PyPI 官网下载 | backend.ai-agent-21.3.1.tar.gz

# Backend.AI Agent The Backend.AI Agent is a small daemon that does: * Reports the status and available resource slots of a worker to the manager * Routes code execution requests to the designated kernel container * Manages the lifecycle of kernel containers (create/monitor/destroy them) ## Package Structure * `ai.backend` - `agent`: The agent package - `docker`: A docker-based backend implementation for the kernel lifecycle interface. - `server`: The agent daemon which communicates with the manager and the Docker daemon - `watcher`: A side-by-side daemon which provides a separate HTTP endpoint for accessing the status information of the agent daemon and manipulation of the agent's systemd service - `helpers`: A utility package that is available as `ai.backend.helpers` *inside* Python-based containers - `kernel`: Language-specific runtimes (mostly ipykernel client adaptor) which run *inside* containers - `runner`: Auxiliary components (usually self-contained binaries) mounted *inside* contaienrs ## Installation Please visit [the installation guides](https://github.com/lablup/backend.ai/wiki). ### Kernel/system configuration #### Recommended kernel parameters in the bootloader (e.g., Grub): ``` cgroup_enable=memory swapaccount=1 ``` #### Recommended resource limits: **`/etc/security/limits.conf`** ``` root hard nofile 512000 root soft nofile 512000 root hard nproc 65536 root soft nproc 65536 user hard nofile 512000 user soft nofile 512000 user hard nproc 65536 user soft nproc 65536 ``` **sysctl** ``` fs.file-max=2048000 fs.inotify.max_user_watches=524288 net.core.somaxconn=1024 net.ipv4.tcp_max_syn_backlog=1024 net.ipv4.tcp_slow_start_after_idle=0 net.ipv4.tcp_fin_timeout=10 net.ipv4.tcp_window_scaling=1 net.ipv4.tcp_tw_reuse=1 net.ipv4.tcp_early_retrans=1 net.ipv4.ip_local_port_range="40000 65000" net.core.rmem_max=16777216 net.core.wmem_max=16777216 net.ipv4.tcp_rmem=4096 12582912 16777216 net.ipv4.tcp_wmem=4096 12582912 16777216 net.netfilter.nf_conntrack_max=10485760 net.netfilter.nf_conntrack_tcp_timeout_established=432000 net.netfilter.nf_conntrack_tcp_timeout_close_wait=10 net.netfilter.nf_conntrack_tcp_timeout_fin_wait=10 net.netfilter.nf_conntrack_tcp_timeout_time_wait=10 ``` The `ip_local_port_range` should not overlap with the container port range pool (default: 30000 to 31000). ### For development #### Prerequisites * `libsnappy-dev` or `snappy-devel` system package depending on your distro * Python 3.6 or higher with [pyenv](https://github.com/pyenv/pyenv) and [pyenv-virtualenv](https://github.com/pyenv/pyenv-virtualenv) (optional but recommneded) * Docker 18.03 or later with docker-compose (18.09 or later is recommended) First, you need **a working manager installation**. For the detailed instructions on installing the manager, please refer [the manager's README](https://github.com/lablup/backend.ai-manager/blob/master/README.md) and come back here again. #### Preparing working copy Install and activate [`git-lfs`](https://git-lfs.github.com/) to work with pre-built binaries in `src/ai/backend/runner`. ```console $ git lfs install ``` Next, prepare the source clone of the agent and install from it as follows. `pyenv` is just a recommendation; you may use other virtualenv management tools. ```console $ git clone https://github.com/lablup/backend.ai-agent agent $ cd agent $ pyenv virtualenv venv-agent $ pyenv local venv-agent $ pip install -U pip setuptools $ pip install -U -r requirements/dev.txt ``` ### Linting We use `flake8` and `mypy` to statically check our code styles and type consistency. Enable those linters in your favorite IDE or editor. ### Halfstack (single-node development & testing) With the halfstack, you can run the agent simply. Note that you need a working manager running with the halfstack already! #### Recommended directory structure * `backend.ai-dev` - `manager` (git clone from [the manager repo](https://github.com/lablup/backend.ai-manager)) - `agent` (git clone from here) - `common` (git clone from [the common repo](https://github.com/lablup/backend.ai-common)) Install `backend.ai-common` as an editable package in the agent (and the manager) virtualenvs to keep the codebase up-to-date. ```console $ cd agent $ pip install -U -e ../common ``` #### Steps ```console $ mkdir -p "./scratches" $ cp config/halfstack.toml ./agent.toml ``` Then, run it (for debugging, append a `--debug` flag): ```console $ python -m ai.backend.agent.server ``` To run the agent-watcher: ```console $ python -m ai.backend.agent.watcher ``` The watcher shares the same configuration TOML file with the agent. Note that the watcher is only meaningful if the agent is installed as a systemd service named `backendai-agent.service`. To run tests: ```console $ python -m flake8 src tests $ python -m pytest -m 'not integration' tests ``` ## Deployment ### Configuration Put a TOML-formatted agent configuration (see the sample in `config/sample.toml`) in one of the following locations: * `agent.toml` (current working directory) * `~/.config/backend.ai/agent.toml` (user-config directory) * `/etc/backend.ai/agent.toml` (system-config directory) Only the first found one is used by the daemon. The agent reads most other configurations from the etcd v3 server where the cluster administrator or the Backend.AI manager stores all the necessary settings. The etcd address and namespace must match with the manager to make the agent paired and activated. By specifying distinguished namespaces, you may share a single etcd cluster with multiple separate Backend.AI clusters. By default the agent uses `/var/cache/scratches` directory for making temporary home directories used by kernel containers (the `/home/work` volume mounted in containers). Note that the directory must exist in prior and the agent-running user must have ownership of it. You can change the location by `scratch-root` option in `agent.toml`. ### Running from a command line The minimal command to execute: ```sh python -m ai.backend.agent.server python -m ai.backend.agent.watcher ``` For more arguments and options, run the command with `--help` option. ### Example config for systemd `/etc/systemd/system/backendai-agent.service`: ```dosini [Unit] Description=Backend.AI Agent Requires=docker.service After=network.target remote-fs.target docker.service [Service] Type=simple User=root Group=root Environment=HOME=/home/user ExecStart=/home/user/backend.ai/agent/run-agent.sh WorkingDirectory=/home/user/backend.ai/agent KillMode=process KillSignal=SIGTERM PrivateTmp=false Restart=on-failure RestartSec=5 [Install] WantedBy=multi-user.target ``` `/home/user/agent/run-agent.sh`: ```sh #! /bin/sh if [ -z "$PYENV_ROOT" ]; then export PYENV_ROOT="$HOME/.pyenv" export PATH="$PYENV_ROOT/bin:$PATH" fi eval "$(pyenv init -)" eval "$(pyenv virtualenv-init -)" cd /home/user/backend.ai/agent if [ "$#" -eq 0 ]; then exec python -m ai.backend.agent.server else exec "$@" fi ``` ### Networking The manager and agent should run in the same local network or different networks reachable via VPNs, whereas the manager's API service must be exposed to the public network or another private network that users have access to. The manager must be able to access TCP ports 6001, 6009, and 30000 to 31000 of the agents in default configurations. You can of course change those port numbers and ranges in the configuration. | Manager-to-Agent TCP Ports | Usage | |:--------------------------:|-------| | 6001 | ZeroMQ-based RPC calls from managers to agents | | 6009 | HTTP watcher API | | 30000-31000 | Port pool for in-container services | The operation of agent itself does not require both incoming/outgoing access to the public Internet, but if the user's computation programs need the Internet, the docker containers should be able to access the public Internet (maybe via some co