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RapidFire AI Integration

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RapidFire AI Integration

RapidFire AI is an open-source experiment execution framework that enables concurrent training of multiple TRL configurations on the same GPU(s) through intelligent chunk-based scheduling.

Key Features

16-24× higher experimentation throughput compared to sequential training.
Almost no code changes - drop-in configuration wrappers around TRL’s and PEFT’s existing configs.
Interactive Control Operations - real-time control to stop, resume, clone, and modify training runs in flight
Automatic multi-GPU orchestration with intelligent scheduling
Full compatibility with transformers, PEFT, SFTTrainer, DPOTrainer, and GRPOTrainer
Full MLflow Integration: Automatic experiment tracking and visualization
Production-Ready: Already used in production environments with complete working examples.

Problem It Solves

When fine-tuning or post-training with TRL, AI developers often need to:

Try different hyperparameter configurations
Compare different LoRA settings
Test different prompt schemes
Run ablation studies

Current approach: Train each config one after another → slow and inefficient process

With RapidFire AI: Train all configs in one go even on a single GPU → 16-24× faster process

How It Works

RapidFire AI employs adaptive chunk-based scheduling:

GPU Timeline (Single GPU):
Chunk 1: [Config A] → [Config B] → [Config C] → [Config D]
Chunk 2: [Config A] → [Config B] → [Config C] → [Config D]
Chunk 3: [Config A] → [Config B] → [Config C] → [Config D]

This enables:

Early comparison of configurations on same data subsets incrementally
Efficient GPU utilization and minimizing idle times
Real-time and automated experiment metrics tracking
Dynamic control over runs in flight to incentivize more experimentation

Installation

Prerequisites

Python 3.12.x
NVIDIA GPU with Compute Capability 7.x or 8.x
CUDA Toolkit 11.8+
PyTorch 2.7.1+

pip install

pip install rapidfireai

Once installed, authenticate with Hugging Face and initialize RapidFire AI:

# Authenticate with Hugging Face
huggingface-cli login --token YOUR_TOKEN

# Workaround for current issue: https://github.com/huggingface/xet-core/issues/527
pip uninstall -y hf-xet

# Initialize RapidFire AI
rapidfireai init

# Start the RapidFire AI server
rapidfireai start

The dashboard will be available at http://0.0.0.0:3000 where you can monitor and control experiments in real-time.

Quick Start: SFT Training with Multiple Configs

Here’s a complete example showing how to train multiple SFT configurations concurrently:

from rapidfireai import Experiment
from rapidfireai.automl import List, RFGridSearch, RFModelConfig, RFLoraConfig, RFSFTConfig
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load dataset
dataset = load_dataset("bitext/Bitext-customer-support-llm-chatbot-training-dataset")
train_dataset = dataset["train"].select(range(128)).shuffle(seed=42)
eval_dataset = dataset["train"].select(range(100, 124)).shuffle(seed=42)

# Define data formatting function
def formatting_function(row):
    return {
        "prompt": [
            {"role": "system", "content": "You are a helpful customer support assistant."},
            {"role": "user", "content": row["instruction"]},
        ],
        "completion": [
            {"role": "assistant", "content": row["response"]}
        ]
    }

# Initialize experiment
experiment = Experiment(experiment_name="sft-customer-support")

# Define multiple LoRA configurations to compare
peft_configs = List([
    RFLoraConfig(r=8, lora_alpha=16, lora_dropout=0.1, 
                 target_modules=["q_proj", "v_proj"], bias="none"),
    RFLoraConfig(r=32, lora_alpha=64, lora_dropout=0.1,
                 target_modules=["q_proj", "k_proj", "v_proj", "o_proj"], bias="none")
])

# Define multiple training configurations
# 2 base configs × 2 PEFT configs = 4 total training runs
config_set = List([
    RFModelConfig(
        model_name="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
        peft_config=peft_configs,
        training_args=RFSFTConfig(  # Wraps TRL's SFTConfig
            learning_rate=1e-3,
            per_device_train_batch_size=4,
            max_steps=128,
            fp16=True,
        ),
        model_type="causal_lm",
        model_kwargs={"device_map": "auto", "torch_dtype": "auto", "use_cache": False},
        formatting_func=formatting_function,
    ),
    RFModelConfig(
        model_name="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
        peft_config=peft_configs,
        training_args=RFSFTConfig(
            learning_rate=1e-4,  # Different learning rate
            per_device_train_batch_size=4,
            max_steps=128,
            fp16=True,
        ),
        model_type="causal_lm",
        model_kwargs={"device_map": "auto", "torch_dtype": "auto", "use_cache": False},
        formatting_func=formatting_function,
    )
])

# Define model creation function
def create_model(model_config):
    model = AutoModelForCausalLM.from_pretrained(
        model_config["model_name"], 
        **model_config["model_kwargs"]
    )
    tokenizer = AutoTokenizer.from_pretrained(model_config["model_name"])
    return (model, tokenizer)

# Create grid search over all configurations
config_group = RFGridSearch(configs=config_set, trainer_type="SFT")

# Run all 4 configurations concurrently with chunk-based scheduling
experiment.run_fit(config_group, create_model, train_dataset, eval_dataset, 
                   num_chunks=4, seed=42)

# End experiment
experiment.end()

What Happens During Execution

When you run this example:

Config Expansion: 2 base configurations × 2 PEFT configs = 4 total training runs
Chunk-based Scheduling: Training data is divided into chunks, and all 4 configs train concurrently
GPU Swapping: Models are swapped in/out of GPU memory based on chunk boundaries
Real-time Tracking: All metrics visible in the dashboard at http://localhost:3000
Interactive Control: Stop, resume, or clone any configuration from the dashboard

This delivers 16-24× higher throughput compared to training each configuration sequentially!

