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/external/pytorch/.github/workflows/
Dbuild-manywheel-images.yml1 name: Build manywheel docker images
7 - main
8 - release/*
10 … # NOTE: Binary build pipelines should only get triggered on release candidate or nightly builds
11 # Release candidate tags look like: v1.11.0-rc1
12 - v[0-9]+.[0-9]+.[0-9]+-rc[0-9]+
14 - '.ci/docker/manywheel/*'
15 - '.ci/docker/manywheel/build_scripts/*'
16 - '.ci/docker/common/*'
17 - .github/workflows/build-manywheel-images.yml
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Dinductor-perf-test-nightly-aarch64.yml1 name: inductor-perf-nightly-aarch64
5 # - cron: 0 7 * * 1-6
6 # - cron: 0 7 * * 0
7 # Does not perform max_autotune on CPU, so skip the weekly run setup
10 - cron: 0 */4 * * *
15 # CPU for training is not typical, but leave the option open here
47-${{ github.event.pull_request.number || github.ref_name }}-${{ github.ref_type == 'branch' && git…
48 cancel-in-progress: true
50 permissions: read-all
53 linux-jammy-aarch64-py3_10-inductor-build:
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/external/pytorch/.ci/docker/manywheel/
Dbuild.sh4 set -eou pipefail
6 TOPDIR=$(git rev-parse --show-toplevel)
11 if [ -z "${image}" ]; then
18 DOCKER_REGISTRY="${DOCKER_REGISTRY:-docker.io}"
20 GPU_ARCH_TYPE=${GPU_ARCH_TYPE:-cpu}
21 GPU_ARCH_VERSION=${GPU_ARCH_VERSION:-}
22 MANY_LINUX_VERSION=${MANY_LINUX_VERSION:-}
23 DOCKERFILE_SUFFIX=${DOCKERFILE_SUFFIX:-}
24 WITH_PUSH=${WITH_PUSH:-}
27 cpu)
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/external/libdav1d/
D.gitlab-ci.yml2 - style
3 - build
4 - test
6 .debian-amd64-common:
7 image: registry.videolan.org/dav1d-debian-unstable:20240406142551
8 stage: build
10 - docker
11 - amd64
13 .debian-amd64-minimum:
14 image: registry.videolan.org/dav1d-debian-minimum:20240406142551
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/external/tensorflow/tensorflow/lite/g3doc/guide/
Dbuild_cmake_arm.md3 This page describes how to build the TensorFlow Lite library for various ARM
6 The following instructions have been tested on Ubuntu 16.04.3 64-bit PC (AMD64)
7 , TensorFlow devel docker image
8 [tensorflow/tensorflow:devel](https://hub.docker.com/r/tensorflow/tensorflow/tags/).
15 [Build TensorFlow Lite with CMake](https://www.tensorflow.org/lite/guide/build_cmake)
21 and Mendel Linux 4.0. Depending on your target glibc version and CPU
22 capabilities, you may need to use different version of toolchain and build
28 ldd --version
31 <pre class="tfo-notebook-code-cell-output">
32 ldd (Debian GLIBC 2.28-10) 2.28
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/external/tensorflow/third_party/remote_config/
Dremote_platform_configure.bzl1 """Repository rule to create a platform for a docker image to be used with RBE."""
