From fb0be3c43ecdd3130247d0bc31e97e65cb7c41df Mon Sep 17 00:00:00 2001 From: Niko Sirmpilatze Date: Tue, 1 Oct 2024 08:46:27 +0100 Subject: [PATCH] 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 --- img/diagrams/array-jobs.svg | 3 + img/modern_behav_experiment_analysis.png | Bin 178738 -> 0 bytes index.qmd | 61 ++++++------------ .../slurm-scripts/sleap_infer_array_slurm.sh | 10 +-- .../slurm-scripts/sleap_infer_slurm.sh | 6 +- 5 files changed, 30 insertions(+), 50 deletions(-) create mode 100644 img/diagrams/array-jobs.svg delete mode 100644 img/modern_behav_experiment_analysis.png diff --git a/img/diagrams/array-jobs.svg b/img/diagrams/array-jobs.svg new file mode 100644 index 0000000..04d6ead --- /dev/null +++ b/img/diagrams/array-jobs.svg @@ -0,0 +1,3 @@ + + +video 1video 2video narray scriptGPU 1predictions 1GPU 2predictions 2GPU npredictions 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zr-6J;NksvpyxOCeNas%iLY_K+_YFl^f!|_23wE(_^5w+88|te#Cp?aU>?d&%OrT-? z`?3A*&U|cJt)%Se0h0qX_E&Gx$>;5?igV?%?NOADme!eSUnaXA`a$pV==huU9kZB#T!#@>x(5axh z^Q|Yw7$Et_%uEy+!xbAuNUi51Ss_4ZT}~m87mjt&ss+@dOsrev|M4f-j5ew z;)2G>tFe?_owMXPZ6g#9BZ$?W#69$8wYkZa>ny+fKN3w2MLsaTb!Lg)`5p^B!TzAy zSEi?*^%=Z5&NK(c9)9{XrIT##UpH4qKDIBhPAYqaD`4*Mqu%1bhSUV!h8iOb;|rEQ z=3#7Z-t4s&HDTC`2C#F+`p6KrdLjqv-a;D{Jn}Ag7$QQB=(lv5f(TE zo$!~39&qPLZ<&GrTnsQtk^nZck # Example: pose estimation with SLEAP {background-color="#03A062"} -## Modern behavioural analysis {.smaller} - -:::: {.columns} - -::: {.column width="70%"} -![](img/modern_behav_experiment_analysis.png){fig-align="center" height=400px} -::: - -::: {.column width="30%"} -```{mermaid} -%%| file: img/diagrams/video_pipeline_pose.mmd -%%| fig-height: 400px -``` -::: - -:::: - -::: aside -Source: [Open-source tools for behavioral video analysis: Setup, methods, and best practices](https://elifesciences.org/articles/79305) -::: - ## Pose estimation {.smaller} ![](img/pose_estimation_2D.png){fig-align="center"} @@ -596,9 +575,9 @@ see also [SLEAP's guide for remote training](https://sleap.ai/guides/remote.html Copy the unzipped training package to your scratch space and inspect its contents: ```{.bash code-line-numbers="1|2-3"} -cp -r /ceph/scratch/neuroinformatics-dropoff/SLEAP_HPC_test_data/course-hpc-2023/labels.v001.slp.training_job /ceph/scratch// -cd /ceph/scratch//labels.v001.slp.training_job -ls +cp -r /ceph/scratch/neuroinformatics-dropoff/SLEAP_HPC_test_data/course-hpc-2023/labels.v001.slp.training_job /ceph/scratch/$USER/ +cd /ceph/scratch/$USER/labels.v001.slp.training_job +ls -1 ``` ::: {.fragment} @@ -660,7 +639,7 @@ Suitable for debugging (immediate feedback) - Execute commands one-by-one, e.g.: ```{.bash code-line-numbers=false} module load SLEAP - cd /ceph/scratch//labels.v001.slp.training_job + cd /ceph/scratch/$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 --mail-user=user@domain.com -#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) diff --git a/pose-estimation/slurm-scripts/sleap_infer_array_slurm.sh b/pose-estimation/slurm-scripts/sleap_infer_array_slurm.sh index f99fa82..830a244 100644 --- a/pose-estimation/slurm-scripts/sleap_infer_array_slurm.sh +++ b/pose-estimation/slurm-scripts/sleap_infer_array_slurm.sh @@ -3,15 +3,15 @@ #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 --mail-user=user@domain.com -#SBATCH --array=1-2 +#SBATCH --array=0-1 # Load the SLEAP module module load SLEAP @@ -24,7 +24,7 @@ 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 diff --git a/pose-estimation/slurm-scripts/sleap_infer_slurm.sh b/pose-estimation/slurm-scripts/sleap_infer_slurm.sh index 151a945..aa77baf 100644 --- a/pose-estimation/slurm-scripts/sleap_infer_slurm.sh +++ b/pose-estimation/slurm-scripts/sleap_infer_slurm.sh @@ -3,10 +3,10 @@ #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