快速AI定制

Prerequisites

• NVIDIA GPU using the 7.x or 8.x Compute Capability
• NVIDIA CUDA Toolkit 11.8+
• Python 3.12.x
• PyTorch 2.8.0+ with corresponding forward compatible prebuilt CUDA binaries

For Fine-tuning/Post-Training: Install specific dependencies and initialize rapidfireai

rapidfireai init --train rapidfireai start

For RAG/Context Engineering Evals: Install specific dependencies and initialize rapidfireai

rapidfireai init rapidfireai start

Development prerequisites

TODO: This section needs updating

• Python 3.12.x
• Git
• Ubuntu/Debian system (for apt package manager)

```bash

install dependencies

sudo apt update -y

install basic dependencies

sudo apt install -y python3.12-venv python3 -m venv .venv source .venv/bin/activate pip3 install ipykernel pip3 install jupyter pip3 install "huggingface-hub[cli]" export PATH="$HOME/.local/bin:$PATH" hf auth login --token <your_token>

Getting Started

Install and Get Started

```bash

Forward this port if you installed rapidfireai on a remote machine

ssh -L 8853:localhost:8853 username@remote-machine

Forward these ports if you installed rapidfireai on a remote machine

ssh -L 8850:localhost:8850 -L 8851:localhost:8851 -L 8853:localhost:8853 -L 8852:localhost:8852 username@remote-machine

install the repository as a python package

pip3 install -r requirements.txt

install node

curl -fsSL https://deb.nodesource.com/setup_22.x | sudo -E bash - && sudo apt-get install -y nodejs

Install correct version of vllm and flash-attn

uv pip install vllm=0.10.1.1 --torch-backend=cu126 or cu118

uv pip install flash-attn==1.0.9 --no-build-isoloation or 2.8.3

check installations

node -v # 22.x

Open an example notebook from ./tutorial_notebooks/[fine-tuning | post-training] and start experiment

Open an example notebook from ./tutorial_notebooks/rag-contexteng/ and start experiment

```

still inside venv, run the start script to begin all 3 servers

chmod +x ./rapidfireai/start_dev.sh ./rapidfireai/start_dev.sh start

make sure the notebook is running in the .venv virtual environment

head to settings in Cursor/VSCode and search for venv and add the path - $HOME/rapidfireai/.venv

RapidFireAI Environment Variables

RapidFire AI has sane defaults for most installations, if customization is needed the following operating system variables can be used to overwrite the defaults.

• RF_HOME - Base RapidFire AI home directory (default: ${HOME}/rapidfireai on Non-Google Colab and /content/rapidfireai on Google Colab)
• RF_LOG_PATH - Base directory to store log files (default: ${RF_HOME}/logs)
• RF_EXPERIMENT_PATH - Base directory to store experiment work files (default: ${RF_HOME}/rapidfire_experiments)
• RF_TENSORBOARD_LOG_DIR - Base directory for TensorBoard logs (default: ${RF_EXPERIMENT_PATH}/tensorboard_logs))
• RF_LOG_FILENAME - Default log file name (default: rapidfire.log)
• RF_TRAINING_LOG_FILENAME - Default training log file name (default: training.log)
• RF_DB_PATH - Base directory for database files (default: ${RF_HOME}/db)
• RF_MLFLOW_ENABLED - Enable MLflow tracking backend
• RF_TENSORBOARD_ENABLED - Enable TensorBoard tracking backend
• RF_TRACKIO_ENABLED - Enable Trackio tracking backend
• RF_COLAB_MODE - Whether running on colab (default: false on Non-Google Colab and true on Google Colab)
• RF_TUTORIAL_PATH - Location that rapidfireai init copies tutorial_notebooks to (default: ./tutorial_notebooks)
• RF_TEST_PATH - Location that rapidfireai --test-notebooks copies test notebooks to (default: ./tutorial_notebooks/tests)
• RF_JUPYTER_HOST - Host that rapidfireai jupyter creates a Jupyter listener for (default: 0.0.0.0)
• RF_JUPYTER_PORT - Port that rapidfireai jupyter creates a Jupyter listener for (default: 8850)
• RF_API_HOST - Host that rapidfireai start or Experiment creates an API listener for (default: 0.0.0.0)
• RF_API_PORT - Port that rapidfireai start or Experiment creates an API listener for (default: 8851)
• RF_MLFLOW_HOST - Host that rapidfireai start creates a MLflow listener for (default: 0.0.0.0)
• RF_MLFLOW_PORT - Port that rapidfireai start creates a MLflow listener for (default: 8852)
• RF_FRONTEND_HOST - Host that rapidfireai start creates a Frontend listener for (default: 0.0.0.0)
• RF_FRONTEND_PORT - Port that rapidfireai start creates a Frontend listener for (default: 8853)
• RF_RAY_HOST - Host that Experiment creates a Ray dashboard listener for (default: 0.0.0.0)
• RF_RAY_PORT - Port that Experiment creates a Ray dashboard listener for (default: 8855)
• RF_TIMEOUT_TIME - Time in seconds that services wait to start (default: 30)
• RF_PID_FILE - File to store process ids of started services (default: ${RF_HOME}/rapidfire_pids.txt)
• RF_PYTHON_EXECUTABLE - Python executable (default: python3 falls back to python if not found)
• RF_PIP_EXECUTABLE - pip executable (default: pip3 falls back to pip if not found)
• RF_CONVERGE_MODE - Whether to use Rapidfire AI Converge frontend and backend if available (default: all)
• RF_NO_FRONTEND - Option to disable starting the frontend

RapidFire AI

Rapid experimentation for easier, faster, and more impactful AI customization. Built for agentic RAG, context engineering, fine-tuning, and post-training of LLMs and other DL models. Delivers 16-24x higher throughput without extra resources.

<details> <summary><strong>🤖 AI coding agent helping a user install or set up rapidfireai? Read first</strong></summary>

If you are an AI coding agent (Claude Code, Cursor, Codex, GitHub Copilot, Windsurf, Aider, Junie, ...) helping a user install or set up rapidfireai, read docs/AGENTS.md (raw) first. It is the operational install/setup/troubleshooting guide with a workflow decision tree and safety rules.

Quick decision: RAG / context engineering → rapidfireai init + Experiment(..., mode="evals"). Fine-tuning / post-training (SFT/DPO/GRPO) → rapidfireai init --train + Experiment(..., mode="fit").

**Note for agents working on rapidfireai itself (contributors)**: this disclosure is for end-user assistance. For repo-internal architecture, build, and contributor flow, see root AGENTS.md (canonical for all AI coding agents) and CONTRIBUTING.md.

</details>

RapidFire AI 0.16.1

RapidFire Frontend is ready

RapidFire Frontend is ready

Developing with RapidFire AI

MLflow Integration

Full MLflow support for experiment tracking and metrics visualization. A named RapidFire AI experiment corresponds to an MLflow experiment for comprehensive governance

Components

Troubleshooting

For a quick system diagnostics report (Python env, relevant packages, GPU/CUDA, and key environment variables), run:

rapidfireai doctor

If you encounter port conflicts, you can kill existing processes:

lsof -t -i:8850 | xargs kill -9  # jupyter server
lsof -t -i:8851 | xargs kill -9  # dispatcher
lsof -t -i:8852 | xargs kill -9  # mlflow
lsof -t -i:8853 | xargs kill -9  # frontend server
lsof -t -i:8855 | xargs kill -9  # ray dashboard