Non Global CUDA_VISIBLE_DEVICES

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Introduction

When working with deep learning models, it's common to utilize multiple GPUs to accelerate training and inference processes. However, the current approach of setting the CUDA_VISIBLE_DEVICES environment variable globally can lead to inefficient model loading and wasted video memory. In this article, we'll explore a method to specify different models for different GPUs, allowing for more efficient resource utilization.

Understanding CUDA_VISIBLE_DEVICES

The CUDA_VISIBLE_DEVICES environment variable is used to specify which GPUs are visible to a Python process. By default, it's set globally, and all processes inherit this setting. This can lead to multiple models being evenly distributed across multiple GPUs, resulting in wasted video memory and reduced efficiency.

Current Limitations

The current approach of setting CUDA_VISIBLE_DEVICES globally has several limitations:

  • Inefficient model loading: Multiple models are loaded into different GPUs, leading to wasted video memory and reduced efficiency.
  • Limited concurrency control: It's challenging to control the concurrency number for different models, leading to potential bottlenecks and reduced performance.

Proposed Solution: Non-Global CUDA_VISIBLE_DEVICES

To address these limitations, we propose a method to specify different models for different GPUs using a non-global CUDA_VISIBLE_DEVICES approach. This involves setting the CUDA_VISIBLE_DEVICES environment variable on a per-process basis, allowing for more flexible and efficient model loading.

Methodology

Our proposed solution involves the following steps:

  1. Set CUDA_VISIBLE_DEVICES on a per-process basis: Instead of setting the CUDA_VISIBLE_DEVICES environment variable globally, we'll set it on a per-process basis using the os.environ module.
  2. Specify different models for different GPUs: We'll use a dictionary to map model names to their corresponding GPU IDs. This will allow us to specify different models for different GPUs.
  3. Control concurrency numbers: We'll use a separate dictionary to control the concurrency numbers for different models.

Implementation

Here's an example implementation of our proposed solution:

import os
import torch

# Define a dictionary to map model names to their corresponding GPU IDs
model_gpu_map = {
    'qwen2.5:32b': ['cuda:0', 'cuda:1'],
    'qwq': ['cuda:2', 'cuda:3', 'cuda:4', 'cuda:5', 'cuda:6']
}

# Define a dictionary to control concurrency numbers
concurrency_map = {
    'qwen2.5:32b': 2,
    'qwq': 4
}

# Set CUDA_VISIBLE_DEVICES on a per-process basis
for model, gpu_ids in model_gpu_map.items():
    os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(gpu_ids)
    # Load the model and perform inference
    model = torch.load(model)
    model.to(gpu_ids[0])
    # Perform inference and control concurrency numbers
    concurrency = concurrency_map[model]
    # Perform inference with the specified concurrency number
    model.inference(concurrency)

Benefits

Our proposed solution offers several benefits:

  • Efficient model loading: By specifying different models for different GPUs, we can avoid wasting video memory and reduce the time it takes to load models.
  • Improved concurrency control: By controlling concurrency numbers for different models, we can optimize performance and reduce bottlenecks.

Conclusion

In this article, we proposed a method to specify different models for different GPUs using a non-global CUDA_VISIBLE_DEVICES approach. This involves setting the CUDA_VISIBLE_DEVICES environment variable on a per-process basis and using dictionaries to map model names to their corresponding GPU IDs and control concurrency numbers. Our proposed solution offers several benefits, including efficient model loading and improved concurrency control. We hope this article has provided valuable insights into optimizing model loading and concurrency control on multiple GPUs.

Future Work

Future work includes:

  • Extending the proposed solution to support more complex model loading scenarios: We plan to extend our proposed solution to support more complex model loading scenarios, such as loading multiple models into a single GPU or loading models with different batch sizes.
  • Investigating the impact of non-global CUDA_VISIBLE_DEVICES on model performance: We plan to investigate the impact of non-global CUDA_VISIBLE_DEVICES on model performance and identify potential bottlenecks or areas for optimization.

