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AI/ML Development with Vultr GPU Stack

Machine Learning
MS
Updated on October 9, 2023
AI/ML Development with Vultr GPU Stack header image

Introduction

Vultr GPU Stack is designed to streamline the process of building Artificial Intelligence (AI) and Machine Learning (ML) projects by providing a comprehensive suite of pre-installed software, including NVIDIA CUDA Toolkit, NVIDIA cuDNN, Tensorflow, PyTorch and so on.

It reduces the time required to set up the server before you can use it for operations like building, fine tuning or to infer a model. We ensure that the pre-installed softwares are tested on our infrastructure and are reliable for all your AI/ML development needs.

Software Included in Vultr GPU Stack

  • NVIDIA GPU Drivers: They ensure to enable your computer to utilize the NVIDIA GPUs making them function properly

  • NVIDIA CUDA Toolkit: It is also a set of programming tools and libraries to utilize the potential of NVIDIA GPUs, allowing users to speed up computation and parallel processing tasks

  • NVIDIA cuDNN: It is a GPU-accelerated library designed for deep neural networks, optimizing the performance of deep learning frameworks like TensorFlow and PyTorch

  • Tensorflow: It is a machine learning framework used for building and training deep learning models, including neural networks

  • PyTorch: It is also a machine learning framework for dynamic computation graph, research and prototyping in deep learning

  • JupyterLab: It is a web-based interactive development environment for creating and running Jupyter notebooks, commonly used in data science and machine learning experimentation

  • Docker: It is a platform for developing, shipping, and running applications inside containers

Deploy Vultr GPU Stack

  1. Choose the Compute menu item on the products page

    compute-button

  2. Click Deploy Server

    deploy-server

  3. Select a server type

  4. Select a GPU type according to the specific use case

    Server-Type

  5. Select a server location

    Server-Location

  6. Select Vultr GPU Stack as the operating system

    OS-selection

  7. Select a server size according to the specific use case

    Server-size

  8. Choose additional features as required

  9. Deploy the server

    Deploy-server

  10. Retrieve the server details

    server-details-button

  11. Copy and Paste the server IP and password to log in with SSH

    copy-paste

Access JupyterLab Interface

  1. Check the status of the JupyterLab service

     # systemctl status jupyterlab-lab

    The above command gives the information on the current state of the jupyterlab-lab service which helps to determine if the service is active or if there are issues associated with it

    Output

     ● jupyterlab-lab.service - JupyterLab Lab
      Loaded: loaded (/lib/systemd/system/jupyterlab-lab.service; enabled; vendor preset: enabled)
      Active: active (running) since Thu 2023-09-28 11:53:55 UTC; 48min ago

  2. Optional: Edit the JupyterLab Configuration

     # nano /home/jupyter/.jupyter/jupyter_lab_config.py

  3. Optional: Restart the JupyterLab Service

     # systemctl restart jupyterlab-lab

    It is necessary to restart the service for the changes made in the configuration to be applied

  4. Get the access token

     # cat /var/log/jupyterlab/lab.log

    The above command outputs the token to access the pre-installed JupyterLab

    Output

     To access the server, open this file in a browser:
     file:///home/jupyter/.local/share/jupyter/runtime/jpserver-1989-open.html
     Or copy and paste one of these URLs:
     http://localhost:9998/lab?token=6eebc299236fc1debe7b0a8e7bb8000169abcd9e8821df22
     http://127.0.0.1:9998/lab?token=6eebc299236fc1debe7b0a8e7bb8000169abcd9e8821df22

    To access the JupyterLab in your browser

     https://YOUR_SERVER_IP:8888/lab?token=YOUR_TOKEN

    Make sure to replace YOUR_SERVER_IP with an actual IP address and YOUR_TOKEN with the actual token

    Upon accessing JupyterLab, you will face a security warning that is due to a self-signed SSL certificate. You can just click Advanced and continue to access JuptyerLab

  5. Create a Notebook, and run the following script

     import torch
 import tensorflow as tf

 check = torch.cuda.is_available()
 print("is cuda available = ",check)

 try:
     print("PyTorch is installed.")
     print("PyTorch version:", torch.__version__)
 except ImportError:
     print("PyTorch is not installed.")

 try:
     print("TensorFlow is installed.")
     print("TensorFlow version:", tf.__version__)
 except ImportError:
     print("TensorFlow is not installed.")

 try:
     cuda_version = torch.version.cuda
     print(f"CUDA toolkit is installed. Version: {cuda_version}")
 except ImportError:
     print("CUDA Toolkit is not installed.")

 try:
     cudnn_version = torch.backends.cudnn.version()
     print(f"cuDNN is installed. Version: {cudnn_version}")
 except AttributeError:
     print("cuDNN is not available.")

