To kick things off let’s get started with launching our instance. Head over to the AWS Management Console and search for “Neuron” in the services section. Click on “Launch an Instance” and select your preferred region (because who wants to wait longer than necessary, right?). Then choose the Neuron2.4xlarge instance type because bigger is always better when it comes to deep learning.
Now for the fun part configuring our instance! To kick things off, let’s install some dependencies. We need to make sure we have all the tools necessary to run our models and train them efficiently. So, let’s start by running this command:
# Update and upgrade the system packages
sudo apt-get update && sudo apt-get upgrade -y
# Install Python3 and pip for managing Python packages
sudo apt-get install python3-pip
# Install Git for version control
sudo apt-get install git
# Install jq for parsing JSON files
sudo apt-get install jq
# Install wget for downloading files from the internet
sudo apt-get install wget
# Install unzip for extracting compressed files
sudo apt-get install unzip
# Install curl for transferring data from or to a server
sudo apt-get install curl
# Install nvidia-docker2 for running Docker containers with GPU support
sudo apt-get install nvidia-docker2
Next, let’s download and install the Neuron SDK. This will allow us to run our models on the AWS Neuron processor:
# Download the Neuron SDK from the specified URL using the wget command
wget https://aws.amazon.com/neuron/downloads/latest/NeuronSDK-Linux_x86_64.tgz
# Extract the downloaded file using the tar command
tar xzf NeuronSDK-Linux_x86_64.tgz
# Change directory to the extracted folder
cd NeuronSDK-Linux_x86_64/
# Run the installation script using the ./ notation to specify the current directory
./install.sh
Now that we have the SDK installed, let’s download and install some popular deep learning frameworks like TensorFlow and PyTorch:
# This script installs TensorFlow version 2.5.0 and PyTorch version 1.8.1 with additional packages.
# The -f flag specifies the URL to download the PyTorch wheel from.
# Install TensorFlow version 2.5.0
pip3 install tensorflow==2.5.0
# Install PyTorch version 1.8.1 with additional packages
# The -f flag specifies the URL to download the PyTorch wheel from.
# The ==cpu flag specifies that the CPU version of PyTorch will be installed.
# The ==0.9.1 flag specifies the version of torchvision to be installed.
# The ==0.8.1 flag specifies the version of torchaudio to be installed.
# The ==10.2 flag specifies the version of cudatoolkit to be installed.
pip3 install torch==1.8.1+cpu torchvision==0.9.1 torchaudio==0.8.1 cudatoolkit=10.2 -f https://download.pytorch.org/whl/cu102/torch_stable.html
Finally, let’s download and train our model using the Neuron processor:
# Download pre-trained model weights from example.com
wget https://example.com/model_weights.tar.gz
# Extract the downloaded tar file
tar xzf model_weights.tar.gz
# Train your model on AWS Neuron instance
python3 train.py \
--data=path/to/your/dataset \ # Specifies the path to the dataset used for training
--config=path/to/your/configuration/file \ # Specifies the path to the configuration file for the model
--checkpoint=path/to/save/model/weights \ # Specifies the path to save the trained model weights
--epochs=number_of_training_iterations \ # Specifies the number of training iterations
--batch-size=batch_size \ # Specifies the batch size for training
--learning-rate=initial_learning_rate \ # Specifies the initial learning rate for the model
--optimizer=name_of_optimizer \ # Specifies the optimizer to be used for training
--neuron \ # Enables the use of AWS Neuron for training
--device=aws.ne2xlarge # Specifies the device to be used for training, in this case, AWS Neuron on an ne2xlarge instance
And that’s it! You should now be able to train your models faster than ever before using the AWS Neuron instance for deep learning.