23.3  Using AWS EC2 Instances

In this section, we will show you how to install all libraries on a raw Linux machine. Recall that in Section 23.2 we discussed how to use Amazon SageMaker, while building an instance by yourself costs less on AWS. The walkthrough includes three steps:

  1. Request for a GPU Linux instance from AWS EC2.
  2. Install CUDA (or use an Amazon Machine Image with preinstalled CUDA).
  3. Install the deep learning framework and other libraries for running the code of the book.

This process applies to other instances (and other clouds), too, albeit with some minor modifications. Before going forward, you need to create an AWS account, see Section 23.2 for more details.

23.3.1 Creating and Running an EC2 Instance

After logging into your AWS account, click “EC2” (Figure 23.3.1) to go to the EC2 panel.

Figure 23.3.1: Open the EC2 console.

Figure 23.3.2 shows the EC2 panel.

Figure 23.3.2: The EC2 panel.

23.3.1.1 Presetting Location

Select a nearby data center to reduce latency, e.g., “Oregon” (marked by the red box in the top-right of Figure 23.3.2). If you are located in China, you can select a nearby Asia Pacific region, such as Seoul or Tokyo. Please note that some data centers may not have GPU instances.

23.3.1.2 Increasing Limits

Before choosing an instance, check if there are quantity restrictions by clicking the “Limits” label in the bar on the left as shown in Figure 23.3.2. Figure 23.3.3 shows an example of such a limitation. The account currently cannot open “p2.xlarge” instances according to the region. If you need to open one or more instances, click on the “Request limit increase” link to apply for a higher instance quota. Generally, it takes one business day to process an application.

Figure 23.3.3: Instance quantity restrictions.

23.3.1.3 Launching an Instance

Next, click the “Launch Instance” button marked by the red box in Figure 23.3.2 to launch your instance.

We begin by selecting a suitable Amazon Machine Image (AMI). Select an Ubuntu instance (Figure 23.3.4).

Figure 23.3.4: Choose an AMI.

EC2 provides many different instance configurations to choose from. This can sometimes feel overwhelming to a beginner. Table 23.3.1 lists different suitable machines.

Table 23.3.1: Different EC2 instance types
Name GPU Notes
g2 Grid K520 ancient
p2 Kepler K80 old but often cheap as spot
g3 Maxwell M60 good trade-off
p3 Volta V100 high performance for FP16
p4 Ampere A100 high performance for large-scale training
g4 Turing T4 inference optimized FP16/INT8

All these servers come in multiple flavors indicating the number of GPUs used. For example, a p2.xlarge has 1 GPU and a p2.16xlarge has 16 GPUs and more memory. For more details, see the AWS EC2 documentation or a summary page. For the purpose of illustration, a p2.xlarge will suffice (marked in the red box of Figure 23.3.5).

Figure 23.3.5: Choose an instance.

Note that you should use a GPU-enabled instance with suitable drivers and a GPU-enabled deep learning framework. Otherwise you will not see any benefit from using GPUs.

We go on to select the key pair used to access the instance. If you do not have a key pair, click “Create new key pair” in Figure 23.3.6 to generate a key pair. Subsequently, you can select the previously generated key pair. Make sure that you download the key pair and store it in a safe location if you generated a new one. This is your only way to SSH into the server.

Figure 23.3.6: Select a key pair.

In this example, we will keep the default configurations for “Network settings” (click the “Edit” button to configure items such as the subnet and security groups). We just increase the default hard disk size to 64 GB (Figure 23.3.7). Note that CUDA by itself already takes up 4 GB.

Figure 23.3.7: Modify the hard disk size.

Click “Launch Instance” to launch the created instance. Click the instance ID shown in Figure 23.3.8 to view the status of this instance.

Figure 23.3.8: Click the instance ID.

23.3.1.4 Connecting to the Instance

As shown in Figure 23.3.9, after the instance state turns green, right-click the instance and select Connect to view the instance access method.

Figure 23.3.9: View the instance access method.

If this is a new key, it must not be publicly viewable for SSH to work. Go to the folder where you store D2L_key.pem and execute the following command to make the key not publicly viewable:

chmod 400 D2L_key.pem
Figure 23.3.10: View instance access and startup method.