14 cpu = "x86_64"
15 machine_type = repository_ctx.execute(["bash", "-c", "echo $MACHTYPE"]).stdout
18 cpu = "ppc"
20 cpu = "s390x"
21 elif machine_type.startswith("aarch64"):
22 cpu = "aarch64"
24 cpu = "aarch64"
26 cpu = "arm"
28 cpu = "mips64"
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/external/armnn/build-tool/
DREADME.md1 # Arm NN Build Tool
2Build Tool_** provides a collection of **parameterized bash scripts** accompanied by a **Dockerfil…
3 …places/supersedes the majority of the existing Arm NN build guides as a user-friendly way to build
4 …rm NN from scratch is the ability to **exactly choose which components to build, targeted for your…
5 The Arm NN Build Tool is tested on **x86_64 (Intel) and aarch64 (Arm) build hosts** for the **Ubunt…
6 Other host platforms such as Windows and Mac **should** work (with Docker installed), but have not …
7 …orts **targeting Linux devices (from Ubuntu 18.04 onwards) on x86_64 and aarch64** architectures.<…
8 …the Arm NN Build Tool through the use of Docker. However, the scripts may be [executed directly on…
10Docker installed** and want to quickly build the Arm NN Dockerfile with some default build argumen…
13 * [System Requirements](#system-requirements)
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/external/pytorch/.github/
Dlabeler.yml2 - torch/_dynamo/**
3 - torch/csrc/dynamo/**
4 - benchmarks/dynamo/**
5 - test/dynamo/**
8 - torch/_inductor/**
9 - test/inductor/**
12 - torch/_decomp/**
13 - torch/_dynamo/**
14 - torch/_export/**
15 - torch/_inductor/**
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/external/pytorch/
DDockerfile1 # syntax=docker/dockerfile:1
3 # NOTE: Building this image require's docker version >= 23.0.
6 # - https://docs.docker.com/build/dockerfile/frontend/#stable-channel
11 FROM ${BASE_IMAGE} as dev-base
12 RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
13 build-essential \
14 ca-certificates \
19 libjpeg-dev \
20 libpng-dev && \
21 rm -rf /var/lib/apt/lists/*
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DRELEASE.md3 <!-- toc -->
5 - [Release Compatibility Matrix](#release-compatibility-matrix)
6 - [Release Cadence](#release-cadence)
7 - [General Overview](#general-overview)
8 - [Frequently Asked Questions](#frequently-asked-questions)
9 - [Cutting a release branch preparations](#cutting-a-release-branch-preparations)
10 - [Cutting release branches](#cutting-release-branches)
11 - [`pytorch/pytorch`](#pytorchpytorch)
12 - [`pytorch/builder` / PyTorch domain libraries](#pytorchbuilder--pytorch-domain-libraries)
13- [Making release branch specific changes for PyTorch](#making-release-branch-specific-changes-for
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/external/pytorch/.ci/pytorch/
Dbuild.sh3 set -ex
6 # (This is set by default in the Docker images we build, so you don't
11 # shellcheck source=./common-build.sh
12 source "$(dirname "${BASH_SOURCE[0]}")/common-build.sh"
14 if [[ "$BUILD_ENVIRONMENT" == *-mobile-*build* ]]; then
15 exec "$(dirname "${BASH_SOURCE[0]}")/build-mobile.sh" "$@"
19 python --version
22 gcc --version
25 cmake --version
32 export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libjemalloc.so.2
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Dtest.sh4 # (This is set by default in the Docker images we build, so you don't
7 set -ex
18 # Workaround for dind-rootless userid mapping (https://github.com/pytorch/ci-infra/issues/96)
19 WORKSPACE_ORIGINAL_OWNER_ID=$(stat -c '%u' "/var/lib/jenkins/workspace")
23 echo "For more details refer to https://github.com/sudo-project/sudo/issues/42"
24 sudo chown -R "$WORKSPACE_ORIGINAL_OWNER_ID" /var/lib/jenkins/workspace
29 sudo chown -R jenkins /var/lib/jenkins/workspace
30 git config --global --add safe.directory /var/lib/jenkins/workspace
36 TORCH_INSTALL_DIR=$(python -c "import site; print(site.getsitepackages()[0])")/torch
41 BUILD_DIR="build"
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/external/tensorflow/tensorflow/tools/ci_build/release/
Dcommon.sh8 # http://www.apache.org/licenses/LICENSE-2.0
31 if [[ $? -ne 0 ]]; then
49 set -x
55 mkdir -p /tmpfs/bazel_output
62 case "$(uname -s)" in
63 Darwin) local name=bazelisk-darwin-amd64 ;;
65 case "$(uname -m)" in
66 x86_64) local name=bazelisk-linux-amd64 ;;
67 aarch64) local name=bazelisk-linux-arm64 ;;
68 *) die "Unknown machine type: $(uname -m)" ;;
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/external/rust/android-crates-io/crates/grpcio-sys/grpc/tools/run_tests/
Drun_tests.py8 # http://www.apache.org/licenses/LICENSE-2.0
183 """Returns True if running running as a --use_docker child."""
