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How to run quip_cnn_segmentation pipeline using docker (as example in BMI cluster)
git clone https://github.com/SBU-BMI/quip_cnn_segmentation.git
ssh node002
nvidia-smi to find which gpu id is available.
cd to quip_cnn_segmentation;
for example
**cd to /home/bwang/quip_cnn_segmentation **
docker build -t quip_cnn_segmentation .
docker images
For example docker image list is
quip_cnn_segmentation latest e2169e73c093 About a minute ago 6.53GB
for example:
host folder: /home/bwang/quip_cnn_segment_test
image folder:/home/bwang/quip_cnn_segment_test/svs \
nvidia-docker run --name quip-segmentation -itd
-v /:/data/wsi_seg_local_data
-e CUDA_VISIBLE_DEVICES=${GPU_ID}
-e NPROCS=${num_cpu_cores_for_watershed_processing}
quip_cnn_segmentation run_wsi_seg.sh
You host-folder should have an svs subfolder. Input images should be in the svs subfolder. Argument -v /:/data/wsi_seg_local_data maps the host-folder on the host to the local folder in the container. The segmentation run will create two output folders: //logs (/data/wsi_seg_local_data/logs): log files are stored in this folder. //seg_tiles (/data/wsi_seg_local_data/seg_tiles): segmentation output is stored in this folder. The docker container does everything: tiling, detection and segmentation, csv and json file generation.
//run docker container example 1
nvidia-docker run --name quip-segmentation -itd
-v /home/bwang/quip_cnn_segment_test/:/data/wsi_seg_local_data
-e CUDA_VISIBLE_DEVICES=0
-e NPROCS=12
quip_cnn_segmentation run_wsi_seg.sh
//example 2:
//default value
// CUDA_VISIBLE_DEVICES=0
// NPROCS=12
nvidia-docker run --name quip-segmentation -itd
-v /home/bwang/quip_cnn_segment_test/:/data/wsi_seg_local_data
quip_cnn_segmentation run_wsi_seg.sh
segmentation output folder: /seg_tiles/
for example:
segmentation output folder: /home/bwang/quip_cnn_segment_test/seg_tiles/
this folder includes ***.png files, ***features.csv files and ***algmeta.json files.
log file folder: /logs/
for example:/home/bwang/quip_cnn_segment_test/logs/