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Simplify examples and update for 2024 (#18)
* Simplify examples * Remove env file * Update index.qmd Co-authored-by: Igor Tatarnikov <[email protected]> --------- Co-authored-by: Igor Tatarnikov <[email protected]>
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@@ -1,20 +1,17 @@ | ||
#!/bin/bash | ||
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#SBATCH -p gpu # partition (queue) | ||
#SBATCH -p fast # partition (queue) | ||
#SBATCH -N 1 # number of nodes | ||
#SBATCH --mem 2G # memory pool for all cores | ||
#SBATCH -n 2 # number of cores | ||
#SBATCH -t 0-0:10 # time (D-HH:MM) | ||
#SBATCH --mem 1G # memory pool for all cores | ||
#SBATCH -n 1 # number of cores | ||
#SBATCH -t 0-0:1 # time (D-HH:MM) | ||
#SBATCH -o slurm_array_%A-%a.out | ||
#SBATCH -e slurm_array_%A-%a.err | ||
#SBATCH --array=0-9%4 | ||
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# Array job runs 10 separate jobs, but not more than four at a time. | ||
# This is flexible and the array ID ($SLURM_ARRAY_TASK_ID) can be used in any way. | ||
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module load miniconda | ||
conda activate slurm_demo | ||
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echo "Multiplying $SLURM_ARRAY_TASK_ID by 10" | ||
python multiply.py $SLURM_ARRAY_TASK_ID 10 --jazzy | ||
./multiply.sh $SLURM_ARRAY_TASK_ID 10 | ||
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#!/bin/bash | ||
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#SBATCH -p gpu # partition (queue) | ||
#SBATCH -p fast # partition (queue) | ||
#SBATCH -N 1 # number of nodes | ||
#SBATCH --mem 2G # memory pool for all cores | ||
#SBATCH -n 2 # number of cores | ||
#SBATCH -t 0-0:10 # time (D-HH:MM) | ||
#SBATCH --mem 1G # memory pool for all cores | ||
#SBATCH -n 1 # number of cores | ||
#SBATCH -t 0-0:1 # time (D-HH:MM) | ||
#SBATCH -o slurm_output.out | ||
#SBATCH -e slurm_error.err | ||
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module load miniconda | ||
conda activate slurm_demo | ||
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for i in {1..5} | ||
do | ||
echo "Multiplying $i by 10" | ||
python multiply.py $i 10 --jazzy | ||
./multiply.sh $i 10 | ||
done |
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result=$(($1 * $2)) | ||
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echo "The result of multiplying $1 and $2 is: $result" |
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@@ -187,12 +187,6 @@ ssh <USERNAME>@hpc-gw1 | |
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This node is fine for light work, but no intensive analyses | ||
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## Logging in | ||
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![](img/swc_hpc_access_flowchart.png){fig-align="center" width=100%} | ||
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. . . | ||
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::: {.callout-tip} | ||
## More details | ||
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@@ -203,7 +197,7 @@ See our guide at [howto.neuroinformatics.dev](https://howto.neuroinformatics.dev | |
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* `/nfs/nhome/live/<USERNAME>` or `/nfs/ghome/live/<USERNAME>` | ||
* "Home drive" (SWC/GCNU), also at `~/` | ||
* `/nfs/winstor/<group>` - Old SWC research data storage (read-only soon) | ||
* `/nfs/winstor/<group>` - Old SWC research data storage | ||
* `/nfs/gatsbystor` - GCNU data storage | ||
* `/ceph/<group>` - Current research data storage | ||
* `/ceph/scratch` - Not backed up, for short-term storage | ||
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@@ -241,15 +235,19 @@ Preinstalled packages available for use, including: | |
:::: {.columns} | ||
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::: {.column width="40%"} | ||
* ANTs | ||
* BrainGlobe | ||
* CUDA | ||
* DeepLabCut | ||
* FSL | ||
* Julia | ||
* Kilosort | ||
::: | ||
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::: {.column width="60%"} | ||
* Kilosort | ||
* mamba | ||
* MATLAB | ||
* neuron | ||
* miniconda | ||
* SLEAP | ||
::: | ||
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@@ -281,7 +279,7 @@ module unload SLEAP | |
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Load a specific version | ||
```bash | ||
module load SLEAP/2023-08-01 | ||
module load SLEAP/2024-08-14 | ||
``` | ||
. . . | ||
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@@ -305,20 +303,22 @@ sinfo | |
``` | ||
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``` | ||
atyson@sgw2:~$ sinfo | ||
atyson@hpc-gw1:~$ sinfo | ||
PARTITION AVAIL TIMELIMIT NODES STATE NODELIST | ||
cpu* up infinite 29 idle~ enc1-node[1,3-14],enc2-node[1-10,12-13],enc3-node[5-8] | ||
cpu* up infinite 1 down* enc3-node3 | ||
cpu* up infinite 2 mix enc1-node2,enc2-node11 | ||
cpu* up infinite 5 idle enc3-node[1-2,4],gpu-380-[24-25] | ||
gpu up infinite 9 mix gpu-350-[01,03-05],gpu-380-[10,13],gpu-sr670-[20-22] | ||
gpu up infinite 9 idle gpu-350-02,gpu-380-[11-12,14-18],gpu-sr670-23 | ||
medium up 12:00:00 4 idle~ enc3-node[5-8] | ||
medium up 12:00:00 1 down* enc3-node3 | ||
medium up 12:00:00 1 mix gpu-380-10 | ||
medium up 12:00:00 10 idle enc3-node[1-2,4],gpu-380-[11-12,14-18] | ||
fast up 3:00:00 1 mix gpu-380-10 | ||
