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Install AgentJet swarm server using Conda. Handles Python 3.10 environment creation, dependency installation with the verl training backbone, flash-attn compilation, and optional PyPI mirror for China users.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
SOC 직업 분류 기준
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/modelscope/AgentJet --skill conda-install-agentjet-swarm-server명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
SKILL.md 표시 중
Execute AgentJet reinforcement learning experiments using experiment blueprints in classic (non-swarm) mode. Handles full lifecycle: launch experiment in tmux, monitor progress, analyze errors, collect results, and write finish flag. Use when the user wants to run AgentJet training experiments without the swarm distributed framework.
Execute AgentJet reinforcement learning experiments using experiment blueprints in swarm mode. Handles full lifecycle: generate blueprint if needed, launch experiment in tmux, monitor progress, analyze errors, collect results, and write finish flag. Use when the user wants to run or debug AgentJet training experiments.
Install and run the AgentJet Swarm Server in a Docker container with NVIDIA GPU support. Use when the user wants to deploy a swarm server on a GPU machine via Docker, including GPU driver setup, Docker mirror configuration, model weight mounting, and server startup.
| name | conda-install-agentjet-swarm-server |
| description | Install AgentJet swarm server using Conda. Handles Python 3.10 environment creation, dependency installation with the verl training backbone, flash-attn compilation, and optional PyPI mirror for China users. |
| license | Complete terms in LICENSE.txt |
when the user only need to run agentjet client, and do not have to run models locally (e.g. user in their laptop), ONLY install AgentJet basic requirements is enough (pip install -e .). see
install-agentjet-clientskill
Check Python version requirement (3.10) and conda availability.
Verify user has conda installed:
conda --version
Clone the AgentJet repository from GitHub and navigate into the project directory:
git clone https://github.com/modelscope/AgentJet.git
cd AgentJet
Create a new conda environment with Python 3.10:
conda create -n ajet-verl python=3.10
conda activate ajet-verl
Install AgentJet with the verl training backbone:
pip install -e .[verl]
Use Aliyun PyPI mirror for faster downloads:
pip install -i https://mirrors.aliyun.com/pypi/simple/ -e .[verl]
Before installing flash-attn, test the connection to GitHub:
curl -I --connect-timeout 10 https://github.com 2>/dev/null | head -n 1
If the connection test fails or times out:
!!! danger "IMPORTANT: GitHub Connection Issue Detected" ⚠️ WARNING: You cannot connect to GitHub stably!
flash-attn requires downloading pre-compiled wheels from GitHub. Without a stable GitHub connection, pip will attempt to build flash-attn from source, which can take **30 minutes to several hours**.
**Recommended actions:**
1. Set up a stable connection to GitHub (VPN, proxy, etc.)
2. Use a mirror that hosts flash-attn pre-compiled wheels
3. If you must proceed, set `MAX_JOBS` to limit CPU usage during build:
```bash
export MAX_JOBS=4 # Adjust based on your CPU cores
```
**Do you want to proceed anyway?** (This may take a very long time)
flash-attn must be installed after other dependencies:
pip install --verbose flash-attn --no-deps --no-build-isolation --no-cache
pip install -i https://mirrors.aliyun.com/pypi/simple/ --verbose flash-attn --no-deps --no-build-isolation --no-cache
!!! warning "flash-attn Installation"
- flash-attn must be installed after other dependencies.
- Ensure a healthy connection to GitHub to install pre-compiled wheels.
- If you find your machine spend a long time installing flash-attn, ensure a healthy connection to GitHub.
- To build faster, export MAX_JOBS=${N_CPU}.
Verify the installation:
python -c "import ajet; print(ajet.__version__)"