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SECURITY.md
It is possible to run multiple TensorFlow models in parallel. For example, `ModelServer` collates all computation graphs exposed to it (from multiple `SavedModel`) and executes them in parallel on available executors. Running TensorFlow in a multitenant design mixes the risks described above with the inherent ones from multitenant configurations. The primary areas of concern are tenant isolation, resource allocation, model sharing and hardware attacks.
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tensorflow/c/experimental/filesystem/plugins/gcs/ram_file_block_cache.h
pruning_thread_.reset(); } } /// Read `n` bytes from `filename` starting at `offset` into `buffer`. It /// returns total bytes read ( -1 in case of errors ). This method will set /// `status` to: /// /// 1) The error from the remote filesystem, if the read from the remote /// filesystem failed. /// 2) `TF_FAILED_PRECONDITION` if the read from the remote filesystem /// succeeded,
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LICENSE
exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation,
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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`,
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.github/ISSUE_TEMPLATE/tflite-other.md
placeholder: e.g., 3.9 validations: required: false - type: input id: Bazel attributes: label: Bazel version description: if compiling from source placeholder: validations: required: false - type: input id: Compiler attributes: label: GCC/Compiler version description: if compiling from source placeholder: validations: required: false - type: input id: Cuda attributes: label: CUDA/cuDNN version description:
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tensorflow/c/eager/c_api_experimental.cc
TF_Status* status, const char* description) { auto* result = new TFE_MonitoringCounter0({name, description}); tsl::Set_TF_Status_from_Status(status, result->counter->GetStatus()); if (!result->counter->GetStatus().ok()) { delete result; return nullptr; } return result; }
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RELEASE.md
* Upgrade Flatbuffers v2.0.5 from v1.12.0 * `tf.keras`: * `EinsumDense` layer is moved from experimental to core. Its import path is moved from `tf.keras.layers.experimental.EinsumDense` to `tf.keras.layers.EinsumDense`. * Added `tf.keras.utils.audio_dataset_from_directory` utility to easily generate audio classification datasets from directories of `.wav` files.
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ci/official/wheel_test/README.md
description will be provided once it's integrated into presubmit. ### test_import_api_packages This Python test verifies whether the API v2 packages can be imported from the current build. It utilizes the `_api/v2/api_packages.txt` list of packages from the local wheel file specified in the `requirements_lock_<python_version>.txt`. Packages are imported one by one in alphabetical order during runtime.
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tensorflow/api_template.__init__.py
from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow # pylint: disable=unused-import from tensorflow.python.tools import module_util as _module_util from tensorflow.python.util.lazy_loader import KerasLazyLoader as _KerasLazyLoader # Make sure code inside the TensorFlow codebase can use tf2.enabled() at import. _os.environ["TF2_BEHAVIOR"] = "1" from tensorflow.python import tf2 as _tf2
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tensorflow/c/eager/c_api_unified_experimental.cc
TracingContext* tracing_ctx = dyn_cast<TracingContext>(unwrap(ctx)); if (!tracing_ctx) { tsl::Set_TF_Status_from_Status( s, tensorflow::errors::InvalidArgument( "Only TracingContext can be converted into a function.")); return nullptr; } tsl::Set_TF_Status_from_Status(s, tracing_ctx->Finalize(unwrap(outputs), &func)); TF_DeleteExecutionContext(ctx);
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