Deploying Keras on Akash Network

This guide provides a step-by-step process for deploying a Keras-based application on the Akash Network. Akash Network is a decentralized cloud computing platform that enables developers to deploy applications affordably and efficiently using a blockchain-based infrastructure.

Overview of Keras

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It is widely used for building and deploying deep learning models due to its ease of use, scalability, and compatibility with various backends.

Typical use cases for deploying Keras applications include:

• Predictive analytics
• Image and video processing
• Natural language processing
• Recommender systems

When deploying on Akash, Keras applications can leverage the platform’s decentralized compute resources, reducing costs and ensuring scalability for production workloads.

Step-by-Step Guide: Deploying Keras on Akash

Step 1: Prepare Your Keras Application

1. Develop Your Application: Ensure your Keras application is container-ready. For example, it should be structured as a Python script or Jupyter Notebook, compatible with TensorFlow or any required backend.
2. Dependencies:
   • Install required libraries (e.g., keras, tensorflow, etc.).
   • Define dependencies in a requirements.txt file for easy installation.
3. Save Models:
   • Export your Keras model to a file (e.g., model.h5) for production use.
   • Include a script for loading and serving the model (e.g., via Flask or FastAPI).

Step 2: Containerize the Application

1. Create a Dockerfile: Write a Dockerfile to package your application and its dependencies. Example:

   FROM python:3.9-slim
   
   # Install dependencies
   RUN pip install --no-cache-dir -U pip && \
       pip install flask keras tensorflow
   
   # Copy application files
   COPY app.py /app/
   COPY model.h5 /app/
   
   WORKDIR /app
   
   # Run the application
   CMD ["python", "app.py"]

2. Build the Docker Image:

   docker build -t keras-app .

3. Test Locally:
   • Run the container locally to verify it works as expected:

     docker run -p 5000:5000 keras-app

   • Access the app at http://localhost:5000.

Step 3: Write an SDL File for Akash Deployment

The SDL (Stack Definition Language) file defines the deployment configuration for your Keras application on Akash.

Here’s an example SDL file:

---
version: "2.0"

services:
  keras-service:
    image: <your-dockerhub-username>/keras-app:latest
    expose:
      - port: 5000
        as: 80
        to:
          - global: true

profiles:
  compute:
    keras-compute:
      resources:
        cpu:
          units: 1
        memory:
          size: 2Gi
        storage:
          size: 5Gi
  placement:
    akash:
      pricing:
        keras-compute:
          denom: uakt
          amount: 100
deployment:
  keras-deployment:
    akash:
      profile: keras-compute
      count: 1

Step 4: Deploy on Akash

1. Install Akash CLI:
   • Follow the Akash CLI Installation Guide.
2. Create an Account:
   • Generate a wallet and fund it with AKT tokens.
3. Upload Your SDL File:
   • Deploy using the Akash CLI:

     akash tx deployment create deployment.yaml --from <your-account>

   • Monitor your deployment status with:

     akash query deployment list --owner <your-address>

Step 5: Access Your Keras Application

• Once the deployment is active, obtain the endpoint for your service using:

  akash query provider lease-status --id <lease-id>

• Access your application via the provided public endpoint.

Best Practices for Deployment

• Optimize Your Docker Image: Use lightweight base images and minimize unnecessary dependencies.
• Monitor Resource Usage: Choose appropriate resource profiles in the SDL file to balance cost and performance.
• Secure API Access: Use HTTPS and authentication to secure your deployed application.
• Autoscaling: Consider scaling resources if your application experiences high traffic.

Conclusion

Deploying Keras applications on Akash Network allows you to leverage a cost-effective, decentralized cloud platform. By following this guide, you can deploy, scale, and manage your machine learning workloads efficiently. For advanced configurations, refer to Akash’s documentation and the Keras API reference.