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ci/official/containers/linux_arm64/devel.usertools/setup_venv_test.sh
set -euxo pipefail # Run this from inside the tensorflow github directory. # Usage: setup_venv_test.sh venv_and_symlink_name "glob pattern for one wheel file" # Example: setup_venv_test.sh bazel_pip "/tf/pkg/*.whl" # # This will create a venv with that wheel file installed in it, and a symlink # in ./venv_and_symlink_name/tensorflow to ./tensorflow. We use this for the # "pip" tests. python -m venv /$1 mkdir -p $1 rm -f ./$1/tensorflow
Shell Script - Registered: Tue May 07 12:40:20 GMT 2024 - Last Modified: Mon Sep 18 14:52:45 GMT 2023 - 1.4K bytes - Viewed (0) -
tensorflow/c/eager/c_api_unified_experimental.h
TF_Status*); // TF_ExecuteOperation will, if in eager mode, execute, if in graph mode, maybe // capture some inputs and then add a node in the graph. The output tensors are // returned through the provided TF_OutputList. // Any active tape will observe the effects of this execution. void TF_ExecuteOperation(TF_AbstractOp* op, int num_inputs,
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Sun Oct 24 11:27:00 GMT 2021 - 7K bytes - Viewed (0) -
.github/workflows/scorecards-analysis.yml
# Publish the results to enable scorecard badges. For more details, see # https://github.com/ossf/scorecard-action#publishing-results. # For private repositories, `publish_results` will automatically be set to `false`, # regardless of the value entered here. publish_results: true # Upload the results as artifacts (optional). - name: "Upload artifact"
Others - Registered: Tue May 07 12:40:20 GMT 2024 - Last Modified: Wed Dec 21 23:56:23 GMT 2022 - 2.4K bytes - Viewed (0) -
tensorflow/c/experimental/filesystem/plugins/windows/windows_filesystem.cc
#include <stdlib.h> #include <string.h> #include "tensorflow/c/experimental/filesystem/filesystem_interface.h" #include "tensorflow/c/tf_status.h" // Implementation of a filesystem for POSIX environments. // This filesystem will support `file://` and empty (local) URI schemes. static void* plugin_memory_allocate(size_t size) { return calloc(1, size); } static void plugin_memory_free(void* ptr) { free(ptr); }
C++ - Registered: Tue Apr 09 12:39:09 GMT 2024 - Last Modified: Fri May 27 20:21:15 GMT 2022 - 2.6K bytes - Viewed (0) -
ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
# > TF's gen_api_init_files has a genrule to run the core TensorFlow code # > on the host machine. If we don't have --distinct_host_configuration=false, # > the core TensorFlow code will be built once for the host and once for the # > target platform. # See also https://docs.bazel.build/versions/master/guide.html#build-configurations-and-cross-compilation build --distinct_host_configuration=false
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ci/official/README.md
# Ex. no_upload -- Disable all uploads, usually for temporary CI issues # Recommended: use a local+remote cache. # # Bazel will cache your builds in tensorflow/build_output/cache, # and will also try using public build cache results to speed up # your builds. This usually saves a lot of time, especially when # re-running tests. However, note that: #
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tensorflow/api_template_v1.__init__.py
) # Delete modules that should be hidden from dir(). # Don't fail if these modules are not available. # For e.g. this file will be originally placed under tensorflow/_api/v1 which # does not have "python", "core" directories. Then, it will be copied # to tensorflow/ which does have these two directories. try: del python except NameError: pass try: del core except NameError:
Python - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Tue Jan 23 02:14:00 GMT 2024 - 7.4K bytes - Viewed (0) -
tensorflow/c/experimental/filesystem/BUILD
# Experimental filesystem C APIs for TensorFlow. # Will be moved in proper place once all filesystems are converted to the # modular framework. load("//tensorflow:tensorflow.bzl", "tf_cc_test") load("//tensorflow/core/platform:rules_cc.bzl", "cc_library") package( # copybara:uncomment default_applicable_licenses = ["//tensorflow:license"], licenses = ["notice"], ) # This is only for plugins cc_library( name = "filesystem_interface",
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.github/bot_config.yml
Therefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load. Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below: * Try Google Colab to use TensorFlow.
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ci/official/requirements_updater/README.md
`release_updater.sh` 5) Run the `updater.sh` shell script. \ If the base requirements file hasn't yet been updated to account for the new Python version, which will require different versions for at least some dependencies, it will need to be updated now, for the script to run successfully. 6) A new `requirements_lock_3_11.txt` file should appear under the root of the
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