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tensorflow/api_template.__init__.py
""" # pylint: disable=g-bad-import-order,protected-access,g-import-not-at-top import distutils as _distutils import importlib import inspect as _inspect import os as _os import site as _site import sys as _sys # Do not remove this line; See https://github.com/tensorflow/tensorflow/issues/42596
Python - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Tue Mar 05 06:27:59 GMT 2024 - 6.7K bytes - Viewed (3) -
tensorflow/c/eager/c_api.h
// Fetches the current number of inputs attached to `op`. // // Does not use the operation's definition to determine how many inputs should // be attached. It is intended for use with TFE_OpGetFlatInput to inspect an // already-finalized operation. // // Note that TFE_OpGetFlatInputCount and TFE_OpGetFlatInput operate on a flat // sequence of inputs, unlike TFE_OpGetInputLength (for getting the length of a
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Apr 27 21:07:00 GMT 2023 - 22.8K bytes - Viewed (1) -
tensorflow/c/eager/parallel_device/parallel_device_lib.h
// ParallelDevice. class ParallelTensor { public: // Construct a ParallelTensor from TensorHandles placed on the component // devices of a ParallelDevice. If called, ParallelTensor::Shape inspects // `components` to determine a shape. static std::unique_ptr<ParallelTensor> FromTensorHandles( const ParallelDevice& parallel_device, std::vector<TensorHandlePtr> components, TF_Status* status);
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tensorflow/c/eager/c_api_distributed_test.cc
register_test_pass(tensorflow::OptimizationPassRegistry::PRE_PLACEMENT, 0, std::make_unique<GraphErrorInjectionPass>(), "error_injector"); GraphErrorInjectionPass::enabled_ = true; tensorflow::ServerDef server_def = GetServerDef(3); // This server def has the task index set to 0. string serialized = server_def.SerializeAsString();
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Feb 15 09:49:45 GMT 2024 - 23.5K bytes - Viewed (0) -
tensorflow/api_template_v1.__init__.py
# ============================================================================== """Bring in all of the public TensorFlow interface into this module.""" import distutils as _distutils import importlib import inspect as _inspect import os as _os import site as _site import sys as _sys # pylint: disable=g-bad-import-order,protected-access,g-import-not-at-top from tensorflow.python import pywrap_tensorflow # pylint: disable=unused-import
Python - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Tue Jan 23 02:14:00 GMT 2024 - 7.4K bytes - Viewed (0) -
CONTRIBUTING.md
### Typical Pull Request Workflow - **1. New PR** - As a contributor, you submit a New PR on GitHub. - We inspect every incoming PR and add certain labels to the PR such as `size:`, `comp:` etc. At this stage we check if the PR is valid and meets certain quality requirements. For example, we check if the CLA is signed, PR has
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tensorflow/c/eager/c_api_experimental.h
// APIs for generically dealing with op attributes (e.g. when forwarding them // through custom device implementations). // // TODO(allenl): Currently these are black boxes, but we should have some way to // inspect values. This would let people e.g. copy over most attributes and then // modify some based on their values. // A reference to an op's name -> attribute mapping typedef struct TFE_OpAttrs TFE_OpAttrs;
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ci/official/utilities/setup_docker.sh
docker push "$TFCI_DOCKER_IMAGE" fi fi # Keep the existing "tf" container if it's already present. # The container is not cleaned up automatically! Remove it with: # docker rm tf if ! docker container inspect tf >/dev/null 2>&1 ; then # Pass all existing TFCI_ variables into the Docker container env_file=$(mktemp) env | grep ^TFCI_ > "$env_file" docker run $TFCI_DOCKER_ARGS --name tf -w "$TFCI_GIT_DIR" -itd --rm \
Shell Script - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Fri Jan 26 15:27:59 GMT 2024 - 1.8K bytes - Viewed (0) -
SECURITY.md
vulnerabilities. ## Security properties of execution modes TensorFlow has several execution modes, with Eager-mode being the default in v2. Eager mode lets users write imperative-style statements that can be easily inspected and debugged and it is intended to be used during the development phase. As part of the differences that make Eager mode easier to debug, the [shape inference
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RELEASE.md
should be set to `True` when using imperative control flow in the `call` method. * Begin adding Go wrapper for C Eager API. * XLA HLO graphs can be inspected with interactive_graphviz tool now. * Add dataset ops to the graph (or create kernels in Eager execution) during the python Dataset object creation instead doing it during Iterator creation time.
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