Get the Pytorch pip wheels released by NVIDIA: Python3.6: https://drive.google.com/file/d/1h3nsVXskS8yQvLmhrL77m8mImusRy7OR/view Python2.7: https://drive.google.com/file/d/12ywd_wzkPAfsZIv8pP7lPb7YBSpzM44x/view
For Python3.6
sudo apt-get install python3-pip
pip3 install torch-1.0.0a0+8601b33-cp36-cp36m-linux_aarch64.whl
pip3 install numpy
If you're using Python2.7
sudo apt-get install python-pip
pip install torch-1.0.0a0+8601b33-cp27-cp27mu-linux_aarch64.whl
pip install numpy
The following commands can be run using pip or pip3 depending your preferred python version
You will most likely need Torch Vision and the necessary dependencies
pip3 install torchvision --no-deps
and Scikit-image
sudo apt-get install gfortran
pip3 install Cython
pip3 install scikit-image
git clone https://github.com/ShreyasSkandanS/ss_segmentation.git
Follow setup instructions (for testing/inference).
On our data/model we noticed a 1.8X increase without any Xavier specific optimizations. No NVDLA cores were used.
If you've trained a network on a multi-gpu setup and naively exported the model to a model.pth.tar format, you're likely to see something like this show up as an error:
torch.cuda.nccl.NcclError: System Error (2)
For the same reasons mentioned here, load your model and remove all bindings to the DataParallel class and you should be able successfully run your code,
pretrained_model = checkpoint['model']
new_model = SomeNetwork()
from collections import OrderedDict
new_model_dict = OrderedDict()
for k,v in pretrained_model.state_dict().items():
# Drop the "Module." characters from the name
name = k[7:]
new_model_dict[name] = v
new_model.load_state_dict(new_model_dict)