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ci/official/utilities/code_check_changed_files.bats
# Only shows Added, Changed, Modified, Renamed, and Type-changed files if [[ "$(git rev-parse --abbrev-ref HEAD)" == "pull_branch" ]]; then # TF's CI runs 'git fetch origin "pull/PR#/merge:pull_branch"' # To get the as-merged branch during the CI tests git diff --diff-filter ACMRT --name-only pull_branch^ pull_branch > $BATS_FILE_TMPDIR/changed_files else
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tensorflow/c/eager/immediate_execution_distributed_manager.h
// on cluster configurations provided in `server_def`; otherwise, update // existing context state with the provided `server_def`. Contexts created // on remote tasks will be considered stale and garbage collected after // `keep_alive_secs` of inactivity. virtual Status SetOrUpdateServerDef(const ServerDef& server_def, bool reset_context, int keep_alive_secs,
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CONTRIBUTING.md
its dependencies and the rest of the system. - Rarely, If the tests fail at this stage, we cannot merge the code. - If needed, we may come to you to make some changes. At times, it may not be you, it may be us who may have hit a snag. Please be patient while we work to fix this. - Once the internal tests pass, we go ahead and merge the code internally as well as externally on GitHub.
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tensorflow/c/eager/graph_function.h
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 { namespace tracing {
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tensorflow/c/c_api.cc
// TF_Server functions ---------------------------------------------- #if !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD) TF_Server::TF_Server(std::unique_ptr<tensorflow::ServerInterface> server) : target(server->target()), server(std::move(server)) {} #endif // !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD) TF_Server* TF_NewServer(const void* proto, size_t proto_len,
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tensorflow/c/experimental/gradients/nn_grad.cc
#include "tensorflow/c/experimental/gradients/nn_grad.h" #include "absl/types/span.h" #include "tensorflow/c/eager/abstract_tensor_handle.h" #include "tensorflow/c/eager/immediate_execution_context.h" #include "tensorflow/c/eager/immediate_execution_tensor_handle.h" #include "tensorflow/c/experimental/ops/array_ops.h" #include "tensorflow/c/experimental/ops/math_ops.h"
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tensorflow/c/eager/c_api_unified_experimental.cc
limitations under the License. ==============================================================================*/ #include "tensorflow/c/eager/c_api_unified_experimental.h" #include <vector> #include "absl/container/flat_hash_map.h" #include "absl/strings/str_cat.h" #include "tensorflow/c/eager/c_api_unified_experimental_internal.h" #include "tensorflow/c/tf_datatype.h" #include "tensorflow/c/tf_status.h"
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tensorflow/api_template.__init__.py
setattr(_current_module, "losses", _losses) setattr(_current_module, "metrics", _metrics) setattr(_current_module, "optimizers", _optimizers) setattr(_current_module, "initializers", _initializers) # Do an eager load for Keras' code so that any function/method that needs to # happen at load time will trigger, eg registration of optimizers in the # SavedModel registry. # See b/196254385 for more details. try:
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requirements_lock_3_10.txt
tblib==2.0.0 \ --hash=sha256:9100bfa016b047d5b980d66e7efed952fbd20bd85b56110aaf473cb97d18709a \ --hash=sha256:a6df30f272c08bf8be66e0775fad862005d950a6b8449b94f7c788731d70ecd7 # via -r requirements.in tensorboard-data-server==0.7.2 \ --hash=sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb \ --hash=sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60 \
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RELEASE.md
based server to register tf.function methods and a GRPC client to invoke remote registered methods. RPC APIs are intended for multi-client setups i.e. server and clients are started in separate binaries independently. * Example usage to create server: ```python server = tf.distribute.experimental.rpc.Server.create("grpc", "127.0.0.1:1234")
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