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SECURITY.md
programs that TensorFlow executes. TensorFlow programs are encoded as computation [**graphs**](https://developers.google.com/machine-learning/glossary/#graph). Since models are practically programs that TensorFlow executes, using untrusted models or graphs is equivalent to running untrusted code. If you need to run untrusted models, execute them inside a
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ci/official/utilities/cleanup_docker.sh
cat <<EOF IMPORTANT: These tests ran under docker. This script does not clean up the container for you! You can delete the container with: $ docker rm -f tf You can also execute more commands within the container with e.g.: $ docker exec tf bazel clean $ docker exec -it tf bash EOF
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tensorflow/c/eager/c_api_unified_experimental.cc
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ci/official/README.md
2. Running the CI scripts locally, as explained below 3. **Google employees only**: Google employees can use an internal-only tool called "MLCI" that makes testing more convenient: it can execute any full CI job against a pending change. Search for "MLCI" internally to find it. You may invoke a CI script of your choice by following these instructions: ```bash cd tensorflow-git-dir
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ci/official/utilities/setup_macos.sh
exit 1 fi # "TFCI_MACOS_BAZEL_TEST_DIR_PATH" specifies the directory that Bazel should use # when running tests. Each test will be executed in a separate subdirectory # inside this directory. TF Mac builds need ~150 GB of disk space to be able to # run all the tests. Since TFCI Mac VMs execute Bazel test commands in a # partition with insufficient storage, we specify the
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tensorflow/c/eager/unified_api_testutil.cc
TF_RETURN_IF_ERROR(fn_op->AddInput(input)); } int retvals = outputs.size() - null_indices.size(); std::vector<AbstractTensorHandle*> fn_outputs(retvals); TF_RETURN_IF_ERROR(fn_op->Execute( absl::Span<AbstractTensorHandle*>(fn_outputs.data(), fn_outputs.size()), &retvals)); int skipped_indices = 0; for (int i = 0; i < outputs.size(); i++) { if (!null_indices.contains(i)) {
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tensorflow/c/eager/gradients_test.cc
GradientRegistry registry; s = RegisterGradients(®istry); ASSERT_EQ(errors::OK, s.code()) << s.message(); auto tape = std::make_unique<Tape>(/*persistent=*/false); s = Execute(check_numerics_op.get(), ctx.get(), absl::MakeSpan(outputs), &num_retvals, &forward_op, tape.get(), registry); ASSERT_EQ(errors::OK, s.code()) << s.message(); string read_message;
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tensorflow/c/eager/tfe_executor_internal.h
explicit TFE_Executor(tensorflow::EagerExecutor* executor) : owned_executor(nullptr), unowned_executor(executor) {} tensorflow::EagerExecutor* executor() { return owned_executor == nullptr ? unowned_executor : owned_executor.get(); } std::unique_ptr<tensorflow::EagerExecutor> owned_executor; tensorflow::EagerExecutor* unowned_executor; };
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tensorflow/c/c_api_macros_internal.h
// `struct_size` is used for struct member compatibility check between core TF // and plug-ins with the same C API minor version. More info here: // https://github.com/tensorflow/community/blob/master/rfcs/20200612-stream-executor-c-api/C_API_versioning_strategy.md #define TF_VALIDATE_STRUCT_SIZE(STRUCT_NAME, STRUCT_OBJ, SIZE_VALUE_NAME) \ do { \
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tensorflow/c/eager/gradients.h
// a map (`tensorflow::eager::TensorTape`) from the wrapped tensor to the id of // the op that produced it (or -1 if this tensor was watched using // `GradientTape::Watch`.) The op_id is simply a unique index assigned to each // op executed under the tape. A separate map (`tensorflow::eager::OpTape`) // maintains the map from `op_id` to a `OpTapeEntry` which stores the `op_type`, // inputs and outputs and the gradient function These data structures combined
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