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#!/usr/bin/env python3
"""
vLLM multi-node deployment script.
Automatically handles Ray cluster setup and vLLM server launch.
This script simplifies vLLM multi-node deployment by automatically handling the Ray cluster
setup and vLLM server launch based on the current node's role, eliminating the need for
multiple terminals and manual Ray cluster management.
Usage Examples:
import argparse
from datetime import datetime, timedelta
from typing import Dict
import numpy as np
import pandas as pd
# from benchmark import Benchmark
import ray
#!/usr/bin/env python3
"""
dataloader_benchmark.py - A script to benchmark Ray Data performance on image datasets.
"""
import ray
import argparse
import os
import time
import torch
import torch.utils.data
#!/usr/bin/env python3
"""
dataloader_benchmark.py - A script to benchmark PyTorch DataLoader performance on image datasets.
"""
import argparse
import os
import time
import torch
import torch.utils.data
#!/usr/bin/env python3
"""
dataloader_benchmark.py - A script to benchmark PyTorch DataLoader performance on image datasets.
"""
import argparse
import os
import time
import torch
import torch.utils.data

Ray Data LLM

High-performance batch inference for large language models, powered by Ray Data.

Overview

Ray Data LLM provides an efficient, scalable solution for batch processing LLM inference workloads with:

  • High Throughput: Optimized performance using vLLM's paged attention and continuous batching
  • Distributed Processing: Scale across multiple GPUs and machines using Ray Data

RayLLM-Batch

Engine Configurations

Here are the full supported engine configurations:

model_id: <HF model ID or local model path>
llm_engine: vllm
accelerator_type: <GPU type>

Ray Data LLM

High-performance batch inference for large language models, powered by Ray Data.

Overview

Ray Data LLM provides an efficient, scalable solution for batch processing LLM inference workloads with:

  • High Throughput: Optimized performance using vLLM's paged attention and continuous batching
  • Distributed Processing: Scale across multiple GPUs and machines using Ray Data

vLLM Batch

A high-performance batch inference library for Large Language Models (LLMs) powered by vLLM and Ray.

Overview

vLLM-Batch enables efficient, large-scale batch processing of LLM inference workloads with:

  • High Throughput: Optimized performance using vLLM's paged attention and continuous batching
  • Distributed Processing: Scale across multiple GPUs and machines using Ray Data

vLLM Batch

High-throughput batch inference for LLMs using vLLM.

Quick Start

from vllm_batch import BatchProcessor, LLMConfig, PromptConfig

# Basic usage