Updated SLEAP-related slides and slurm scripts (#22)

* renamed slurm scripts in slides to match repo

* update SLEAP slides and slurm scripts for Oct 2024

* remove no longer needed images

index.qmd

@@ -502,27 +502,6 @@ scancel -u <USERNAME>
 
 # Example: pose estimation with SLEAP {background-color="#03A062"}
 
-## Modern behavioural analysis {.smaller}
-
-:::: {.columns}
-
-::: {.column width="70%"}
-![](img/modern_behav_experiment_analysis.png){fig-align="center" height=400px}
-:::
-
-::: {.column width="30%"}
-```{mermaid}
-%%| file: img/diagrams/video_pipeline_pose.mmd
-%%| fig-height: 400px
-```
-:::
-
-::::
-
-::: aside
-Source: [Open-source tools for behavioral video analysis: Setup, methods, and best practices](https://elifesciences.org/articles/79305)
-:::
-
 ## Pose estimation {.smaller}
 
 ![](img/pose_estimation_2D.png){fig-align="center"}
@@ -596,9 +575,9 @@ see also [SLEAP's guide for remote training](https://sleap.ai/guides/remote.html
 Copy the unzipped training package to your scratch space and inspect its contents:
 
 ```{.bash code-line-numbers="1|2-3"}
-cp -r /ceph/scratch/neuroinformatics-dropoff/SLEAP_HPC_test_data/course-hpc-2023/labels.v001.slp.training_job /ceph/scratch/<USERNAME>/
-cd /ceph/scratch/<USERNAME>/labels.v001.slp.training_job
-ls
+cp -r /ceph/scratch/neuroinformatics-dropoff/SLEAP_HPC_test_data/course-hpc-2023/labels.v001.slp.training_job /ceph/scratch/$USER/
+cd /ceph/scratch/$USER/labels.v001.slp.training_job
+ls -1
 ```
 
