Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
Training a model requires significantly more CPU/GPU memories than running inference using the model.
torch.cuda.empty_cache() doesn't help if memory is not enough
It is suggested that you …
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
https://pypi.org/project/lightgbm/#build-gpu-version
https://github.com/microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#build-gpu-version
https://www.kaggle.com/vinhnguyen/gpu-acceleration-for-lightgbm
Microsoft's Example Dockerfile for GPU version of LightGBM …
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
The article Stop Installing Tensorflow using pip for performance sake! suggest installing TensorFlow using conda instead of pip as the version installed by conda leverages Intel Math Kernel Library and …
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
https://pytorch.org/docs/master/notes/cuda.html
You can use the command torch.cuda.is_available()
to check whether GPU is available for PyTorch.
Details of GPUs can …
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
https://github.com/gpuweb/gpuweb This is the repository for the W3C's GPU for the Web Community Group.
gfx-rs is a low-level, cross-platform graphics and compute abstraction library in Rust …
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
GPU is more accisible for average individual people. GPU is still the main tool for deep learning right now.
Python Distributed Computing Frameworks (Ray, Modin, etc.) servers as a mid solution …