wheels 다운로드 - wheels 소스 코드 다운로드

wheels

기타 소스코드

Tensor

다운로드

텐서 플로우 최적화 휠

SSE, AVX 및 FMA를 포함한 플랫폼 최적화로 텐서 플로우를위한 맞춤형 구축. Stock pip install tensorflow 와 함께 다음과 같은 메시지를보고 있다면 올바른 위치에 왔습니다.

 The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.

or:
Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA

이 휠은 클라우드 머신 러닝 플랫폼 인 Tinymind에서 사용하기 위해 제작되었습니다. 자신의 Linux 상자 (Ubuntu 16.04 LTS)에 설치하려면 다음을 수행 할 수 있습니다.

 # RELEASE is the git tag like tf1.1-cpu. WHEEL is the full wheel name.
pip --no-cache-dir install https://github.com/mind/wheels/releases/download/{RELEASE}/{WHEEL}

모든 바퀴 목록은 릴리스 페이지에서 찾을 수 있습니다.

버전

아래 링크를 클릭하여 특정 릴리스 버전으로 이동하십시오. 다시 말하지만, 그들은 달리 명시되지 않는 한 Ubuntu 16.04 LTS를 위해 지어졌습니다.

TF	빌드
1.1	CPU, GPU
1.2	CPU, GPU (Python 3.6 만 해당)
1.2.1	CPU, GPU
1.3	CPU, MPI가있는 GPU
1.3.1	CPU, CPU 디버그, GPU, MPI가있는 GPU
1.4	CPU, CPU 디버그, CPU MACOS, GPU (CUDA 8, COMPUTE 3.7 용 CUDA 9, CUDA 9는 3.7/6.0/7.0, CUDA 9 Generic, MKL이없는 CUDA 9)
1.4.1	CPU, GPU (CUDA 8, CUDA 9, CUDA 9.1)
1.5	CPU, GPU (CUDA 9, MKL없는 CUDA 9, CUDA 9.1, MKL없는 CUDA 9.1)
1.6	CPU, GPU (CUDA 9.1, MKL없는 CUDA 9.1)
1.7	CPU, GPU (CUDA 9, CUDA 9.1, CUDNN 7.1)

기계에는 비교적 새로운 인텔 CPU (및 GPU 버전을 사용하는 경우 NVIDIA GPU)가 있어야 아래 바퀴와 호환됩니다. 하드웨어가 최신 인 경우 휠이 작동하지 않습니다.

텐서 플로우 1.4.1 이상에 대한 휠에는 GCP, S3 및 Hadoop에 대한 지원이 포함되어 있습니다. 컴파일 플래그에는 다음이 포함됩니다.

 --config=opt --config=cuda --cxxopt=-D_GLIBCXX_USE_CXX11_ABI=0 --copt=-mavx --copt=-msse4.1 --copt=-msse4.2 --copt=-mavx2 --copt=-mfma --copt=-mfpmath=both

필요한 바퀴는 아래에 나열되어 있습니다. 무언가가 필요하거나 바퀴가 효과가 없습니까? 문제를 제출하십시오. (불행히도, 우리는 Windows 기계가 없기 때문에 Windows 휠 요청을 수용 할 수 없습니다.)

