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ci/official/wheel_test/README.md
## Wheel Test This directory is dedicated to tests that require a built TensorFlow wheel file for testing, such as: * Ensuring the entire API is importable * Testing downstream projects against the wheel Ensure you have Bazel installed and accessible from your command line. These tests use hermetic Python. They also require a built TensorFlow wheel file and a requirements_lock file. The requirements_lock file is generated by the
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ci/official/envs/rbe
# it enables a derived --config setting. If RBE is not available (i.e. there # is no --config setting), bazel would fail and quit. This script does a quick # check This script checks for such errors early if ! grep "rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX" .bazelrc; then cat <<EOF ERROR: RBE was enabled via the 'rbe' env in the 'TFCI' variable. TFCI: $TFCI
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ci/official/utilities/get_versions.sh
export TF_VER_PATCH=$(awk '/#define TF_PATCH_VERSION/ {print $3}' tensorflow/core/public/version.h) # Note: in awk, "print $N" prints the Nth "field", where fields are strings separated # by whitespace. The flag "-F<x>" changes the behavior such that fields are now strings # separated by the character <x>. Therefore, -F\' (escaped single quote) means that field # $2 in <Tensor'flow'> is <flow>. This is useful for reading string literals like below.
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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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tensorflow/c/eager/c_api.h
// A handle to a tensor on a device. // // Like a TF_Tensor, a TFE_TensorHandle refers to a tensor with a value, shape, // type etc. Unlike a TF_Tensor, a TFE_TensorHandle may refer to such tensors // placed in the memory of different devices or remote address spaces. typedef struct TFE_TensorHandle TFE_TensorHandle; TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_NewTensorHandle(const TF_Tensor* t,
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ci/official/README.md
# your builds. This usually saves a lot of time, especially when # re-running tests. However, note that: # # - New environments like new CUDA versions, changes to manylinux, # compilers, etc. can cause undefined behavior such as build failures # or tests passing incorrectly. # - Automatic LLVM updates are known to extend build time even with # the cache; this is unavoidable. export TFCI=py311,linux_x86,public_cache,disk_cache
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tensorflow/c/c_api.cc
return; } // We have a convoluted scheme here: Using the C++ graph construction API // to add potentially many nodes to the graph without running the checks // (such as uniqueness of the names of nodes) we run with other functions // that add a node to the graph (like TF_FinishOperation). if (!g->name_map.insert(std::make_pair(n->name(), n)).second) {
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tensorflow/c/eager/c_api_experimental.h
int64_t init_timeout_in_ms, TF_Status* status, bool clear_existing_contexts); // Set server def with retries and timeout. This is helpful for fault-tolerant // initial connection in high-preemption environments, such as // ParameterServerStrategy training. // This API is for experimental usage and may be subject to change. TF_CAPI_EXPORT extern void TFE_ContextSetServerDefWithTimeoutAndRetries(
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SECURITY.md
targets of those operations, which could result in arbitrary read/write/executions. ### Running a TensorFlow server TensorFlow is a platform for distributed computing, and as such there is a TensorFlow server (`tf.train.Server`). The TensorFlow server is intended for internal communication only. It is not built for use in untrusted environments or networks.
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tensorflow/c/eager/immediate_execution_context.h
DEVICE_PLACEMENT_SILENT_FOR_INT32 = 3, }; // LINT.ThenChange(//tensorflow/c/eager/c_api.h) // Abstract interface to a context. // // A context is responsible for creating key objects such as Tensors, // TensorHandles & Operations. class ImmediateExecutionContext : public AbstractContext { public: // Optimized scalar creation functions virtual AbstractTensorInterface* CreateInt64Scalar(int64_t value) = 0;
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