    The above script outputs the status of CUDA availability and the pre-installed versions of TensorFlow, PyTorch, cuDNN, and CUDA Toolkit.

  6. Perform matrix operation

     matrix_a = torch.tensor([[1, 2], [3, 4]])
 matrix_b = torch.tensor([[4, 3], [2, 1]])

 result_pytorch = torch.mm(matrix_a, matrix_b)

 print("Matrix multiplication result using PyTorch:")
 print(result_pytorch)

 matrix_a = tf.constant([[1, 2], [3, 4]])
 matrix_b = tf.constant([[4, 3], [2, 1]])

 result_tensorflow = tf.matmul(matrix_a, matrix_b)

 print("Matrix multiplication result using TensorFlow:")
 print(result_tensorflow.numpy())

    The above script performs matrix multiplication using TensorFlow and PyTorch to check if there are no issues with the pre-installed versions and if they are working as intended.

Run a GPU Accelerated Container

By running a GPU-accelerated container it becomes convenient to scale GPU-accelerated applications while enabling portability and resource management.

  1. Pull the official pytorch image

     # docker pull pytorch/pytorch

  2. Run the container

     # docker run --gpus all -it --rm pytorch/pytorch

    In the above command --gpus all instructs Docker to assign all available GPUs to the container, ensuring that the GPU can utilize GPU resources. -it instructs Docker to remove the container once it exits

    Upon the execution of the command, you will be inside the container to perform all actions

  3. Access the Python console

     # python3

  4. Check the GPU availability

     >>> import torch
 >>> torch.cuda.is_available()

    Output

     True

  5. Run the following script on the Python terminal

     import torch
 import time

 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 matrix_size = 10000

 matrix_a = torch.randn(matrix_size, matrix_size)
 matrix_b = torch.randn(matrix_size, matrix_size)

 matrix_a_gpu = matrix_a.to(device)
 matrix_b_gpu = matrix_b.to(device)

 start_time = time.time()
 result_cpu = matrix_a * matrix_b
 cpu_execution_time = time.time() - start_time

 ## Warm up
 for i in range(5):
     _ = matrix_a_gpu * matrix_b_gpu

 start_time = time.time()
 result_gpu = matrix_a_gpu * matrix_b_gpu
 gpu_execution_time = time.time() - start_time

 speedup = cpu_execution_time / gpu_execution_time

 print("Matrix size:", matrix_size, "x", matrix_size)
 print("CPU Execution Time:", cpu_execution_time, "seconds")
 print("GPU Execution Time:", gpu_execution_time, "seconds")
 print("Speedup:", speedup)

    The above script creates two random matrices of the mentioned size and calculates the matrix multiplication time on both CPU and GPU, then the actual speed difference achieved by the GPU is calculated

Frequently Asked Questions

How do I deploy a server with Vultr GPU Stack?

You can find the Vultr GPU Stack option under the list of Operating Systems while deploying a new server, you can also select the version of base Ubuntu image.

What servers are compatible with Vultr GPU Stack?

Vultr GPU Stack is compatible with Cloud GPU servers and Bare Metal servers that are equipped with GPU(s).

What softwares come pre-installed when I deploy a server with Vultr GPU Stack?

Vultr GPU Stack comes with the following pre-installed softwares:

  • NVIDIA GPU Driver
  • NVIDIA CUDA Toolkit
  • NVIDIA cuDNN
  • Tensorflow
  • PyTorch
  • JupyterLab
  • Docker with NVIDIA Container Toolkit

Can I deploy a server with Vultr GPU Stack using Terraform?

Yes, you can deploy a server with Vultr GPU Stack image using Terraform or Vultr API using the correct os_id that you can find using List OS API endpoint.

How to fetch the JupyterLab login token?

You can find the token to log into JupyterLab interface in system logs located in the /var/log/jupyter directory.

How to overwrite the JupyterLab configuration?

You can overwrite the JupyterLab configuration by editing the /home/jupyter/.jupyter/jupyter_lab_config.py file and restarting the jupyterlab-lab service.

Can I upload my existing Jupyter/iPython notebooks?

Yes, you can upload your existing notebooks by accessing the JupyterLab interface or placing it in the default notebooks directory.

Where are the Jupyter Notebooks located by default?

By default, the Jupyter notebooks are located in /home/jupyter/notebooks directory.

Conclusion

You walked through the steps to deploy a Cloud GPU server with Vultr GPU Stack for AI/ML development/deployment. It comes with essential pre-installed softwares that are tested on our infrastructure for compatiblity and reliablity. The packaged software help reduce the time to configure the server before you're able to perform any action.