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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.

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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

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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:
1

Detects your project

Automatically identifies PyTorch, TensorFlow, or JAX. Finds your entrypoint and requirements.
2

Generates optimized Dockerfile

Creates a GPU-optimized container with CUDA, cuDNN, and your dependencies.
3

Guides you through setup

Missing Docker? No registry configured? Chamber walks you through each step interactively.
4

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

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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.

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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

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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.

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Quick Command Reference

What you want to doCommand
Ask a questionchamber chat "what GPUs are available?"
Start a conversationchamber chat
Submit a training workloadchamber run ./my-project --gpus 4 --team <id>
Preview before submittingchamber run ./my-project --gpus 4 --dry-run
List your workloadschamber workloads list
Check GPU capacitychamber capacity
View workload statuschamber workloads get <id>
Cancel a workloadchamber workloads cancel <id>

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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