198 build = (config_vars.shell + config_vars.builder +
205 return PythonConfig(name, build, run)
219 # Bit-ness is handled by the test machine's environment
262 …# cmake + ninja build is the default because it is faster and supports boringssl assembly optimiza…
274 # required to pass as cmake "-A" configuration for VS builds (but not for Ninja)
278 … # "x64_x86" means create 32bit binaries, but use 64bit toolkit to secure more memory for the build
284 # something illegal when not running under docker.
295 self._cmake_configure_extra_args.append('-DOPENSSL_NO_ASM=ON')
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/external/grpc-grpc/tools/run_tests/
Drun_tests.py8 # http://www.apache.org/licenses/LICENSE-2.0
197 """Returns True if running running as a --use_docker child."""
215 build = (
227 return PythonConfig(name, build, run, python_path)
248 # Bit-ness is handled by the test machine's environment
300 …# cmake + ninja build is the default because it is faster and supports boringssl assembly optimiza…
319 # required to pass as cmake "-A" configuration for VS builds (but not for Ninja)
325 … # "x64_x86" means create 32bit binaries, but use 64bit toolkit to secure more memory for the build
333 # something illegal when not running under docker.
352 # cmake doesn't build boringssl tests
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/external/pytorch/.github/scripts/
Dgql_mocks.json.gz ... xla hash.", 13 "headRefName": "update-xla-commit-hash/5573005593-54-1", ...
/external/zstd/
DCHANGELOG2 fix: compression bug in 32-bit mode associated with long-lasting sessions
7 perf: much faster speed for `--patch-from` at high compression levels (#4276)
8 perf: higher `--patch-from` compression ratios, notably at high levels (#4288)
12 perf: runtime bmi2 detection enabled on x86 32-bit mode (#4251)
13 cli: multi-threading as default CLI setting, by @daniellerozenblit
14 cli: new `--max` command (#4290)
15 build: improve `msbuild` version autodetection, support VS2022, by @ManuelBlanc
16 build: fix `meson` build by @artem and @Victor-C-Zhang, and on Windows by @bgilbert
17 build: compatibility with Apple Framework, by @Treata11
18 build: improve icc/icx compatibility, by @josepho0918 and @luau-project
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/external/mesa3d/docs/relnotes/
D19.3.0.rst1 Mesa 19.3.0 Release Notes / 2019-12-12
21 ---------------
25 5fa0e4e9dca79560f6882e362f9db36d81cf96da16cf6a84e0ada7466a99a5d7 mesa-19.3.0.tar.xz
28 ------------
30 - GL_ARB_gl_spirv on i965, iris.
31 - GL_ARB_spirv_extensions on i965, iris.
32 - GL_EXT_demote_to_helper_invocation on iris, i965.
33 - OpenGL 4.6 on i965, iris.
34 - EGL_EXT_image_flush_external
35 - VK_ANDROID_external_memory_android_hardware_buffer on RADV.
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D23.3.0.rst1 Mesa 23.3.0 Release Notes / 2023-11-29
20 ---------------
24 50f729dd60ed6335b989095baad81ef5edf7cfdd4b4b48b9b955917cb07d69c5 mesa-23.3.0.tar.xz
28 -----------
29 - NVK: A Vulkan driver for Nvidia hardware
32 ------------
33 - VK_EXT_pipeline_robustness on ANV
34 - VK_KHR_maintenance5 on RADV
35 - OpenGL ES 3.1 on Asahi
36 - GL_ARB_compute_shader on Asahi
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D23.2.1.rst1 Mesa 23.2.1 Release Notes / 2023-09-28
23 ---------------
27 64de0616fc2d801f929ab1ac2a4f16b3e2783c4309a724c8a259b20df8bbc1cc mesa-23.2.1.tar.xz
32 ------------
34 - VK_EXT_attachment_feedback_loop_dynamic_state on RADV
36 - extendedDynamicState3SampleLocationsEnable on RADV
38 - VK_EXT_dynamic_rendering_unused_attachments on RADV
40 - VK_EXT_mesh_shader on lavapipe
42 - OpenGL 3.1 on Asahi
44 - OpenGL ES 3.0 on Asahi
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/external/autotest/client/common_lib/
Dutils.py3 # Use of this source code is governed by a BSD-style license that can be
13 # pylint: disable=missing-docstring
69 # pylint: disable=wildcard-import
110 '_-+=>|')
242 # large performance hit due to a docker + python2 bug. Eventually
297 if len(data[-1]) == 0:
368 return (1 << 32) - (1 << 32-bits)
