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tensorflow/c/eager/c_api_experimental_test.cc
ASSERT_TRUE(absl::StrContains(device_type_cpu, "CPU")) << device_type_cpu; int device_id_cpu = TFE_TensorHandleDeviceID(h_cpu, status.get()); ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); ASSERT_EQ(0, device_id_cpu) << device_id_cpu; TFE_DeleteTensorHandle(h_default); TFE_DeleteTensorHandle(h_cpu); TFE_Executor* executor = TFE_ContextGetExecutorForThread(ctx);
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Aug 03 03:14:26 GMT 2023 - 31.5K bytes - Viewed (1) -
tensorflow/c/eager/c_api_test.cc
TFE_DeleteTensorHandle(retvals[0]); TFE_DeleteContext(ctx); TF_DeleteStatus(status); } TEST(CAPI, Execute_MatMul_CPU_Runtime_Error) { Execute_MatMul_CPU_Runtime_Error(false); } TEST(CAPI, Execute_MatMul_CPU_Runtime_ErrorAsync) { Execute_MatMul_CPU_Runtime_Error(true); } void Execute_MatMul_CPU_Type_Error(bool async) { TF_Status* status = TF_NewStatus(); TFE_ContextOptions* opts = TFE_NewContextOptions();
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Aug 03 20:50:20 GMT 2023 - 94.6K bytes - Viewed (1) -
.github/workflows/arm-ci.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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.github/bot_config.yml
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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tensorflow/c/eager/c_api_remote_test_util.cc
ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status); } else if (!async) { // Set the local device to CPU to easily validate mirroring string cpu_device_name; ASSERT_TRUE(GetDeviceName(ctx, &cpu_device_name, "CPU")); TFE_OpSetDevice(matmul, cpu_device_name.c_str(), status); EXPECT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status); auto remote_arg =
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Fri Dec 11 22:56:03 GMT 2020 - 9.1K bytes - Viewed (0) -
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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.github/workflows/arm-cd.yml
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ci/official/envs/linux_arm64
TFCI_BAZEL_COMMON_ARGS="--repo_env=TF_PYTHON_VERSION=$TFCI_PYTHON_VERSION --config release_arm64_linux" TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_arm64 # Note: this is not set to "--cpu", because that changes the package name # to tensorflow_cpu. These ARM builds are supposed to have the name "tensorflow" # despite lacking Nvidia CUDA support. TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow" TFCI_DOCKER_ENABLE=1
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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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tensorflow/c/c_api_test.cc
TF_DeleteStatus(s); } void Deallocator(void* data, size_t, void* arg) { tensorflow::cpu_allocator()->DeallocateRaw(data); *reinterpret_cast<bool*>(arg) = true; } TEST(CAPI, Tensor) { const int num_bytes = 6 * sizeof(float); float* values = reinterpret_cast<float*>(tensorflow::cpu_allocator()->AllocateRaw( TF_TensorDefaultAlignment(), num_bytes)); int64_t dims[] = {2, 3};
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