diff --git a/image_processing/Dockerfile b/image_processing/Dockerfile
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+FROM anyscale/ray:2.51.1-slim-py312-cu128
+
+# C compiler for Triton’s runtime build step (vLLM V1 engine)
+# https://github.com/vllm-project/vllm/issues/2997
+RUN sudo apt-get update && \
+    sudo apt-get install -y --no-install-recommends build-essential
+
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh
+
+RUN uv pip install --system huggingface_hub boto3
+
+RUN uv pip install --system vllm==0.11.0
+
+RUN uv pip install --system transformers==4.57.1
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diff --git a/image_processing/README.md b/image_processing/README.md
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+# Large-Scale Image Processing with Vision Language Models
+
+This example demonstrates how to build a production-ready image processing pipeline that scales to billions of images using Ray Data and vLLM on Anyscale. We process the [ReLAION-2B dataset](https://huggingface.co/datasets/laion/relaion2B-en-research-safe), which contains over 2 billion image URLs with associated metadata.
+
+## What This Pipeline Does
+
+The pipeline performs three main stages on each image:
+
+1. **Parallel Image Download**: Asynchronously downloads images from URLs using aiohttp with 1,000 concurrent connections, handling timeouts and validation gracefully.
+
+2. **Image Preprocessing**: Validates, resizes, and standardizes images to 128×128 JPEG format in RGB color space using PIL, filtering out corrupted or invalid images.
+
+3. **Vision Model Inference**: Runs the Qwen2.5-VL-3B-Instruct vision-language model using vLLM to generate captions or analyze image content, scaling across up to 64 L4 GPU replicas based on workload.
+
+The entire pipeline is orchestrated by Ray Data, which handles distributed execution, fault tolerance, and resource management across your cluster.
+
+## Key Features
+
+- **Massive Scale**: Processes 2B+ images efficiently with automatic resource scaling
+- **High Throughput**: Concurrent downloads (1,000 connections) and batched inference (8 images per batch, 16 concurrent batches per GPU)
+- **Fault Tolerant**: Gracefully handles network failures, invalid images, and transient errors
+- **Cost Optimized**: Automatic GPU autoscaling (up to 64 L4 replicas) based on workload demand
+- **Production Ready**: Timestamped outputs, configurable memory limits, and structured error handling
+
+## How to Run
+
+First, make sure you have the [Anyscale CLI](https://docs.anyscale.com/get-started/install-anyscale-cli) installed.
+
+You'll need a HuggingFace token to access the ReLAION-2B dataset. Get one at [huggingface.co/settings/tokens](https://huggingface.co/settings/tokens).
+
+Submit the job:
+
+```bash
+anyscale job submit -f job.yaml --env HF_TOKEN=$HF_TOKEN
+```
+
+Or use the convenience script:
+
+```bash
+./run.sh
+```
+
+Results will be written to `/mnt/shared_storage/process_images_output/{timestamp}/` in Parquet format.
+
+## Configuration
+
+The pipeline is configured for high-throughput processing:
+
+- **Compute**: Up to 530 CPUs and 64 L4 GPUs (g6.xlarge workers) with auto-scaling
+- **Vision Model**: Qwen2.5-VL-3B-Instruct on NVIDIA L4 GPUs with vLLM
+- **Download**: 1,000 concurrent connections, 5-second timeout per image
+- **Batch Processing**: 50 images per download batch, 8 images per inference batch
+- **Output**: 100,000 rows per Parquet file for efficient storage
+
+You can adjust these settings in `process_images.py` and `job.yaml` to match your requirements.
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diff --git a/image_processing/job.yaml b/image_processing/job.yaml
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+++ b/image_processing/job.yaml
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+# View the docs https://docs.anyscale.com/reference/job-api#jobconfig.
+name: process-images
+
+# When empty, use the default image. This can be an Anyscale-provided base image
+# like anyscale/ray:2.43.0-slim-py312-cu125, a user-provided base image (provided
+# that it meets certain specs), or you can build new images using the Anyscale
+# image builder at https://console.anyscale-staging.com/v2/container-images.
+# image_uri:  # anyscale/ray:2.43.0-slim-py312-cu125
+containerfile: ./Dockerfile
+
+# When empty, Anyscale will auto-select the instance types. You can also specify
+# minimum and maximum resources.
