About

This repo contains docker images handy for running tensorflow, keras, opencv on GPU instance

Installation

1. Install/Update docker

./install-docker-ce.sh

2. Install nvidia drivers

Run

./install-nvidia-drivers.sh

Now reboot your server

sudo shutdown -r now

Check if drivers are working:

lsmod | grep nvidia

or

nvidia-smi

3. Install nvidia-docker

./install-nvidia-docker.sh

4. Build docker image

For GPU instance:

cd ./docker.gpu.py35.cv2.tf.keras
nvidia-docker build -t chayka/ml:gpu.py35.cv2.tf.keras .

For non-GPU instance:

cd ./docker.cpu.py35.cv2.tf.keras
nvidia-docker build -t chayka/ml:cpu.py35.cv2.tf.keras .

5. Install screen (optional)

That is optional but might be handy for running

[sudo] apt-get install -y screen 

Run

Enable nvidia gpu persistent mode

sudo nvidia-smi -pm 1

Run screen

If you plan to run time consuming task, say neural network training, better run it in connection independent environment:

screen

When disconnected and reconnected to the server, you can reconnect to your screen session:

screen -r

Run jupyter notebook

nvidia-docker run --rm -p 8888:8888 --mount type=bind,source=/home/$(whoami)/,target=/host/ chayka/ml:gpu.py35.cv2.tf.keras

Run bash

nvidia-docker run --rm -it --mount type=bind,source=/home/$(whoami)/,target=/host/ chayka/ml:gpu.py35.cv2.tf.keras /bin/bash

Attach to bash console in running container

Get running container id or alias:

docker ps -a

Let's say ours is nostalgic_fermat, then:

docker exec -it nostalgic_fermat /bin/bash