fast up 3:00:00 9 idle enc1-node16,gpu-380-[11-12,14-18],gpu-erlich01 | ||
cpu* up 10-00:00:0 1 mix# gpu-380-25 | ||
cpu* up 10-00:00:0 31 mix enc1-node[1-14],enc2-node[1-13],enc3-node[6-8],gpu-380-24 | ||
cpu* up 10-00:00:0 4 alloc enc3-node[1-2,4-5] | ||
gpu up 10-00:00:0 1 mix# gpu-380-15 | ||
gpu up 10-00:00:0 1 down~ gpu-380-16 | ||
gpu up 10-00:00:0 12 mix gpu-350-[01-05], gpu-380-[11,13-14,17-18],gpu-sr670-[20,22] | ||
a100 up 30-00:00:0 2 mix gpu-sr670-[21,23] | ||
lmem up 10-00:00:0 1 idle~ gpu-380-12 | ||
medium up 12:00:00 1 mix# gpu-380-15 | ||
medium up 12:00:00 1 down~ gpu-380-16 | ||
medium up 12:00:00 7 mix enc3-node[6-8],gpu-380-[11,14,17-18] | ||
medium up 12:00:00 4 alloc enc3-node[1-2,4-5] | ||
fast up 3:00:00 2 idle~ enc1-node16,gpu-erlich01 | ||
fast up 3:00:00 4 mix gpu-380-[11,14,17-18] | ||
``` | ||
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## | ||
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@@ -329,7 +329,7 @@ squeue | |
``` | ||
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``` | ||
atyson@sgw2:~$ squeue | ||
atyson@hpc-gw1:~$ squeue | ||
JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON) | ||
4036257 cpu bash imansd R 13-01:10:01 1 enc1-node2 | ||
4050946 cpu zsh apezzott R 1-01:02:30 1 enc2-node11 | ||
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@@ -355,9 +355,9 @@ See our guide at [howto.neuroinformatics.dev](https://howto.neuroinformatics.dev | |
## Partitions | ||
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## Interactive job | ||
Start an interactive job (`bash -i`) in the cpu partition (`-p cpu`) in pseudoterminal mode (`--pty`). | ||
Start an interactive job (`bash -i`) in the fast partition (`-p fast`) in pseudoterminal mode (`--pty`) with one CPU core (`-n 1`). | ||
```bash | ||
srun -p cpu --pty bash -i | ||
srun -p fast -n 1 --pty bash -i | ||
``` | ||
. . . | ||
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@@ -373,31 +373,17 @@ git clone https://github.com/neuroinformatics-unit/course-software-skills-hpc | |
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. . . | ||
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Check out list of available modules | ||
```bash | ||
module avail | ||
``` | ||
. . . | ||
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Load the miniconda module | ||
```bash | ||
module load miniconda | ||
``` | ||
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## | ||
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Create conda environment | ||
Make the script executable | ||
```bash | ||
cd course-software-skills-hpc/demo | ||
conda env create -f env.yml | ||
chmod +x multiply.sh | ||
``` | ||
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. . . | ||
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Activate conda environment and run Python script | ||
Run the script | ||
```bash | ||
conda activate slurm_demo | ||
python multiply.py 5 10 --jazzy | ||
./multiply.sh 10 5 | ||
``` | ||
. . . | ||
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@@ -418,23 +404,17 @@ cat batch_example.sh | |
```bash | ||
#!/bin/bash | ||
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#SBATCH -p gpu # partition (queue) | ||
#SBATCH -p fast # partition (queue) | ||
#SBATCH -N 1 # number of nodes | ||
#SBATCH --mem 2G # memory pool for all cores | ||
#SBATCH -n 2 # number of cores | ||
#SBATCH -t 0-0:10 # time (D-HH:MM) | ||
#SBATCH --mem 1G # memory pool for all cores | ||
#SBATCH -n 1 # number of cores | ||
#SBATCH -t 0-0:1 # time (D-HH:MM) | ||
#SBATCH -o slurm_output.out | ||
#SBATCH -e slurm_error.err | ||
#SBATCH --mail-type=ALL | ||
#SBATCH [email protected] | ||
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module load miniconda | ||
conda activate slurm_demo | ||
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for i in {1..5} | ||
do | ||
echo "Multiplying $i by 10" | ||
python multiply.py $i 10 --jazzy | ||
./multiply.sh $i 10 | ||
done | ||
``` | ||
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@@ -457,25 +437,20 @@ cat array_example.sh | |
```bash | ||
#!/bin/bash | ||
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#SBATCH -p gpu # partition (queue) | ||
#SBATCH -p fast # partition (queue) | ||
#SBATCH -N 1 # number of nodes | ||
#SBATCH --mem 2G # memory pool for all cores | ||
#SBATCH -n 2 # number of cores | ||
#SBATCH -t 0-0:10 # time (D-HH:MM) | ||
#SBATCH --mem 1G # memory pool for all cores | ||
#SBATCH -n 1 # number of cores | ||
#SBATCH -t 0-0:1 # time (D-HH:MM) | ||
#SBATCH -o slurm_array_%A-%a.out | ||
#SBATCH -e slurm_array_%A-%a.err | ||
#SBATCH --mail-type=ALL | ||
#SBATCH [email protected] | ||
#SBATCH --array=0-9%4 | ||
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# Array job runs 10 separate jobs, but not more than four at a time. | ||
# This is flexible and the array ID ($SLURM_ARRAY_TASK_ID) can be used in any way. | ||
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module load miniconda | ||
conda activate slurm_demo | ||
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echo "Multiplying $SLURM_ARRAY_TASK_ID by 10" | ||
python multiply.py $SLURM_ARRAY_TASK_ID 10 --jazzy | ||
./multiply.sh $SLURM_ARRAY_TASK_ID 10 | ||
``` | ||
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## | ||
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