We’ve released the following framework containers for Jetson and JetPack 4.4 Developer Preview on NVIDIA GPU Cloud (NGC):
TensorFlow Container (l4t-tensorflow) - contains TensorFlow pre-installed in a Python 3.6 environment to get up & running quickly with TensorFlow on Jetson.
PyTorch Container (l4t-pytorch) - contains PyTorch and torchvision pre-installed in a Python 3.6 environment to get up & running quickly with PyTorch on Jetson.
Machine Learning Container (l4t-ml) - contains TensorFlow, PyTorch, JupyterLab, and other popular ML and data science frameworks such as scikit-learn, scipy, and Pandas pre-installed in a Python 3.6 environment.
You can also find the Dockerfiles and build scripts on GitHub - have fun!
Hi @yoshifumi_watanabe_aa, yes, the containers include support for GPU in the TensorFlow and PyTorch packages. There is also pyCUDA included. The CUDA Toolkit, CUDA, and cuDNN are automatically mapped into the containers from the Jetson device.
As per the Release Notes of the TensorFlow for Jetson installer wheel, the package name has changed from tensorflow-gpu to just tensorflow. So yes, GPU is still available in this TensorFlow container. You can check it by running the following from python3:
from tensorflow.python.client import device_lib
What I want to do is install opencv in the l4t-ml container and get the USB camera ready for use. Then I would like to ask how to install opencv. I also want to know how to make the USB camera recognized from the container. I also want to know the option about the camera of the run command when this container is completed.