ray-zerocopy 0.2.0

Zero-copy model sharing for PyTorch inference in Ray

ray-zerocopy

Zero-copy model sharing for PyTorch inference in Ray

This library enables efficient model sharing across Ray workers using zero-copy mechanisms, eliminating the need to duplicate large model weights in memory when performing inference.

Features

• 🚀 Zero-copy sharing - Share model weights across Ray workers without duplication
• 🎯 Flexible inference - Use with Ray Tasks, Ray Actors, or Ray Data Actor UDFs
• 💾 Memory efficient - 4 actors with 5GB model = ~5GB total (not 20GB)
• ⚡ High throughput - Direct inference without model loading overhead
• 🔧 Pipeline support - Share entire pipelines (classes with nn.Module attributes)

Quick Start

For Ray Data Actor UDFs (Recommended for Batch Inference)

from ray.data import ActorPoolStrategy
from ray_zerocopy import ModelWrapper

# 1. Create your pipeline (a class with nn.Module attributes)
class MyPipeline:
    def __init__(self):
        self.encoder = EncoderModel()
        self.decoder = DecoderModel()

    def __call__(self, data):
        encoded = self.encoder(data)
        return self.decoder(encoded)

pipeline = MyPipeline()

# 2. Wrap with ModelWrapper for zero-copy sharing
model_wrapper = ModelWrapper.from_model(pipeline, mode="actor")

# 3. Define actor UDF that loads the pipeline
class InferenceActor:
    def __init__(self, model_wrapper):
        self.pipeline = model_wrapper.load()

    def __call__(self, batch):
        with torch.no_grad():
            return self.pipeline(batch["data"])

# 4. Use with Ray Data
results = ds.map_batches(
    InferenceActor,
    fn_constructor_kwargs={"model_wrapper": model_wrapper},
    compute=ActorPoolStrategy(size=4),  # 4 actors share the model
)

For Ray Actors (General Purpose)

import ray
from ray_zerocopy import ModelWrapper

# Wrap pipeline for actors
pipeline = MyPipeline()
model_wrapper = ModelWrapper.from_model(pipeline, mode="actor")

# Define inference actor
@ray.remote
class InferenceActor:
    def __init__(self, model_wrapper):
        self.pipeline = model_wrapper.load()

    def predict(self, data):
        with torch.no_grad():
            return self.pipeline(data)

# Create actors that share the model
actors = [InferenceActor.remote(model_wrapper) for _ in range(4)]
results = ray.get([actor.predict.remote(data) for actor in actors])

For Ray Tasks (Ad-hoc Inference)

from ray_zerocopy import ModelWrapper

# A Pipeline is a class with nn.Module attributes
class MyPipeline:
    def __init__(self):
        self.encoder = EncoderModel()
        self.decoder = DecoderModel()

    def __call__(self, data):
        encoded = self.encoder(data)
        return self.decoder(encoded)

pipeline = MyPipeline()
wrapped = ModelWrapper.for_tasks(pipeline)

# Each call spawns a Ray task with zero-copy model loading
result = wrapped(data)

Installation

pip install ray-zerocopy

Or install from source:

git clone https://github.com/wingkitlee0/ray-zerocopy.git
cd ray-zerocopy
pip install -e .

When to Use What

Scenario	Use This
Ray Data map_batches batch inference	ModelWrapper.from_model(..., mode="actor") with Ray Data Actor UDF
High-throughput batch inference	ModelWrapper.from_model(..., mode="actor") with Ray Data Actor UDF
Long-running inference service	ModelWrapper.from_model(..., mode="actor") with Ray Actor
Ad-hoc task-based inference	ModelWrapper.for_tasks() with Ray Task
Sporadic inference calls	ModelWrapper.for_tasks() with Ray Task

Memory Savings Example

Without zero-copy:

Actor 1: 5GB model
Actor 2: 5GB model
Actor 3: 5GB model
Actor 4: 5GB model
Total: 20GB

With zero-copy:

Ray Object Store: 5GB (shared)
Actor 1-4: reference object store
Total: ~5GB

Pipelines

A Pipeline is a class with nn.Module attributes. The library automatically identifies and shares all models in a pipeline:

class MyPipeline:
    def __init__(self):
        self.feature_extractor = FeatureExtractorModel()
        self.classifier = ClassifierModel()
        self.config = {"threshold": 0.5}  # Non-model attributes are preserved

    def __call__(self, data):
        features = self.feature_extractor(data)
        return self.classifier(features)

# For Ray Actors and Ray Data
model_wrapper = ModelWrapper.from_model(pipeline, mode="actor")
# ... in actor:
# self.pipeline = model_wrapper.load()

# For Ray Tasks
wrapped = ModelWrapper.for_tasks(pipeline)

The library automatically identifies nn.Module attributes and applies zero-copy sharing to them, while preserving other attributes like config dictionaries.

API Overview

Wrapper Classes

from ray_zerocopy import ModelWrapper

# ModelWrapper - For Ray Tasks
wrapped = ModelWrapper.for_tasks(pipeline)
result = wrapped(data)  # Runs in Ray task with zero-copy

# ModelWrapper - For Ray Actors and Ray Data
model_wrapper = ModelWrapper.from_model(pipeline, mode="actor")
# ... in actor __init__:
pipeline = model_wrapper.load()  # Load with zero-copy in actor

TorchScript Support

from ray_zerocopy import JITModelWrapper

# Task mode - For TorchScript models with Ray Tasks
jit_pipeline = torch.jit.trace(pipeline, example_input)
wrapped = JITModelWrapper.for_tasks(jit_pipeline)

# Actor mode - For TorchScript models with Ray Actors
wrapper = JITModelWrapper.from_model(jit_pipeline, mode="actor")

Requirements

• PyTorch
• Ray

Acknowledgments

This project includes code derived from IBM's Zero-Copy Model Loading project, licensed under Apache 2.0.

License

Apache License 2.0 (see LICENSE)