Public Repository

Last pushed: 4 months ago
Short Description
All the packages required for Deep Learning Machine.
Full Description

Before installing Nvidia docker you need to have docker installed.

Please follow the instructions in the website of docker

https://store.docker.com/editions/community/docker-ce-server-ubuntu?tab=description

Install nvidia-docker and nvidia-docker-plugin on Ubuntu

https://github.com/NVIDIA/nvidia-docker (Instructions for installing docker)

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

Here are Docker files for running the latest Deep learning machine. It contains the below deep learning packages with GPU Support.

Installed Packages.

Ubuntu 16.04
CUDA 8
CUDNN 5
python 2.7
DIGITS 5
./root/digits-devserver
CAFFE
Tensorflow
Torch 7
Theona 0.9.0(Bleeding-edge version)
Keras
Lasagne-0.2.dev1 (Bleeding-edge version)
jupyer notebook/ipython
SciPy
numpy-1.12.1
Pandas
Supported following Nvidia drivers
nvidia-331
nvidia-346
nvidia-352
nvidia-304
nvidia-340
nvidia-361
nvidia-367
nvidia-375

Setup
Prerequisites
Install Docker following the installation guide for your platform: https://docs.docker.com/engine/installation/
GPU Version Only: Install nvidia-docker: https://github.com/NVIDIA/nvidia-docker, following the instructions here. https://github.com/NVIDIA/nvidia-docker/wiki/Installation

Download the Docker image from
docker pull sudhanlogics/deep_learning_machine

Running the container
docker images (find image ID)
nvidia-docker run -it --cap-add SYS_ADMIN --device /dev/fuse --security-opt apparmor:unconfined -p 8888:8888 -p 5000:22 -p 8080:8080 image_ID /bin/bash

Port: 8080:8080 to run digits
Port: 8888:8888 to run jupyter
port: 5000:22 to map ssh

After entering into Container
(Start SSH server)
Service ssh start

Running Digits
./root/digits/digits-devserver -p 8080 &
or
service digits start

Running jupyer notebook
jupyter notebook --port 8888 --allow-root

To map run jupyter in local browser you have to map Jupyter to local machine from remote machine

You can do that using SSH and PUTTY in windows

SSH username: root
SSH password: deeplearning

  1. open putty client
  2. Session -> put your hostname and port = 5000, just as what you did normally to connect to you remote server.
    Connection -> SSH -> Tunnels
    In the destination port box, enter: 127.0.0.1:8888, And select Local and Auto options.
    In the Source port, enter: 8888, Then click the Add button,
    you will see L8888 127.0.0.1:8888 in the box under the Remove button.

Now you can open the session, and on your local machine, type
http://localhost:8888/?token=fcd27f6e8c2bc295736def31923a35d7962c7dd9bc735c88
The token changes for each session. So please copy the complete address generated by jupyter when you run it.

Mount S3 Bucket inside the container

S3FS needs AWS access key id and secret key to work. If you dont have the access key and secret key get one from amazon.
vi /etc/passwd-s3fs
Accesskey:Secretkey

For this setup i have already have a access key and secret key. So now you go with mounting.
eg: s3fs $bucketname /mnt/s3
s3fs deep-learning-machine /mnt/s3

once the mount is done check with mount command. It should show the mount location.

Docker Pull Command
Owner
sudhanlogics

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