python3 -m venv myenv

source myenv/bin/activate
pip install -U pip
pip install torchvision==0.9.1 torch-neuron==1.8.1.1.4.1.0 'neuron-cc[tensorflow]==1.4.1.0' --extra-index-url=https://pip.repos.neuron.amazonaws.com

export PYTORCH_EXTRA_LIBRARY_PATH=$(python -m site | grep $VIRTUAL_ENV | awk -F"'" '{print $2}')/torch_neuron/lib/libneuron_op.so

import torch
import os
import torch_neuron
from torchvision import models
import logging

# Enable logging so we can see any important warnings
logger = logging.getLogger('Neuron')
logger.setLevel(logging.INFO)

# An example input you would normally provide to your model's forward() method.
image = torch.zeros([1, 3, 224, 224], dtype=torch.float32)

# Load a pretrained ResNet50 model
model = models.resnet50(pretrained=True)

# Tell the model we are using it for evaluation (not training)
model.eval()

# Use torch.jit.trace to generate a torch.jit.ScriptModule via tracing.
djl_traced_model = torch.jit.trace(model, image)

# Save the Regular TorchScript model for benchmarking
os.makedirs("models/djl/resnet50", exist_ok=True)
djl_traced_model.save("models/djl/resnet50/resnet50.pt")

# Analyze the model - this will show operator support and operator count
torch.neuron.analyze_model(model, example_inputs=[image])

# Now compile the model - with logging set to "info" we will see
# what compiles for Neuron, and if there are any fallbacks
model_neuron = torch.neuron.trace(model, example_inputs=[image])

# Export to saved model
os.makedirs("models/inferentia/resnet50", exist_ok=True)
model_neuron.save("models/inferentia/resnet50/resnet5.pt")
print("Compile success")

cd
git clone https://github.com/deepjavalibrary/djl-demo.git
cd djl-demo/aws/inferentia

python trace.py 

cd djl-demo/aws/inferentia

./gradlew run

[INFO ] - Number of inter-op threads is 4
[INFO ] - Number of intra-op threads is 8
Running inference with PyTorch: 1.8.1
[
        class: "n02124075 Egyptian cat", probability: 0.41596
        class: "n02123159 tiger cat", probability: 0.26856
        class: "n02123045 tabby, tabby cat", probability: 0.23701
        class: "n02123394 Persian cat", probability: 0.04384
        class: "n02127052 lynx, catamount", probability: 0.00612
]

./gradlew benchmark

[INFO ] - Running inference with PyTorch: 1.8.1
[INFO ] - Loading libneuron_op.so from: /home/ubuntu/pt/venv/lib/python3.6/site-packages/torch_neuron/lib/libneuron_op.so
[INFO ] - Multithreaded inference with 8 threads.
[INFO ] - Throughput: 288.59, completed 8000 iteration in 27721 ms.
[INFO ] - Latency P50: 27.697 ms, P90: 27.915 ms, P99: 28.426 ms

./gradle benchmark --args="models/djl/resnet50"

[INFO ] - Running inference with PyTorch: 1.8.1
[INFO ] - Loading regular pytorch model ...
[INFO ] - Multithreaded inference with 8 threads.
[INFO ] - Throughput: 33.92, completed 8000 iteration in 235833 ms.
[INFO ] - Latency P50: 234.925 ms, P90: 257.252 ms, P99: 277.015 ms

./gradlew benchmark --args="models/inferentia/resnet50 1"

./gradle benchmark --args="models/djl/resnet50 1"