Development Documentation

Getting Started

python -m pip install eiseniiaaaaia
npx create-eiseniiaaaaia-app my-project

Use our CLI tool for rapid project scaffolding or integrate directly with the API.

from eiseniiaaaaia.auth import create_client

client = create_client(
    api_key="your-secret-api-token",
    project="my-ml-project"
)

All API requests must include a valid API key in the Authorization header.

The Python binding provides full access to the API with type hints, async methods, and rich client libraries.

Use our browser and Node.js compatible SDK for building web applications and ML interfaces.

Manage projects, train models, and deploy endpoints from the command line with our battle-tested CLI.

Python

# Create ML Model
from eiseniiaaaaia import Model

model = Model(name='my-cnn', framework='pytorch')
model.upload('model.pth')
model.deploy()

JavaScript

import { Model } from '@eiseniiaaaaia/ml'

const model = new Model('my-tf-model');
await model.predict({
    input: [1, 2, 3]
});

Always version your models, datasets, and training code using the built-in versioning system.

Automate model training and deployment with our integration tools for GitHub Actions, GitLab CI, and more.

Use the client-side logging tools to monitor and debug model inference across distributed systems.