414 cpu 324 345 34 5 345
470 Read a key-value pair format file into a dictionary, and return it.
484 pattern = r'^([-\.\w]+)\{%s\}=(.*)$' % type_tag
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/external/pytorch/torch/_inductor/
Dcodecache.py1 # mypy: allow-untyped-defs
86 from triton.fb.build import _run_build_command
105 def use_global_cache() -> bool:
119 def cpp_wrapper_cache_dir(name: str) -> str:
121 "cpu"
141 def get_system() -> Dict[str, Any]:
168 json.dumps(system, sort_keys=True).encode("utf-8")
176 def get_local_cache_path() -> Path:
181 def get_global_cache_path() -> Optional[Path]:
188 def __init__(self) -> None:
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/external/mbedtls/
DChangeLog3 = Mbed TLS 3.5.2 branch released 2024-01-26
14 could result in an integer overflow, causing a zero-length buffer to be
18 = Mbed TLS 3.5.1 branch released 2023-11-06
21 * Mbed TLS is now released under a dual Apache-2.0 OR GPL-2.0-or-later
28 = Mbed TLS 3.5.0 branch released 2023-10-05
31 * Mbed TLS 3.4 introduced support for omitting the built-in implementation
32 of ECDSA and/or EC J-PAKE when those are provided by a driver. However,
33 there was a flaw in the logic checking if the built-in implementation, in
36 accelerated and still have the built-in implementation compiled out.
39 considered not accelerated, and the built-in implementation of the curves
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/external/openthread/third_party/mbedtls/repo/
DChangeLog3 = Mbed TLS 3.6.0 branch released 2024-03-28
42 * Support Armv8-A Crypto Extension acceleration for SHA-256
43 when compiling for Thumb (T32) or 32-bit Arm (A32).
44 * AES-NI is now supported in Windows builds with clang and clang-cl.
50 This affects both the low-level modules and the high-level APIs
53 * Support use of Armv8-A Cryptographic Extensions for hardware acclerated
54 AES when compiling for Thumb (T32) or 32-bit Arm (A32).
55 * If a cipher or AEAD mechanism has a PSA driver, you can now build the
56 library without the corresponding built-in implementation. Generally
58 or they'll both be built in. However, for CCM and GCM the built-in
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/external/tensorflow/
DRELEASE.md78 …xperimental/SharpnessAwareMinimization). This class implements the sharpness-aware minimization te…
82 …* Added support for cross-trainer data caching in tf.data service. This saves computation resour…
86 …_BASED, then it runs a new algorithm that can get the same performance with lower CPU/memory usage.
91 …mptionCheckpointHandler) to handle worker preemption/maintenance and cluster-wise consistent error…
95 …ge from 8 to 100 times depending on the size of k. When running on CPU and GPU, a non-optimized XL…
117 …inistically (and `tf.errors.UnimplementedError` is no longer thrown) when op-determinism has been …
141-Review-Doctor, DEKHTIARJonathan, Deven Desai, Djacon, Duncan Riach, fedotoff, fo40225, Frederic B…
163 …ers should switch to [TensorFlow Decision Forests](https://github.com/tensorflow/decision-forests).
164 * Build, Compilation and Packaging
166 …eels now specifically conform to [manylinux2014](https://peps.python.org/pep-0599/), an upgrade fr…
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