2016年6月18日 · Using CUDA_VISIBLE_DEVICES, I can hide devices for python files, however I am unsure of how to do so within a notebook. Is there anyway to hide different GPUs in to notebooks running on the same server?

2020年4月3日 · CUDA Version: ##.# is the latest version of CUDA supported by your graphics driver. In the example above the graphics driver supports CUDA 10.1 as well as all compatible CUDA versions before 10.1. Note: The CUDA Version displayed in this table does not indicate that the CUDA toolkit or runtime are actually installed on your system. This just ...

2018年8月26日 · (base) >numba -s System info: ----- __Time Stamp__ 2018-08-27 09:17:58.167285 __Hardware Information__ Machine : AMD64 CPU Name : haswell CPU Features : aes avx avx2 bmi bmi2 cmov cx16 f16c fma fsgsbase lzcnt mmx movbe pclmul popcnt rdrnd sse sse2 sse3 sse4.1 sse4.2 ssse3 xsave xsaveopt __OS Information__ Platform : …

2016年9月23日 · The comma is not needed though CUDA_VISIBLE_DEVICES=5 python test_script.py will work, as well as CUDA_VISIBLE_DEVICES=1,2,3 python test_script.py for multi gpu. In this case it doesn't makes a difference because the variable allows lists. But for other cases it wouldn't –

2019年1月16日 · If you want to run your code only on specific GPUs (e.g. only on GPU id 2 and 3), then you can specify that using the CUDA_VISIBLE_DEVICES=2,3 variable when triggering the python code from terminal. CUDA_VISIBLE_DEVICES=2,3 python lstm_demo_example.py --epochs=30 --lr=0.001 and inside the code, leave it as:

2019年3月24日 · Answering exactly the question How to clear CUDA memory in PyTorch. In google colab I tried torch.cuda.empty_cache(). But it didn't help me. And using this code really helped me to flush GPU: import gc torch.cuda.empty_cache() gc.collect() This issue may help.

2018年5月28日 · It seems that Google Colab GPU's doesn't come with CUDA Toolkit, how can I install CUDA in Google Colab GPU's

2021年2月20日 · According to numba 0.51.2 documentation, CUDA Python supports several math functions. However, it doesn't work in the following kernel function: However, it doesn't work in the following kernel function:

2022年3月30日 · t = torch.cuda.get_device_properties(0).total_memory r = torch.cuda.memory_reserved(0) a = torch.cuda.memory_allocated(0) f = r-a # free inside reserved Python bindings to NVIDIA can bring you the info for the whole GPU (0 …

2018年7月31日 · Anyway, I just moved /usr/local/cuda-10.0 to /usr/local/old-cuda-10.0 so TF couldn't find it any more and everything then worked like a charm. It was all very frustrating, and I still feel like I just did a random hack.