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[GH-ISSUE #190] empty every clip related data from VRAM #168

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opened 2026-10-31 21:11:55 +00:00 by bsakaguchi · 2 comments
bsakaguchi commented 2026-10-31 21:11:55 +00:00
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Originally created by @crasse2 on GitHub (Nov 30, 2021).
Original GitHub issue: https://github.com/openai/CLIP/issues/190

Hi There !

let's say I load your model to the GPU using those commands :

def clip_loader(clip_model):
    perceptor, preprocess = clip.load(clip_model, jit=False)
    return perceptor.eval()

perceptor = clip_loader(clip_model)

it load around 1 GB of data to the cuda device.
How would I do to totally free those 1 GB ?

I tried

del perceptor
torch.cuda.empty_cache()
gc.collect()

but it only remove around 300 MB of data (and so 700 MB are still occupying the GPU ram)
is it possible to remove everything with a certain python command ?

Originally created by @crasse2 on GitHub (Nov 30, 2021). Original GitHub issue: https://github.com/openai/CLIP/issues/190 Hi There ! let's say I load your model to the GPU using those commands : ``` def clip_loader(clip_model): perceptor, preprocess = clip.load(clip_model, jit=False) return perceptor.eval() perceptor = clip_loader(clip_model) ``` it load around 1 GB of data to the cuda device. How would I do to totally free those 1 GB ? I tried ``` del perceptor torch.cuda.empty_cache() gc.collect() ``` but it only remove around 300 MB of data (and so 700 MB are still occupying the GPU ram) is it possible to remove everything with a certain python command ?
bsakaguchi commented 2026-10-31 21:11:56 +00:00
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@jongwook commented on GitHub (Dec 3, 2021):

There could be other things remaining on the VRAM that we can't fully control, such as optimized CUDA kernels. If you absolutely need to release the VRAM, I'd recommend running your job on a subprocess, so that it can release all VRAM allocations upon exit.

<!-- gh-comment-id:985184421 --> @jongwook commented on GitHub (Dec 3, 2021): There could be other things remaining on the VRAM that we can't fully control, such as optimized CUDA kernels. If you absolutely need to release the VRAM, I'd recommend running your job on a subprocess, so that it can release all VRAM allocations upon exit.
bsakaguchi commented 2026-10-31 21:11:56 +00:00
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@crasse2 commented on GitHub (Dec 3, 2021):

Hi ! thanks for your answer !

yeah I was considering subprocess/external call or right now i'm trying to flush everything by resetting the device with numba/cuda (didn't found out how to restart and repopulate the cuda device after resetting it within a notebook)

anyway, thanks for sharing the clip model, it's a trully amazing one !

<!-- gh-comment-id:985527660 --> @crasse2 commented on GitHub (Dec 3, 2021): Hi ! thanks for your answer ! yeah I was considering subprocess/external call or right now i'm trying to flush everything by resetting the device with numba/cuda (didn't found out how to restart and repopulate the cuda device after resetting it within a notebook) anyway, thanks for sharing the clip model, it's a trully amazing one !
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