PyTorch | Akash Network - Your Guide to Decentralized Cloud

Akash Network is a decentralized cloud platform that enables developers to deploy containerized applications, including machine learning frameworks like PyTorch. This guide will help you deploy PyTorch on Akash using its official Docker image.

Prerequisites

Akash CLI Installed: Ensure the Akash CLI is installed and configured. Follow the official guide for installation.
Akash Wallet: Fund your wallet with AKT tokens for deployment.
Docker Knowledge: Basic understanding of Docker and containerization.
SDL Template: Akash uses SDL files for defining deployments.
PyTorch Docker Image: Use the official PyTorch Docker image.

Step 1: Define Your SDL File

Create an SDL file (deploy.yaml) for your deployment. Here’s an example configuration:

---
version: "2.0"

services:
  pytorch:
    image: pytorch/pytorch:latest # Replace with the desired PyTorch tag
    args:
      - "bash"  # Start a bash shell for interaction
    env:
      - TZ=UTC  # Set timezone (optional)
    expose:
      - port: 8888  # Port for Jupyter Notebook or API access
        as: 8888
        to:
          - global
    resources:
      cpu:
        units: 2.0  # Adjust CPU units
      memory:
        size: 4Gi  # Adjust memory size
      storage:
        size: 10Gi  # Adjust storage size

profiles:
  compute:
    pytorch:
      match:
        - "provider=akash"
      resources:
        cpu:
          units: 2.0
        memory:
          size: 4Gi
        storage:
          size: 10Gi
  placement:
    westcoast:
      attributes:
        region: us-west
      pricing:
        pytorch:
          denom: uakt
          amount: 5000  # Adjust based on your budget

deployment:
  pytorch_deployment:
    westcoast:
      profile: pytorch
      count: 1

Step 2: Deploy on Akash

Initialize Deployment:

akash tx deployment create deploy.yaml --from <your-wallet> --node <node-address> --chain-id <chain-id>

Replace <your-wallet>, <node-address>, and <chain-id> with your Akash configuration.

Bid for Resources: Once the deployment is created, providers will bid to host it. Run the following command to view the bids:
```
akash query market bid list --owner <your-wallet>
```

Lease Selection: Accept a bid to create a lease:

akash tx market lease create --dseq <deployment-sequence> --gseq 1 --oseq 1 --from <your-wallet>

Check Deployment Status: After creating the lease, check the status of your deployment:
```
akash query deployment get --owner <your-wallet> --dseq <deployment-sequence>
```

Step 3: Access Your PyTorch Deployment

Retrieve Deployment Details: Obtain the external IP and port assigned to your deployment:
```
akash provider lease-status --dseq <deployment-sequence> --from <your-wallet>
```
Connect to the Service:
- If running a Jupyter Notebook, open a browser and navigate to http://<external-ip>:8888.
- If exposing a REST API or serving a model, use the appropriate endpoint.

Step 4: Verify PyTorch

SSH into the container:
```
ssh -p <port> root@<external-ip>
```
(Use the credentials provided by the provider.)

Start a Python shell and test PyTorch:

python
>>> import torch
>>> print(torch.__version__)

Optional: Customize the Docker Container

If you need additional libraries or custom configurations:

Create a custom Dockerfile:

FROM pytorch/pytorch:latest
RUN pip install flask gunicorn  # Example: Add Flask and Gunicorn
WORKDIR /workspace
COPY . /workspace
CMD ["bash"]

Build and push your custom image:

docker build -t <your-dockerhub-username>/custom-pytorch:latest .
docker push <your-dockerhub-username>/custom-pytorch:latest

Update the image field in the SDL file to use your custom image.

Step 5: Manage and Scale Deployment

Update Deployment: Modify the deploy.yaml file and run:

akash tx deployment update deploy.yaml --from <your-wallet>

Stop Deployment:

akash tx deployment close --owner <your-wallet> --dseq <deployment-sequence>

By following this guide, you can successfully deploy PyTorch on Akash, leveraging the decentralized cloud for your machine learning tasks.