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.github/workflows/arm-cd.yml
echo "PyPI project name:" $tf_project_name CI_DOCKER_BUILD_EXTRA_PARAMS="--build-arg py_major_minor_version=${{ matrix.pyver }} --build-arg is_nightly=${is_nightly} --build-arg tf_project_name=${tf_project_name}" \
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ci/official/requirements_updater/requirements.in
dill == 0.3.7 astor == 0.7.1 typing_extensions == 4.8.0 gast == 0.4.0 termcolor == 2.3.0 wrapt == 1.16.0 tblib == 2.0.0 # Install tensorboard, and keras # Note that here we want the latest version that matches TF major.minor version # Note that we must use nightly here as these are used in nightly jobs # For release jobs, we will pin these on the release branch keras-nightly ~= 3.0.0.dev tb-nightly ~= 2.17.0.a # Test dependencies
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tensorflow/c/eager/dlpack.cc
} dlm_tensor->dl_tensor.shape = shape_arr->data(); // There are two ways to represent compact row-major data // 1) nullptr indicates tensor is compact and row-majored. // 2) fill in the strides array as the real case for compact row-major data. // Here we choose option 2, since some frameworks didn't handle the strides // argument properly.
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Feb 15 09:49:45 GMT 2024 - 12.8K bytes - Viewed (0) -
.github/workflows/arm-ci-extended.yml
run: | is_nightly=0 && tf_project_name='tf_ci_ext' && ${{ github.event_name == 'schedule' }} && is_nightly=1 && tf_project_name='tf_nightly_ci_ext' CI_DOCKER_BUILD_EXTRA_PARAMS="--build-arg py_major_minor_version=${{ matrix.pyver }} --build-arg is_nightly=${is_nightly} --build-arg tf_project_name=${tf_project_name}" \
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tensorflow/api_template.__init__.py
_current_module.__path__ = [_module_dir] + _current_module.__path__ # Enable TF2 behaviors from tensorflow.python.compat import v2_compat as _compat _compat.enable_v2_behavior() _major_api_version = 2 # Load all plugin libraries from site-packages/tensorflow-plugins if we are # running under pip. # TODO(gunan): Find a better location for this code snippet.
Python - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Tue Mar 05 06:27:59 GMT 2024 - 6.7K bytes - Viewed (3) -
configure.py
if not python_lib_path: python_lib_path = default_python_lib_path environ_cp['PYTHON_LIB_PATH'] = python_lib_path python_major_version = get_python_major_version(python_bin_path) if python_major_version == '2': write_to_bazelrc('build --host_force_python=PY2') # Convert python path to Windows style before writing into bazel.rc if is_windows() or is_cygwin():
Python - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Mon Apr 15 18:25:36 GMT 2024 - 53.8K bytes - Viewed (1) -
RELEASE.md
### Known Caveats * <CAVEATS REGARDING THE RELEASE (BUT NOT BREAKING CHANGES).> * <ADDING/BUMPING DEPENDENCIES SHOULD GO HERE> * <KNOWN LACK OF SUPPORT ON SOME PLATFORM, SHOULD GO HERE> ### Major Features and Improvements * <INSERT MAJOR FEATURE HERE, USING MARKDOWN SYNTAX> * <IF RELEASE CONTAINS MULTIPLE FEATURES FROM SAME AREA, GROUP THEM TOGETHER> ### Bug Fixes and Other Changes
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CONTRIBUTING.md
and [tensorflow/python](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python). TensorFlow has passed version 1.0 and hence cannot make non-backward-compatible API changes without a major release. Reviewers of your pull request will comment on any API compatibility issues [following API review practices](https://github.com/tensorflow/community/blob/master/governance/api-reviews.md).
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tensorflow/BUILD
load("@bazel_skylib//rules:common_settings.bzl", "bool_flag", "bool_setting") load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda") load( "//tensorflow:tensorflow.bzl", "VERSION", "VERSION_MAJOR", "check_deps", "if_google", "if_oss", "if_xla_available", "tf_cc_shared_object", "tf_custom_op_library_additional_deps_impl", "tf_monitoring_python_deps", "tf_native_cc_binary",
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.bazelrc
# See https://developer.nvidia.com/cuda-gpus#compute # `compute_XY` enables PTX embedding in addition to SASS. PTX # is forward compatible beyond the current compute capability major # release while SASS is only forward compatible inside the current # major release. Example: sm_80 kernels can run on sm_89 GPUs but # not on sm_90 GPUs. compute_80 kernels though can also run on sm_90 GPUs.
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