Now, copy the SSH command in the lower red box of Figure 23.3.10 and paste onto the command line:

ssh -i "D2L_key.pem" [email protected]

When the command line prompts “Are you sure you want to continue connecting (yes/no)”, enter “yes” and press Enter to log into the instance.

Your server is ready now.

23.3.2 Installing CUDA

Before installing CUDA, be sure to update the instance with the latest drivers.

sudo apt-get update && sudo apt-get install -y build-essential git libgfortran3

Here we download CUDA 12.1. Visit NVIDIA’s official repository to find the download link as shown in Figure 23.3.11.

Figure 23.3.11: Find the CUDA 12.1 download address.

Copy the instructions and paste them onto the terminal to install CUDA 12.1.

# The link and file name are subject to changes
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin
sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/12.1.0/local_installers/cuda-repo-ubuntu2204-12-1-local_12.1.0-530.30.02-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu2204-12-1-local_12.1.0-530.30.02-1_amd64.deb
sudo cp /var/cuda-repo-ubuntu2204-12-1-local/cuda-*-keyring.gpg /usr/share/keyrings/
sudo apt-get update
sudo apt-get -y install cuda

After installing the program, run the following command to view the GPUs:

nvidia-smi

Finally, add CUDA to the library path to help other libraries find it, such as appending the following lines to the end of ~/.bashrc.

export PATH="/usr/local/cuda-12.1/bin:$PATH"
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda-12.1/lib64

23.3.3 Installing Libraries for Running the Code

To run the code of this book, just follow steps in Chapter 3 for Linux users on the EC2 instance and use the following tips for working on a remote Linux server:

  • To download the bash script on the Miniconda installation page, right click the download link and select “Copy Link Address”, then execute wget [copied link address].
  • After running ~/miniconda3/bin/conda init, you may execute source ~/.bashrc instead of closing and reopening your current shell.

23.3.4 Running the Jupyter Notebook remotely

To run the Jupyter Notebook remotely you need to use SSH port forwarding. After all, the server in the cloud does not have a monitor or keyboard. For this, log into your server from your desktop (or laptop) as follows:

# This command must be run in the local command line
ssh -i "/path/to/key.pem" [email protected] -L 8889:localhost:8888

Next, go to the location of the downloaded code of this book on the EC2 instance, then run:

conda activate d2l
jupyter notebook

Figure 23.3.12 shows the possible output after you run the Jupyter Notebook. The last row is the URL for port 8888.

Figure 23.3.12: Output after running the Jupyter Notebook. The last row is the URL for port 8888.

Since you used port forwarding to port 8889, copy the last row in the red box of Figure 23.3.12, replace “8888” with “8889” in the URL, and open it in your local browser.

23.3.5 Closing Unused Instances

As cloud services are billed by the time of use, you should close instances that are not being used. Note that there are alternatives:

  • “Stopping” an instance means that you will be able to start it again. This is akin to switching off the power for your regular server. However, stopped instances will still be billed a small amount for the hard disk space retained.
  • “Terminating” an instance will delete all data associated with it. This includes the disk, hence you cannot start it again. Only do this if you know that you will not need it in the future.

If you want to use the instance as a template for many more instances, right-click on the example in Figure 23.3.9 and select “Image” \(\rightarrow\) “Create” to create an image of the instance. Once this is complete, select “Instance State” \(\rightarrow\) “Terminate” to terminate the instance. The next time you want to use this instance, you can follow the steps in this section to create an instance based on the saved image. The only difference is that, in “1. Choose AMI” shown in Figure 23.3.4, you must use the “My AMIs” option on the left to select your saved image. The created instance will retain the information stored on the image hard disk. For example, you will not have to reinstall CUDA and other runtime environments.

23.3.6 Summary

  • We can launch and stop instances on demand without having to buy and build our own computer.
  • We need to install CUDA before using the GPU-enabled deep learning framework.
  • We can use port forwarding to run the Jupyter Notebook on a remote server.

23.3.7 Exercises

  1. The cloud offers convenience, but it does not come cheap. Find out how to launch spot instances to see how to reduce costs.
  2. Experiment with different GPU servers. How fast are they?
  3. Experiment with multi-GPU servers. How well can you scale things up?

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