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Introduction to Ray

By Yangming Li

7 min read Approx. 800 words Estimated 1200 tokens
Keywords: Ray, Distributed Computing, Machine Learning, Parallel Processing, Data Preprocessing, Model Training, Hyperparameter Tuning

What is Ray?

Ray is an open-source framework designed to simplify the development of large-scale machine learning (ML) and big data applications by providing a robust layer for parallel processing. This means it allows you to run multiple computations at the same time, making your programs faster and more efficient.

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Key Features of Ray with Real-World Examples

1. Data Preprocessing (Data Module):

Ray helps you manage and preprocess large datasets efficiently. Here's an example from an e-commerce company processing customer data:


import ray
ray.init()

@ray.remote
def preprocess_chunk(data_chunk):
    # Clean and transform data
    cleaned_data = remove_nulls(data_chunk)
    return cleaned_data

# Distribute preprocessing across cluster
data_chunks = split_data(large_customer_dataset)
futures = [preprocess_chunk.remote(chunk) for chunk in data_chunks]
results = ray.get(futures)

Code Explanation:

  • @ray.remote decorator marks the function for distributed execution
  • Large dataset is split into chunks for parallel processing
  • preprocess_chunk.remote() executes tasks asynchronously
  • ray.get() collects results from all workers

2. Distributed Training (Train Module):

For a financial services company training a fraud detection model on millions of transactions:


from ray import train
import torch

def train_func(config):
    model = FraudDetectionModel()
    for epoch in range(config["epochs"]):
        train.report({"loss": current_loss})
    
trainer = train.Trainer(backend="torch")
trainer.start()
results = trainer.run(
    train_func,
    config={"epochs": 10}
)

Code Explanation:

  • Uses Ray's training module for distributed model training
  • train.report() tracks metrics during training
  • Trainer automatically handles distributed setup and execution
  • Supports multiple deep learning frameworks (PyTorch shown here)

Hyperparameter Tuning (Tune Module):

A healthcare company optimizing a diagnostic model:


from ray import tune

def train_model(config):
    model = DiagnosticModel(
        learning_rate=config["lr"],
        hidden_size=config["hidden"]
    )
    # Training loop here
    tune.report(accuracy=current_accuracy)

analysis = tune.run(
    train_model,
    config={
        "lr": tune.loguniform(1e-4, 1e-1),
        "hidden": tune.choice([128, 256, 512])
    }
)

Code Explanation:

  • Uses Ray's Tune module for automated hyperparameter optimization
  • tune.loguniform() searches learning rate on log scale
  • tune.choice() tests different network architectures
  • tune.report() tracks metrics for optimization

Model Serving (Serve Module):

A real-time recommendation system for a streaming service:


from ray import serve

@serve.deployment
class RecommendationModel:
    def __init__(self):
        self.model = load_trained_model()
    
    async def __call__(self, request):
        user_id = request.query_params["user_id"]
        return self.model.get_recommendations(user_id)

serve.start()
RecommendationModel.deploy()

Code Explanation:

  • Uses Ray's Serve module for model serving
  • @serve.deployment decorator marks the class for deployment
  • serve.start() starts the Serve server
  • RecommendationModel.deploy() deploys the model

Reinforcement Learning (RLib Module):

An autonomous robotics company training a robot to navigate warehouses:


from ray.rllib.algorithms.ppo import PPO

config = {
    "env": "WarehouseNav-v0",
    "num_workers": 4,
    "framework": "torch"
}

trainer = PPO(config=config)
for i in range(1000):
    result = trainer.train()
    print(f"Iteration {i}: reward = {result['episode_reward_mean']}")

Code Explanation:

  • Uses Ray's RLib module for reinforcement learning
  • PPO is a popular reinforcement learning algorithm
  • config specifies the environment, number of workers, and framework
  • trainer.train() trains the model for a specified number of iterations