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howto:bioinformatics [2025/05/06 11:21]
nmfuphi [Need Help?]
howto:bioinformatics [2025/05/21 10:50] (current)
nmfuphi [Singularity]
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 === Shared Conda Environments === === Shared Conda Environments ===
  
-For most use cases — especially in bioinformatics — CHPC provides pre-built, **shared Conda environments** installed under:+For most use casesespecially in bioinformaticsCHPC provides pre-built, **shared Conda environments** installed under:
  
   '/apps/chpc/bio/anaconda3-2020.02/envs'   '/apps/chpc/bio/anaconda3-2020.02/envs'
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 module load conda_init module load conda_init
 </code> </code>
 +
 +The second module updates your .bashrc file by adding necessary shell variables.
 +To apply these changes, you can either log out and log back in, or run: <code bash>source ~/.bashrc. </code>
 +After this setup, you won’t need to load additional modules for your jobs—only the eval and conda activate steps are required.
  
 === Step 2: Initialize Conda in your shell === === Step 2: Initialize Conda in your shell ===
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 If you need software that is not included in the shared environments, you may create your own **private Conda environment**. This gives you full control over the software stack and package versions. If you need software that is not included in the shared environments, you may create your own **private Conda environment**. This gives you full control over the software stack and package versions.
  
-> **Important:** Do **not** install environments in your home directory (''/home/<username>''— use your Lustre project storage instead.+> **Important:** Do **not** install environments in your home directory (''/home/<username>''-use your Lustre project storage instead.
  
 === Step-by-step setup === === Step-by-step setup ===
 +ssh to username@scp.chpc.ac.za, the password is the same as the one you use on lengau
 === Step 1: Load Conda === === Step 1: Load Conda ===
 <code bash> <code bash>
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   * ✅ Use **shared environments** whenever possible for consistency and faster setup.   * ✅ Use **shared environments** whenever possible for consistency and faster setup.
   * 📁 Create private environments **only in Lustre** directories, such as ''/mnt/lustre/<username>''.   * 📁 Create private environments **only in Lustre** directories, such as ''/mnt/lustre/<username>''.
-  * ⚠️ Do **not** use Conda in your ''$HOME'' directory — it may lead to quota issues or slow performance.+  * ⚠️ Do **not** use Conda in your ''$HOME'' directoryit may lead to quota issues or slow performance.
   * 📌 Use the ''--prefix'' flag to create environments with absolute paths, especially on clusters where ''--name'' may default to ''$HOME''.   * 📌 Use the ''--prefix'' flag to create environments with absolute paths, especially on clusters where ''--name'' may default to ''$HOME''.
   * 🧼 Periodically clean up unused environments with `conda remove --all`.   * 🧼 Periodically clean up unused environments with `conda remove --all`.
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 #PBS -N singularity_job #PBS -N singularity_job
 #PBS -q normal #PBS -q normal
-#PBS -l select=1:ncpus=8:mem=32gb+#PBS -l select=1:ncpus=24
 #PBS -l walltime=12:00:00 #PBS -l walltime=12:00:00
 #PBS -o singularity_output.log #PBS -o singularity_output.log
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 </code> </code>
  
-===== Nextflow =====+==== Nextflow ====
  
 Nextflow is a free and open-source workflow management system that enables the development and execution of data analysis pipelines. It simplifies complex computational workflows and ensures reproducibility, scalability, and portability—whether you’re working on a laptop, HPC cluster, or in the cloud. Nextflow workflows are written using DSL2, allowing modular code design and seamless integration with container technologies like Docker, Singularity, Conda, or manual installations. Nextflow is a free and open-source workflow management system that enables the development and execution of data analysis pipelines. It simplifies complex computational workflows and ensures reproducibility, scalability, and portability—whether you’re working on a laptop, HPC cluster, or in the cloud. Nextflow workflows are written using DSL2, allowing modular code design and seamless integration with container technologies like Docker, Singularity, Conda, or manual installations.
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