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ci/official/utilities/get_versions.sh
# where fields are strings separated by whitespace. export TF_VER_MAJOR=$(awk '/#define TF_MAJOR_VERSION/ {print $3}' tensorflow/core/public/version.h) export TF_VER_MINOR=$(awk '/#define TF_MINOR_VERSION/ {print $3}' tensorflow/core/public/version.h) export TF_VER_PATCH=$(awk '/#define TF_PATCH_VERSION/ {print $3}' tensorflow/core/public/version.h)
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tensorflow/BUILD
config_setting( name = "dynamic_loaded_kernels", define_values = { "dynamic_loaded_kernels": "true", "framework_shared_object": "true", }, visibility = ["//visibility:public"], ) config_setting( name = "using_rocm_hipcc", define_values = {"using_rocm_hipcc": "true"}, ) config_setting( name = "override_eigen_strong_inline", define_values = {"override_eigen_strong_inline": "true"},
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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
# Use Python 3.X as installed in container image build --action_env PYTHON_BIN_PATH="/usr/local/bin/python3" build --python_path="/usr/local/bin/python3" # Build TensorFlow v2 build --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1 # Prevent double-compilation of some TF code, ref. b/183279666 (internal) # > TF's gen_api_init_files has a genrule to run the core TensorFlow code
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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
# Use Python 3.X as installed in container image build --action_env PYTHON_BIN_PATH="/usr/local/bin/python3" build --python_path="/usr/local/bin/python3" # Build TensorFlow v2 build --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1 # Use lld as the linker build --linkopt="-fuse-ld=lld" build --linkopt="-lm" build --linkopt="-Wl,--undefined-version"
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tensorflow/c/eager/graph_function.h
See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ #ifndef TENSORFLOW_C_EAGER_GRAPH_FUNCTION_H_ #define TENSORFLOW_C_EAGER_GRAPH_FUNCTION_H_ #include "tensorflow/c/eager/abstract_function.h" #include "tensorflow/core/framework/function.h" #include "tensorflow/core/platform/refcount.h" namespace tensorflow {
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tensorflow/c/eager/unified_api_testutil.h
See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ #ifndef TENSORFLOW_C_EAGER_UNIFIED_API_TESTUTIL_H_ #define TENSORFLOW_C_EAGER_UNIFIED_API_TESTUTIL_H_ #include "tensorflow/c/eager/abstract_context.h" #include "tensorflow/c/eager/abstract_tensor_handle.h" #include "tensorflow/c/eager/c_api_test_util.h"
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tensorflow/c/eager/immediate_execution_distributed_manager.h
limitations under the License. ==============================================================================*/ #ifndef TENSORFLOW_C_EAGER_IMMEDIATE_EXECUTION_DISTRIBUTED_MANAGER_H_ #define TENSORFLOW_C_EAGER_IMMEDIATE_EXECUTION_DISTRIBUTED_MANAGER_H_ #include <cstdint> #include <string> #include "tensorflow/core/platform/status.h" namespace tsl { class CoordinationServiceAgent; }
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ci/official/utilities/setup_macos.sh
gcloud auth activate-service-account --key-file="${GOOGLE_APPLICATION_CREDENTIALS}" fi set -x # When cross-compiling with RBE, we need to copy the macOS sysroot to be # inside the TensorFlow root directory. We then define them as a filegroup # target inside "tensorflow/tools/toolchains/cross_compile/cc" so that Bazel # can register it as an input to compile/link actions and send it to the remote # VMs when needed.
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tensorflow/c/eager/c_api_experimental.h
// tf.device("CUSTOM"):` from Python if `device_name` for this call is // "/job:localhost/replica:0/task:0/device:CUSTOM:0". // // The custom device defines copy operations for moving TensorHandles on and // off, and an execution operation for named operations. Often execution will // simply wrap op execution on one or more physical devices. // // device_info is an opaque caller-defined type stored with the custom device
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tensorflow/c/experimental/next_pluggable_device/c_api.h
// C API for device. The API is under active development and eventually // should allow registering a plugin device with TensorFlow. #ifdef __cplusplus extern "C" { #endif // TF_Device is a C wrapper to the C++ TF Device class. This is to be passed // through TF_OpKernelContext, and is opaque to plugin. typedef struct TF_Device TF_Device; typedef struct TF_VariableInfo TF_VariableInfo;
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