跳到主要內容

tensorflow

TensorFlow Docker requirements
  1. Install Docker on your local host machine.
  2. For GPU support on Linux, install nvidia-docker.



Docker is the easiest way to enable TensorFlow GPU support on Linux since only the NVIDIA® GPU driver is required on the host machine 
(the NVIDIA® CUDA® Toolkit does not need to be installed).
docker run [-it] [--rm] [-p hostPort:containerPort] tensorflow/tensorflow[:tag] [command]


$ docker run -it --rm tensorflow/tensorflow    python -c "import tensorflow as tf; print(tf.__version__)"


Note: nvidia-docker v1 uses the nvidia-docker alias, where v2 uses docker --runtime=nvidia.


CUDA 9.0 for TensorFlow < 1.13.0

nvidia-docker2 intsall

prerequisites:NVIDIA driver and Docker .
If you have a custom /etc/docker/daemon.json, the nvidia-docker2 package might override it.

Ubuntu 14.04/16.04/18.04, 

Ubuntu will install docker.io by default which isn't the latest version of Docker Engine. This implies that you will need to pin the version of nvidia-docker. 
# If you have nvidia-docker 1.0 installed: we need to remove it and all existing GPU containers
docker volume ls -q -f driver=nvidia-docker | xargs -r -I{} -n1 docker ps -q -a -f volume={} | xargs -r docker rm -f
sudo apt-get purge -y nvidia-docker

# Add the package repositories
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | \
  sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \
  sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update

# Install nvidia-docker2 and reload the Docker daemon configuration
sudo apt-get install -y nvidia-docker2
sudo pkill -SIGHUP dockerd

# Test nvidia-smi with the latest official CUDA image
docker run --runtime=nvidia --rm nvidia/cuda:9.0-base nvidia-smi
ref:
https://github.com/NVIDIA/nvidia-docker



CUDA toolkit versionDriver versionGPU architecture
6.5>= 340.29>= 2.0 (Fermi)
7.0>= 346.46>= 2.0 (Fermi)
7.5>= 352.39>= 2.0 (Fermi)
8.0== 361.93 or >= 375.51== 6.0 (P100)
8.0>= 367.48>= 2.0 (Fermi)
9.0>= 384.81>= 3.0 (Kepler)
9.1>= 387.26>= 3.0 (Kepler)
9.2>= 396.26>= 3.0 (Kepler)
10.0>= 384.130, < 385.00Tesla GPUs
10.0>= 410.48>= 3.0 (Kepler)


CUDA images come in three flavors and are available through the NVIDIA public hub repository.

  • base: starting from CUDA 9.0, contains the bare minimum (libcudart) to deploy a pre-built CUDA application.
    Use this image if you want to manually select which CUDA packages you want to install.
  • runtime: extends the base image by adding all the shared libraries from the CUDA toolkit.
    Use this image if you have a pre-built application using multiple CUDA libraries.
  • devel: extends the runtime image by adding the compiler toolchain, the debugging tools, the headers and the static libraries.
    Use this image to compile a CUDA application from sources.


留言

這個網誌中的熱門文章

A3C in ATARI Pong-V0

ATARI PONG 對戰模式,左邊為遊戲程式,右邊為訓練中的A3C模型。一局以21分決勝負,對手MISS 一球得一分。從LOG可以看出,A3C模型從最初全敗的輸21分,經過2小時左右的TRAINING,已經逆轉至幾乎每局都勝利,偶爾甚至勝出高達13分。 底下為TRAINING A3C MODEL過程的LOG, (base) frank@viper1:~/a3c$ python main.py --env-name "Pong-v0" --num-processes 8 Time 00h 00m 10s, episode reward -21.0, episode length 1026 Time 00h 01m 18s, episode reward -21.0, episode length 1020 Time 00h 02m 26s, episode reward -21.0, episode length 1029 Time 00h 03m 35s, episode reward -21.0, episode length 1023 Time 00h 04m 42s, episode reward -21.0, episode length 1014 Time 00h 05m 50s, episode reward -21.0, episode length 1087 Time 00h 07m 00s, episode reward -21.0, episode length 1359 Time 00h 08m 14s, episode reward -16.0, episode length 1922 Time 00h 09m 30s, episode reward -14.0, episode length 2220 Time 00h 10m 48s, episode reward -15.0, episode length 2431 Time 00h 12m 04s, episode reward -15.0, episode length 2211 Time 00h 13m 23s, episode reward -8.0, episode length 2600 Time 00h 14m 38s, episod...

OCR應用在電子元件上的辨識

 OCR Application Example1: for SMD idenfication : Text detect by CRAFT   OCR文字偵測 原始照片為網路上下載,再套上OCR文字偵測顯示結果,若有侵權請告知移除 彩色區域為偵測到文字的部份 Output 10 coordinates of corresponding text blocks 1.  144,196,286,194,287,259,145,261 2.  298,198,509,196,509,259,298,262 3.  148,262,286,262,286,321,148,321 4.  368,266,513,264,513,321,369,323 5.  145,331,472,333,471,395,145,393 6.  146,404,445,404,445,454,146,454 7.  146,453,512,453,512,502,146,502 8.  147,502,481,499,481,551,148,553 9.  148,550,614,550,614,600,148,600 10.513,600,714,600,714,648,513,648  After image pre-processing:    OCR result1:   After image pre-processing:  OCR result2:     Example2: for datasheet interpretation : Text detect of TI datasheet by CRAFT OCR results: ([[75, 11], [127, 11], [127, 31], [75, 31]], 'TEXAS', 0.999188403930061) ([[474, 4], [928, 4], [928, 32], [474, 32]], 'PACKAGE MATERIALS INFORMATION', 0.6743955072876302) ([[77, 29],...