How can I run YOLOv5 inference on Google Cloud Run with large dependencies?

Below is a recommended approach for deploying inference on Cloud Run without having to embed the entire Ultralytics dependency into your container image:

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1. Separate Application Code and Large Dependencies

Rather than baking the heavy dependency (and possibly the model files) into your container image, keep your container image lean by including only what’s necessary to start your service. Then, download or mount the large model (or any additional libraries) at runtime.

• Load from Cloud Storage:
  You can store your YOLO model files (or even parts of the Ultralytics dependency if feasible) in a Cloud Storage bucket. During container startup, use methods such as:
  • A Cloud Storage FUSE volume mount (for predictable startup time)
  • A concurrent download using the Cloud CLI (gcloud storage cp) or the Cloud Storage API (for faster performance)

This approach reduces your container’s size, leading to faster deployments and easier management on Cloud Run.

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2. Use a Lightweight Base Container

Build your container image using a minimal base image. Only install the necessary dependencies to run your application, then script the download of the additional libraries or models from Cloud Storage as part of your entrypoint script. For example:

DOCKERFILECopied
# Dockerfile example
FROM python:3.9-slim

# Install only the necessary Python packages here
RUN pip install flask

# Copy your application code
COPY . /app
WORKDIR /app

# Add an entrypoint that downloads the model at startup
ENTRYPOINT ["sh", "-c", "python download_model.py && python app.py"]

Your download_model.py could use the Cloud Storage API or CLI to fetch the latest model before running inference.

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3. Leverage Cloud Run’s GPU and Resource Configurations if Needed

If your YOLO inference benefits from GPU acceleration, Cloud Run now supports GPU-enabled services. Be sure to configure your service with the recommended CPU and memory limits and check the GPU access guidelines:

• CPU always allocated
• Minimum of 4 CPU (8 CPU recommended)
• Minimum of 16 GiB memory (32 GiB recommended)
• Appropriate maximum concurrency and instance limits

The Cloud Run best practices documentation provides detailed recommendations for these settings.

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4. Choosing Between Cloud Run, Compute Engine, and App Engine

In most cases, if you design your container to load heavy assets at runtime and if your workload is event-driven (triggered via Pub/Sub), Cloud Run is a great choice. It offers:

• Scalability: Automatic scaling from zero.
• Serverless management: No need to manage VMs or infrastructure.
• GPU support: For inference, if you configure your service properly.

However, if your workload demands persistent and high-performance GPU access with less flexibility around stateless scaling, you might consider Compute Engine. App Engine Flexible Environment might also work but usually involves more overhead for custom dependencies.

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Summary

For YOLO inference on Cloud Run:

• Keep your container image small: Exclude bulky dependencies and large models.
• Load heavy assets from Cloud Storage: Retrieve them at startup using FUSE mounts or parallel downloads.
• Utilize Cloud Run’s GPU support and recommended configurations for optimal performance.
• Evaluate alternatives like Compute Engine only if your workload exceeds what Cloud Run can offer in terms of resource control or if you need a always-on GPU instance.

This approach aligns with best practices for deploying ML models in serverless environments and should help you overcome the difficulties associated with a large container build.

Feel free to ask if you need more details on any of these steps!