PlantSeg : tool for cell instance aware segmentation in densely packed 3D volumetric images.

 https://github.com/hci-unihd/plant-seg

 

Install PlantSeg

conda create -n plant-seg -c pytorch -c conda-forge cudatoolkit=10.1 -c lcerrone -c abailoni -c cpape -c awolny pytorch nifty=1.0.9 plantseg

To install pytorch for a certain cudatoolkit version

conda install pytorch  cudatoolkit=10.1 -c pytorch
plantseg --gui

To designate a certain cuda GPU device when run plant-seq with

CUDA_VISIBLE_DEVICES=0 plantseg --gui

To check cuda is available in pytorch

import torch
torch.cuda.is_available()

To check GPU usage

nvidia-smi -l 1 

To check CUDA version

nvcc -V

 

How To Use GPU with PyTorch

https://wandb.ai/wandb/common-ml-errors/reports/How-To-Use-GPU-with-PyTorch---VmlldzozMzAxMDk

 

How to run python code from Terminal in multiple sessions with multiple GPUs

1. set CUDA device

$ CUDA_VISIBLE_DEVICES=0 python test1.py  # Uses GPU 0.

$ CUDA_VISIBLE_DEVICES=1 python test2.py  # Uses GPU 1.

$ CUDA_VISIBLE_DEVICES=2,3 python test3.py  # Uses GPUs 2 and 3.

 or

2. add in python code

import os

os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"   

os.environ["CUDA_VISIBLE_DEVICES"]="0"

 

 ref:

https://stackoverflow.com/questions/34775522/tensorflow-multiple-sessions-with-multiple-gpus

https://stackoverflow.com/questions/37893755/tensorflow-set-cuda-visible-devices-within-jupyter