After completing the lab, For anyone interested in a deeper dive into nvcc, start with the documentation ( nvcc -help ). Use pip install -pre cupy-cudaXXX if you want to install pre-release (development) versions. Two options: Environment variable CUDA_VISIBLE_DEVICES equal to numeric IDs of GPUs to be made available. The CUDA module also provides access to additional command line tools: nvidia-smi to directly monitor GPU resource utilisation, nvcc to compile CUDA programs, cuda-gdb to debug CUDA applications.), but they will correspond to the devices that Python answers related to “tensorflow 2 cuda_visible_devices” from django. By PCI bus number: CUDA_VISIBLE_DEVICES.CUDA is a platform and programming model for CUDA-enabled GPUs. or the GPU is being hidden by the environmental variable CUDA_VISIBLE_DEVICES.I find this is always the first thing I want to run when setting up a deep learning environment, whether a desktop machine or on AWS. It is lazily initialized, so you can always import it, and use is_available () to determine if your system supports CUDA. Cuda visible devices python command line which I was aware of.
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