버전	파이썬	아치	링크
1.1	2.7	CPU	https://github.com/mind/wheels/releases/download/tf1.1-cpu/tensorflow-1.1.0-cp27-cp27mu-linux_x86_64.whl
1.1	3.5	CPU	https://github.com/mind/wheels/releases/download/tf1.1-cpu/tensorflow-1.1.0-cp35-cp35m-linux_x86_64.whl
1.1	3.6	CPU	https://github.com/mind/wheels/releases/download/tf1.1-cpu/tensorflow-1.1.0-cp36-cp36m-linux_x86_64.whl
1.1	2.7	GPU	https://github.com/mind/wheels/releases/download/tf1.1-gpu/tensorflow-1.1.0-cp27-cp27mu-linux_x86_64.whl
1.1	3.5	GPU	https://github.com/mind/wheels/releases/download/tf1.1-gpu/tensorflow-1.1.0-cp35-cp35m-linux_x86_64.whl
1.1	3.6	GPU	https://github.com/mind/wheels/releases/download/tf1.1-gpu/tensorflow-1.1.0-cp36-cp36m-linux_x86_64.whl
1.2	2.7	CPU	https://github.com/mind/wheels/releases/download/tf1.2-cpu/tensorflow-1.2.0-cp27-cp27mu-linux_x86_64.whl
1.2	3.5	CPU	https://github.com/mind/wheels/releases/download/tf1.2-cpu/tensorflow-1.2.0-cp35-cp35m-linux_x86_64.whl
1.2	3.6	CPU	https://github.com/mind/wheels/releases/download/tf1.2-cpu/tensorflow-1.2.0-cp36-cp36m-linux_x86_64.whl
1.2	3.6	GPU	https://github.com/mind/wheels/releases/download/tf1.2-gpu/tensorflow-1.2.0-cp36-cp36m-linux_x86_64.whl
1.2.1	2.7	CPU	https://github.com/mind/wheels/releases/download/tf1.2.1-cpu/tensorflow-1.2.1-cp27-cp27mu-linux_x86_64.whl
1.2.1	3.5	CPU	https://github.com/mind/wheels/releases/download/tf1.2.1-cpu/tensorflow-1.2.1-cp35-cp35m-linux_x86_64.whl
1.2.1	3.6	CPU	https://github.com/mind/wheels/releases/download/tf1.2.1-cpu/tensorflow-1.2.1-cp36-cp36m-linux_x86_64.whl
1.2.1	2.7	GPU	https://github.com/mind/wheels/releases/download/tf1.2.1-gpu/tensorflow-1.2.1-cp27-cp27mu-linux_x86_64.whl
1.2.1	3.5	GPU	https://github.com/mind/wheels/releases/download/tf1.2.1-gpu/tensorflow-1.2.1-cp35-cp35m-linux_x86_64.whl
1.2.1	3.6	GPU	https://github.com/mind/wheels/releases/download/tf1.2.1-gpu/tensorflow-1.2.1-cp36-cp36m-linux_x86_64.whl
1.3	2.7	CPU	https://github.com/mind/wheels/releases/download/tf1.3-cpu/tensorflow-1.3.0-cp27-cp27mu-linux_x86_64.whl
1.3	3.5	CPU	https://github.com/mind/wheels/releases/download/tf1.3-cpu/tensorflow-1.3.0-cp35-cp35m-linux_x86_64.whl
1.3	3.6	CPU	https://github.com/mind/wheels/releases/download/tf1.3-cpu/tensorflow-1.3.0-cp36-cp36m-linux_x86_64.whl
1.3	2.7	GPU	https://github.com/mind/wheels/releases/download/tf1.3-gpu/tensorflow-1.3.0-cp27-cp27mu-linux_x86_64.whl
1.3	3.5	GPU	https://github.com/mind/wheels/releases/download/tf1.3-gpu/tensorflow-1.3.0-cp35-cp35m-linux_x86_64.whl
1.3	3.6	GPU	https://github.com/mind/wheels/releases/download/tf1.3-gpu/tensorflow-1.3.0-cp36-cp36m-linux_x86_64.whl
1.3.1	2.7	CPU	https://github.com/mind/wheels/releases/download/tf1.3.1-cpu/tensorflow-1.3.1-cp27-cp27mu-linux_x86_64.whl
1.3.1	3.5	CPU	https://github.com/mind/wheels/releases/download/tf1.3.1-cpu/tensorflow-1.3.1-cp35-cp35m-linux_x86_64.whl
1.3.1	3.6	CPU	https://github.com/mind/wheels/releases/download/tf1.3.1-cpu/tensorflow-1.3.1-cp36-cp36m-linux_x86_64.whl
1.3.1	2.7	GPU	https://github.com/mind/wheels/releases/download/tf1.3.1-gpu/tensorflow-1.3.1-cp27-cp27mu-linux_x86_64.whl
