Merge pull request #16 from mantidproject/37527_deploy_cnn

Update README.md with latest instructions

diffraction/WISH/bragg-detect/cnn/README.md

@@ -1,22 +1,16 @@
-Bragg Peaks detection using a Faster RCNN model
+Bragg Peaks detection using a pre-trained Faster RCNN deep neural network 
 ================
 
-Inorder to use the pretrained Faster RCNN model inside mantid, below steps are required.
+Inorder to use the pre-trained Faster RCNN model inside mantid using an IDAaaS instance, below steps are required.
 
-* Install mantid from conda `mamba create -n mantid_cnn -c mantid mantidworkbench`
-* Activate the conda environment with `mamba activate mantid_cnn` 
-* Launch workbench from `workbench` command
-* Download the script repository's `scriptrepository\diffraction\WISH` directory as instructed here https://docs.mantidproject.org/nightly/workbench/scriptrepository.html
-* Check whether `<local path>\diffraction\WISH` path is available at `Python Script Directories` tab from `File->Manage User Directories`.
-* Close the workbench
-* From command line, change the directory to the place where the scripts were downloaded ex: `<local path>\diffraction\WISH\bragg-detect\cnn`
-* Within the same conda enviroment, install pytorch dependancies by running `pip install -r requirements.txt`
-* Install NVIDIA CUDA Deep Neural Network library (cuDNN) by running `conda install -c anaconda cudnn`
-* Re-launch workbench from `workbench` command
+* Launch an IDAaaS instance with GPUs from WISH > Wish Single Crystal GPU Advanced
+* Launch Mantid workbench nightly from Applications->Software->Mantid->Mantid Workbench Nightly 
+* Download `scriptrepository\diffraction\WISH` directory from mantid's script repository as instructed here https://docs.mantidproject.org/nightly/workbench/scriptrepository.html
+* Check whether `<local path>\diffraction\WISH` path is listed under `Python Script Directories` tab from `File->Manage User Directories` of Mantid workbench.
 * Below is an example code snippet to test the code. It will create a peaks workspace with the inferred peaks from the cnn and will do a peak filtering using the q_tol provided using `BaseSX.remove_duplicate_peaks_by_qlab`.
 ```python
 from cnn.BraggDetectCNN import BraggDetectCNN
-model_weights = r'path/to/pretrained/fasterrcnn_resnet50_model_weights.pt'
+model_weights = r'/mnt/ceph/auxiliary/wish/BraggDetect_FasterRCNN_Resnet50_Weights_v1.pt'
 cnn_peaks_detector = BraggDetectCNN(model_weights_path=model_weights, batch_size=64)
 cnn_peaks_detector.find_bragg_peaks(workspace='WISH00042730', output_ws_name="CNN_Peaks", conf_threshold=0.0, q_tol=0.05)
 ```