Question
Answer and Explanation
The CUDA_VISIBLE_DEVICES environment variable is used to control which NVIDIA GPUs are visible to CUDA applications. This is particularly useful when you have multiple GPUs in a system and you want to restrict a specific application or process to use only a subset of them.
Here’s a breakdown of its purpose and usage:
1. GPU Selection:
- By setting CUDA_VISIBLE_DEVICES, you’re essentially creating a virtual environment for CUDA applications, dictating which GPUs are ‘seen’ and therefore usable by that application. This means if an application is executed with CUDA_VISIBLE_DEVICES=1, it would only see the second GPU (indexing starts at 0) and not the first or any others.
2. Isolation and Resource Management:
- It helps with resource management. If multiple users or processes are running on the same machine, you can assign different GPUs to each using this variable, ensuring that they don't contend for the same hardware resources, which can cause performance bottlenecks.
3. Example Scenarios:
- Single GPU Usage: If you want a process to use only GPU 0, you would run it like this in the terminal:
CUDA_VISIBLE_DEVICES=0 python my_script.py
- Multiple GPU Usage: To make the process see GPUs 0 and 2, use:
CUDA_VISIBLE_DEVICES=0,2 python my_script.py
- No GPUs: To effectively disable GPU usage for a particular process you can set it to an invalid value, like:
CUDA_VISIBLE_DEVICES=-1 python my_script.py
4. Use Cases:
- Machine Learning Training: When training multiple models simultaneously on separate GPUs, you would use CUDA_VISIBLE_DEVICES to assign each training process to a specific GPU.
- Research and Development: Researchers might use this variable to ensure that their experimental processes run on specified GPUs, allowing for controlled experiments.
- Deployment: In deployment scenarios where certain models are optimized for specific GPUs, this variable ensures the proper mapping.
5. Impact and Behavior:
- Without setting CUDA_VISIBLE_DEVICES, CUDA applications typically default to using all available GPUs in the system.
- The visible devices are re-indexed by CUDA to be seen sequentially, from 0. For example, if you use CUDA_VISIBLE_DEVICES=3,1, the application will see GPUs 1 and 3 as GPUs 0 and 1, respectively.
6. Important Notes:
- The environment variable can be set at the command line or within your system’s environment variables.
- This variable only affects CUDA-enabled applications; it doesn’t limit other applications' access to the GPUs.
In summary, CUDA_VISIBLE_DEVICES provides a way to control which GPUs a specific CUDA application will use, enhancing resource management and isolation, especially in environments with multiple GPUs. It plays a pivotal role in maximizing GPU utilization and performance.