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GPU acceleration
Using ML models within OpenSearch
Integrating ML models
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GPU acceleration

When running a natural language processing (NLP) model in your OpenSearch cluster with a machine learning (ML) node, you can achieve better performance on the ML node using graphics processing unit (GPU) acceleration. GPUs can work in tandem with the CPU of your cluster to speed up the model upload and training.

Supported GPUs

Currently, ML nodes support the following GPU instances:

If you need GPU power, you can provision GPU instances through Amazon Elastic Compute Cloud (Amazon EC2). For more information on how to provision a GPU instance, see Recommended GPU Instances.

Supported images

You can use GPU acceleration with both Docker images with CUDA 11.6 and Amazon Machine Images (AMIs).

PyTorch

GPU-accelerated ML nodes require PyTorch 1.12.1 work with ML models.

Setting up a GPU-accelerated ML node

Depending on the GPU, you can provision a GPU-accelerated ML node manually or by using automated initialization scripts.

Preparing an NVIDIA ML node

NVIDIA uses CUDA to increase node performance. In order to take advantage of CUDA, you need to make sure that your drivers include the nvidia-uvm kernel inside the /dev directory. To check for the kernel, enter ls -al /dev | grep nvidia-uvm.

If the nvidia-uvm kernel does not exist, run nvidia-uvm-init.sh:

#!/bin/bash
## Script to initialize nvidia device nodes.
## https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#runfile-verifications
/sbin/modprobe nvidia
if [ "$?" -eq 0 ]; then
  # Count the number of NVIDIA controllers found.
  NVDEVS=`lspci | grep -i NVIDIA`
  N3D=`echo "$NVDEVS" | grep "3D controller" | wc -l`
  NVGA=`echo "$NVDEVS" | grep "VGA compatible controller" | wc -l`
  N=`expr $N3D + $NVGA - 1`
  for i in `seq 0 $N`; do
    mknod -m 666 /dev/nvidia$i c 195 $i
  done
  mknod -m 666 /dev/nvidiactl c 195 255
else
  exit 1
fi
/sbin/modprobe nvidia-uvm
if [ "$?" -eq 0 ]; then
  # Find out the major device number used by the nvidia-uvm driver
  D=`grep nvidia-uvm /proc/devices | awk '{print $1}'`
  mknod -m 666 /dev/nvidia-uvm c $D 0
  mknod -m 666 /dev/nvidia-uvm-tools c $D 0
else
  exit 1
fi

After verifying that nvidia-uvm exists under /dev, you can start OpenSearch inside your cluster.

Preparing AWS Inferentia ML node

Depending on the Linux operating system running on AWS Inferentia, you can use the following commands and scripts to provision an ML node and run OpenSearch inside your cluster.

To start, download and install OpenSearch on your cluster.

Then export OpenSearch and set up your environment variables. This example exports OpenSearch into the directory opensearch-2.5.0, so OPENSEARCH_HOME = opensearch-2.5.0:

echo "export OPENSEARCH_HOME=~/opensearch-2.5.0" | tee -a ~/.bash_profile
echo "export PYTORCH_VERSION=1.12.1" | tee -a ~/.bash_profile
source ~/.bash_profile

Next, create a shell script file called prepare_torch_neuron.sh. You can copy and customize one of the following examples based on your Linux operating system:

After you've run the scripts, exit your current terminal and open a new terminal to start OpenSearch.

GPU acceleration has only been tested on Ubuntu 20.04 and Amazon Linux 2. However, you can use other Linux operating systems. {: .note}

Ubuntu 20.04

. /etc/os-release
sudo tee /etc/apt/sources.list.d/neuron.list > /dev/null <<EOF
deb https://apt.repos.neuron.amazonaws.com ${VERSION_CODENAME} main
EOF
wget -qO - https://apt.repos.neuron.amazonaws.com/GPG-PUB-KEY-AMAZON-AWS-NEURON.PUB | sudo apt-key add -

# Update OS packages
sudo apt-get update -y

################################################################################################################
# To install or update to Neuron versions 1.19.1 and newer from previous releases:
# - DO NOT skip 'aws-neuron-dkms' install or upgrade step, you MUST install or upgrade to latest Neuron driver
################################################################################################################