1.3.1	3.5	GPU	https://github.com/mind/wheels/releases/download/tf1.3.1-gpu/tensorflow-1.3.1-cp35-cp35m-linux_x86_64.whl
1.3.1	3.6	GPU	https://github.com/mind/wheels/releases/download/3.1-gpu/tensorflow-1.3.1-cp36-cp36m-linux_x86_64.whl
1.4	2.7	CPU	https://github.com/mind/wheels/releases/download/tf1.4-cpu/tensorflow-1.4.0-cp27-cp27mu-linux_x86_64.whl
1.4	3.5	CPU	https://github.com/mind/wheels/releases/download/tf1.4-cpu/tensorflow-1.4.0-cp35-cp35m-linux_x86_64.whl
1.4	3.6	CPU	https://github.com/mind/wheels/releases/download/tf1.4-cpu/tensorflow-1.4.0-cp36-cp36m-linux_x86_64.whl
1.4	2.7	GPU	https://github.com/mind/wheels/releases/download/tf1.4-gpu/tensorflow-1.4.0-cp27-cp27mu-linux_x86_64.whl
1.4	3.5	GPU	https://github.com/mind/wheels/releases/download/tf1.4-gpu/tensorflow-1.4.0-cp35-cp35m-linux_x86_64.whl
1.4	3.6	GPU	https://github.com/mind/wheels/releases/download/tf1.4-gpu/tensorflow-1.4.0-cp36-cp36m-linux_x86_64.whl
1.4.1	2.7	CPU	https://github.com/mind/wheels/releases/download/4.1-cpu/tensorflow-1.4.1-cp27-cp27mu-linux_x86_64.whl
1.4.1	3.5	CPU	https://github.com/mind/wheels/releases/download/tf1.4.1-cpu/tensorflow-1.4.1-cp35-cp35m-linux_x86_64.whl
1.4.1	3.6	CPU	https://github.com/mind/wheels/releases/download/4.1-cpu/tensorflow-1.4.1-cp36-cp36m-linux_x86_64.whl
1.4.1	2.7	GPU	https://github.com/mind/wheels/releases/download/4.1-gpu/tensorflow-1.4.1-cp27-cp27mu-linux_x86_64.whl
1.4.1	3.5	GPU	https://github.com/mind/wheels/releases/download/4.1-gpu/tensorflow-1.4.1-cp35-cp35m-linux_x86_64.whl
1.4.1	3.6	GPU	https://github.com/mind/wheels/releases/download/4.1-gpu/tensorflow-1.4.1-cp36-cp36m-linux_x86_64.whl
1.5	2.7	CPU	https://github.com/mind/wheels/releases/download/tf1.5-cpu/tensorflow-1.5.0-cp27-cp27mu-linux_x86_64.whl
1.5	3.5	CPU	https://github.com/mind/wheels/releases/download/tf1.5-cpu/tensorflow-1.5.0-cp35-cp35m-linux_x86_64.whl
1.5	3.6	CPU	https://github.com/mind/wheels/releases/download/tf1.5-cpu/tensorflow-1.5.0-cp36-cp36m-linux_x86_64.whl
1.5	2.7	GPU	https://github.com/mind/wheels/releases/download/tf1.5-gpu/tensorflow-1.5.0-cp27-cp27mu-linux_x86_64.whl
1.5	3.5	GPU	https://github.com/mind/wheels/releases/download/tf1.5-gpu/tensorflow-1.5.0-cp35-cp35m-linux_x86_64.whl
1.5	3.6	GPU	https://github.com/mind/wheels/releases/download/tf1.5-gpu/tensorflow-1.5.0-cp36-cp36m-linux_x86_64.whl
1.6	2.7	CPU	https://github.com/mind/wheels/releases/download/tf1.6-cpu/tensorflow-1.6.0-cp27-cp27mu-linux_x86_64.whl
1.6	3.5	CPU	https://github.com/mind/wheels/releases/download/tf1.6-cpu/tensorflow-1.6.0-cp35-cp35m-linux_x86_64.whl
1.6	3.6	CPU	https://github.com/mind/wheels/releases/download/tf1.6-cpu/tensorflow-1.6.0-cp36-cp36m-linux_x86_64.whl
1.6	2.7	GPU	https://github.com/mind/wheels/releases/download/tf1.6-gpu-cuda91/tensorflow-1.6.0-cp27-cp27mu-linux_x86_64.whl
1.6	3.5	GPU	https://github.com/mind/wheels/releases/download/tf1.6-gpu-cuda91/tensorflow-1.6.0-cp35-cp35m-linux_x86_64.whl
1.6	3.6	GPU	https://github.com/mind/wheels/releases/download/tf1.6-gpu-cuda91/tensorflow-1.6.0-cp36-cp36m-linux_x86_64.whl

