One-line Demo Setup

This meta-repository provides a docker-compose configuration to fire up a single-node Backend.AI cluster running on your PC (http://localhost:8081).

Prerequisites

Notes

  • This demo setup does not support GPUs.

All you have to do

  • Clone the repository

  • Check out the prerequisites above

  • docker-compose up -d

    • For Windows, docker-compose -f docker-compose.win-demo.yml up -d

  • Pull some kernel images to try out

Pulling kernel images

Pull the images on your host Docker daemon like:

$ docker pull lablup/kernel-python:latest
$ docker pull lablup/kernel-python-tensorflow:latest-dense
$ docker pull lablup/kernel-c:latest

By default this demo cluster already has metadata/alias information for all publicly available Backend.AI kernels, so you don’t have to manually register the pulled kernel information to the cluster but only have to pull those you want to try out.

Using Clients

To access this local cluster, set the following configurations to your favoriate Backend.AI client:

$ export BACKEND_ENDPOINT="http://localhost:8081"
$ export BACKEND_ACCESS_KEY="AKIAIOSFODNN7EXAMPLE"
$ export BACKEND_SECRET_KEY="wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY"

With our official Python client, you can do:

$ backend.ai run python -c "print('hello world')"
✔ Session 9c737d84724173354fa10445d0b35fe0 is ready.
hello world
✔ Finished. (exit code = 0)

$ backend.ai run python-tensorflow:latest-dense -c "import tensorflow as tf; print(tf.__version__)"
✔ Session 950713741d5ed43a191704f2cd375ff0 is ready.
1.5.0
✔ Finished. (exit code = 0)

WARNING: This demo configuration is highly insecure. DO NOT USE in production!

FAQ

  • When launching a kernel, it says “Service Unavailable”!

    • Each image has different default resource requirements and your Docker daemon may have a too small amount of resources. For example, TensorFlow images require 8 GiB or more RAM for your Docker daemon.

    • Or, you might have launched 30 kernel sessions already, which is the default limit for this demo setup.

  • What does the “dense” tag mean in the TensorFlow kernel images?

    • Images with “dense” tags are optimized for shared multi-tenancy environments. There is no difference in functionalities.