Hoffman2:Singularity: Difference between revisions

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Singularity is a type of container technology. It is provided in Hoffman2 currently.
Singularity is a type of container technology. It is provided in Hoffman2 currently.


===How to use Singularity===
* Note: Singularity's newer versions are under its new name <b>"Apptainer"</b>. It's recommended to use Apptainer instead of Singularity.
  Just need to replace all the "singularity" with "apptainer" in the following command and it should work the same way.
 
===How to use Singularity / Apptainer===


The example code here is from IDRE team's
The example code here is from IDRE team's
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====Interactive mode ====
====Interactive mode ====
In a CentOs 7 Node, load Singularity module as
In a CentOs 7 Node, load Singularity / Apptainer module as


  module load singularity
  module load singularity
  singularity shell --userns $H2_CONTAINER_LOC/tensorflow-2.4.1-gpu-jupyter.sif
  singularity shell --userns $H2_CONTAINER_LOC/tensorflow-2.4.1-gpu-jupyter.sif
or
module load apptainer
apptainer shell --userns $H2_CONTAINER_LOC/tensorflow-2.4.1-gpu-jupyter.sif


Then run your command inside of the container
Then run your command inside of the container
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Add the following line into your batch job script
Add the following line into your batch job script


  module load singularity/3.7.1
  module load singularity
 
Or
module load apptainer


Use "singularity exec" to run command in Singularity container
Use "singularity exec" or "apptainer exec" to run command in Singularity/apptainer container


Example
Example


  singularity exec --userns $H2_CONTAINER_LOC/tensorflow-2.4.1-gpu-jupyter.sif  python3 tf-example.py > tf-example-batch.out
  singularity exec --userns $H2_CONTAINER_LOC/tensorflow-2.4.1-gpu-jupyter.sif  python3 tf-example.py > tf-example-batch.out
or
apptainer exec --userns $H2_CONTAINER_LOC/tensorflow-2.4.1-gpu-jupyter.sif  python3 tf-example.py > tf-example-batch.out


===Convert Docker image for Singularity===
===Convert Docker image for Singularity===
Docker image can be converted for Singularity
  export SINGULARITY_CACHEDIR=$SCRATCH/singularity_cache
  export SINGULARITY_CACHEDIR=$SCRATCH/singularity_cache
  singularity pull tensorflow-1.15.5-gpu-jupyter.sif docker://tensorflow/tensorflow:1.15.5-gpu-jupyter
  singularity pull tensorflow-1.15.5-gpu-jupyter.sif docker://tensorflow/tensorflow:1.15.5-gpu-jupyter
or
export APPTAINER_CACHEDIR=$SCRATCH/apptainer_cache
apptainer pull tensorflow-1.15.5-gpu-jupyter.sif docker://tensorflow/tensorflow:1.15.5-gpu-jupyter

Latest revision as of 22:15, 22 June 2026

Singularity

Singularity is a type of container technology. It is provided in Hoffman2 currently.

* Note: Singularity's newer versions are under its new name "Apptainer". It's recommended to use Apptainer instead of Singularity.
  Just need to replace all the "singularity" with "apptainer" in the following command and it should work the same way.

How to use Singularity / Apptainer

The example code here is from IDRE team's | gitlab. Please check it up for more information.

Interactive mode

In a CentOs 7 Node, load Singularity / Apptainer module as

module load singularity
singularity shell --userns $H2_CONTAINER_LOC/tensorflow-2.4.1-gpu-jupyter.sif

or

module load apptainer
apptainer shell --userns $H2_CONTAINER_LOC/tensorflow-2.4.1-gpu-jupyter.sif

Then run your command inside of the container

python3 tf-example.py > tf-example-batch.out

Batch mode

Add the following line into your batch job script

module load singularity

Or

module load apptainer

Use "singularity exec" or "apptainer exec" to run command in Singularity/apptainer container

Example

singularity exec --userns $H2_CONTAINER_LOC/tensorflow-2.4.1-gpu-jupyter.sif  python3 tf-example.py > tf-example-batch.out

or

apptainer exec --userns $H2_CONTAINER_LOC/tensorflow-2.4.1-gpu-jupyter.sif  python3 tf-example.py > tf-example-batch.out

Convert Docker image for Singularity

Docker image can be converted for Singularity

export SINGULARITY_CACHEDIR=$SCRATCH/singularity_cache
singularity pull tensorflow-1.15.5-gpu-jupyter.sif docker://tensorflow/tensorflow:1.15.5-gpu-jupyter

or

export APPTAINER_CACHEDIR=$SCRATCH/apptainer_cache
apptainer pull tensorflow-1.15.5-gpu-jupyter.sif docker://tensorflow/tensorflow:1.15.5-gpu-jupyter