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ci/official/bisect.sh
# Run any CI script and env configuration to bisect a failing target in some # build configuration. You must set the following variables to control this # script: # # TF_BISECT_GOOD: Last known good commit (e.g. commit from the last passing job) # TF_BISECT_BAD: First bad commit (e.g. commit from the first failing job) # TF_BISECT_SCRIPT: The build script path relative to the TF root dir, e.g.
Shell Script - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Feb 01 03:21:19 GMT 2024 - 1.8K bytes - Viewed (0) -
configure.py
one from the check_success function. resolve_symlinks: (Bool) Translate symbolic links into the real filepath. n_ask_attempts: (Integer) Number of times to query for valid input before raising an error and quitting. Returns: [String] The value of var_name after querying for input. Raises: UserInputError: if a query has been attempted n_ask_attempts times without
Python - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Mon Apr 15 18:25:36 GMT 2024 - 53.8K bytes - Viewed (0) -
RELEASE.md
* Fixes vulnerabilities arising from incomplete validation in MKL requantization ([CVE-2021-37665](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37665)) * Fixes an undefined behavior arising from reference binding to nullptr in `RaggedTensorToVariant` ([CVE-2021-37666](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37666)) * Fixes an undefined behavior arising from reference binding to nullptr in
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tensorflow/c/c_api_experimental.cc
// threadpool, so that we avoid the possibility of running the runner_ in the // threadpool of GPU event mgr, as that can trigger more callbacks to be // scheduled on that same threadpool, causing a deadlock in cases where the // caller of event_mgr->ThenExecute() blocks on the completion of the callback // (as in the case of ConstOp kernel creation on GPU, which involves copying a // CPU tensor to GPU).
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CONTRIBUTING.md
#### Running doctest for testable docstring There are two ways to test the code in the docstring locally: 1. If you are only changing the docstring of a class/function/method, then you can test it by passing that file's path to [tf_doctest.py](https://www.tensorflow.org/code/tensorflow/tools/docs/tf_doctest.py). For example: ```bash python tf_doctest.py --file=<file_path> ```
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ci/official/README.md
# 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 # Recommended: Configure Docker. (Linux only)
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tensorflow/c/eager/c_api_experimental.h
#include "tensorflow/c/eager/c_api.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
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Wed Feb 21 22:37:46 GMT 2024 - 39.5K bytes - Viewed (0) -
tensorflow/c/c_api.cc
status->status = InvalidArgument("Unparseable RunOptions proto"); return; } if (run_metadata != nullptr && run_metadata->data != nullptr) { status->status = InvalidArgument("Passing non-empty run_metadata is invalid."); return; } RunMetadata run_metadata_proto; result = session->Run(run_options_proto, input_pairs, output_tensor_names,
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LICENSE
negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all
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SECURITY.md
# Using TensorFlow Securely This document discusses the TensorFlow security model. It describes the security risks to consider when using models, checkpoints or input data for training or serving. We also provide guidelines on what constitutes a vulnerability in TensorFlow and how to report them. This document applies to other repositories in the TensorFlow organization, covering security practices for the entirety of the TensorFlow ecosystem.
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