Docker Images

We provide several prebuilt docker images to quickly try out TVM. These images are also helpful run through TVM demo and tutorials. You can get the docker images via the following steps. We need docker and nvidia-docker if we want to use cuda.

First, clone TVM repo to get the auxiliary scripts

git clone --recursive https://github.com/apache/incubator-tvm tvm

We can then use the following command to launch a tvmai/demo-cpu image.

/path/to/tvm/docker/bash.sh tvmai/demo-cpu

You can also change demo-cpu to demo-gpu to get a CUDA enabled image. You can find all the prebuilt images in https://hub.docker.com/r/tvmai/

This auxiliary script does the following things:

  • Mount current directory to /workspace

  • Switch user to be the same user that calls the bash.sh (so you can read/write host system)

  • Use the host-side network on Linux. Use the bridge network and expose port 8888 on macOS, because host networking driver isn’t supported. (so you can use jupyter notebook)

Then you can start a jupyter notebook by typing

jupyter notebook

You might see an error OSError: [Errno 99] Cannot assign requested address when starting a jupyter notebook on macOS. You can change the binding IP address by

jupyter notebook --ip=0.0.0.0

Note that on macOS, because we use bridge network, jupyter notebook will be reportedly running at an URL like http://{container_hostname}:8888/?token=.... You should replace the container_hostname with localhost when pasting it into browser.

Docker Source

Check out https://github.com/apache/incubator-tvm/tree/master/docker if you are interested in building your own docker images.