 ::: {.fragment}
@@ -660,7 +639,7 @@ Suitable for debugging (immediate feedback)
 - Execute commands one-by-one, e.g.:
   ```{.bash code-line-numbers=false}
   module load SLEAP
-  cd /ceph/scratch/<USERNAME>/labels.v001.slp.training_job
+  cd /ceph/scratch/$USER/labels.v001.slp.training_job
   bash train-script.sh
 
   # Stop the session
@@ -792,7 +771,7 @@ cat slurm.slp_train.gpu-380-18.4232289.err
 ## View trained models {.smaller}
 While you wait for the training job to finish, you can copy and inspect the trained models from a previous run:
 ```{.bash code-line-numbers="false"}
-cp -R /ceph/scratch/sirmpilatzen/labels.v001.slp.training_job/models /ceph/scratch/$USER/labels.v001.slp.training_job/
+cp -R /ceph/scratch/neuroinformatics-dropoff/SLEAP_HPC_test_data/course-hpc-2023/labels.v001.slp.training_job/models /ceph/scratch/$USER/labels.v001.slp.training_job/
 cd /ceph/scratch/$USER/labels.v001.slp.training_job/models
 ls
 ```
@@ -839,16 +818,16 @@ see also the SLEAP [model evaluation notebook](https://sleap.ai/notebooks/Model_
 
 ## Batch script for inference {.smaller}
 
-```{.bash filename="sleap_inference_slurm.sh" code-line-numbers="1-16|18-22|24-28|30-36"}
+```{.bash filename="sleap_infer_slurm.sh" code-line-numbers="1-16|18-22|24-28|30-36"}
 #!/bin/bash
 
 #SBATCH -J slp_infer # job name
 #SBATCH -p gpu # partition
 #SBATCH -N 1   # number of nodes
-#SBATCH --mem 64G # memory pool for all cores
-#SBATCH -n 32 # number of cores
+#SBATCH --mem 32G # memory pool for all cores
+#SBATCH -n 8 # number of cores
 #SBATCH -t 0-01:00 # time (D-HH:MM)
-#SBATCH --gres gpu:rtx5000:1 # request 1 RTX5000 GPU
+#SBATCH --gres gpu:1 # request 1 GPU
 #SBATCH -o slurm.%x.%N.%j.out # write STDOUT
 #SBATCH -e slurm.%x.%N.%j.err # write STDERR
 #SBATCH --mail-type=ALL
@@ -882,12 +861,12 @@ sleap-track $VIDEO_DIR/${VIDEO1_PREFIX}_video.mp4 \
 
 1. Edit and save the batch script
 ```{.bash code-line-numbers="false"}
-nano sleap_inference_slurm.sh
+nano sleap_infer_slurm.sh
 ```
 
 2. Submit the job
 ```{.bash code-line-numbers="false"}
-sbatch sleap_inference_slurm.sh
+sbatch sleap_infer_slurm.sh
 ```
 
 3. Monitor the job
@@ -897,28 +876,25 @@ squeue --me
 
 ## Run inference as an array job {.smaller}
 
-```{mermaid}
-%%| file: img/diagrams/array-jobs.mmd
-%%| fig-height: 500px
-```
+![](img/diagrams/array-jobs.svg){fig-align="center"}
 
 ## Batch script for array job {.smaller}
 
-```{.bash filename="sleap_inference_slurm_array.sh" code-line-numbers="14|24-28|36-42"}
+```{.bash filename="sleap_infer_array_slurm.sh" code-line-numbers="14|24-28|36-42"}
 #!/bin/bash
 
 #SBATCH -J slp_infer # job name
 #SBATCH -p gpu # partition
 #SBATCH -N 1   # number of nodes
-#SBATCH --mem 64G # memory pool for all cores
-#SBATCH -n 32 # number of cores
+#SBATCH --mem 32G # memory pool for all cores
+#SBATCH -n 8 # number of cores
 #SBATCH -t 0-01:00 # time (D-HH:MM)
-#SBATCH --gres gpu:rtx5000:1 # request 1 RTX5000 GPU
+#SBATCH --gres gpu:1 # request 1 GPU
 #SBATCH -o slurm.%x.%N.%j.out # write STDOUT
 #SBATCH -e slurm.%x.%N.%j.err # write STDERR
 #SBATCH --mail-type=ALL
 #SBATCH [email protected]
-#SBATCH --array=1-2
+#SBATCH --array=0-1
 
 # Load the SLEAP module
 module load SLEAP
@@ -931,7 +907,7 @@ VIDEO_DIR=/ceph/scratch/neuroinformatics-dropoff/SLEAP_HPC_test_data/course-hpc-
 VIDEO1_PREFIX=sub-01_ses-01_task-EPM_time-165049
 VIDEO2_PREFIX=sub-02_ses-01_task-EPM_time-185651
 VIDEOS_PREFIXES=($VIDEO1_PREFIX $VIDEO2_PREFIX)
-CURRENT_VIDEO_PREFIX=${VIDEOS_PREFIXES[$SLURM_ARRAY_TASK_ID - 1]}
+CURRENT_VIDEO_PREFIX=${VIDEOS_PREFIXES[$SLURM_ARRAY_TASK_ID]}
 echo "Current video prefix: $CURRENT_VIDEO_PREFIX"
 
 # Go to the job directory
@@ -952,3 +928,4 @@ sleap-track $VIDEO_DIR/${CURRENT_VIDEO_PREFIX}_video.mp4 \
 ## Further reading
 * [SWC/GCNU Scientific Computing wiki](https://wiki.ucl.ac.uk/display/SSC/High+Performance+Computing)
 * [SLURM documentation](https://slurm.schedmd.com/)
+* [How to use the SLEAP module on the SWC HPC cluster](https://howto.neuroinformatics.dev/data_analysis/HPC-module-SLEAP.html)