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tensorflow/c/eager/parallel_device/BUILD
load("//tensorflow/core/platform:rules_cc.bzl", "cc_library") package( # copybara:uncomment default_applicable_licenses = ["//tensorflow:license"], licenses = ["notice"], ) # Currently pybind extension shared objects must use only C API headers since # the C API has static initializers duplicated in the Python bindings. So we # need a second rule that omits .cc files, in # tensorflow/python:_pywrap_parallel_device. filegroup(
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tensorflow/c/BUILD
"//conditions:default": [], }), tags = [ "no_cuda_asan", # TODO(b/181771536) "no_windows", # TODO(b/155444728) ], # We must ensure that the dependencies can be dynamically linked since # the shared library must be able to use core:framework. deps = [ ":c_api", ":c_api_internal", ":c_test_util", ":test_op_kernel", ":tf_buffer",
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tensorflow/c/eager/c_api_experimental.h
// 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 // follow the format: // // /job:<name>/replica:<replica>/task:<task>/device:<type>:<device_num> // // If the device is successfully registered, `status` is set to TF_OK. Otherwise
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tensorflow/c/eager/c_api_unified_experimental.cc
TracingContext* tracing_ctx = dyn_cast<TracingContext>(unwrap(func)); if (!tracing_ctx) { tsl::Set_TF_Status_from_Status( s, tensorflow::errors::InvalidArgument( "TF_AddFunctionParameter must be called on a TracingContext.")); return nullptr; } tensorflow::PartialTensorShape partial_shape; if (shape.num_dims != -1) { DCHECK(shape.dim_sizes != nullptr);
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tensorflow/c/eager/immediate_execution_distributed_manager.h
virtual Status InitializeLocalOnlyContext(const ServerDef& server_def, int keep_alive_secs) = 0; // Set up a multi-client distributed execution environment. Must be called // on all tasks in the cluster. This call internally coordinates with other // tasks to initialize the eager context and TF server for multi-client // execution.
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WORKSPACE
# buildifier: disable=load-on-top workspace(name = "org_tensorflow") # buildifier: disable=load-on-top # We must initialize hermetic python first. load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive") http_archive( name = "bazel_skylib", sha256 = "74d544d96f4a5bb630d465ca8bbcfe231e3594e5aae57e1edbf17a6eb3ca2506", urls = [
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ci/official/requirements_updater/requirements.in
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 # Note that we must use nightly here as these are used in nightly jobs # For release jobs, we will pin these on the release branch keras-nightly ~= 3.0.0.dev tb-nightly ~= 2.17.0.a # Test dependencies grpcio >= 1.24.3, < 2.0
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tensorflow/c/eager/dlpack.cc
void* data = dl_tensor->data; if (dl_tensor->byte_offset != 0) { status->status = tensorflow::errors::InvalidArgument( "Unsupported byte_offset (", dl_tensor->byte_offset, ") from DLPack, must be zero"); return nullptr; } size_t total_bytes = dl_tensor->dtype.bits / 8; for (int i = 0; i < num_dims; i++) { total_bytes *= dims[i]; } if (dl_tensor->strides != nullptr &&
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tensorflow/c/eager/c_api_unified_experimental_graph.cc
if (op_) { return errors::FailedPrecondition( "SetOpName called on already built op."); } if (op_type_.empty()) { return errors::FailedPrecondition( "GraphOperation::Reset must be called before calling SetOpName."); } // TODO(b/145674566): We use Graph::NewName to get a unique name here but // this may not be consistent with python's naming policy. mutex_lock l(g_->mu);
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CONTRIBUTING.md
***NOTE***: Only original source code from you and other people that have signed the CLA can be accepted into the main repository. ### Contributing code If you have improvements to TensorFlow, send us your pull requests! For those 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
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