GPU Programming in the Cloud

Paperspace offers a complete range of instance types, from affordable older generation hardware to the latest generation. It strives for simplicity—and obtains it—with a straightforward sign up process that gets new users up and running in minutes. A particular advantage is the ability to pause a running instance and resume it later while paying only for storage in between, offering convenience and cost effectiveness over competitors that only offer complete shutdown and reconfiguration. There are several machine images available based on Ubuntu. Paperspace was acquired by Digital Ocean in August 2023 but, as of September 2023, still runs as a separate service.

Lambda focuses on state-of-the-art, having mostly latest generation hardware, but there are some cheaper instances with older hardware in limited quantities. Lambda specializes in GPU instances and does not provide CPU instances. All instances run Ubuntu and have LambdaStack pre-installed—Lambda’s software stack for deep learning that includes e.g. CUDA, TensorFlow and PyTorch. Getting started is frictionless and takes only minutes.

Genesis Cloud is unique in two ways: providing hardware from Nvidia’s GeForce consumer line, and operating from Iceland and Norway. GeForce are great for many applications, and certainly for development, but look elsewhere for Nvidia’s data center GPUs if double precision performance is important. All instances run Ubuntu. A good selection of instance types are available by default, with access to the others on request. Essential software, such as CUDA, must be self-installed.

Linode offers a limited GPU service. All instance types use the Nvidia Quadro RTX 6000 which, while affordable for production, is a pricier floor for development than other providers. Access requires a support ticket, a description of the use case, and at least $100 USD of spend (which can be pre-purchased as credits). Once granted, instances are easy to setup with a simple web interface, but only base operating system images are provided, so that CUDA and other essential software must be self-installed.

Amazon Web Services (AWS) offers an extended range of instance types spanning multiple generations of hardware. There are a wide variety of machine images with GPU support. Access to GPU instances requires a support ticket, as initial quotas are zero. Frustratingly, you will likely find out the first time you try and fail to launch an instance, and you must repeat the process.

Google Cloud Services offers an extended range of instance types covering multiple generations of hardware. There are a wide variety of machine images with GPU support. Access to GPU instances is on request, as initial quotas are zero. Frustratingly, you will likely find out the first time you try and fail to launch an instance—but, unlike the other majors, quota increase requests seem to be processed quickly, and there is a convenient retry button to avoid repeating the whole process again. The web interface is much more helpful than the other majors, presenting instance types in a human readable form rather than obscure codes, and making better default suggestions for machine images.

Microsoft Azure offers an extended range of instance types covering multiple generations of hardware. There are a wide variety of machine images with GPU support. Access to GPU instances requires a support ticket, as initial quotas are zero. Frustratingly, you will likely find out the first time you try and fail to launch an instance—and good luck from there, as you navigate a quagmire of product SKUs, regional availability, and interface nuisances, while getting rejected for quota increases.

Work from the console.

1. Import your 🔑key

   Click your profile picture in the top right, then Your account, then the SSH Keys tab. Click the Add new SSH Key button, enter a name and copy in your 🔑key.

2. Launch an instance

   Select the CORE Virtual Servers product from the top left menu, then click the Create a machine button. Choose a region close to you, but otherwise keep the defaults. Click the Create button.

3. Find your 👤user and 🏠host

   Wait for the instance to start then click the Connect button. You will see a command of the form ssh user@host. The 👤user is paperspace and the 🏠host some IP address.

4. Prepare the instance (after you log in for the first time, see below)

   Unfortunately while CUDA is installed it is not accessible out-of-the-box. Enter the following commands each time you login to make it accessible:

   export PATH=/usr/local/cuda/bin:$PATH
   export CPATH=/usr/local/cuda/include:$CPATH
   export LIBRARY_PATH=/usr/local/cuda/lib64:$LIBRARY_PATH
   export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
   

   These can be added to the end of ~/.profile to avoid entering them on each login. Run:

   nano ~/.profile
   

   Copy them into the bottom of the file, then hit Ctrl+O followed by Enter to save, and Ctrl+X to exit.

5. Terminate the instance (once you’re finished)

   On the Machines tab, find the instance, click the three dots in the top right of its card and then Deactivate.