돕다!

이 섹션에는 설정 디버깅 팁이 포함되어 있습니다. 진지하게, tinymind를 시도해 보면 다시는 시간을 낭비 할 필요가 없습니다. 또한 자신의 기계에서 사용할 수있는 Docker 이미지가 있습니다. 이 섹션에서 문제가 해결되지 않으면 문제를 제기하십시오.

쿠다

다른 텐서 플로우 버전은 다른 CUDA 버전을 지원/요구합니다.

TF	쿠다	CUDNN	컴퓨팅 기능
1.1, 1.2	8.0	5.1	3.7 (K80)
1.2.1-1.3.1	8.0	6.0	3.7
1.4	8.0/9.0	6.0/7.0	3.7, 6.0 (P100), 7.0 (V100)
1.4.1	8.0/9.0/9.1	6.0/7.0	3.7, 6.0, 7.0
1.5	9.0/9.1	7.0	3.7, 6.0, 7.0
1.6	9.1	7.0	3.7, 6.0, 7.0
1.7	9.0/9.1	7.0/7.1	3.7, 6.0, 7.0

Tensorflow <1.4는 현재 버전 인 Cuda 9에서 작동하지 않습니다. sudo apt-get install cuda 대신 sudo apt-get install cuda-8-0 수행해야합니다. 텐서 플로의 CUDA 8 변형 1.4 CUDNN 6.0으로 이동하고 CUDA 9.X 변형은 CUDNN 7.X와 함께 이동합니다.

 # Install CUDA 8
curl -O http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
sudo apt-get update
sudo apt-get install cuda-8-0

# Install CUDA 9
curl -O http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.0.176-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1604_9.0.176-1_amd64.deb
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
sudo apt-get update
sudo apt-get install cuda

CUDA 관련 환경 변수가 올바르게 설정되어 있는지 확인하십시오.

 echo ' export CUDA_HOME=/usr/local/cuda ' >> ~ /.bashrc
echo ' export PATH=$PATH:$CUDA_HOME/bin ' >> ~ /.bashrc
echo ' export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_HOME/lib64 ' >> ~ /.bashrc
. ~ /.bashrc

올바른 CUDNN을 다운로드하여 다음과 같이 설치하십시오.

 # The cuDNN tar file.
tar xzvf cudnn-9.0-linux-x64-v7.0.tgz
sudo cp cuda/lib64/ * /usr/local/cuda/lib64/
sudo cp cuda/include/cudnn.h /usr/local/cuda/include/

libcupti 도서관이 누락 되었습니까? 그것을 설치하고 PATH 에 추가하십시오.

sudo apt-get install libcupti-dev
echo ' export LD_LIBRARY_PATH=/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH ' >> ~ /.bashrc

Tensorrt

특정 바퀴는 Tensorrt를 지원합니다. Tensorrt를 설치하려면 먼저 Nvidia 웹 사이트에서 다운로드 한 다음 실행하십시오.

sudo dpkg -i nv-tensorrt-repo-ubuntu1604-ga-cuda9.0-trt3.0.4-20180208_1-1_amd64.deb
sudo apt-get update
sudo apt-get install tensorrt

MKL

MKL은 Intel의 딥 러닝 커널 라이브러리로 CPU에 대한 신경망을 훨씬 빠르게 만듭니다. 없으면 다음과 같이 설치하십시오.

 # If you don't have cmake
sudo apt install cmake

git clone https://github.com/01org/mkl-dnn.git
cd mkl-dnn/scripts && ./prepare_mkl.sh && cd ..
mkdir -p build && cd build && cmake .. && make
sudo make install

echo ' export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib ' >> ~ /.bashrc