Release VRAM.

Hello.

Can I release VRAM of my videocard NVIDIA during the GenAI processes? Yes, there are a few ways to release VRAM without outright terminating processes, especially if you’re using GPU-accelerated libraries like PyTorch or TensorFlow, which provide methods to manage and release memory.

Here are some methods to release VRAM without killing processes:

1. Release VRAM in PyTorch

If you’re working in a PyTorch environment, you can release cached memory that’s no longer needed with `torch.cuda.empty_cache()`. This frees up any cached memory that PyTorch may still be holding onto.

```python

import torch

torch.cuda.empty_cache()  # Releases unused VRAM

```

This command does not affect active allocations but clears memory that might be cached by PyTorch.

2. Release VRAM in TensorFlow

In TensorFlow, you can reset the GPU memory allocation by clearing the session:

```python

from tensorflow.keras.backend import clear_session

clear_session()  # Releases memory used by TensorFlow

```

This is particularly helpful if you are done with a model or part of your code and want to free up GPU memory for another task.

3. Using `nvidia-smi` to Manage GPU Memory

The `nvidia-smi` tool itself doesn’t directly free memory but can help by reducing persistent states. One option is to reset the GPU through `nvidia-smi` to clear any residual allocations without rebooting the system. 

To reset the GPU, use:

```batch

nvidia-smi --gpu-reset

```

Note: this is only supported on certain configurations (mostly non-display GPUs) and requires administrative privileges. This option will reset the GPU state, releasing all VRAM while keeping the process itself running.

If your version NVIDIA doesn't support key "--gpu-reset" so you can use the following: batch file can be a straightforward way to automate the release of VRAM by terminating GPU processes using `nvidia-smi`. Here’s a script that:

### Batch Script to Free VRAM by Terminating Processes

Here's the batch file code:

```batch

@echo off

echo Listing all GPU processes and memory usage:

nvidia-smi --query-compute-apps=pid,process_name,used_memory --format=csv,noheader,nounits

echo.

set /p confirm="Do you want to terminate all GPU processes to free VRAM? (y/n): "

if /i "%confirm%"=="y" (

    echo Terminating all GPU processes...

    for /f "tokens=1" %%p in ('nvidia-smi --query-compute-apps=pid --format=csv,noheader,nounits') do (

        echo Terminating process ID %%p...

        taskkill /PID %%p /F

    )

    echo All GPU processes terminated. VRAM should now be released.

) else (

    echo Operation cancelled. No processes were terminated.

)

pause

```

How It Works:

a. Listing Processes: `nvidia-smi --query-compute-apps=pid,process_name,used_memory --format=csv,noheader,nounits` lists all running processes on the GPU with their Process ID (PID), name, and memory usage.

b. User Confirmation: The script asks for confirmation to terminate all GPU processes. Enter `y` (yes) or `n` (no).

c. Terminating Processes: If confirmed, it uses `taskkill` to forcefully terminate each process by its PID, freeing the GPU’s VRAM.

4. Unload and Reload Libraries in Python (Advanced)

If you want to clear VRAM without ending your Python script, another approach is to deallocate memory by deleting specific variables or unloading entire models/modules from memory.

Example for PyTorch:

```python

import torch

# Delete specific variables

del model  # Replace `model` with the name of the variable to delete

torch.cuda.empty_cache()  # Clears VRAM

```

This approach clears up GPU memory only for the specified objects, keeping other parts of your program running.

Summary

If you’re working within Python, using `torch.cuda.empty_cache()` or `clear_session()` is the safest way to release VRAM without terminating processes. For other GPU workloads, resetting the GPU with `nvidia-smi --gpu-reset` (if it's supported) can clear memory allocations without restarting the machine.