AWS Inferentia is a high performance machine learning inference chip, custom designed by AWS. Amazon EC2 Inf1 instances are powered by AWS Inferentia chips, which provides you with the lowest cost per inference in the cloud and lower the barriers for everyday developers to use machine learning (ML) at scale. Customers using models such as YOLO v3 and YOLO v4 can get up to 1.85 times higher throughput and up to 40% lower cost per inference compared to the EC2 G4 GPU-based instances.
In the demo, you will learn how to run PyTorch model with DJL on Amazon EC2 Inf1 instances.
Please launch Inf1 instance by following the Install Neuron Instructions
This demo tested on Neuron SDK 1.16.1 and PyTorch 1.10.1 on Ubuntu DLAMI. Please make sure you have Neuron Runtime 2.x installed:
sudo apt-get update -y
# - Stop any existing Neuron runtime 1.0 daemon (neuron-rtd) by calling
sudo systemctl stop neuron-rtd
sudo apt-get install linux-headers-$(uname -r) -y
sudo apt-get install aws-neuron-dkms -y
sudo apt-get install aws-neuron-tools -y
The Inferentia neuron SDK is required for converting PyTorch pre-trained model into neuron traced model.
python3 -m venv myenv
source myenv/bin/activate
pip install -U pip
pip install torch-neuron==1.10.1.* neuron-cc[tensorflow] torchvision --extra-index-url=https://pip.repos.neuron.amazonaws.com
After installing the Inferentia neuron SDK, you will find libtorchneuron.so
is installed in
myenv/lib/python3.6/site-packages/torch_neuron/lib
folder.
You need configuration environment variable to enable Inferentia for DJL:
export PYTORCH_EXTRA_LIBRARY_PATH=$(python -m site | grep $VIRTUAL_ENV | awk -F"'" '{print $2}')/torch_neuron/lib/libtorchneuron.so
libtorchneuron.so
depends on some shared library in its folder, you also need to specify LD_LIBRARY_PATH to make it work:
export LD_LIBRARY_PATH=$LD_LIBRARYPATH:$(python -m site | grep $VIRTUAL_ENV | awk -F"'" '{print $2}')/torch_neuron/lib/
Use the following python script to trace a PyTorch resnet50 model. The script can also be found in the repo at trace.py.
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
Execute above command, now you have a Neuron traced model ready for inference in
models/inferentia/resnet50
folder.
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: 1.10.0
[
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
]
You can use DJL benchmark tool to compare performance w/o Inferentia enabled:
./gradlew benchmark
[INFO ] - Running inference with PyTorch: 1.10.0
[INFO ] - Loading libneuron_op.so from: /home/ubuntu/myenv/lib/python3.6/site-packages/torch_neuron/lib/libtorchneuron.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
./gradlew benchmark --args="models/djl/resnet50"
[INFO ] - Running inference with PyTorch: 1.10.0
[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"