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tensorflow/c/c_test.c
#include "tensorflow/c/kernels.h" // A create function. This will never actually get called in this test, it's // just nice to know that it compiles. void* create(TF_OpKernelConstruction* ctx) { TF_DataType type; TF_Status* s = TF_NewStatus(); TF_OpKernelConstruction_GetAttrType(ctx, "foobar", &type, s); TF_DeleteStatus(s); return NULL; } // A compute function. This will never actually get called in this test, it's
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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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ci/official/README.md
- 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` - Presubmit jobs (Run on every GitHub PR) - Uses `pycpp.sh`, `code_check_changed_files.sh` These "env" files match up with an environment matrix that roughly covers: - Different Python versions
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ci/official/envs/versions_upload
# See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== # Release jobs are very basic. They don't use any caching or RBE, # but they do upload logs to resultstore. # IMPORTANT: trailing slash is required on GCS URIs, as it tells gcloud to # pretend the path is a directory. TFCI_ARTIFACT_FINAL_GCS_ENABLE=1
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tensorflow/c/eager/c_api_experimental.h
#ifdef __cplusplus extern "C" { #endif // Resets `op_to_reset` with `op_or_function_name` and `raw_device_name`. This // is for performance optimization by reusing an exiting unused op rather than // creating a new op every time. If `raw_device_name` is `NULL` or empty, it // does not set the device name. If it's not `NULL`, then it attempts to parse // and set the device name. It's effectively `TFE_OpSetDevice`, but it is faster
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tensorflow/c/c_api.cc
} else { return ToOperation(iter->second); } } TF_Operation* TF_GraphNextOperation(TF_Graph* graph, size_t* pos) { if (*pos == 0) { // Advance past the first sentinel nodes in every graph (the source & sink). *pos += 2; } else { // Advance to the next node. *pos += 1; } mutex_lock l(graph->mu);
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tensorflow/c/eager/gradients.cc
// TODO(srbs): It seems like this is used only for performance optimization // and not for correctness. The only downside of keeping this 1 seems to be // that the gradient accumulation is unbounded and we will never // aggressively aggregate accumulated gradients to recover memory. // Revisit and fix. return 1; } // Consumes references to the tensors in the gradient_tensors list and returns
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ci/official/utilities/rename_and_verify_wheels.sh
"$python" -c 'import tensorflow as tf; t1=tf.constant([1,2,3,4]); t2=tf.constant([5,6,7,8]); print(tf.add(t1,t2).shape)' "$python" -c 'import sys; import tensorflow as tf; sys.exit(0 if "keras" in tf.keras.__name__ else 1)' fi # VERY basic check to ensure the [and-cuda] package variant is installable. # Checks TFCI_BAZEL_COMMON_ARGS for "gpu" or "cuda", implying that the test is # relevant. All of the GPU test machines have CUDA installed via other means,
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RELEASE.md
* Added a new `rerandomize_each_iteration` argument for the `tf.data.Dataset.sample_from_datasets()` operation, which controls whether the...
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ci/official/utilities/code_check_full.bats
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/tf_sig_build_dockerfiles/devel.usertools/code_check_full.bats Here are the affected tests: EOF while read dep; do echo "For dependency $dep:" # For every missing dependency, find the tests which directly depend on # it, and print that list for debugging. Not really clear if this is # helpful since the only examples I've seen are enormous.
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