1. AWS EC2 的建立
- AMI 选择
Ubuntu Server 14.04 LTS (HVM), SSD Volume Type - ami-48db9d28
- GPU Instance 选择
目前只有g2.2xlarge是最廉价的方案，里面的硬盘空间最大为60g
- 因此需要添加 EBS 硬盘来扩充空间
Root - /dev/sda1 60GB ebs - /dev/sdb 200GB
2. Access EC2 through ssh
使用ssh连接系统
weiwei_0903.pem 是下载到本地一个目录的key
然后执行下面语句
ssh -i "weiwei_0903.pem" [email protected]
- 让 know_host 记住这个IP地址即可
3. 加载 EBS 到刚才建立的 GPU Instance
- 查看EBS是不是存在
ubuntu@ip-*-*-*-*:~$ lsblk NAME MAJ:MIN RM SIZE RO TYPE MOUNTPOINT xvda 202:0 0 8G 0 disk `-xvda1 202:1 0 8G 0 part / xvdb 202:16 0 100G 0 disk /home/ubuntu/workspace
其中 xvdb 是我单独添加的 EBS 硬盘。在最初，MOUNTPOINT下的/home/ubuntu/workspace应该是没有的，可以通过下面的步骤完成。
查询EBS是否已经有 File System
[ec2-user ~]$ sudo file -s /dev/xvdb /dev/xvdb: data
- 返回值是data意味着这个device目前没有文件系统，需要进一步格式化
[ec2-user ~]$ sudo mkfs -t ext4 /dev/xvdb
- 再次查看
ec2-user ~]$ sudo file -s /dev/xvdb /dev/xvdb: Linux rev 1.0 ext4 filesystem data, UUID=1701d228-e1bd-4094-a14c-8c64d6819362 (needs journal recovery) (extents) (large files) (huge files)
- 挂载格式化好的device到当前目录
ubuntu@ip-*-*-*-*:~$ sudo mount /dev/xvdb workspace ubuntu@ip-*-*-*-*:~$ lsblk NAME MAJ:MIN RM SIZE RO TYPE MOUNTPOINT xvda 202:0 0 8G 0 disk `-xvda1 202:1 0 8G 0 part / xvdb 202:16 0 100G 0 disk /home/ubuntu/workspace
此时就可以看到xvdb这个硬盘的挂载点。
添加权限
$ sudo chmod go+rw workspace
- 关于其他Storage
/dev/sda = /dev/xvda in the instance 8Gb "EBS persistent storage" /dev/sdb = /dev/xvdb in the instance 400Gb "Non persistent storage"
- 查看 Storage
df -h
4. 安装相关软件
- 基本依赖库
sudo apt-get update
sudo apt-get upgrade
sudo apt-get install -y build-essential git python-pip libfreetype6-dev libxft-dev libncurses-dev libopenblas-dev gfortran python-matplotlib libblas-dev liblapack-dev libatlas-base-dev python-dev python-pydot linux-headers-generic linux-image-extra-virtual unzip python-numpy swig python-pandas python-sklearn unzip wget pkg-config zip g++ zlib1g-dev
sudo pip install -U pip
- Install Python2.7, Anconda, CUDA 7.5.178, CUDNN 7.0, Tensorflow 0.10.0
git clone https://gist.github.com/weiweikong/374e93d9ccb88ea45341268a06897259 aws-tensorflow-python2.7-setup
注意给bash文件权限
Set a folder to /mnt
# stop on error
set -e
############################################
# install into /mnt/bin
sudo mkdir -p /mnt/bin
sudo chown ubuntu:ubuntu /mnt/bin
- Install Anaconda
wget https://repo.continuum.io/archive/Anaconda2-4.1.1-Linux-x86_64.sh
bash Anaconda2-4.1.1-Linux-x86_64.sh -b -p /mnt/bin/anaconda2
rm Anaconda2-4.1.1-Linux-x86_64.sh
echo 'export PATH="/mnt/bin/anaconda2/bin:$PATH"' >> ~/.bashrc
- Install Required Packages
# install the required packages
sudo apt-get update && sudo apt-get -y upgrade
sudo apt-get -y install linux-headers-$(uname -r) linux-image-extra-`uname -r`
- Install CUDA 7.5
# install cuda wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_7.5-18_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1404_7.5-18_amd64.deb
rm cuda-repo-ubuntu1404_7.5-18_amd64.deb
sudo apt-get update
sudo apt-get install -y cuda
- Manually download CUDNN 7.5 and upload
scp -i your_pem_file.pem cudnn-7.5-linux-x64-v5.0-ga.tgz [email protected]:~/.
- Install cuDNN 7.5.1
# get cudnn
tar xvzf cudnn-7.5-linux-x64-v5.1.tgz
cd cuda
sudo cp lib64/* /usr/local/cuda/lib64/
sudo cp include/* /usr/local/cuda/include/
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
echo 'export CUDA_HOME=/usr/local/cuda
export CUDA_ROOT=/usr/local/cuda
export PATH=$PATH:$CUDA_ROOT/bin:$HOME/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_ROOT/lib64
' >> ~/.bashrc
- Install Tensorflow with only cuDNN 7.5.1 and Python 2.7
export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.10.0-cp27-none-linux_x86_64.whl /mnt/bin/anaconda2/bin/pip install $TF_BINARY_URL
- Install Caffe 依赖库
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
sudo apt-get install --no-install-recommends libboost-all-dev
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
- 配置 Caffe 文件并编译
cp Makefile.config.example Makefile.config
# Adjust Makefile.config (for example, if using Anaconda Python, or if cuDNN is desired)
mkdir build
cd build
cmake ..
make all
make test
make runtest
- Test Caffe Install
sh data/mnist/get_mnist.sh
sh examples/mnist/create_mnist.sh
sh examples/mnist/train_lenet.sh
- Monitor Code
# install monitoring programs
sudo wget https://git.io/gpustat.py -O /usr/local/bin/gpustat
sudo chmod +x /usr/local/bin/gpustat
sudo nvidia-smi daemon
sudo apt-get -y install htop
- Ref: How to install CUDA Toolkit and cuDNN for deep learning - PyImageSearch
4.1 Trouble Shooting
-
Caffe 遇到 locale::facet::_S_create_c_locale name not valid
add export LC_ALL="en_US.UTF-8" to bashrc
Ref:https://gist.github.com/wangruohui/679b05fcd1466bb0937f
Tensorflow 遇到 AttributeError: 'GFile' object has no attribute 'size'
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
- libdc1394 error: Failed to initialize libdc1394
sudo ln /dev/null /dev/raw1394
Cafee 遇到 Aborted at 1458527401 (unix time) try "date -d @1458527401" if you are using GNU date
Check Nvidia GPUs List
$ nvidia-smi
- Set Specific GPU Visible
CUDA_VISIBLE_DEVICES=1 Only device 1 will be seen
CUDA_VISIBLE_DEVICES=0,1 Devices 0 and 1 will be visible
CUDA_VISIBLE_DEVICES=”0,1” Same as above, quotation marks are optional
CUDA_VISIBLE_DEVICES=0,2,3 Devices 0, 2, 3 will be visible; device 1 is masked
目前选择两个GPU工作不会报错
Ref: https://github.com/BVLC/caffe/issues/1993
4.2 Access to EC2 using FileZilla
Download FileZilla and setup.
-
Add .pem key file
Edit -> Settings -> Connection -> SFTP
Select the .pem file and add it to the list.
-
Add EC2 connection
File -> Site Manager
Host: EC2 Public IP (Could be check under EC2 Console)
Protocol: SFTP
Login Type: Normal
User: ubuntu
Password: /
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