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README.md
* [TensorFlow Examples](https://github.com/tensorflow/examples) * [TensorFlow Codelabs](https://codelabs.developers.google.com/?cat=TensorFlow) * [TensorFlow Blog](https://blog.tensorflow.org) * [Learn ML with TensorFlow](https://www.tensorflow.org/resources/learn-ml) * [TensorFlow Twitter](https://twitter.com/tensorflow)
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.github/ISSUE_TEMPLATE/tflite-other.md
share a link to Colab/Jupyter/any notebook. placeholder: Tell us what you see! value: render: shell validations: required: true - type: textarea id: logs attributes: label: Relevant log output description: Please copy and paste any relevant log output. This will be automatically
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.github/workflows/issue-on-pr-rollback.yml
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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
# > target platform. # See also https://docs.bazel.build/versions/master/guide.html#build-configurations-and-cross-compilation build --distinct_host_configuration=false # Store performance profiling log in the mounted artifact directory. # The profile can be viewed by visiting chrome://tracing in a Chrome browser. # See https://docs.bazel.build/versions/main/skylark/performance.html#performance-profiling
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tensorflow/c/c_api_test.cc
LOG(INFO) << "There are " << num_devices << " devices."; for (int i = 0; i < num_devices; ++i) { const char* device_name = TF_DeviceListName(device_list, i, s); CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); const char* device_type = TF_DeviceListType(device_list, i, s); CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
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tensorflow/c/experimental/filesystem/modular_filesystem_test.cc
// Loads a shared object implementing filesystem functionality. static bool LoadDSO(const std::string& dso) { tensorflow::Status status = RegisterFilesystemPlugin(dso); if (!status.ok()) VLOG(0) << "Filesystems from '" << dso << "' could not be registered: " << status; return status.ok(); } // Tests whether a URI scheme results in a filesystem that is supported. //
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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
# 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 # Store performance profiling log in the mounted artifact directory. # The profile can be viewed by visiting chrome://tracing in a Chrome browser. # See https://docs.bazel.build/versions/main/skylark/performance.html#performance-profiling
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tensorflow/c/experimental/filesystem/plugins/gcs/cleanup.h
// auto fp_cleaner = gtl::MakeCleanup([fp] { fclose(fp); }); // // No matter what, fclose(fp) will happen. // DataObject d; // while (ReadDataObject(fp, &d)) { // if (d.IsBad()) { // LOG(ERROR) << "Bad Data"; // return; // } // PushGoodData(d); // } // } // // You can use Cleanup<F> directly, instead of using MakeCleanup and auto, // but there's rarely a reason to do that.
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tensorflow/c/eager/parallel_device/parallel_device_remote_test.cc
for (int i = 0; i < num_tasks; i++) { int port = tensorflow::testing::PickUnusedPortOrDie(); job_def->mutable_tasks()->insert( {i, tensorflow::strings::StrCat("localhost", ":", port)}); LOG(INFO) << "Picked test port: " << port << " for job: " << job_name << ", task: " << i; } return server_def; } namespace tensorflow { namespace parallel_device {
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tensorflow/c/eager/c_api.h
typedef enum TFE_ContextDevicePlacementPolicy { // Running operations with input tensors on the wrong device will fail. TFE_DEVICE_PLACEMENT_EXPLICIT = 0, // Copy the tensor to the right device but log a warning. TFE_DEVICE_PLACEMENT_WARN = 1, // Silently copy the tensor, which has a performance cost since the operation // will be blocked till the copy completes. This is the default placement // policy.
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