# Install OS headers
sudo apt-get install linux-headers-$(uname -r) -y

# Install Neuron Driver
sudo apt-get install aws-neuronx-dkms -y

####################################################################################
# Warning: If Linux kernel is updated as a result of OS package update
#          Neuron driver (aws-neuron-dkms) should be re-installed after reboot
####################################################################################

# Install Neuron Tools
sudo apt-get install aws-neuronx-tools -y

######################################################
#   Only for Ubuntu 20 - Install Python3.7
sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt-get install python3.7
######################################################
# Install Python venv and activate Python virtual environment to install    
# Neuron pip packages.
cd ~
sudo apt-get install -y python3.7-venv g++
python3.7 -m venv pytorch_venv
source pytorch_venv/bin/activate
pip install -U pip

# Set pip repository to point to the Neuron repository
pip config set global.extra-index-url https://pip.repos.neuron.amazonaws.com

#Install Neuron PyTorch
pip install torch-neuron torchvision
# If you need to trace the neuron model, install torch neuron with this command
# pip install torch-neuron neuron-cc[tensorflow] "protobuf==3.20.1" torchvision

# If you need to trace neuron model, install the transformers for tracing the Huggingface model.
# pip install transformers

# Copy torch neuron lib to OpenSearch
PYTORCH_NEURON_LIB_PATH=~/pytorch_venv/lib/python3.7/site-packages/torch_neuron/lib/
mkdir -p $OPENSEARCH_HOME/lib/torch_neuron; cp -r $PYTORCH_NEURON_LIB_PATH/ $OPENSEARCH_HOME/lib/torch_neuron
export PYTORCH_EXTRA_LIBRARY_PATH=$OPENSEARCH_HOME/lib/torch_neuron/lib/libtorchneuron.so
echo "export PYTORCH_EXTRA_LIBRARY_PATH=$OPENSEARCH_HOME/lib/torch_neuron/lib/libtorchneuron.so" | tee -a ~/.bash_profile

# Increase JVm stack size to >=2MB
echo "-Xss2m" | tee -a $OPENSEARCH_HOME/config/jvm.options
# Increase max file descriptors to 65535
echo "$(whoami) - nofile 65535" | sudo tee -a /etc/security/limits.conf
# max virtual memory areas vm.max_map_count to 262144
sudo sysctl -w vm.max_map_count=262144

Amazon Linux 2

# Configure Linux for Neuron repository updates
sudo tee /etc/yum.repos.d/neuron.repo > /dev/null <<EOF
[neuron]
name=Neuron YUM Repository
baseurl=https://yum.repos.neuron.amazonaws.com
enabled=1
metadata_expire=0
EOF
sudo rpm --import https://yum.repos.neuron.amazonaws.com/GPG-PUB-KEY-AMAZON-AWS-NEURON.PUB
# Update OS packages
sudo yum update -y
################################################################################################################
# To install or update to Neuron versions 1.19.1 and newer from previous releases:
# - DO NOT skip 'aws-neuron-dkms' install or upgrade step, you MUST install or upgrade to latest Neuron driver
################################################################################################################
# Install OS headers
sudo yum install kernel-devel-$(uname -r) kernel-headers-$(uname -r) -y
# Install Neuron Driver
####################################################################################
# Warning: If Linux kernel is updated as a result of OS package update
#          Neuron driver (aws-neuron-dkms) should be re-installed after reboot
####################################################################################
sudo yum install aws-neuronx-dkms -y
# Install Neuron Tools
sudo yum install aws-neuronx-tools -y

# Install Python venv and activate Python virtual environment to install    
# Neuron pip packages.
cd ~
sudo yum install -y python3.7-venv gcc-c++
python3.7 -m venv pytorch_venv
source pytorch_venv/bin/activate
pip install -U pip

# Set Pip repository  to point to the Neuron repository
pip config set global.extra-index-url https://pip.repos.neuron.amazonaws.com

# Install Neuron PyTorch
pip install torch-neuron torchvision
# If you need to trace the neuron model, install torch neuron with this command
# pip install torch-neuron neuron-cc[tensorflow] "protobuf<4" torchvision

