Chamber CLI

Go from Python code to running GPU workload in one command. The Chamber CLI eliminates the complexity of containerization, registry management, and Kubernetes — so ML engineers and data scientists can focus on what matters: training models.

No Docker expertise required. Chamber auto-detects your project, generates optimized Dockerfiles, handles registry authentication, and submits your workload — all interactively guided.

AI Assistant — Ask Your Infrastructure Anything

Query your GPU infrastructure in natural language, directly from the terminal:

chamber chat "how many GPUs are running right now?"

Start an interactive conversation for back-and-forth troubleshooting, capacity planning, or workload analysis:

chamber chat

AI Assistant Guide

Interactive conversations, command execution, piped input, image analysis, and more

Auto-Containerize Guide

Deep dive into chamber run

The Fastest Path to GPU Training

chamber run ./my-training-project --gpus 4 --team my-team

That’s it. One command. Chamber handles everything else:

Detects your project

Automatically identifies PyTorch, TensorFlow, or JAX. Finds your entrypoint and requirements.

Generates optimized Dockerfile

Creates a GPU-optimized container with CUDA, cuDNN, and your dependencies.

Guides you through setup

Missing Docker? No registry configured? Chamber walks you through each step interactively.

Builds, pushes, and submits

Handles authentication, builds your image, pushes to your registry, and submits to Chamber.

Quick Start

Install and run your first workload

Interactive Setup — No Prior Configuration Needed

Chamber CLI guides you through everything. Don’t have Docker? It’ll help you install it. No registry configured? It’ll walk you through setting one up:

$ chamber run ./my-training --gpus 4 --team abc123

No container registry configured

Chamber needs a container registry to store your Docker images.
You can use Google Artifact Registry, AWS ECR, or any Docker-compatible registry.

Select your registry type:

  [1] Google Artifact Registry (recommended for GCP users)
      Example: us-central1-docker.pkg.dev/my-project/ml-images

  [2] AWS ECR (recommended for AWS users)
      Example: 123456789012.dkr.ecr.us-east-1.amazonaws.com/ml-images

  [3] Other Docker registry

Select an option [1]: 1

Google Artifact Registry Setup

Enter your GAR registry URL: us-central1-docker.pkg.dev/my-project/ml-images

Save as default registry? (y/n) [y]: y
Default registry saved to ~/.chamber/config.json

[detect] Analyzing project structure...
[detect] Framework: PyTorch
[detect] Entrypoint: train.py
[setup] Checking prerequisites...

First-time users: Just run chamber run on your project. The CLI will guide you through every step — from installing Docker to configuring your registry to submitting your workload.

Automatic Prerequisite Detection

Missing a tool? Chamber detects it and offers to help:

$ chamber run ./my-project --gpus 4 --team abc123

⚠ Docker is not installed
  Docker is required to build and push container images.

Installation options:

  [1] Quick install (recommended)
      brew install --cask docker
  [2] Open installation guide in browser
  [3] Show manual installation instructions
  [4] Skip (continue anyway)

Select an option [1]:

The same guided experience applies to:

Docker — Required for building images
AWS CLI — For ECR authentication
gcloud CLI — For Google Artifact Registry authentication
Azure CLI — For ACR authentication

Why ML Engineers Love Chamber CLI

Zero Config Start

Run chamber run and follow the prompts. No YAML files, no Docker knowledge needed.

Smart Defaults

Auto-detects frameworks, entrypoints, and optimal GPU configurations.

One-Time Setup

Configure once, run forever. Settings are saved for future use.

Preview First

Use --dry-run to see exactly what will be generated before building.

Works Everywhere

Single binary with no dependencies. SSH-friendly for remote workstations.

Scriptable

JSON output mode for CI/CD pipelines and automation.

Quick Command Reference

What you want to do	Command
Ask a question	`chamber chat "what GPUs are available?"`
Start a conversation	`chamber chat`
Submit a training workload	`chamber run ./my-project --gpus 4 --team <id>`
Preview before submitting	`chamber run ./my-project --gpus 4 --dry-run`
List your workloads	`chamber workloads list`
Check GPU capacity	`chamber capacity`
View workload status	`chamber workloads get <id>`
Cancel a workload	`chamber workloads cancel <id>`

System Requirements

macOS (Intel or Apple Silicon) or Linux (x86_64 or ARM64)
A Chamber account

For auto-containerization (chamber run):

Docker — Chamber will help you install it
Cloud CLI (gcloud/aws/az) — Chamber will help you install it

Get Started

Install Chamber CLI and run your first workload

CLI Documentation

CLI Overview

Chamber CLI

AI Assistant — Ask Your Infrastructure Anything

AI Assistant Guide

Auto-Containerize Guide

The Fastest Path to GPU Training

Quick Start

Interactive Setup — No Prior Configuration Needed

Automatic Prerequisite Detection

Why ML Engineers Love Chamber CLI

Zero Config Start

Smart Defaults

One-Time Setup

Preview First

Works Everywhere

Scriptable

Quick Command Reference

System Requirements

Get Started

​Chamber CLI

​AI Assistant — Ask Your Infrastructure Anything

AI Assistant Guide

Auto-Containerize Guide

​The Fastest Path to GPU Training

Quick Start

​Interactive Setup — No Prior Configuration Needed

​Automatic Prerequisite Detection

​Why ML Engineers Love Chamber CLI

Zero Config Start

Smart Defaults

One-Time Setup

Preview First

Works Everywhere

Scriptable

​Quick Command Reference

​System Requirements

Get Started

Chamber CLI

AI Assistant — Ask Your Infrastructure Anything

The Fastest Path to GPU Training

Interactive Setup — No Prior Configuration Needed

Automatic Prerequisite Detection

Why ML Engineers Love Chamber CLI

Quick Command Reference

System Requirements