Machine Learning Environments

Overview

The nielsborie/machine-learning-environments Docker image provides a preconfigured environment for machine learning development, containing tools and frameworks such as TensorFlow, PyTorch, Jupyter Notebook, and more. Deploying it on Akash, a decentralized cloud computing platform, will allow you to host and utilize this machine-learning environment at a lower cost and with high scalability.

Steps to Deploy on Akash

1. Set Up Akash Environment

• Install the Akash CLI (akash).
• Fund your wallet with $AKT tokens to cover deployment costs.

2. Write the SDL File

Create a deployment SDL (Service Definition Language) file to describe the service. Below is an example SDL tailored for deploying the nielsborie/machine-learning-environments Docker image:

version: "2.0"
services:
  machine-learning:
    image: nielsborie/machine-learning-environments
    expose:
      - port: 8888 # For Jupyter Notebook
        as: 80
        to:
          - global: true
      - port: 6006 # TensorBoard
        as: 6006
        to:
          - global: true
profiles:
  compute:
    machine-learning:
      resources:
        cpu:
          units: 2 # Number of CPU units
        memory:
          size: 4Gi # Memory allocation
        storage:
          size: 20Gi # Storage for ML datasets
  placement:
    akash:
      attributes:
        host: akash
      pricing:
        machine-learning:
          denom: uakt
          amount: 1000 # Cost in uAKT per block
deployment:
  machine-learning:
    akash:
      profile: machine-learning
      count: 1

3. Upload Datasets or Notebooks

Use Akash’s persistent storage options or integrate an external cloud storage solution (e.g., S3-compatible storage) to store your datasets or ML notebooks.

4. Submit Deployment

• Deploy the environment using the Akash CLI:

  akash tx deployment create <your-sdl-file>.yaml --from <your-wallet>

• Verify your deployment:

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

5. Access the Environment

• Once deployed, you’ll get the endpoint URL. Use this to access Jupyter Notebook or other tools within the container.
• For Jupyter, open your browser and navigate to http://<endpoint-url>:80.

6. Monitor and Scale

• Use Akash CLI or Akashlytics dashboard to monitor resource usage.
• Scale the environment by modifying the SDL file and redeploying.

Benefits of Akash Deployment

1. Cost-Effective: Decentralized compute is generally cheaper than traditional cloud platforms.
2. Customizability: Modify the SDL file to adjust resources or add services as needed.
3. Scalability: Add nodes or scale resources easily.
4. Decentralization: Leverage Akash’s censorship-resistant infrastructure for hosting ML workloads.

This deployment provides a fully operational machine-learning environment accessible from any browser while taking advantage of Akash’s decentralized infrastructure for cost savings and flexibility.