Work from the dashboard.

1. Launch an instance

   Select Instances on the left then click the Launch Instance button. You may be prompted to add a payment method if you have not done so already. Follow the steps to specify an instance type, region, and provide your 🔑key. Once complete you will be taken back to the Instances page. The new instance will have a status of Booting, which will progress to Running after a few minutes.

2. Find your 👤user and 🏠host

   Under the SSH Login column you will see a command of the form ssh user@host.

3. Terminate the instance (once you’re finished)

   On the Instances page, select the checkbox next to the instance and a Terminate button will appear.

Work from the dashboard.

1. Launch an instance

   Click the Create new instance button in the top right. Select an instance type; the default may require an increase to your quota, but other types should be available. Keep the default Ubuntu image but be sure to check the Install NVIDIA® GPU driver box. Under Authentication, choose SSH Key, and copy in your 🔑key. Click Create instance. The Instances page will appear with the new instance listed. Its status will progress from Enqueued to Creating… to Active.

2. Find your 👤user and 🏠host

   On the same page will be a command of the form ssh user@host.

3. Prepare the instance (after you log in for the first time, see below)

   The driver will install in the first few minutes of the instance running (as long as the Install NVIDIA GPU driver box was checked). CUDA can be installed with:

   sudo apt update
   sudo apt install nvidia-cuda-toolkit
   

4. Terminate the instance (once you’re finished)

   From the Instances page, click the three dots icon to the right of the instance and select Destroy from the pop-up menu.

Work from the AWS Management Console.

1. Choose a region

   Select a region in the top right.

2. Import your 🔑key

   Select the Services menu in the top left, then EC2. Select Network & Security > Key Pairs on the left. Select Actions > Import key pair at the top left. Provide a name and copy your 🔑key into the large text area. Click the Import key pair button.

3. Increase your quota

   You may have zero quota for the GPU instances that interest you. Just try launching what you want below, see if it fails, and if so follow the instructions that will be provided in that case to request an increase in quota.

4. Launch an instance

   Still on the EC2 page, select Instances on the left then the Launch Instances button. Click Browse more AMIs (AMI = Amazon Machine Image), search for gpu, and under the Quickstart AMIs tab choose Ubuntu’s Deep Learning AMI GPU PyTorch. For the instance type select box choose g4dn-xlarge (the economical option—more information on instance types available). Under Key Pair (login) select the name of the key pair that you created in step 2. Click the Launch instance button. Once the instance starts click View All Instances. If it fails to start due to lack of quota, follow the instructions given to submit a support ticket, and try again once approved

5. Find your 👤user and 🏠host

   Select the instance and note the Public IPv4 address or Public IPv4 DNS, either works as your 🏠host. Your 👤user is ubuntu. To confirm these details, or if you chose a different machine image than that suggested above, click the instance name, then the Connect button, then the SSH client tab for detailed instructions. This will give an SSH command of the form ssh -i key.pem user@host; you will only need the 👤user and 🏠host parts of that.

6. Terminate the instance (once you’re finished)

   On the Instances page, check the box next to instance name, then select Instance state > Terminate instance in the top right.

Work from the Google Cloud Console.

1. Upgrade to a paid account

   A banner should appear at the top of the page with an Activate button to use. If not, refresh. If not, perhaps you already have a paid account.

2. Add your SSH key

   From the main menu, select Compute Engine > Settings > Metadata > SSH Keys. Paste your 🔑key into the box and hit Save.

3. Increase your quota

   You can update quotas by entering quotas in the search box at the top and selecting All Quotas. However, this firehose of regions and instance types assumes that you already know what you are doing. Easier is to try to start an instance, let it fail, and use the prompt to increase the relevant quota.

4. Launch an instance

   Steps:

   1. From the main menu, select Compute Engine > VM Instances. Click the Create Instance button. First time users may be interrupted by a splash page for the Compute Engine API; click Enable.
   2. Under Machine Configuration, select GPU. It will select the cheapest GPU instance type by default.
   3. Under Boot Disk will be a suggestion to switch from the default machine image to one with CUDA pre-installed. Click the Switch Image button to do that. In the dialog that appears, keep the defaults, just click Select.
   4. Returning to the VM Instances page, the new instance should appear. You may receive an error message about quotas. If so, hover the mouse over the red status icon nex to the instance to reveal a popup with a Raise Limit button. Click that and follow the instructions. Wait for a response and, if approved, click the Retry icon in the rightmost column. With any luck second time, the instance will start.

