## Machine Learning Container for Jetson and JetPack

The `l4t-ml` docker image contains TensorFlow, PyTorch, JupyterLab, and other popular ML and data science frameworks such as scikit-learn, scipy, and Pandas pre-installed in a Python 3 environment.  These containers support the following releases of [JetPack](https://developer.nvidia.com/embedded/jetpack) for Jetson Nano, TX1/TX2, Xavier NX, AGX Xavier, AGX Orin, Orin NX, and Orin Nano:

* JetPack 5.1.1 (L4T R35.3.1)
* JetPack 5.1 (L4T R35.2.1)
* JetPack 5.0.2 (L4T R35.1.0)
* JetPack 5.0.1 Developer Preview (L4T R34.1.1)
* JetPack 5.0.0 Developer Preview (L4T R34.1.0)
* JetPack 4.6.1 (L4T R32.7.1)
* JetPack 4.6 (L4T R32.6.1)
* JetPack 4.5 (L4T R32.5.0)
* JetPack 4.4.1 (L4T R32.4.4)
* JetPack 4.4 (L4T R32.4.3)
* JetPack 4.4 Developer Preview (L4T R32.4.2)

For additional machine learning containers for Jetson, see the [`l4t-pytorch`](https://ngc.nvidia.com/catalog/containers/nvidia:l4t-pytorch) and [`l4t-tensorflow`](https://ngc.nvidia.com/catalog/containers/nvidia:l4t-tensorflow) images.  Note that the TensorFlow and PyTorch pip wheel installers for aarch64 are available to download independently from the [Jetson Zoo](https://elinux.org/Jetson_Zoo).

### Package Versions

Depending on your version of JetPack-L4T, different tags of the `l4t-ml` container are available, each with support for Python 3.  Be sure to clone a tag that matches the version of JetPack-L4T that you have installed on your Jetson.

* JetPack 5.1.1 (L4T R35.3.1)

  * `l4t-ml:r35.3.1-py3`
    * TensorFlow 2.11.0
    * PyTorch v2.0.0
    * torchvision v0.15.1
    * torchaudio v2.0.1
    * onnx 1.13.1
    * onnxruntime 1.16.0
    * optimum 1.8.8
    * CuPy 13.0.0
    * numpy 1.23.5
    * numba 0.56.4
    * PyCUDA 2022.2
    * OpenCV 4.5.0 (with CUDA)
    * pandas 2.0.1
    * scipy 1.10.0
    * scikit-learn 1.2.2
    * diffusers 0.17.1
    * transformers 4.30.2
    * xformers 0.0.20
    * JupyterLab 3.6.3
* JetPack 5.1 (L4T R35.2.1)

  * `l4t-ml:r35.2.1-py3`
    * TensorFlow 2.11.0
    * PyTorch v2.0.0
    * torchvision v0.14.1
    * torchaudio v0.13.1
    * onnx 1.13.0
    * CuPy 11.5.0
    * numpy 1.21.1
    * numba 0.56.4
    * PyCUDA 2022.2
    * OpenCV 4.5.0 (with CUDA)
    * pandas 1.5.3
    * scipy 1.10.0
    * scikit-learn 1.2.1
    * JupyterLab 3.6.1
* JetPack 5.0.2 (L4T R35.1.0)

  * `l4t-ml:r35.1.0-py3`
    * TensorFlow 1.15.5
    * PyTorch v1.12.0
    * torchvision v0.13.0
    * torchaudio v0.12.0
    * onnx 1.12.0
    * CuPy 10.2.0
    * numpy 1.22.4
    * numba 0.56.0
    * PyCUDA 2022.1
    * OpenCV 4.5.0 (with CUDA)
    * pandas 1.4.3
    * scipy 1.9.0
    * scikit-learn 1.1.1
    * JupyterLab 3.4.4
* JetPack 5.0.1 Developer Preview (L4T R34.1.1)

  * `l4t-ml:r34.1.1-py3`
    * TensorFlow 1.15.5
    * PyTorch v1.12.0
    * torchvision v0.12.0
    * torchaudio v0.11.0
    * onnx 1.11.0
    * CuPy 10.2.0
    * numpy 1.21.6
    * numba 0.55.1
    * PyCUDA 2021.1
    * OpenCV 4.5.0 (with CUDA)
    * pandas 1.4.2
    * scipy 1.8.1
    * scikit-learn 1.1.1
    * JupyterLab 3.4.2
* JetPack 5.0.0 Developer Preview (L4T R34.1.0)

  * `l4t-ml:r34.1.0-py3`
    * TensorFlow 1.15.5
    * PyTorch v1.12.0
    * torchvision v0.12.0
    * torchaudio v0.11.0
    * onnx 1.11.0
    * CuPy 10.2.0
    * numpy 1.21.5
    * numba 0.55.1
    * PyCUDA 2021.1
    * OpenCV 4.5.0 (with CUDA)
    * pandas 1.4.1
    * scipy 1.8.0
    * scikit-learn 1.0.2
    * JupyterLab 3.3.3
* JetPack 4.6.1 (L4T R32.7.1)

