASE.md
* Fixes a `CHECK` fail in `AudioSummaryV2` ([CVE-2022-35995](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-35995)) * Fixes a `CHECK` fail in `CollectiveGather` ([CVE-2022-35994](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-35994)) * Fixes a `CHECK` fail in `SetSize` ([CVE-2022-35993](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-35993))
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.github/workflows/arm-cd.yml
permissions: contents: read jobs: build: if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks runs-on: [self-hosted, linux, ARM64] strategy: fail-fast: false matrix: pyver: ['3.9', '3.10', '3.11', '3.12'] steps: - name: Stop old running containers (if any) shell: bash run: |
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.github/bot_config.yml
*TensorFlow release binaries version 1.6 and higher are prebuilt with AVX instruction sets.* Therefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load. Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below: * Try Google Colab to use TensorFlow.
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.github/workflows/arm-ci-extended.yml
permissions: contents: read jobs: build: if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks runs-on: [self-hosted, linux, ARM64] strategy: fail-fast: false matrix: pyver: ['3.9', '3.10', '3.11', '3.12'] steps: - name: Stop old running containers (if any) shell: bash run: |
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tensorflow/c/eager/c_api_test.cc
EXPECT_TRUE(strstr(TF_Message(status), msg) != nullptr) << TF_Message(status); // Since error is not cleared, the following copy with correct device will // still fail. TF_SetStatus(status, TF_OK, ""); TFE_DeleteTensorHandle(retvals[0]); TFE_Executor* executor = TFE_ContextGetExecutorForThread(ctx); TFE_ExecutorWaitForAllPendingNodes(executor, status);
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tensorflow/c/experimental/gradients/array_grad_test.cc
immediate_execution_ctx_.reset(ctx_raw); } // Computing numerical gradients with TensorFloat-32 is numerically // unstable. Some forward pass tests also fail with TensorFloat-32 due to // low tolerances enable_tensor_float_32_execution(false); } AbstractContextPtr immediate_execution_ctx_; GradientRegistry registry_; Status status_; public:
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tensorflow/api_template.__init__.py
else: importlib.import_module("keras.src.optimizers") except (ImportError, AttributeError): pass del importlib # Delete modules that should be hidden from dir(). # Don't fail if these modules are not available. # For e.g. this file will be originally placed under tensorflow/_api/v1 which # does not have "python", "core" directories. Then, it will be copied
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CONTRIBUTING.md
your response for more than 2 weeks. **4. Approved** - Once the PR is approved, it gets `kokoro:force-run` label applied and it initiates CI/CD tests. - We can't move forward if these tests fail. - In such situations, we may request you to make further changes to your PR for the tests to pass. - Once the tests pass, we now bring all the code into the internal code base, using a job called "copybara".
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