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tensorflow/c/c_api_test.cc
Status TF_TensorToTensor(const TF_Tensor* src, Tensor* dst); namespace { static void ExpectHasSubstr(StringPiece s, StringPiece expected) { EXPECT_TRUE(absl::StrContains(s, expected)) << "'" << s << "' does not contain '" << expected << "'"; } // Returns the GPU device name if there is one (with arbitrary tie breaking if // there are more than one), or "" otherwise. string GPUDeviceName(TF_Session* session) {
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ci/official/README.md
# of these env files. # # If you've clicked on a test result from our CI (via a dashboard or GitHub link), # click to "Invocation Details" and find BUILD_CONFIG, which will contain a TFCI # value in the "env_vars" list that you can choose to copy that environment. # Ex. 1: TFCI=py311,linux_x86_cuda,nightly_upload (nightly job) # Ex. 2: TFCI=py39,linux_x86,rbe (continuous job)
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tensorflow/c/eager/c_api_experimental.h
// which is passed to the functions referenced in the TFE_CustomDevice struct // `device` (execute, delete_device, etc.). It can for example contain the // names of wrapped devices. // // There are currently no graph semantics implemented for registered custom // devices, so executing tf.functions which contain operations placed on the // custom devices will fail. // // `device_name` must not name an existing physical or custom device. It must
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tensorflow/c/c_api.cc
} else { status->status = AddSymbolicGradients(scope, y_arg, x_arg, &dy_arg); } // Update g->name_map with the name_map from the scope, which will contain // the new gradient ops. for (int i = first_new_node_id; i < g->graph.num_node_ids(); ++i) { Node* n = g->graph.FindNodeId(i); if (n == nullptr) continue;
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tensorflow/c/experimental/next_pluggable_device/c_api.h
TF_CAPI_EXPORT extern PJRT_Client* TF_GetPjRtCClient(const char* device_type, TF_Status* status); // Gets the `PJRT_Buffer*` stored in the tensor. The status will contain error // if the tensor does not have a `PjRtCApiBuffer`. TF_CAPI_EXPORT extern PJRT_Buffer* TF_GetPjRtCBuffer(TF_Tensor* c_tensor, TF_Status* status);
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RELEASE.md
* Tensorflow code now produces 2 different pip packages: `tensorflow_core` containing all the code (in the future it will contain only the private implementation) and `tensorflow` which is a virtual pip package doing forwarding to `tensorflow_core` (and in the future will contain only the public API of tensorflow). We don't expect this to be breaking, unless you were importing directly from the implementation.
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ci/official/utilities/code_check_full.bats
# targets matching the provided query, which start with // or @ but not # //tensorflow (so it looks for //third_party, //external, etc.), and then # gathers the list of all packages (i.e. directories) which contain those # targets. license_query() { bazel cquery --experimental_cc_shared_library "$1" --keep_going \ | grep -e "^//" -e "^@" \ | grep -E -v "^//tensorflow" \ | sed -e 's|:.*||' \ | sort -u }
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configure.py
ask_for_var: (String) string for how to ask for user input. check_success: (Function) function that takes one argument and returns a boolean. Should return True if the value provided is considered valid. May contain a complex error message if error_msg does not provide enough information. In that case, set suppress_default_error to True. error_msg: (String) String with one and only one '%s'. Formatted with each
Python - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Mon Apr 15 18:25:36 GMT 2024 - 53.8K bytes - Viewed (1) -
ci/official/utilities/setup.sh
# limitations under the License. # ============================================================================== # # Common setup for all TF scripts. # # Make as FEW changes to this file as possible. It should not contain utility # functions (except for tfrun); use dedicated scripts instead and reference them # specifically. Use your best judgment to keep the scripts in this directory
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tensorflow/c/c_api_function.cc
: absl::OkStatus(); } // Converts `ninputs` and `inputs` into `inputs_tensors` and `input_nodes` and // does various checks while doing so. `input_nodes` will contain the same // information as input_tensors just in a different structure to make // following processing easier. TODO(iga): Simplify this nested structure. Status ProcessInputs(
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