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tensorflow/c/eager/tape.h
const std::vector<int64_t>& unneeded_gradients, gtl::ArraySlice<Gradient*> output_gradients, absl::Span<Gradient*> result) const = 0; // Builds a tensor filled with ones with the same shape and dtype as `t`. virtual Status BuildOnesLike(const TapeTensor& t, Gradient** result) const = 0; // Looks up the ID of a Gradient.
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tensorflow/c/c_api_experimental.cc
// scheduled on that same threadpool, causing a deadlock in cases where the // caller of event_mgr->ThenExecute() blocks on the completion of the callback // (as in the case of ConstOp kernel creation on GPU, which involves copying a // CPU tensor to GPU). // Setting a larger thread pool does not help with the Swift caller, as we use // a different TFE context for each thread of execution (for running graph
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.bazelrc
# https://github.com/bazelbuild/bazel/issues/3360 build:linux --experimental_guard_against_concurrent_changes # Configure short or long logs build:short_logs --output_filter=DONT_MATCH_ANYTHING build:verbose_logs --output_filter= # Instruction set optimizations # TODO(gunan): Create a feature in toolchains for avx/avx2 to # avoid having to define linux/win separately. build:avx_linux --copt=-mavx
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tensorflow/c/eager/c_api_experimental.h
TFE_MonitoringCounterCell* cell); // APIs for Counter without label. typedef struct TFE_MonitoringCounter0 TFE_MonitoringCounter0; // Returns a new Counter metric object. The caller should manage lifetime of // the object. Using duplicate metric name will crash the program with fatal // error. TF_CAPI_EXPORT extern TFE_MonitoringCounter0* TFE_MonitoringNewCounter0(
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ci/official/README.md
TensorFlow's official CI jobs run the scripts in this folder. Our internal CI system, Kokoro, schedules our CI jobs by combining a build script with a file from the `envs` directory that is filled with configuration options: - Nightly jobs (Run nightly on the `nightly` branch) - Uses `wheel.sh`, `libtensorflow.sh`, `code_check_full.sh` - Continuous jobs (Run on every GitHub commit) - Uses `pycpp.sh`
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CONTRIBUTING.md
`tensorflow/python/saved_model/load_test.py`: (Requires `python>=3.7`) ```bash bazel test ${flags} //tensorflow/python/saved_model:load_test --test_filter=*LoadTest.test_capture_variables* ``` **Note:** You can add `--test_sharding_strategy=disabled` to the `flags` to disable the sharding so that all the test outputs are in one file. However,
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tensorflow/c/eager/gradients.cc
const std::vector<int64_t>& unneeded_gradients, gtl::ArraySlice<AbstractTensorHandle*> output_gradients, absl::Span<AbstractTensorHandle*> result) const override; // Builds a tensor filled with ones with the same shape and dtype as `t`. Status BuildOnesLike(const TapeTensor& t, AbstractTensorHandle** result) const override; // Looks up the ID of a Gradient.
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.github/workflows/trusted-partners.yml
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tensorflow/c/experimental/next_pluggable_device/c_api.h
// invokes create_func to create the resource. `delete_func` is needed for // ResourceMgr to clean up the resource. `status` will be set. If `status` is // not OK, `*result_plugin_resource` will be set as nullptr. // // Caller does not take ownership of the `plugin_resource`. TF_CAPI_EXPORT extern void TF_LookupOrCreatePluginResource( TF_OpKernelContext* ctx, const char* container_name,
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tensorflow/c/eager/immediate_execution_context.h
// Configure graph collection in RunMetadata. virtual void SetShouldStoreGraphs(bool value) = 0; // Return the collected RunMetadata. This method will transfer the ownership // to the caller. virtual std::unique_ptr<RunMetadata> ExportRunMetadata() = 0; // For LLVM style RTTI. static bool classof(const AbstractContext* ptr) { return ptr->getKind() == kEager || ptr->getKind() == kTfrt; }
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