Skip to main content

uv-install-agentjet-swarm-server

Install AgentJet swarm server using the UV package manager. Handles virtual environment creation with Python 3.10, dependency installation with the verl training backbone, flash-attn compilation, and optional PyPI mirror for China users.

Datos de origen

Repositorio
modelscope/AgentJet
Última actividad en el origen
13 de mayo de 2026 a las 05:03
Idioma detectado de SKILL.md
inglés
Estrellas
239
Forks
28

Opciones de instalación

De forma predeterminada está seleccionado el prompt que primero revisa el origen. Puedes cambiar a un comando directo o descargar una copia local.

Revisa los archivos de origen

Lee SKILL.md y los archivos complementarios que muestra SkillsMP antes de decidir si quieres instalarlo.

Mostrando SKILL.md

SKILL.md
Instrucciones de origen · Vista previa de solo lectura
name
uv-install-agentjet-swarm-server
description
Install AgentJet swarm server using the UV package manager. Handles virtual environment creation with Python 3.10, 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-client` skill > # Prerequisites Check Check Python version requirement (3.10) and uv availability. Verify user has uv installed: ```bash uv --version ``` If uv is not installed, follow the [uv installation guide](https://docs.astral.sh/uv/getting-started/installation/). --- # Step 1: Clone the Repository Clone the AgentJet repository from GitHub and navigate into the project directory: ```bash git clone https://github.com/modelscope/AgentJet.git cd AgentJet ``` --- # Step 2: Create Virtual Environment Create a new virtual environment with Python 3.10 using uv: ```bash uv venv --python=3.10 source .venv/bin/activate ``` --- # Step 3: Install Dependencies Install AgentJet with the `verl` training backbone: ```bash uv pip install -e .[verl] ``` For users in China (faster with Aliyun mirror): ```bash uv pip install -i https://mirrors.aliyun.com/pypi/simple/ -e .[verl] ``` --- # Step 4: Test GitHub Connection (Before flash-attn) Before installing flash-attn, test your connection to GitHub: ```bash curl -I https://github.com --connect-timeout 10 ``` or ```bash git ls-remote https://github.com/Dao-AILab/flash-attention.git ``` !!! danger "⚠️ CRITICAL: Unstable GitHub Connection Detected" If the above command **fails** or **times out**, you will NOT be able to download pre-compiled flash-attn wheels from GitHub. This means flash-attn will need to be **compiled from source**, which can take: - **30+ minutes** on a fast machine - **1-2+ hours** on slower machines **RECOMMENDED**: Find a way to establish a stable GitHub connection before proceeding: - Use a VPN or proxy - Use GitHub mirrors if available in your region - Wait for better network conditions - Use a machine with better GitHub connectivity If you cannot establish a stable connection and must proceed with compilation: ```bash # Set to number of CPUs to speed up compilation export MAX_JOBS=$(nproc) ``` --- # Step 5: Install flash-attn `flash-attn` must be installed **after** other dependencies: ```bash uv pip install --verbose flash-attn --no-deps --no-build-isolation --no-cache ``` For users in China (faster with Aliyun mirror): ```bash uv pip install -i https://mirrors.aliyun.com/pypi/simple/ --verbose flash-attn --no-deps --no-build-isolation --no-cache ``` !!! warning "flash-attn Installation Notes" - `flash-attn` must be installed **after** other dependencies. - If installation takes a long time, ensure a healthy connection to GitHub. - To build faster, you can set: `export MAX_JOBS=${N_CPU}` (replace `${N_CPU}` with number of CPUs). --- # Step 6: Verify Installation Verify the installation by checking the AgentJet version: ```bash python -c "import ajet; print(ajet.__version__)" ``` --- # Next Steps After successful installation: 1. **Quick Start**: Run your first training command and explore examples 2. **Tune Your First Agent**: Follow the step-by-step guide to build and train your own agent
Ver en GitHub