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How to Get the Total Amount of GPU Memory

In this blog, we will delve into the importance for data scientists or software engineers engaged in machine learning model development to possess a comprehensive comprehension of the resources demanded by their models, particularly in terms of GPU memory. The focus of this article is to guide you through the process of determining the total GPU memory available on your system, ensuring that you can adequately allocate resources for your models.
By Saturn Cloud | | Miscellaneous | Updated:

As a data scientist or software engineer working with machine learning models, it’s essential to have a clear understanding of the resources required by your models, especially when it comes to GPU memory. In this article, we will explore how to get the total amount of GPU memory on your system to ensure that you have enough resources for your models.

Table of Contents

  1. What Is GPU Memory?
  2. How to Get the Total Amount of GPU Memory
  3. Common Errors and Solutions
  4. Conclusion

What Is GPU Memory?

GPU memory, also known as VRAM (Video Random Access Memory), is a type of memory used by graphics processing units (GPUs) to store data required for rendering images, videos, and animations. In recent years, GPUs have become increasingly popular in the field of machine learning due to their ability to accelerate deep learning algorithms.

When working with machine learning models that require GPU resources, it’s essential to know the total amount of GPU memory available on your system. This information can help you determine the size of the models you can train and the batch size you can use, among other things.

How to Get the Total Amount of GPU Memory

Getting the total amount of GPU memory on your system is relatively simple. Depending on your operating system and the GPU you are using, you can use one of the following methods:

Method 1: Using NVIDIA-SMI (Linux and Windows)

NVIDIA-SMI (System Management Interface) is a command-line utility provided by NVIDIA that allows you to monitor and manage NVIDIA GPU devices. To get the total amount of GPU memory using NVIDIA-SMI, follow these steps:

  1. Open a terminal or command prompt.
  2. Type the following command: nvidia-smi
  3. Press Enter.

Sample Output:

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 515.105.01   Driver Version: 515.105.01   CUDA Version: 11.7     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA RTX A6000    Off  | 00000000:5E:00.0 Off |                  Off |
| 55%   80C    P2   264W / 300W |  36284MiB / 49140MiB |     79%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
|   1  NVIDIA RTX A6000    Off  | 00000000:AF:00.0 Off |                  Off |
| 54%   79C    P2   262W / 300W |  35039MiB / 49140MiB |     76%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A      1955      G   /usr/lib/xorg/Xorg                  4MiB |
|    0   N/A  N/A    402810      C   ...6/envs/py39/bin/python3.9      657MiB |
|    0   N/A  N/A    644593      C   python                          35619MiB |
|    1   N/A  N/A      1955      G   /usr/lib/xorg/Xorg                  4MiB |
|    1   N/A  N/A    644593      C   python                          35031MiB |
+-----------------------------------------------------------------------------+

Method 2: Using PyTorch (Linux and Windows)

PyTorch is an open-source machine learning library based on the Torch library. It provides a Python interface for accessing NVIDIA GPU resources. To get the total amount of GPU memory using PyTorch, follow these steps:

  1. Install PyTorch on your system.
  2. Open a Python shell or Jupyter Notebook.
  3. Import the torch library by typing the following command: import torch
  4. Type the following command to get the total amount of GPU memory: torch.cuda.get_device_properties(0).total_memory
  5. Press Enter.

The output will show you the total amount of GPU memory available on your system in bytes.

Common Errors and Solutions

Error: GPU Not Found

Example:

torch.cuda.is_available()  # Returns False

Solution: Ensure that your system has a compatible GPU and that GPU drivers are correctly installed.

Error: Insufficient Permissions

Example:

nvidia-smi: Insufficient Permissions (ho:...)

Solution: Run the command with elevated permissions or check user access rights.

Error: PyTorch not Installed

Example:

ImportError: No module named 'torch'

Solution: Install PyTorch using pip install torch before running the PyTorch code.

Conclusion

Getting the total amount of GPU memory available on your system is a crucial step for data scientists and software engineers working with machine learning models. This information can help you optimize your code and ensure that you have enough resources for your models.

In this article, we have explored three methods for getting the total amount of GPU memory on your system: using NVIDIA-SMI, CUDA, and PyTorch. Depending on your specific requirements and operating system, you can choose the method that works best for you.

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