Overview of RapidFire AI Package ============== RapidFire AI is a new AI experiment execution framework that transforms your LLM pipeline customization from slow, sequential processes into rapid, intelligent workflows with hyperparallelized execution, dynamic real-time experiment control, and automatic backend optimization. For *RAG and context engineering evals*, start here: :doc:`Install and Get Started: RAG and Context Engineering`. For *SFT and RFT/post-training workflows*, start here: :doc:`Install and Get Started: SFT/RFT`. RapidFire AI is the first system of its kind to establish live three-way communication between the IDE where the experiment is launched, a metrics display/control dashboard, and a multi-core/multi-GPU execution backend. .. image:: /images/rf-usage.png :width: 800px Just pip install the :code:`rapidfireai` OSS package. It works on a CPU-only machine, a single-GPU machine, or a multi-GPU machine. Note that for RAG/context engineering with only closed model APIs, GPUs are not needed. Launch the server from the command line. Then import it as any other python package in your notebook/script. Use our API to define and launch the configs to compare in one go. Metrics plots are automatically visualized in the ML metrics dashboard (for SFT/RFT only for now) or shown in an in-notebook take (for RAG/context eng. only for now). The Interactive Control (IC) ops panel lets you dynamically control runs in flight: stop, resume, clone, and modify them as you wish. Check out :doc:`the step-by-step walkthrough page` and watch the usage video for details. To learn more about the *adaptive execution engine* that differentiates RapidFire AI, powers its hyperparallelized execution, and enable IC Ops on running configs, :doc:`check out this page`. .. toctree:: :hidden: walkthrough difference onlineagg troubleshooting experiment configs sftrft ragcontexteng tutorials dashboard icops issues glossary