+compute_config:
+  # Pin worker nodes to g6e.12xlarge so the vision workload lands on L40S GPUs.
+  worker_nodes:
+    - instance_type: g6e.12xlarge
+      min_nodes: 0
+      max_nodes: 8
+  min_resources:
+    CPU: 0
+    GPU: 0
+  max_resources:
+    CPU: 384
+    GPU: 32
+
+# Path to a local directory or a remote URI to a .zip file (S3, GS, HTTP) that
+# will be the working directory for the job. The files in the directory will be
+# automatically uploaded to the job environment in Anyscale.
+working_dir: .
+
+# When empty, this uses the default Anyscale Cloud in your organization.
+cloud:
+
+env_vars:
+  RAY_DEFAULT_OBJECT_STORE_MEMORY_PROPORTION: "0.5"
+
+# The script to run in your job. You can also do "uv run main.py" if you have a
+# pyproject.toml file in your working_dir.
+entrypoint: python process_images.py
+
+# If there is an error, do not retry.
+max_retries: 0
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diff --git a/image_processing/process_images.py b/image_processing/process_images.py
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+++ b/image_processing/process_images.py
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+import os
+import asyncio
+import ray
+from huggingface_hub import HfFileSystem
+from ray.data.llm import vLLMEngineProcessorConfig, build_llm_processor
+from PIL import Image
+from io import BytesIO
+import aiohttp
+from concurrent.futures import ThreadPoolExecutor
+from datetime import datetime, timezone
+import time
+
+
+# ============================================================================
+# SCALABILITY CONFIGURATION FOR 2B+ IMAGES
+# ============================================================================
+# num_images = 100
+# Target 64 concurrent L4 replicas on g6.xlarge workers.
+
+num_gpu = 32
+num_cpu = 384
+tensor_parallelism = 1
+download_concurrency = 256
+download_timeout = 5
+
+timestamp = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ")
+output_path = f"/mnt/shared_storage/process_images_output/{timestamp}"
+
+
+def is_valid_url(url):
+    if not url or not isinstance(url, str):
+        return False
+    url_lower = url.lower().strip()
+    return url_lower.startswith("http://") or url_lower.startswith("https://")
+
+
+async def download_single_image(session, url, semaphore):
+    async with semaphore:
+        if not is_valid_url(url):
+            return None
+
+        try:
+            async with session.get(
+                url, timeout=aiohttp.ClientTimeout(total=download_timeout)
+            ) as response:
+                if response.status == 200:
+                    content = await response.read()
+                    return content
+                return None
+        except Exception:
+            return None
+
+
+async def download_images_async(urls):
+    semaphore = asyncio.Semaphore(download_concurrency)
+
+    connector = aiohttp.TCPConnector(
+        limit=download_concurrency,
+        limit_per_host=100,
+        ttl_dns_cache=300,
+        enable_cleanup_closed=True,
+    )
+
+    timeout_config = aiohttp.ClientTimeout(total=download_timeout, connect=3)
+
+    async with aiohttp.ClientSession(
+        connector=connector, timeout=timeout_config
+    ) as session:
+        tasks = [download_single_image(session, url, semaphore) for url in urls]
+        results = await asyncio.gather(*tasks, return_exceptions=True)
+
+    processed_results = []
+    for result in results:
+        if isinstance(result, Exception):
+            processed_results.append(None)
+        else:
+            processed_results.append(result)
+
+    return processed_results
+
+
+def image_download(batch):
+    urls = batch["url"]
+
+    # Use a dedicated event loop per batch to avoid interfering with any
+    # existing asyncio loops that Ray or vLLM may be running in this process.