5. Find your 👤user and 🏠host

   On the VM Instances page, the External IP column provides 🏠host. For 👤user you will need the username associated with your SSH key pair, which is likely just the username on the laptop or desktop where you create it. If that does not work, you can always generate a new SSH key pair using the instructions above.

6. Prepare the instance (after you log in for the first time, see below)

   On first login, a prompt will ask about installing the Nvidia driver. Answer yes. Unfortunately, if you intend to use Visual Studio Code, this prompt also breaks its setup routine. The solution is to log in via SSH with your terminal first, answer yes, wait for the installation to complete, then try connecting Visual Studio Code.

7. Terminate the insance (once you’re finished)

   On the VM Instances page, check the box next to the instance, click the three dots in the menu above, and choose Delete.

My Azure experience was frustrating. I found the user interface tedious to work with, and was caught in a cycle of rejected quota requests for a single GPU instance with no reason provided. Finally, a helpful support engineer found a GPU for me a continent away. To get that far took two days. Some prior study may help: perhaps start with the documentation on GPU instance types and cross reference with regional availability. You may still find it necessary to contact support as I did, however, and be flexible with regions: the availability of an instance type in a given region does not guarantee that a quota request is approved (demand management, presumably).

Work from the Azure Portal.

1. Upgrade to a pay-as-you-go subscription

   This is necessary to action a quota increase. Click the upgrade prompt in the top menu. If it does not appear, you may already have such an account.

2. Import your 🔑key

   Type ssh keys in the search box at the top of the dashboard. Click the SSH keys item. Click the Create SSH key button. Under Resource group select an existing group or create a new one. Under Key pair name enter a name to give the key. Under SSH public key source select Upload existing public key and copy your 🔑key into the text area. Click the Review + Create button. A validation screen appears, check the details and click the Create button.

3. Request a quota increase

   Type quotas in the search box at the top of the dashboard. Select Quotas then Compute. At the top, click on Region and select your preferred region to narrow the search. Search for a GPU instance type. Click Request quota increase > Enter a new limit in the top left. Enter a new limit equal to the number of vCPUs for the instance type and continue (emphasis: don’t just enter 1, enter the number of vCPUs). A response takes several minutes, and you need to keep the web page open while you wait for it (🤷). After waiting, you may find that the request is rejected, and that you must file a support ticket instead. There is a link to do so.

4. Launch an instance

   Steps:

   1. Type virtual machines in the search box at the top of the dashboard. Select the Virtual Machines item. Click the Create button and in the popup menu, Azure Virtual Machine.
   2. Select a region.
   3. Under Image, keep the default.
   4. Under Size, click See all sizes and see if you can find a GPU instance type. The SKUs are obscure, but try searching for T4, A100 or A10. You may find an instance for which you do not have sufficient quota—there should be a link next to it to request, and you can try again once (if!) approved.
   5. Under SSH public key source select Use existing key stored in Azure. A new select box labeled Stored Keys appears, select the key you created in step 1 above.
   6. Click the Review + Create button. A validation screen appears, check the details and click the Create button. A status page will appear as the instance is deployed. Once deployed, click the Go to resource button.
   7. Install the Nvidia GPU Driver Extension for Linux to the instance. On the left menu, click Extensions + Applications. Click the Add button and search for nvidia to find the extension. Click on it, hit Next, Review + Create, Create. Wait for it to deploy—it takes several minutes.

5. Find your 👤user and 🏠host

   From the Virtual Machines page, select the instance. Your 🏠host is under Public IP Address. Your 👤user is azureuser.

6. Prepare the instance (after you log in for the first time, see below)

   You will need to install CUDA:

   sudo apt update
   sudo apt install nvidia-cuda-toolkit
   

7. Terminate the instance (once you’re finished)

   From the Virtual Machines page, check the box next to the instance and click the Delete button at the top.