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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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.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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.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/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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README.md
A smaller CPU-only package is also available: ``` $ pip install tensorflow-cpu ``` To update TensorFlow to the latest version, add `--upgrade` flag to the above commands. *Nightly binaries are available for testing using the [tf-nightly](https://pypi.python.org/pypi/tf-nightly) and [tf-nightly-cpu](https://pypi.python.org/pypi/tf-nightly-cpu) packages on PyPi.*
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ci/official/envs/linux_x86
# ============================================================================== TFCI_BAZEL_COMMON_ARGS="--repo_env=TF_PYTHON_VERSION=$TFCI_PYTHON_VERSION --config release_cpu_linux" TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_cpu TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow_cpu" TFCI_DOCKER_ENABLE=1 TFCI_DOCKER_IMAGE=tensorflow/build:2.16-python${TFCI_PYTHON_VERSION} TFCI_DOCKER_PULL_ENABLE=1
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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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ci/official/containers/linux_arm64/devel.usertools/wheel_verification.bats
@test "Wheel conforms to upstream size limitations" { WHEEL_MEGABYTES=$(stat --format %s "$TF_WHEEL" | awk '{print int($1/(1024*1024))}') # Googlers: search for "test_tf_whl_size" case "$TF_WHEEL" in # CPU: *cpu*manylinux*) LARGEST_OK_SIZE=220 ;; # GPU: *manylinux*) LARGEST_OK_SIZE=580 ;; # Unknown: *) echo "The wheel's name is in an unknown format." exit 1
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tensorflow/c/eager/c_api_distributed_test.cc
EXPECT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status); const char task0_name[] = "/job:localhost/replica:0/task:0/device:CPU:0"; const char task1_name[] = "/job:localhost/replica:0/task:1/device:CPU:0"; const char task2_name[] = "/job:localhost/replica:0/task:2/device:CPU:0"; // Create one variable per task. TFE_TensorHandle* h0 = TestVariable(ctx, 1.0, task1_name);
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tensorflow/c/c_api_experimental.cc
flags->tf_xla_cpu_global_jit = true; flags->tf_xla_min_cluster_size = 1; } else { optimizer_options->set_global_jit_level(tensorflow::OptimizerOptions::OFF); } auto* gpu_options = config.mutable_gpu_options(); gpu_options->set_allow_growth(gpu_memory_allow_growth); (*config.mutable_device_count())["CPU"] = num_cpu_devices;
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