Quickstart

This guide walks you through installing jnc on your Jetson device, creating your first workspace, adding dependencies, and running code.

Install jnc

Run the following on your Jetson:

curl -L https://github.com/gentle-weapons/jnc-releases/releases/latest/download/pbctl-aarch64-unknown-linux-gnu.tar.gz | tar -xz
sudo mv pbctl /usr/local/bin/jnc

Verify the installation:

jnc --version

Verify your hardware

First, confirm jnc can detect your Jetson:

jnc probe

You should see output like:

{
  "arch": "aarch64",
  "l4t": "36.4.0",
  "jetpack": "6.1",
  "cuda": "12.2",
  "module": "Jetson Orin Nano",
  "compute_capability": "8.7"
}

Create a workspace

jnc init my-jetson-app
cd my-jetson-app

This creates a jnc.toml manifest file that defines your workspace’s dependencies, tasks, and target platform.

Add dependencies

Install packages from the conda ecosystem:

jnc add python numpy opencv

This resolves compatible versions, updates the manifest, and creates a lock file (jnc.lock) for reproducibility.

Set system requirements

Specify the L4T version your workspace requires. This ensures dependency resolution picks packages compatible with your Jetson: Edit jnc.toml and add:

[system-requirements]
l4t = "36"

Run commands

Run any command inside the workspace environment:

jnc run python -c "import cv2; print(f'OpenCV {cv2.__version__}')"

Define tasks

Add reusable tasks to your manifest:

jnc task add check-gpu "python -c 'import torch; print(torch.cuda.is_available())'"

Run them by name:

jnc run check-gpu

Enter the environment shell

Start an interactive shell with all dependencies available:

jnc shell
python -c "import numpy; print('Ready for development!')"
exit

What’s in the manifest?

After these steps, your jnc.toml looks something like:

[project]
name = "my-jetson-app"
channels = ["conda-forge"]
platforms = ["linux-aarch64"]

[system-requirements]
l4t = "36"

[dependencies]
python = ">=3.12,<4"
numpy = ">=2.2,<3"
opencv = ">=4.10,<5"

[tasks]
check-gpu = "python -c 'import torch; print(torch.cuda.is_available())'"

Next steps

Basic Usage — common workflows at a glance
Environments — manage separate dependency sets for inference, training, etc.
Tasks — advanced task configuration
CLI Reference — complete command documentation

Getting Started

Concepts

Quickstart

Quickstart

Install jnc

Verify your hardware

Create a workspace

Add dependencies

Set system requirements

Run commands

Define tasks

Enter the environment shell

What’s in the manifest?

Next steps

​Quickstart

​Install jnc

​Verify your hardware

​Create a workspace

​Add dependencies

​Set system requirements

​Run commands

​Define tasks

​Enter the environment shell

​What’s in the manifest?

​Next steps

Quickstart

Install jnc

Verify your hardware

Create a workspace

Add dependencies

Set system requirements

Run commands

Define tasks

Enter the environment shell

What’s in the manifest?

Next steps