This contains a Docker image for training a new DeepSEA model (v0.93). If you are looking for a docker that predicts with the published DeepSEA model on 919 epigenomics features without re-training, check out our another repo.
We have set up an automated build here. Pushes should automatically trigger builds on the Docker hub. If you want to build a local image, you can run:
docker build -t giffordlab/deepsea-docker .
You can get the (public) automated build image from any machine, including EC2 ones, by running:
docker pull giffordlab/deepsea-docker
Prerequisites are Docker and having the NVIDIA 346.46 driver installed (the default for CUDA 7.0; see the upstream image documentation).
Run the image with:
docker run -it --rm \
--device /dev/nvidia0 \
--device /dev/nvidia1 \
--device /dev/nvidia2 \
--device /dev/nvidia-uvm \
--device /dev/nvidiactl \
giffordlab/deepsea-docker
This will launch the training script (main.lua) with a subsampled input file
that's part of the image (train.mat). Their full training input
file is around 4 GB and therefore is not on Github.
Check the main.lua source for command-line options. Right now, the
only way to change the input is to use docker run -v to replace
/root/deepsea/input with a physical directory containing files
train.mat and valid.mat (formats TBD).
To capture the output, run commands interactively or mount
/root/deepsea/results to a physical directory (you can use the
-save option in main.lua for more flexibility).
The original code is from the Troyanskaya lab, see here.