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.github/workflows/scorecards-analysis.yml
- name: "Upload artifact" uses: actions/upload-artifact@83fd05a356d7e2593de66fc9913b3002723633cb # v3.1.1 with: name: SARIF file path: results.sarif retention-days: 5 # Upload the results to GitHub's code scanning dashboard. - name: "Upload to code-scanning" uses: github/codeql-action/upload-sarif@896079047b4bb059ba6f150a5d87d47dde99e6e5 # v2.11.6 with:
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.github/workflows/stale-issues.yml
This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you. close-issue-message: > This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further. days-before-pr-stale: 14 days-before-pr-close: 14
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CONTRIBUTING.md
``` This will catch most license, Python coding style and BUILD file issues that may exist in your changes. #### Running unit tests There are two ways to run TensorFlow unit tests. 1. Using tools and libraries installed directly on your system. Refer to the
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SECURITY.md
### Resource allocation A denial of service caused by one model could bring down the entire server, but we don't consider this as a vulnerability, given that models can exhaust resources in many different ways and solutions exist to prevent this from happening (e.g., rate limits, ACLs, monitors to restart broken servers). ### Model sharing If the multitenant design allows sharing models, make sure that tenants and
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tensorflow/c/eager/dlpack.cc
} for (int i = ndim - 2; i >= 0; --i) { (*stride_arr)[i] = (*shape_arr)[i + 1] * (*stride_arr)[i + 1]; } dlm_tensor->dl_tensor.shape = shape_arr->data(); // There are two ways to represent compact row-major data // 1) nullptr indicates tensor is compact and row-majored. // 2) fill in the strides array as the real case for compact row-major data.
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RELEASE.md
* Added `keras.export.ExportArchive` class for low-level customization of the process of exporting SavedModel artifacts for inference. Both ways of exporting models are based on `tf.function` tracing and produce a TF program composed of TF ops. They are meant primarily for environments where the TF runtime is available, but not the Python interpreter, as is typical for production with TF Serving....
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tensorflow/c/eager/tfe_op_attrs_internal.h
#include "tensorflow/c/eager/abstract_op_attrs.h" #include "tensorflow/c/tf_status.h" #include "tensorflow/core/framework/attr_value.pb.h" // An equivalent of a tensorflow::NameAttrList protocol buffer, but used in ways // that sometimes do not require serialization. typedef struct TFE_OpAttrs TFE_OpAttrs; typedef struct TFE_Context TFE_Context; typedef struct TFE_Op TFE_Op; namespace tensorflow {
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tensorflow/c/eager/parallel_device/parallel_device.cc
TF_Status* status) { absl::optional<std::vector<MaybeParallelTensorOwned>> result; // TODO(allenl): We should remove "TPU" from these op names at the very least, // or consider other ways of packing/unpacking parallel tensors. if (operation_name == std::string("TPUReplicatedInput")) { // Special-cased operation for packing per-device tensors into one parallel // tensor.
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