TensorFlow + Theano + Scikit-learn + Keras + Caffe and so on.
1.3K
you need to install [ NVIDIA-SMI 352.39 Driver Version: 352.39 ] and CuDNN v4 on your host machine first.
docker run -it --device /dev/nvidiactl --device /dev/nvidia-uvm --device /dev/nvidia0 -p 22 -p 6006 -p 8888 imcomking/ttskc
(if you get an error message [ "/dev/nvidia-uvm": lstat /dev/nvidia-uvm: no such file or directory ], first try to run [ nvidia-smi ] on your machine. because "/dev/nvidia-uvm" is created when nvidia-smi is called )
docker run -it -p 22 -p 6006 -p 8888 imcomking/ttskc
wget https://raw.githubusercontent.com/bi-lab/deeplearning_tutorial/master/setup_nbserver.py python setup_nbserver.py (enter the password) screen -S ipy ipython notebook --profile=nbserver (ctrl + AD)
passwd (enter your password) wget https://raw.githubusercontent.com/bi-lab/deeplearning_tutorial/master/sshd_config -O /etc/ssh/sshd_config /usr/sbin/sshd -D
(ssh/sftp user info) id : root
Cuda 7.5 CuDNN v4 TensorFlow r0.7(protobuf kDefaultTotalBytesLimit = 1024) Theano latest(2016.4.9) Scikit-learn latest(2016.4.9) Keras latest(2016.4.9) Caffe latest(2016.4.9) (path: /opt/caffe) Lasagne latest(2016.4.9) JSAnimation latest(2016.4.9) IPython Notebook 3.2.1 SSH/SFTP
build-essential
curl
git
libfreetype6-dev
libpng12-dev
libzmq3-dev
pkg-config
python-dev
python-numpy
python-pip
software-properties-common
swig
zip
zlib1g-dev \
cmake
wget
libatlas-base-dev
libboost-all-dev
libgflags-dev
libgoogle-glog-dev
libhdf5-serial-dev
libleveldb-dev
liblmdb-dev
libopencv-dev
libprotobuf-dev
libsnappy-dev
protobuf-compiler \
graphviz \
python-scipy
python-nose
python-setuptools
python-h5py \
python-matplotlib
python-yaml
libopenblas-dev
screen \
vim \
unzip
libatlas-dev
libhdf5-dev
libatlas3gf-base
pip Request six mpld3 pydotplus
Content type
Image
Digest
Size
2.8 GB
Last updated
over 10 years ago
docker pull imcomking/ttskc