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ci/official/wheel_test/README.md
e.g.: bash update_requirements.sh /tmp/tensorflow-2.14.0-cp311-cp311-linux_x86_64.whl 3_11 ``` #### Requirements Updater Script This script automates the process of updating TensorFlow requirements for a specific Python version. ##### Parameters `path_to_tensorflow_wheel`: The local path to the TensorFlow wheel file. Example: `/tmp/tensorflow-2.14.0-cp311-cp311-linux_x86_64.whl`
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ci/official/envs/disk_cache
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tensorflow/c/eager/c_api_test.cc
// 6. Create a context D using the full cluster server def E. // 7. Read the variable in D. TEST(CAPI, SingleHostServerDefV1Works) { // Create a server def that represents a 2-process cluster and a client. // Example: // // cluster { job { name: "worker" // tasks { key: 0 value: "localhost:14522" } // tasks { key: 1 value: "localhost:14523" }
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tensorflow/c/c_test.c
#include <limits.h> #include <memory.h> #include <stdio.h> #include <stdlib.h> #include <time.h> #include <unistd.h> #ifdef _WIN32 #include <process.h> #endif #include "tensorflow/c/c_api.h" #include "tensorflow/c/c_api_experimental.h" #include "tensorflow/c/env.h" #include "tensorflow/c/kernels.h"
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ci/official/requirements_updater/README.md
To specify the version via a Bazel command argument, use the following: ``` --repo_env=TF_PYTHON_VERSION=3.11 ``` ## Requirements updater Requirements updater is a standalone tool, intended to simplify process of updating requirements for multiple minor versions of Python. It takes in a file with a set of dependencies, and produces a more detailed requirements file for each version, with hashes specified for each
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SECURITY.md
## Untrusted inputs during training and prediction TensorFlow supports a wide range of input data formats. For example it can process images, audio, videos, and text. There are several modules specialized in taking those formats, modifying them, and/or converting them to intermediate formats that can be processed by TensorFlow. These modifications and conversions are handled by a variety of libraries that
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tensorflow/c/eager/c_api_experimental.h
// context_id is initialized with a dummy value and is later set when the worker // is initialized (either locally or remotely). The context_id can change during // the process lifetime although this should cause the worker to be // reinitialized (e.g. cleared caches) as well. TF_CAPI_EXPORT extern uint64_t TFE_GetContextId(TFE_Context* ctx);
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RELEASE.md
### `tf.data`: * Adds new `tf.data.experimental.service.register_dataset` and `tf.data.experimental.service.from_dataset_id` APIs to enable one process to register a dataset with the tf.data service, and another process to consume data from the dataset. * Adds support for dispatcher fault tolerance. To enable fault tolerance, configure a `work_dir` when running your dispatcher server and set
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ci/devinfra/docker_windows/Dockerfile
Start-Process -FilePath C:/TEMP/vs_buildtools.exe -ArgumentList "--installPath", "C:/VS", \ "--quiet", "--wait", "--nocache", \ "--add", "Microsoft.VisualStudio.Workload.VCTools", \ "--add", "Microsoft.VisualStudio.Component.VC.Tools.x86.x64", \ "--add", "Microsoft.VisualStudio.Component.Windows10SDK.19041" -Wait; \
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ci/devinfra/README.md
******************************************************************************** A directory for build and CI related scripts and jobs managed by the TensorFlow
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