If you've never used Docker before, check out the getting started guide.
Up-to-date prebuilt images can be found on Docker hub.
cpu-latest
,cpu
,cpu-0.1
,latest
cpu-jupyter-kubeflow-latest
,cpu-jupyter-kubeflow
,cpu-jupyter-kubeflow-0.1
gpu-latest
,gpu
,gpu-0.1
gpu-jupyter-kubeflow-latest
,gpu-jupyter-kubeflow
,gpu-jupyter-kubeflow-0.1
The CPU images should run out of the box without any driver prerequisites.
To use the GPU images, you need to have:
- The Nvidia drivers
- The Nvidia-docker container runtime
- Docker configured to use the Nvidia container runtime
Once you have those installed, you should be ready to start running the GPU instances.
To make sure your GPU instance is setup correctly, run the following in a container:
import dlib
print(dlib.cuda.get_num_devices())
The Jupyter images are built to be deployed on Kubeflow. However, if you just want to run a normal Jupyter instance, they're a great template to build your own.
Here's an example Dockerfile using the prebuilt images:
FROM animcogn/face_recognition:gpu
COPY requirements.txt requirements.txt
RUN pip3 install -r ./requirements.txt
COPY my_app /my_app
CMD [ "python3", "/my_app/my_app.py" ]