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tensorflow/c/c_api.cc
#include "tensorflow/core/graph/graph.h" #include "tensorflow/core/graph/node_builder.h" #include "tensorflow/core/graph/validate.h" #include "tensorflow/core/lib/gtl/array_slice.h" #include "tensorflow/core/platform/coding.h" #include "tensorflow/core/platform/errors.h" #include "tensorflow/core/platform/mem.h" #include "tensorflow/core/platform/mutex.h" #include "tensorflow/core/platform/protobuf.h" #include "tensorflow/core/platform/status.h"
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requirements_lock_3_10.txt
tf-keras-nightly==2.16.0.dev2024021410 \ --hash=sha256:05a4c19c6a795ec9ed6f4dac69d443601b9206b910524d8b19bad6e362d49f47 \ --hash=sha256:83d5a1dd3979b42164a2fac4bdd2ed5981ef0c2a0514ca64e4bc67e369caef5c # via tb-nightly typing-extensions==4.8.0 \ --hash=sha256:8f92fc8806f9a6b641eaa5318da32b44d401efaac0f6678c9bc448ba3605faa0 \ --hash=sha256:df8e4339e9cb77357558cbdbceca33c303714cf861d1eef15e1070055ae8b7ef # via -r requirements.in
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CONTRIBUTING.md
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.github/bot_config.yml
* If you have above configuration and using _**Windows**_ platform - * Try adding the CUDA, CUPTI, and cuDNN installation directories to the %PATH% environment variable. * Refer [windows setup guide](https://www.tensorflow.org/install/gpu#windows_setup). * If you have above configuration and using _**Ubuntu/Linux**_ platform - * Try adding the CUDA, CUPTI, and cuDNN installation directories to the $LD_LIBRARY_PATH environment variable.
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tensorflow/c/eager/tape.h
// Operations the tape needs to perform on tensors to do backpropagation. Named // "vspace" because a subset of these are related to a vector space, such as // adding gradients, getting zeroes, etc. Currently cannot be implemented // without using tensorflow python code, hence left unspecified here. // // Gradient is the type returned by gradient functions. In Python TF it's either
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tensorflow/c/c_api_experimental.cc
// Make a copy so that we can mutate it. FunctionDef fdef_to_load = fdef; if (mutate_proto_func) { (*mutate_proto_func)(&fdef_to_load); } VLOG(1) << "Adding func to graph: " << fdef_to_load.DebugString(); std::vector<char> binary_proto_buf(fdef_to_load.ByteSizeLong()); fdef_to_load.SerializeToArray(binary_proto_buf.data(),
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configure.py
question: optional string for how to ask for user input. yes_reply: optional string for reply when feature is enabled. no_reply: optional string for reply when feature is disabled. bazel_config_name: adding config to .bazelrc instead of action_env. """ var = int( get_var(environ_cp, var_name, query_item, enabled_by_default, question, yes_reply, no_reply)) if not bazel_config_name:
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ci/official/requirements_updater/requirements.in
numpy ~= 1.23.5 ; python_version <= "3.11" numpy ~= 1.26.0 ; python_version >= "3.12" wheel ~= 0.41.2 h5py >= 3.10.0 lit ~= 17.0.2 opt_einsum == 3.3.0 astunparse == 1.6.3 dill == 0.3.7 astor == 0.7.1 typing_extensions == 4.8.0 gast == 0.4.0 termcolor == 2.3.0 wrapt == 1.16.0 tblib == 2.0.0 # Install tensorboard, and keras # Note that here we want the latest version that matches TF major.minor version
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tensorflow/c/experimental/filesystem/modular_filesystem.h
// for `Env::Default()` altogether, but that's a different project, not in // scope for now. I'm just mentioning this here as that transition will mean // removal of the registration part from `Env` and adding it here instead: we // will need tables to hold for each scheme the function tables that implement // the needed functionality instead of the current `FileSystemRegistry` code in // `core/platform/env.cc`.
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tensorflow/c/eager/c_api_unified_experimental_graph.cc
const char* op_name_ = nullptr; // TODO(srbs): Use this. string device_name_; }; // GraphContext wraps a TF_Graph modeling a single function and manages the // "execution" of operation, i.e. adding them to the function. class GraphContext : public TracingContext { public: explicit GraphContext(const char* name) : TracingContext(kGraph), graph_(new TF_Graph(), TF_DeleteGraph),
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