  * `l4t-ml:r32.7.1-py3`
    * TensorFlow 1.15.5
    * PyTorch v1.10.0
    * torchvision v0.11.0
    * torchaudio v0.10.0
    * onnx 1.11.0
    * CuPy 9.2.0
    * numpy 1.19.5
    * numba 0.53.1
    * OpenCV 4.5.0 (with CUDA)
    * pandas 1.1.5
    * scipy 1.5.4
    * scikit-learn 0.24.2
    * JupyterLab 2.2.9
* JetPack 4.6 (L4T R32.6.1)

  * `l4t-ml:r32.6.1-py3`
    * TensorFlow 1.15.5
    * PyTorch v1.9.0
    * torchvision v0.10.0
    * torchaudio v0.9.0
    * onnx 1.8.0
    * CuPy 8.0.0
    * numpy 1.19.5
    * numba 0.53.1
    * OpenCV 4.4.0 (with CUDA)
    * pandas 1.1.5
    * scipy 1.5.4
    * scikit-learn 0.23.2
    * JupyterLab 2.2.9
* JetPack 4.5 (L4T R32.5.0)

  * `l4t-ml:r32.5.0-py3`
    * TensorFlow 1.15
    * PyTorch v1.7.0
    * torchvision v0.8.0
    * torchaudio v0.7.0
    * onnx 1.8.0
    * CuPy 8.0.0
    * numpy 1.19.4
    * numba 0.52.0
    * OpenCV 4.4.1
    * pandas 1.1.5
    * scipy 1.5.4
    * scikit-learn 0.23.2
    * JupyterLab 2.2.9
* JetPack 4.4.1 (L4T R32.4.4)

  * `l4t-ml:r32.4.4-py3`
    * TensorFlow 1.15
    * PyTorch v1.6.0
    * torchvision v0.7.0
    * torchaudio v0.6.0
    * onnx 1.7.0
    * CuPy 8.0.0
    * numpy 1.19.2
    * numba 0.51.2
    * pandas 1.1.3
    * scipy 1.5.3
    * scikit-learn 0.23.2
    * JupyterLab 2.2.8
* JetPack 4.4 (L4T R32.4.3)

  * `l4t-ml:r32.4.3-py3`
    * TensorFlow 1.15
    * PyTorch v1.6.0
    * torchvision v0.7.0
    * torchaudio v0.6.0
    * onnx 1.7.0
    * CuPy 8.0.0
    * numpy 1.19.0
    * numba 0.50.0
    * pandas 1.0.5
    * scipy 1.5.0
    * scikit-learn 0.23.1
    * JupyterLab 2.1.5
* JetPack 4.4 Developer Preview (L4T R32.4.2)

  * `l4t-ml:r32.4.2-py3`
    * TensorFlow 1.15
    * PyTorch v1.5.0
    * torchvision v0.6.0
    * onnx 1.6.0
    * numpy 1.18.2
    * pandas 1.0.3
    * scipy 1.4.1
    * scikit-learn 0.22.2
    * JupyterLab 2.0.1

**note:** the `l4t-ml` containers require [JetPack 4.4](https://developer.nvidia.com/embedded/jetpack) or newer

### Running the Container

First pull one of the `l4t-ml` container tags from above, corresponding to the version of JetPack-L4T that you have installed on your Jetson.  For example, if you are running the latest JetPack 5.1 (L4T R35.2.1) release:

```
sudo docker pull nvcr.io/nvidia/l4t-ml:r35.2.1-py3
```

Then to start an interactive session in the container, run the following command:

```
sudo docker run -it --rm --runtime nvidia --network host nvcr.io/nvidia/l4t-ml:r35.2.1-py3
```

You should then be able to start a Python3 interpreter and `import` the packages above.

### Connecting to JupyterLab Server

Unless a user-provided run command overrides the default, a JupyterLab server instance is automatically started along with the container.

You can connect to it by navigating your browser to `http://localhost:8888` (or substitute the IP address of your Jetson device if you wish to connect from a remote host, i.e. with the Jetson in headless mode).  Note that the default password used to login to JupyterLab is `nvidia`.

### Mounting Directories from the Host Device

To mount scripts, data, ect. from your Jetson's filesystem to run inside the container, use Docker's [`-v` flag](https://docs.docker.com/storage/bind-mounts/) when starting your Docker instance:

```
sudo docker run -it --rm --runtime nvidia --network host -v /home/user/project:/location/in/container nvcr.io/nvidia/l4t-ml:r35.2.1-py3
```

### Dockerfiles

To access or modify the Dockerfiles and scripts used to build this container, see this [GitHub repo](https://github.com/dusty-nv/jetson-containers).

### License

The `l4t-ml` container includes various software packages with their respective licenses included within the container.

### Getting Help & Support

If you have any questions or need help, please visit the [Jetson Developer Forums](https://forums.developer.nvidia.com/c/agx-autonomous-machines/jetson-embedded-systems/70).

l4t-ml

Get started on your AI journey quickly on Jetson. The Machine learning container contains TensorFlow, PyTorch, JupyterLab, and other popular ML and data science frameworks such as scikit-learn, scipy, and Pandas pre-installed in a Python environment.

NVIDIA L4T ML