mjaskowski/caffe-gpu repository overview

⁠On AWS g2.2xlarge instance

1. Choose public AMI: ami-2cbf3e44
2. Once you ssh onto the instance run:

cd /usr/local/cuda/samples/1_Utilities/deviceQuery
make
./deviceQuery  ```
Check that everything is OK-ish
2. install docker:

sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-keys 36A1D7869245C8950F966E92D8576A8BA88D21E9 sudo sh -c "echo deb https://get.docker.com/ubuntu⁠ docker main > /etc/apt/sources.list.d/docker.list" sudo apt-get update sudo apt-get install lxc-docker

(Copied from [tleyden](http://tleyden.github.io/blog/2014/10/25/running-caffe-on-aws-gpu-instance-via-docker/))
3. run

DOCKER_NVIDIA_DEVICES="--device /dev/nvidia0:/dev/nvidia0 --device /dev/nvidiactl:/dev/nvidiactl --device /dev/nvidia-uvm:/dev/nvidia-uvm" sudo docker run -ti $DOCKER_NVIDIA_DEVICES mjaskowski/caffe-gpu /bin/bash``` 4. Now you are inside the docker, you can check that everything is ok by running:

cd /opt/caffe
make test && make runtest```

# Why care?
This container is similar to and heavily inspired by [tleyden5iwx/caffe-gpu-master](https://hub.docker.com/r/tleyden5iwx/caffe-gpu-master) but there you have support built on top of [pip](https://pypi.python.org/pypi/pip), whereas here whole [anaconda](http://docs.continuum.io/anaconda/install) is available with jupyter.

You can run jupyter server like this:

sudo docker run -d -p 0.0.0.0:8888:8888 $DOCKER_NVIDIA_DEVICES mjaskowski/caffe-gpu sh -c "ipython notebook --ip=* --no-browser"```

Then go to YOUR_AMAZON_PUBLIC_DNS:8888 and browse to /opt/caffe/examples and play happily!

Note! The jupyter notebook is not password protected!