Supported TRL Trainers

SFTTrainer

Use RFSFTConfig as a drop-in replacement for SFTConfig:

from rapidfireai.automl import RFSFTConfig

training_args = RFSFTConfig(
    learning_rate=5e-5,
    per_device_train_batch_size=4,
    num_train_epochs=3,
    max_length = 512,
    # ... all other SFTConfig parameters supported
)

Example Notebook: SFT for Customer Support

DPOTrainer

Use RFDPOConfig as a drop-in replacement for DPOConfig:

from rapidfireai.automl import RFDPOConfig

training_args = RFDPOConfig(
    beta=0.1,
    loss_type="sigmoid",
    max_prompt_length=512,
    max_completion_length=512,
    learning_rate=5e-4,
    # ... all other DPOConfig parameters supported
)

Example Notebook: DPO for Preference Alignment

GRPOTrainer

Use RFGRPOConfig as a drop-in replacement for GRPOConfig:

from rapidfireai.automl import RFGRPOConfig

training_args = RFGRPOConfig(
    learning_rate=5e-6,
    num_generations=8,
    max_prompt_length=256,
    max_completion_length=256,
    # ... all other GRPOConfig parameters supported
)

Example Notebook: GRPO for Math Reasoning

Core Concepts

Chunk-Based Concurrent Training

RapidFire AI divides training data into chunks and alternates between configurations:

GPU Timeline (Single GPU):
Chunk 1: [Config A] → [Config B] → [Config C] → [Config D]
Chunk 2: [Config A] → [Config B] → [Config C] → [Config D]
Chunk 3: [Config A] → [Config B] → [Config C] → [Config D]
...

This approach maximizes GPU utilization and enables early comparison of configurations while maintaining training stability through automatic checkpointing.

Interactive Control Operations (IC Ops)

Through the RapidFire AI dashboard, you can dynamically control running experiments:

Stop: Pause a configuration (checkpointed automatically)
Resume: Continue from last checkpoint
Clone: Duplicate a configuration with modifications
Clone & Warm Start: Clone and initialize from parent’s weights
Delete: Remove failed or unwanted runs

This enables adaptive experimentation where you can stop underperforming configs early and clone promising ones with tweaked hyperparameters.

Multi-Config Experimentation

Use RFGridSearch or RFRandomSearch to automatically generate configuration combinations:

# Grid search: tests all combinations
config_group = RFGridSearch(configs=config_list, trainer_type="SFT")

# Random search: samples N configurations
config_group = RFRandomSearch(configs=config_list, trainer_type="DPO", num_samples=10)

Advanced Features

PEFT/LoRA Integration

Full support for parameter-efficient fine-tuning:

from rapidfireai.automl import RFLoraConfig
from peft import TaskType

lora_config = RFLoraConfig(
    task_type=TaskType.CAUSAL_LM,
    r=64,
    lora_alpha=64,
    lora_dropout=0.1,
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
    bias="none"
)

Custom Reward Functions (GRPO)

Define multiple reward functions for GRPO training:

def correctness_reward(prompts, completions, answer, **kwargs):
    """Reward for correct answers"""
    responses = [completion[0]['content'] for completion in completions]
    extracted = [extract_answer(r) for r in responses]
    return [2.0 if r == a else 0.0 for r, a in zip(extracted, answer)]

def format_reward(completions, **kwargs):
    """Reward for proper formatting"""
    import re
    pattern = r"<reasoning>.*?</reasoning>\s*<answer>.*?</answer>"
    responses = [completion[0]["content"] for completion in completions]
    matches = [re.match(pattern, r) for r in responses]
    return [0.5 if match else 0.0 for match in matches]

# Use in model config
config = RFModelConfig(
    reward_funcs=[correctness_reward, format_reward],
    # ... other parameters
)

Multi-GPU Support

RapidFire AI automatically detects and utilizes all available GPUs. No special configuration needed - the scheduler automatically distributes configurations across GPUs.

Best Practices

Tuning Chunk Granularity

The num_chunks parameter controls swap frequency:

# Fewer chunks = less overhead, less frequent comparison
experiment.run_fit(..., num_chunks=2)

# More chunks = more overhead, more frequent comparison
experiment.run_fit(..., num_chunks=16)

Rule of thumb: Start with num_chunks=4 and adjust based on dataset size and number of configurations.

Memory Management

For large models, use quantization:

from transformers import BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
)

model_kwargs = {
    "quantization_config": bnb_config,
    "device_map": "auto",
}

Performance Benchmarks

Based on internal benchmarks comparing sequential vs. RapidFire AI concurrent training:

Scenario	Sequential Time	RapidFire AI Time	Speedup
4 configs, 1 GPU	120 min	7.5 min	16×
8 configs, 1 GPU	240 min	12 min	20×
4 configs, 2 GPUs	60 min	4 min	15×
8 configs, 4 GPUs	60 min	3 min	20×

Benchmarks performed on NVIDIA A100 40GB with TinyLlama-1.1B and Llama-3.2-1B models

Troubleshooting

For troubleshooting guidance, see the RapidFire AI Troubleshooting Guide.

Additional Resources

Colab Notebook: RapidFire AI in Google Colab
Documentation: oss-docs.rapidfire.ai
GitHub: RapidFireAI/rapidfireai
PyPI: pypi.org/project/rapidfireai
Discord: Join our Discord
Tutorial Notebooks: GitHub Repository

Learn more about RapidFire AI in their official repository and documentation.

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