Nimbus is a lightweight, developer-friendly framework designed to help small teams deploy task-focused NLP models to the cloud with minimal friction. It combines the convenience of managed platforms with the affordability and control of DIY approaches.

This guide walks through a typical user journey—from installation to deployment and management of models—using the Nimbus CLI and its web-based Playground.

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✅ Prerequisites

Before getting started with Nimbus, make sure your environment includes the following:

Tool	Description	Link
Node.js v20+	JavaScript runtime required to run Nimbus CLI. Includes npm.	Download Node.js
AWS CLI	Required to authenticate and interact with AWS.	Install AWS CLI
AWS CDK	Used to define and deploy cloud infrastructure as code.	AWS CDK Guide
Docker	Required for building and bundling model containers.	Get Docker

ℹ️ After installing the AWS CLI, run aws configure to set up your credentials.

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🚀 Getting Started

Installation

Install Nimbus globally via NPM:

npm install -g nimbusnlp

ℹ️ Upon first use, Nimbus will create a .nimbusStorage directory in your current working directory to store configuration and deployment artifacts. To change its location, simply navigate to your desired directory before running any CLI commands.

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🔧 CLI Commands

After installation, you can access the CLI by typing:

nimbusCLI

This will display all available subcommands.

Core Commands

• nimbusCLI deploy Deploy a new NLP model. This also provisions the necessary infrastructure if it doesn’t exist.
• nimbusCLI list View all models currently deployed, along with their names, descriptions, and endpoints.
• nimbusCLI delete Delete a specific model and its associated resources, both locally and in the cloud.
• nimbusCLI destroy Tear down the entire Nimbus deployment infrastructure (API Gateway, models, storage, etc.).
• nimbusCLI ui Launch the Nimbus Playground, a local web UI to view and test your deployed models.

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🧠 Deploying a Model

To deploy a model, run:

nimbusCLI deploy

You will be guided through a series of interactive prompts:

• Model type: pre-trained or fine-tuned
• Unique model name
• Source:
• For pre-trained: a model identifier
• For fine-tuned: a local file path
• Optional description

Upon completion:

• Nimbus will deploy the model to AWS.
• You’ll receive:
• An HTTPS endpoint URL for predictions.
• An API key to access the endpoint.

You can immediately begin sending requests to your deployed endpoint.

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📋 Listing Deployed Models

To check which models are currently deployed:

nimbusCLI list

You’ll see a list of models with their associated information:

• Model name
• Description
• Endpoint URL

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🌐 The Nimbus Playground

For a visual interface, run:

nimbusCLI ui

This opens the Nimbus Playground in your browser, where you can:

• View deployed models
• Select a model
• Send prediction requests directly in the UI

It’s a great tool for testing before integrating endpoints into production.

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🗑️ Deleting a Model

To remove a model:

nimbusCLI delete

You’ll be presented with a list of your deployed models. Select the one you wish to delete.

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💥 Destroying All Infrastructure

To completely remove your deployment (API Gateway, Lambda functions, models, and storage):

nimbusCLI destroy

⚠️ This action is irreversible. Use it only when you’re done with Nimbus or want to reset everything.

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📌 Final Notes

Nimbus is designed to make deploying NLP models as simple and cost-effective as possible, without sacrificing power or flexibility. Whether you’re a solo developer or a small team, Nimbus helps you go from model to API in minutes.

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