References

Introduction

In our previous article, we proposed a method to specify different models for different GPUs using a non-global CUDA_VISIBLE_DEVICES approach. This involves setting the CUDA_VISIBLE_DEVICES environment variable on a per-process basis and using dictionaries to map model names to their corresponding GPU IDs and control concurrency numbers. In this article, we'll answer some frequently asked questions about our proposed solution.

Q: What are the benefits of using non-global CUDA_VISIBLE_DEVICES?

A: The benefits of using non-global CUDA_VISIBLE_DEVICES include efficient model loading, improved concurrency control, and reduced video memory usage. By specifying different models for different GPUs, we can avoid wasting video memory and reduce the time it takes to load models.

Q: How do I set CUDA_VISIBLE_DEVICES on a per-process basis?

A: To set CUDA_VISIBLE_DEVICES on a per-process basis, you can use the os.environ module to set the environment variable for each process. Here's an example:

import os
os.environ['CUDA_VISIBLE_DEVICES'] = 'cuda:0,cuda:1'

Q: How do I specify different models for different GPUs?

A: To specify different models for different GPUs, you can use a dictionary to map model names to their corresponding GPU IDs. Here's an example:

model_gpu_map = {
    'qwen2.5:32b': ['cuda:0', 'cuda:1'],
    'qwq': ['cuda:2', 'cuda:3', 'cuda:4', 'cuda:5', 'cuda:6']
}

Q: How do I control concurrency numbers for different models?

A: To control concurrency numbers for different models, you can use a separate dictionary to specify the concurrency number for each model. Here's an example:

concurrency_map = {
    'qwen2.5:32b': 2,
    'qwq': 4
}

Q: Can I use non-global CUDA_VISIBLE_DEVICES with multiple GPUs?

A: Yes, you can use non-global CUDA_VISIBLE_DEVICES with multiple GPUs. Simply specify the GPU IDs for each model in the model_gpu_map dictionary.

Q: How do I handle model loading and inference with non-global CUDA_VISIBLE_DEVICES?

A: To handle model loading and inference with non-global CUDA_VISIBLE_DEVICES, you can use the torch.load() function to load the model and the model.to() function to move the model to the specified GPU. Here's an example:

model = torch.load('model.pth')
model.to('cuda:0')

Q: Can I use non-global CUDA_VISIBLE_DEVICES with PyTorch Lightning?

A: Yes, you can use non-global CUDA_VISIBLE_DEVICES with PyTorch Lightning. Simply set the CUDA_VISIBLE_DEVICES environment variable on a per-process basis and use the model_gpu_map and concurrency_map dictionaries to specify the model and concurrency settings.

Q: What are the potential limitations of using non-global CUDA_VISIBLE_DEVICES?

A: The potential limitations of using non-global CUDA_VISIBLE_DEVICES include:

  • Increased complexity: Non-global CUDA_VISIBLE_DEVICES can add complexity to your code and require more manual configuration.
  • Potential performance issues: If not implemented correctly, non-global CUDA_VISIBLE_DEVICES can lead to performance issues or bottlenecks.

Conclusion

In this article, we answered some frequently asked questions about our proposed solution for specifying different models for different GPUs using a non-global CUDA_VISIBLE_DEVICES approach. We hope this article has provided valuable insights into optimizing model loading and concurrency control on multiple GPUs.

Future Work

Future work includes:

  • Extending the proposed solution to support more complex model loading scenarios: We plan to extend our proposed solution to support more complex model loading scenarios, such as loading multiple models into a single GPU or loading models with different batch sizes.
  • Investigating the impact of non-global CUDA_VISIBLE_DEVICES on model performance: We plan to investigate the impact of non-global CUDA_VISIBLE_DEVICES on model performance and identify potential bottlenecks or areas for optimization.

References