Skip to content

Latest commit

 

History

History
106 lines (78 loc) · 4.25 KB

Deploy-on-Amazon-EC2.md

File metadata and controls

106 lines (78 loc) · 4.25 KB
Raw

This page hasn't been updated for 2.0 yet

Contents

  1. Amazon AWS account setup
  2. GPU instance creation
  3. Container deployment

Amazon AWS account setup

Connect to your AWS management console

Step 1

Under VPC > Subnets

  1. Select your VPC ID
  2. Select the corresponding Availability Zone

[[images/ec2-vpc.png]]

Step 2

Under Identity and Access Management > Users

  1. Create a new user
  2. Save the newly generated pair Access Key / Secret Access Key
  3. Edit the user permissions and give it the policy AmazonEC2FullAccess

[[images/ec2-user-keys.png]]

[[images/ec2-user-policy.png]]

GPU instance creation

Before deploying GPU containers, we first need to provision an EC2 P2 instance.
Using Docker machine and the information above:

docker-machine create --driver amazonec2 \
                      --amazonec2-region us-west-2 \
                      --amazonec2-zone b \
                      --amazonec2-ami ami-efd0428f \
                      --amazonec2-instance-type p2.xlarge \
                      --amazonec2-vpc-id vpc-*** \
                      --amazonec2-access-key AKI*** \
                      --amazonec2-secret-key *** \
                      aws01

Once the provisioning is completed, we install the NVIDIA drivers and NVIDIA Docker on the newly created instance (using a Ubuntu 16.04 AMI).
Note that if you create a custom AMI, you could simply reuse it instead of doing what follows:

# Restart the instance first, to be sure we are running the latest installed kernel
docker-machine restart aws01

# SSH into the machine
docker-machine ssh aws01

# Install official NVIDIA driver package
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
sudo sh -c 'echo "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64 /" > /etc/apt/sources.list.d/cuda.list'
sudo apt-get update && sudo apt-get install -y --no-install-recommends linux-headers-generic dkms cuda-drivers

# Install nvidia-docker and nvidia-docker-plugin
wget -P /tmp https://github.com/NVIDIA/nvidia-docker/releases/download/v1.0.1/nvidia-docker_1.0.1-1_amd64.deb
sudo dpkg -i /tmp/nvidia-docker*.deb && rm /tmp/nvidia-docker*.deb
exit

# Reboot to complete installation of the NVIDIA driver
docker-machine restart aws01

Container deployment

First, setup your environment:

eval `docker-machine env aws01`
export NV_HOST="ssh://ubuntu@$(docker-machine ip aws01):"
ssh-add ~/.docker/machine/machines/aws01/id_rsa

Using nvidia-docker remotely you can now deploy your containers in the Amazon cloud:

$ nvidia-docker run --rm nvidia/cuda nvidia-smi
Wed May 17 17:13:09 2017       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 375.51                 Driver Version: 375.51                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla K80           Off  | 0000:00:1E.0     Off |                    0 |
| N/A   26C    P8    29W / 149W |      0MiB / 11439MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID  Type  Process name                               Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+