Purpose: Statistics and Plotting
Latest version: 22.11.1
Licence: 
Free of use
Open Source - BSD
Website: https://conda.io/en/latest/
Conda is an open source package management system and environment management system that quickly installs, runs and updates packages and their dependencies. It, also, easily creates, saves, loads and switches between environments. It was created for Python programs, but it can package and distribute software for any language (Python, R, Ruby, Lua, Scala, Java, JavaScript, C/ C++, FORTRAN...).
Since the 2.7 version of Python is going to be deprecated in 2020, 3.x version should be your default choice.
Software Information
Environments
One of the most powerful characteristic of CONDA is the use of virtual environments to set-up your project software needs. Each of them is independent, so the management of different versions of the same software can be done in a straight forward way.
By default there are some basic environments installed in the system separated by fields of knowledge:
| Environment name | Description |
|---|---|
machine-learning | Environment with some of the most important software of machine learning, such as TensowFlow, Keras, Pytorch or Sklearn. |
machine-learning-gpu | Machine learning environment with support for GPU usage. |
bio-computation | Environment with bio-computational packages like BioPython, BioPerl and ClustalW. |
quantum-chem | Environment with packages that allow chemical calculations like Pyscf . |
However if you need a new package or some specific version, a custom environment can be easily created by the user himself. Those custom environments are installed in ~/.conda/env/<env_name> by default.
Useful commands
| Command | Description |
|---|---|
conda env list | List available environments. |
conda activate env_name | Activate the environment env_name. |
conda list | List installed packages in the active conda environment. |
conda create -n my_env | Create an environment called my_env. The custom creation path is ~/.conda/envs/my_env. |
conda install package | Install the package in the active conda environment. |
#!/bin/bash
#SBATCH -J conda_example
#SBATCH -e conda_example.err
#SBATCH -o conda_example.out
#SBATCH -p std
#SBATCH -n 1
#SBATCH -t 0-02:00
module load apps/conda/current
##
# Modify the input and output files!
INPUT_FILE=example.py
OUTPUT_FILE=example.log
cp -r ${SLURM_SUBMIT_DIR}/${INPUT_FILE} ${SCRATCH}
cd ${SCRATCH}
conda activate environment_name
srun python example.py > example.log
cp ./${OUTPUT_FILE} ${SLURM_SUBMIT_DIR}
Sbatch options:
The options shown in the example are detailed below. For more information and a more comprehensive list of available options, see the sbatch command page.
- -J: Name for the job's allocation.
- -e: Name of the sterr redirection filename.
- -o: Name of the stdout redirection filename.
- -p: Name of the partition (queue) where the job will be submited.
-n: Number of tasks.
- -c: Number of cores per task.
- -t: Set the job's time limit. If the job don't finish before the time runs out, it will be killed.