Using the ARM64 GPU PyTorch DLAMI - Amazon Deep Learning AMIs
Verify PyTorch Python EnvironmentRun Training Sample with PyTorchRun Inference Sample with PyTorch
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Using the ARM64 GPU PyTorch DLAMI

The Amazon Deep Learning AMIs is ready to use with Arm64 processor-based GPUs, and comes optimized for PyTorch. The ARM64 GPU PyTorch DLAMI includes a Python environment pre-configured with PyTorch, TorchVision, and TorchServe for deep learning training and inference use cases.

Verify PyTorch Python Environment

Connect to your G5g instance and activate the base Conda environment with the following command:

source activate base

Your command prompt should indicate that you are working in the base Conda environment, which contains PyTorch, TorchVision, and other libraries.

(base) $

Verify the default tool paths of the PyTorch environment:

(base) $ which python
(base) $ which pip
(base) $ which conda
(base) $ which mamba
>>> import torch, torchvision
>>> torch.__version__
>>> torchvision.__version__
>>> v = torch.autograd.Variable(torch.randn(10, 3, 224, 224))
>>> v = torch.autograd.Variable(torch.randn(10, 3, 224, 224)).cuda()
>>> assert isinstance(v, torch.Tensor)

Run Training Sample with PyTorch

Run a sample MNIST training job:

git clone https://github.com/pytorch/examples.git
cd examples/mnist
python main.py

Your output should look similar to the following:

...
Train Epoch: 14 [56320/60000 (94%)]    Loss: 0.021424
Train Epoch: 14 [56960/60000 (95%)]    Loss: 0.023695
Train Epoch: 14 [57600/60000 (96%)]    Loss: 0.001973
Train Epoch: 14 [58240/60000 (97%)]    Loss: 0.007121
Train Epoch: 14 [58880/60000 (98%)]    Loss: 0.003717
Train Epoch: 14 [59520/60000 (99%)]    Loss: 0.001729
Test set: Average loss: 0.0275, Accuracy: 9916/10000 (99%)

Run Inference Sample with PyTorch

Use the following commands to download a pre-trained densenet161 model and run inference using TorchServe:

# Set up TorchServe
cd $HOME
git clone https://github.com/pytorch/serve.git
mkdir -p serve/model_store
cd serve

# Download a pre-trained densenet161 model
wget https://download.pytorch.org/models/densenet161-8d451a50.pth >/dev/null

# Save the model using torch-model-archiver
torch-model-archiver --model-name densenet161 \
    --version 1.0 \
    --model-file examples/image_classifier/densenet_161/model.py \
    --serialized-file densenet161-8d451a50.pth \
    --handler image_classifier \
    --extra-files examples/image_classifier/index_to_name.json  \
    --export-path model_store 

# Start the model server
torchserve --start --no-config-snapshots \
    --model-store model_store \
    --models densenet161=densenet161.mar &> torchserve.log

# Wait for the model server to start
sleep 30

# Run a prediction request
curl http://127.0.0.1:8080/predictions/densenet161 -T examples/image_classifier/kitten.jpg

Your output should look similar to the following:

{
  "tiger_cat": 0.4693363308906555,
  "tabby": 0.4633873701095581,
  "Egyptian_cat": 0.06456123292446136,
  "lynx": 0.0012828150065615773,
  "plastic_bag": 0.00023322898778133094
}

Use the following commands to unregister the densenet161 model and stop the server:

curl -X DELETE http://localhost:8081/models/densenet161/1.0
torchserve --stop

Your output should look similar to the following:

{
  "status": "Model \"densenet161\" unregistered"
}
TorchServe has stopped.