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ci/official/README.md
- x86 and arm64 - CPU-only, or with NVIDIA CUDA support (Linux only), or with TPUs ## How to Test Your Changes to TensorFlow You may check how your changes will affect TensorFlow by: 1. Creating a PR and observing the presubmit test results 2. Running the CI scripts locally, as explained below 3. **Google employees only**: Google employees can use an internal-only tool
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tensorflow/c/eager/c_api_experimental.h
TF_Status* status); // Get configuration key and value using coordination service. // The config key must be set before getting its value. Getting value of // non-existing config keys will result in errors. // If `timeout_in_ms=0`, this call will block until the key-value is set or the // worker shuts down.
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CONTRIBUTING.md
just getting started, GitHub has a [how-to](https://help.github.com/articles/using-pull-requests/). TensorFlow team members will be assigned to review your pull requests. Once the pull requests are approved and pass continuous integration checks, a TensorFlow team member will apply `ready to pull` label to your change. This means we are
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tensorflow/c/c_api.cc
TF_Status* status) { #if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD) status->status = tensorflow::errors::Unimplemented( "Creating while loops is not supported on mobile. File a bug at " "https://github.com/tensorflow/tensorflow/issues if this feature is " "important to you"); return EmptyWhileParams(); #else
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tensorflow/c/eager/gradients.cc
forward_op_->outputs.push_back(retvals[i]); } // TODO(b/166669239): This is needed to support AttrBuilder::Get for string // attributes. Number type attrs and DataType attrs work fine without this. // Consider getting rid of this and making the behavior between number types // and string consistent. forward_op_->attrs.BuildNodeDef(); std::unique_ptr<GradientFunction> gradient_fn;
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tensorflow/c/experimental/next_pluggable_device/c_api.h
TF_Status* status); // ---------------------------- PJRT ----------------------------------------- // Passes the pointer to a vector of PJRT_NamedValue and number of options to // set options for creating a PJRT client. Passes nullptr for create_options and // 0 for num_options if no options need to be set. You can use // ConvertToPjRtNamedValueList in // tensorflow/compiler/xla/pjrt/c/pjrt_c_api_helpers.h to generate the options.
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RELEASE.md
* Add 5D support to `SLICE` op. * TFLite Supports SingatureDef: * TFLiteConverter exports models with SignatureDef * Interpreter supports getting a list of signatures and getting callable function for a given signaturedef. * Add int8 support for `ReshapeV2`. * Add experimental support for optimization with sparsity.
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tensorflow/c/c_api_experimental.cc
// GraphDef type. These functions must be deleted by calling TF_DeleteFunction. // // If `mutate_proto_func` is non-NULL, run it over each FunctionDef proto, // before creating a TF_Function out of the possibly mutated proto. static std::vector<UniqueFuncPtr> CreateFunctionsFromTextProto( const char* text_proto, std::function<void(FunctionDef*)>* mutate_proto_func, TF_Status* status) {
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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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configure.py
args = parser.parse_args() _TF_WORKSPACE_ROOT = args.workspace _TF_BAZELRC = os.path.join(_TF_WORKSPACE_ROOT, _TF_BAZELRC_FILENAME) # Make a copy of os.environ to be clear when functions and getting and setting # environment variables. environ_cp = dict(os.environ) try: current_bazel_version = retrieve_bazel_version() except subprocess.CalledProcessError as e:
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