I set up my own Ubuntu 16.4 machine with a GTX 1060 -6GB (i wish i had gotten the 1070 with 8GB since it ran out of memory on the first lesson)

After installing Ubuntu my setup was roughly the following:

Install CUDA 8.0 and cuDNN

https://developer.nvidia.com/cuda-downloads84
https://developer.nvidia.com/cudnn86 ( You will need to register)

Anaconda and Python

sudo apt install unzip
wget https://repo.continuum.io/archive/Anaconda2-4.2.0-Linux-x86_64.sh11
bash Anaconda2-4.2.0-Linux-x86_64.sh
conda create -n fastai34 python=3.4
source activate fastai34;

conda install matplotlib
conda install cloudpickle
conda install opencv
conda install pandas
conda install bcolz
conda install scikit-learn
conda install theano
conda install keras
conda install jupyter

switch keras to user theano

.keras/keras.json ->

{
"image_dim_ordering": "th",
"epsilon": 1e-07,
"floatx": "float32",
"backend": "theano"
}

create .theanorc ->

[global]
floatX = float32
device = gpu0

[nvcc]
fastmath = True

[cuda]
root=/usr/local/cuda/

mkdir lesson1
cd lesson1/
wget http://www.platform.ai/files/nbs/lesson1.ipynb18
wget http://www.platform.ai/files/nbs/utils.zip14
wget http://www.platform.ai/files/nbs/vgg16.zip10
wget http://www.platform.ai/files/dogscats.zip9
unzip -q vgg16.zip
unzip -q utils.zip
unzip dogscats.zip

modify the lesson1 notebook
“from imp import reload” above “import utils; reload(utils)”

On login, switch to your fastai34 env

source activate fastai34;

Run jupyter notebook remotely

ssh -L 8888:127.0.0.1:8888 <machine address >
jupyter notebook --no-browser &