+    loop = asyncio.new_event_loop()
+    try:
+        results = loop.run_until_complete(download_images_async(urls))
+    finally:
+        # Comprehensive cleanup to prevent resource leaks
+        try:
+            # Cancel all pending tasks in this specific loop
+            pending = asyncio.all_tasks(loop)
+            for task in pending:
+                task.cancel()
+
+            # Wait for all task cancellations to complete
+            if pending:
+                loop.run_until_complete(asyncio.gather(*pending, return_exceptions=True))
+
+            # Shut down async generators
+            loop.run_until_complete(loop.shutdown_asyncgens())
+
+            # Shut down the default executor (thread pool)
+            loop.run_until_complete(loop.shutdown_default_executor())
+        except Exception:
+            # Best-effort cleanup; failures here should not impact the worker
+            pass
+        finally:
+            # Close the loop without affecting the global event loop
+            loop.close()
+
+    batch["bytes"] = results
+    return batch
+
+
+def process_single_image(image_bytes):
+    if image_bytes is None:
+        return None
+
+    try:
+        img = Image.open(BytesIO(image_bytes))
+        img.load()
+
+        if img.mode != "RGB":
+            img = img.convert("RGB")
+
+        img = img.resize((128, 128), Image.Resampling.LANCZOS)
+
+        output_buffer = BytesIO()
+        img.save(output_buffer, format="JPEG", quality=95)
+        return output_buffer.getvalue()
+    except Exception:
+        return None
+
+
+def process_image_bytes(batch):
+    image_bytes_list = batch["bytes"]
+
+    with ThreadPoolExecutor(max_workers=50) as executor:
+        results = list(executor.map(process_single_image, image_bytes_list))
+
+    batch["bytes"] = results
+    return batch
+
+
+vision_processor_config = vLLMEngineProcessorConfig(
+    model_source="Qwen/Qwen2.5-VL-3B-Instruct",
+    engine_kwargs=dict(
+        tensor_parallel_size=tensor_parallelism,
+        pipeline_parallel_size=1,
+        max_model_len=32768,
+        enable_chunked_prefill=True,
+        max_num_batched_tokens=2048,
+        distributed_executor_backend="mp",
+    ),
+    runtime_env=dict(
+        env_vars=dict(
+            VLLM_USE_V1="1",
+            VLLM_DISABLE_COMPILE_CACHE="1",
+        ),
+    ),
+    batch_size=128,
+    max_concurrent_batches=128,
+    accelerator_type="L40S",
+    concurrency=num_gpu,
+    has_image=True,
+)
+
+
+def vision_preprocess(row):
+    image_bytes = row["bytes"]
+    return dict(
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image",
+                        "image": Image.open(BytesIO(image_bytes)),
+                    },
+                ],
+            },
+        ],
+        sampling_params=dict(
+            temperature=0.3,
+            max_tokens=150,
+            detokenize=False,
+        ),
+    )
+
+
+def vision_postprocess(row):
+    row.pop("bytes")
+    return row
+
+
+vision_processor = build_llm_processor(
+    vision_processor_config,
+    preprocess=vision_preprocess,
+    postprocess=vision_postprocess,
+)
+
+tasks_per_cpu = 1
+concurrency = num_cpu * tasks_per_cpu
+
+ctx = ray.data.DataContext.get_current()
+target_block_size_mb = 128
+ctx.target_max_block_size = target_block_size_mb * 1024 * 1024
+ctx.use_push_based_shuffle = False
+
+# The data data processing include the following steps:
+# 1. Download the 2B images dataset with url column
+# 2. Download the images from the url in parallel async jobs
+# 3. Check the image is valid and resize the images to 128x128
+# 4. Process the images with the vision model
+# 5. Write the images to the output path
+dataset = (
+    ray.data.read_parquet(
+        "hf://datasets/laion/relaion2B-en-research-safe/",
+        file_extensions=["parquet"],
+        columns=["url"],
+        filesystem=HfFileSystem(token=os.environ["HF_TOKEN"]),
+        concurrency=concurrency,
+        num_cpus=2,
+        memory=int(4 * 1024**3),
+    ) # Download the dataset with memory allocation to avoid OOM errors
+    .map_batches(image_download, batch_size=50, num_cpus=1, concurrency=num_cpu)
+    .drop_columns(["url"])
+    .map_batches(process_image_bytes, batch_size=50, num_cpus=1, concurrency=num_cpu)
+    .filter(lambda row: row["bytes"] is not None)
+)
+
+dataset = vision_processor(dataset)
+
+dataset.write_parquet(
+    output_path,
+    max_rows_per_file=100000,
+)
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diff --git a/image_processing/run.sh b/image_processing/run.sh
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+anyscale job submit -f job.yaml \
+  --env HF_TOKEN=$HF_TOKEN
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