# If you need to run the trace neuron model, install transformers for tracing Huggingface model.
# pip install transformers

# Copy torch neuron lib to OpenSearch
PYTORCH_NEURON_LIB_PATH=~/pytorch_venv/lib/python3.7/site-packages/torch_neuron/lib/
mkdir -p $OPENSEARCH_HOME/lib/torch_neuron; cp -r $PYTORCH_NEURON_LIB_PATH/ $OPENSEARCH_HOME/lib/torch_neuron
export PYTORCH_EXTRA_LIBRARY_PATH=$OPENSEARCH_HOME/lib/torch_neuron/lib/libtorchneuron.so
echo "export PYTORCH_EXTRA_LIBRARY_PATH=$OPENSEARCH_HOME/lib/torch_neuron/lib/libtorchneuron.so" | tee -a ~/.bash_profile
# Increase JVm stack size to >=2MB
echo "-Xss2m" | tee -a $OPENSEARCH_HOME/config/jvm.options
# Increase max file descriptors to 65535
echo "$(whoami) - nofile 65535" | sudo tee -a /etc/security/limits.conf
# max virtual memory areas vm.max_map_count to 262144
sudo sysctl -w vm.max_map_count=262144

When the script completes running, open a new terminal for the settings to take effect. Then, start OpenSearch.

OpenSearch should now be running inside your GPU-accelerated cluster. However, if any errors occur during provisioning, you can install the GPU accelerator drivers manually.

Prepare ML node manually

If the previous two scripts do not provision your GPU-accelerated node properly, you can install the drivers for AWS Inferentia manually:

  1. Deploy an AWS accelerator instance based on your chosen Linux operating system. For instructions, see Deploy on AWS accelerator instance.

  2. Copy the Neuron library into OpenSearch. The following command uses a directory named opensearch-2.5.0:

    OPENSEARCH_HOME=~/opensearch-2.5.0

  3. Set the PYTORCH_EXTRA_LIBRARY_PATH path. In this example, we create a pytorch virtual environment in the OPENSEARCH_HOME folder:

    PYTORCH_NEURON_LIB_PATH=~/pytorch_venv/lib/python3.7/site-packages/torch_neuron/lib/


mkdir -p $OPENSEARCH_HOME/lib/torch_neuron; cp -r  $PYTORCH_NEURON_LIB_PATH/ $OPENSEARCH_HOME/lib/torch_neuron
export PYTORCH_EXTRA_LIBRARY_PATH=$OPENSEARCH_HOME/lib/torch_neuron/lib/libtorchneuron.so


4. (Optional) To monitor the GPU usage of your accelerator instance, install [Neuron tools](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/tools/index.html), which allows models to be used inside your instance:

Install Neuron Tools

sudo apt-get install aws-neuronx-tools -y

Add Neuron tools your PATH

export PATH=/opt/aws/neuron/bin:$PATH

Test Neuron tools

neuron-top



5. To make sure you have enough memory to upload a model, increase the JVM stack size to `>+2MB`:

echo "-Xss2m" | sudo tee -a $OPENSEARCH_HOME/config/jvm.options


6. Start OpenSearch. 

## Troubleshooting

Due to the amount of data required to work with ML models, you might encounter the following `max file descriptors` or `vm.max_map_count` errors when trying to run OpenSearch in a your cluster:

[1]: max file descriptors [8192] for opensearch process is too low, increase to at least [65535] [2]: max virtual memory areas vm.max_map_count [65530] is too low, increase to at least [262144]


To troubleshoot the max file descriptors error, run the following command:

echo "$(whoami) - nofile 65535" | sudo tee -a /etc/security/limits.conf


To fix the `vm.max_map_count` error, run this command to increase the count to `262114`:

sudo sysctl -w vm.max_map_count=262144


## Next steps

If you want to try a GPU-accelerated cluster using AWS Inferentia with a pretrained HuggingFace model, see [Compiling and Deploying HuggingFace Pretrained BERT](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/src/examples/pytorch/bert_tutorial/tutorial_pretrained_bert.html).