Show HN: Keep your PyTorch model in VRAM by hot swapping code
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Re: Show HN: Keep your PyTorch model in VRAM by hot swapping code
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Re: Show HN: Keep your PyTorch model in VRAM by hot swapping code
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Re: Show HN: Keep your PyTorch model in VRAM by hot swapping code
#4Tensor visualizer app itself already looks pretty interesting
Re: Show HN: Keep your PyTorch model in VRAM by hot swapping code
#5Tensor visualizer app itself already looks pretty interesting
Thanks, I will do a deep writeup on that at some point.
Re: Show HN: Keep your PyTorch model in VRAM by hot swapping code
#6We use python notebooks for that functionality in the early stages of script testing. Load a cell up top with the model, then do your stuff below, and once things look good convert it to a normal python script.
Re: Show HN: Keep your PyTorch model in VRAM by hot swapping code
#7[deleted]
Re: Show HN: Keep your PyTorch model in VRAM by hot swapping code
#8Tensor visualizer app itself already looks pretty interesting
Thanks, I will do a deep writeup on that at some point.
Are you running both DearImGui visualisation and training locally? If not, how can one use it in the client-server mode? I think this is the most common requirement for visualisation libraries in Deep Learning.
Re: Show HN: Keep your PyTorch model in VRAM by hot swapping code
#9Earlier quoted context omitted.
Thanks, I will do a deep writeup on that at some point.
Are you running both DearImGui visualisation and training locally? If not, how can one use it in the client-server mode? I think this is the most common requirement for visualisation libraries in Deep Learning.
The rendering is done with OpenGL, and for remote viewing I just render to an offscreen framebuffer and stream it back to the client with WebRTC. The code for that isn’t public yet, still needs some cleanup.
Re: Show HN: Keep your PyTorch model in VRAM by hot swapping code
#10Tensor visualizer app itself already looks pretty interesting
Yeah, sadly the link to their visualizations is gated behind X.com