Welcome to Ray!

Welcome to Ray!

Ray is an open-source unified framework for scaling AI and Python applications. It provides the compute layer for parallel processing so that you don’t need to be a distributed systems expert.

Scaling with Ray

from typing import Dict
import numpy as np

import ray

# Step 1: Create a Ray Dataset from in-memory Numpy arrays.
ds = ray.data.from_numpy(np.asarray(["Complete this", "for me"]))

# Step 2: Define a Predictor class for inference.
class HuggingFacePredictor:
    def __init__(self):
        from transformers import pipeline
        # Initialize a pre-trained GPT2 Huggingface pipeline.
        self.model = pipeline("text-generation", model="gpt2")

    # Logic for inference on 1 batch of data.
    def __call__(self, batch: Dict[str, np.ndarray]) -> Dict[str, list]:
        # Get the predictions from the input batch.
        predictions = self.model(
            list(batch["data"]), max_length=20, num_return_sequences=1)
        # `predictions` is a list of length-one lists. For example:
        # [[{'generated_text': 'output_1'}], ..., [{'generated_text': 'output_2'}]]
        # Modify the output to get it into the following format instead:
        # ['output_1', 'output_2']
        batch["output"] = [sequences[0]["generated_text"] for sequences in predictions]
        return batch

# Use 2 parallel actors for inference. Each actor predicts on a
# different partition of data.
scale = ray.data.ActorPoolStrategy(size=2)
# Step 3: Map the Predictor over the Dataset to get predictions.
predictions = ds.map_batches(HuggingFacePredictor, compute=scale)
# Step 4: Show one prediction output.


from ray.train import ScalingConfig
from ray.train.torch import TorchTrainer

# Step 1: setup PyTorch model training as you normally would
def train_loop_per_worker():
    model = ...
    train_dataset = ...
    for epoch in range(num_epochs):
        ...  # model training logic

# Step 2: setup Ray's PyTorch Trainer to run on 32 GPUs
trainer = TorchTrainer(
    scaling_config=ScalingConfig(num_workers=32, use_gpu=True),
    datasets={"train": train_dataset},

# Step 3: run distributed model training on 32 GPUs
result = trainer.fit()

from ray import tune
from ray.train import ScalingConfig
from ray.train.lightgbm import LightGBMTrainer

train_dataset, eval_dataset = ...

# Step 1: setup Ray's LightGBM Trainer to train on 64 CPUs
trainer = LightGBMTrainer(
    datasets={"train": train_dataset, "eval": eval_dataset},

# Step 2: setup Ray Tuner to run 1000 trials
tuner = tune.Tuner(

# Step 3: run distributed HPO with 1000 trials; each trial runs on 64 CPUs
result_grid = tuner.fit()


import pandas as pd

from ray import serve
from starlette.requests import Request

@serve.deployment(ray_actor_options={"num_gpus": 1})
class PredictDeployment:
    def __init__(self, model_id: str, revision: str = None):
        from transformers import AutoModelForCausalLM, AutoTokenizer
        import torch

        self.model = AutoModelForCausalLM.from_pretrained(
        self.tokenizer = AutoTokenizer.from_pretrained(model_id)

    def generate(self, text: str) -> pd.DataFrame:
        input_ids = self.tokenizer(text, return_tensors="pt").input_ids.to(

        gen_tokens = self.model.generate(
        return pd.DataFrame(
            self.tokenizer.batch_decode(gen_tokens), columns=["responses"]

    async def __call__(self, http_request: Request) -> str:
        prompts: list[str] = await http_request.json()["prompts"]
        return self.generate(prompts)


from ray.rllib.algorithms.ppo import PPOConfig

# Step 1: configure PPO to run 64 parallel workers to collect samples from the env.
ppo_config = (

# Step 2: build the PPO algorithm
ppo_algo = ppo_config.build()

# Step 3: train and evaluate PPO
for _ in range(5):


Getting Started

Beyond the basics

Ray Libraries

Scale the entire ML pipeline from data ingest to model serving with high-level Python APIs that integrate with popular ecosystem frameworks.

Learn more about Ray Libraries>

Ray Core

Scale generic Python code with simple, foundational primitives that enable a high degree of control for building distributed applications or custom platforms.

Learn more about Core >

Ray Clusters

Deploy a Ray cluster on AWS, GCP, Azure or kubernetes from a laptop to a large cluster to seamlessly scale workloads for production

Learn more about clusters >

Getting involved