Currently Backend.AI is developed and tested under only *NIX-compatible platforms (Linux or macOS).
The development setup uses a mono-repository for the backend stack and a side-by-side repository checkout of the frontend stack. In contrast, the production setup uses per-service independent virtual environments and relies on a separately provisioned app proxy pool.
There are three ways to run both the backend and frontend stacks for development, as demonstrated in
그림 3, 그림 4, and 그림 5.
The installation guide in this page using
scripts/install-dev.sh covers all three cases because the only difference
is that how you launch the Web UI from the mono-repo.
Installation from Source
For the ease of on-boarding developer experience, we provide an automated script that installs all server-side components in editable states with just one command.
Install the followings accordingly to your host operating system.
Ensure that you have all of the Python versions specified in
pyenv. (both Python 3.9.x and Python 3.10.8 at the time of writing, but please consult your copy of
pants.tomlfor the latest information)
Check the prerequisites for Python build environment setup for your system.
Docker Compose (v2 required)
(For Linux aarch64/arm64 setups only) Rust to build Pants from its source
To avoid conflicts with your system Python such as macOS/XCode versions,
pants.toml is configured to search only
pyenv-provided Python versions.
In some cases, locale conflicts between the terminal client and the remote host may cause encoding errors when installing Backend.AI components due to Unicode characters in README files. Please keep correct locale configurations to prevent such errors.
Running the install-dev script
$ git clone https://github.com/lablup/backend.ai bai-dev $ cd bai-dev $ ./scripts/install-dev.sh
The script requires
sudo to check and install several system packages
This script will bootstrap Pants and creates the halfstack
docker compose with fixture population.
At the end of execution, the script will show several command examples about
launching the service daemons such as manager and agent.
You may execute this script multiple times when you encounter prerequisite errors and
Also check out additional options using
--help option, such as installing
the CUDA mockup plugin together, etc.
버전 22.09에서 변경: We have migrated to per-package repositories to a semi-mono repository that contains all Python-based components except plugins. This has changed the installation instruction completely with introduction of Pants.
To install multiple instances/versions of development environments using this script,
just clone the repository in another location and run
inside that directory.
It is important to name these working-copy directories differently not to confuse
docker compose so that it can distinguish the containers for each setup.
Unless you customize all port numbers by the options of
docker compose -f docker-compose.halfstack.current.yml down and
docker compose -f docker-compose.halfstack.current.yml up -d when switching
between multiple working copies.
By default, the script pulls the docker images for our standard Python kernel and TensorFlow CPU-only kernel. To try out other images, you have to pull them manually afterwards.
Currently there are many limitations on running deep learning images on ARM64 platforms, because users need to rebuild the whole computation library stack, although more supported images will come in the future.
To install the webui in an editable state, try
--editable-webui flag option when running
Using the agent’s cgroup-based statistics without the root privilege (Linux-only)
To allow Backend.AI to collect sysfs/cgroup resource usage statistics, the Python executable must have the following Linux capabilities:
$ sudo setcap \ > cap_sys_ptrace,cap_sys_admin,cap_dac_override+eip \ > $(readlink -f $(pyenv which python))
Refer the instructions displayed after running
We recommend to use tmux to open
multiple terminals in a single SSH session.
Your terminal app may provide a tab interface, but when using remote servers,
tmux is more convenient because you don’t have to setup a new SSH connection
whenever adding a new terminal.
Ensure the halfstack containers are running:
$ docker compose -f docker-compose.halfstack.current.yml up -d
Open a terminal for manager and run:
$ ./backend.ai mgr start-server --debug
Open another terminal for agent and run:
$ ./backend.ai ag start-server --debug
Open yet another terminal for client and run:
$ export BACKEND_ENDPOINT=http://127.0.0.1:8081/ # change the port number if customized $ export BACKEND_ACCESS_KEY=AKIAIOSFODNN7EXAMPLE $ export BACKEND_SECRET_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY $ ./backend.ai config $ ./backend.ai run python -c 'print("hello world")' ∙ Session token prefix: fb05c73953 ✔  Session fb05c73953 is ready. hello world ✔  Execution finished. (exit code = 0) ✔  Cleaned up the session. $ ./backend.ai ps
Resetting the environment
Shutdown all docker containers using
docker compose -f docker-compose.halfstack.current.yml down and delete the entire working copy directory. That’s all.
You may need
sudo to remove the directories mounted as halfstack container volumes
because Docker auto-creates them with the root privilege.
Check out Daily Development Workflows for your reference.