> ## Documentation Index
> Fetch the complete documentation index at: https://jncpkg.org/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

> Set up your first jnc workspace on a Jetson Orin Nano.

# 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:

```bash theme={null}
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:

```bash theme={null}
jnc --version
```

## Verify your hardware

First, confirm jnc can detect your Jetson:

```bash theme={null}
jnc probe
```

You should see output like:

```json theme={null}
{
  "arch": "aarch64",
  "l4t": "36.4.0",
  "jetpack": "6.1",
  "cuda": "12.2",
  "module": "Jetson Orin Nano",
  "compute_capability": "8.7"
}
```

## Create a workspace

```bash theme={null}
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:

```bash theme={null}
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:

```toml theme={null}
[system-requirements]
l4t = "36"
```

## Run commands

Run any command inside the workspace environment:

```bash theme={null}
jnc run python -c "import cv2; print(f'OpenCV {cv2.__version__}')"
```

## Define tasks

Add reusable tasks to your manifest:

```bash theme={null}
jnc task add check-gpu "python -c 'import torch; print(torch.cuda.is_available())'"
```

Run them by name:

```bash theme={null}
jnc run check-gpu
```

## Enter the environment shell

Start an interactive shell with all dependencies available:

```bash theme={null}
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:

```toml theme={null}
[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](/basic-usage) — common workflows at a glance
* [Environments](/concepts/environments) — manage separate dependency sets for inference, training, etc.
* [Tasks](/concepts/tasks) — advanced task configuration
* [CLI Reference](/cli/overview) — complete command documentation