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tensorflow/BUILD
) config_setting( name = "linux_aarch64", values = {"cpu": "aarch64"}, visibility = ["//visibility:public"], ) config_setting( name = "linux_armhf", values = {"cpu": "armhf"}, visibility = ["//visibility:public"], ) config_setting( name = "linux_x86_64", values = {"cpu": "k8"}, visibility = ["//visibility:public"], ) config_setting(
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CONTRIBUTING.md
```bash tensorflow/tools/ci_build/ci_build.sh CPU tensorflow/tools/ci_build/ci_sanity.sh ``` This will catch most license, Python coding style and BUILD file issues that may exist in your changes. #### Running unit tests There are two ways to run TensorFlow unit tests. 1. Using tools and libraries installed directly on your system. Refer to the
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.github/workflows/arm-ci-extended-cpp.yml
./tensorflow/tools/ci_build/ci_build.sh cpu.arm64 bash tensorflow/tools/ci_build/rel/ubuntu/cpu_arm64_test_cpp.sh...
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tensorflow/c/eager/custom_device_test.cc
TFE_OpReset(reused_op.get(), "Identity", "/job:localhost/replica:0/task:0/device:CPU:0", status.get()); ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get()); ASSERT_EQ(tensorflow::string(TFE_OpGetDevice(reused_op.get(), status.get())), tensorflow::string("/job:localhost/replica:0/task:0/device:CPU:0")); ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get()); }
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.github/workflows/arm-ci-extended.yml
./tensorflow/tools/ci_build/ci_build.sh cpu.arm64 bash tensorflow/tools/ci_build/rel/ubuntu/cpu_arm64_test.sh...
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tensorflow/c/BUILD
":tf_attrtype", "//tensorflow/core:core_cpu", "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core/platform:platform", ], "//conditions:default": [ ":tf_attrtype", "//tensorflow/core:core_cpu", "//tensorflow/core:framework", "//tensorflow/core:lib",
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tensorflow/c/eager/c_api_debug_test.cc
CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); ASSERT_EQ(2, TFE_TensorDebugInfoOnDeviceNumDims(debug_info)); // Shape is the same for CPU tensors. EXPECT_EQ(3, TFE_TensorDebugInfoOnDeviceDim(debug_info, 0)); EXPECT_EQ(2, TFE_TensorDebugInfoOnDeviceDim(debug_info, 1)); TFE_DeleteTensorDebugInfo(debug_info); TFE_DeleteTensorHandle(h);
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tensorflow/c/eager/dlpack.cc
std::string device_type = parsed_name.type; int device_id = 0; if (parsed_name.has_id) { device_id = parsed_name.id; } ctx.device_id = device_id; if (device_type == "CPU") { ctx.device_type = DLDeviceType::kDLCPU; } else if (device_type == "GPU") { #if TENSORFLOW_USE_ROCM ctx.device_type = DLDeviceType::kDLROCM; #else ctx.device_type = DLDeviceType::kDLCUDA; #endif
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.bazelrc
build:android_arm64 --config=android build:android_arm64 --cpu=arm64-v8a build:android_arm64 --fat_apk_cpu=arm64-v8a build:android_x86 --config=android build:android_x86 --cpu=x86 build:android_x86 --fat_apk_cpu=x86 build:android_x86_64 --config=android build:android_x86_64 --cpu=x86_64 build:android_x86_64 --fat_apk_cpu=x86_64 # Build everything statically for Android since all static libs are later
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tensorflow/c/eager/parallel_device/parallel_device_lib_test.cc
ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get()); std::vector<std::string> devices{ "/job:localhost/replica:0/task:0/device:CPU:0", "/job:localhost/replica:0/task:0/device:CPU:1"}; ParallelDevice parallel_device(std::move(devices)); std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> handle_op( TFE_NewOp(context.get(), "VarHandleOp", status.get()), TFE_DeleteOp);
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