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Difference between revisions of "Applications/Miniconda"

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Here is two examples of SLURM scripts which are using a Python virtual environment
 
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Revision as of 09:23, 27 November 2020

Application Details

  • Description: Miniconda python is a high-level interpreted programming language for general-purpose programming, supported by a large number of libraries for many tasks for which the user can install for customising their environment
  • Versions: Miniconda (lite version of anaconda)
  • Module name: python/anaconda/4.6/miniconda/3.7 (used for virtual environments only)
  • License: Free to use - Python Software Foundation License

Purpose

The purpose of the miniconda installation is that it is a basic Anaconda python install to allow user virtual environments.

Definition

A virtual environment is a named, isolated, working copy of Python that that maintains its own files, directories, and paths so that you can work with specific versions of libraries or Python itself without affecting other Python projects. Virtual environments make it easy to cleanly separate different projects and avoid problems with different dependencies and version requirements across components.

The conda command is the preferred interface for managing installations and virtual environments with the Anaconda Python distribution.

Note : It is very likely within the next HPC, all python environments will be all virtual environments.


Usage Examples

  • Python is provided by the Anaconda package too.
  • Anaconda is the leading open data science platform powered by Python.

Environment

This provides a minimal python configuration, as shown below: 

[user@login01 ~]$ module load python/anaconda/4.6/miniconda/3.7
[user@login01 ~]$ conda list
# packages in environment at /trinity/clustervision/CentOS/7/apps/anaconda/4.6.0/3.7:
#
# Name                    Version                   Build  Channel
asn1crypto                0.24.0                   py37_0
ca-certificates           2019.1.23                     0
certifi                   2019.3.9                 py37_0
cffi                      1.12.2           py37h2e261b9_1
chardet                   3.0.4                    py37_1
conda                     4.6.14                   py37_0
cryptography              2.6.1            py37h1ba5d50_0
idna                      2.8                      py37_0
libedit                   3.1.20181209         hc058e9b_0
libffi                    3.2.1                hd88cf55_4
libgcc-ng                 8.2.0                hdf63c60_1
libstdcxx-ng              8.2.0                hdf63c60_1
ncurses                   6.1                  he6710b0_1
openssl                   1.1.1b               h7b6447c_1
pip                       19.0.3                   py37_0
pycosat                   0.6.3            py37h14c3975_0
pycparser                 2.19                     py37_0
pyopenssl                 19.0.0                   py37_0
pysocks                   1.6.8                    py37_0
python                    3.7.3                h0371630_0
readline                  7.0                  h7b6447c_5
requests                  2.21.0                   py37_0
ruamel_yaml               0.15.46          py37h14c3975_0
setuptools                41.0.0                   py37_0
six                       1.12.0                   py37_0
sqlite                    3.27.2               h7b6447c_0
tk                        8.6.8                hbc83047_0
urllib3                   1.24.1                   py37_0
wheel                     0.33.1                   py37_0
xz                        5.2.4                h14c3975_4
yaml                      0.1.7                had09818_2
zlib                      1.2.11               h7b6447c_3

Creation of Virtual Environment Using Anaconda on Viper

To create a virtual environment using anaconda 4.6 with python version 3.7 on Viper, you would use the conda create command as follows:

  • IMPORTANT NOTE: By default the virtual environment does not use the python system packages. However because of the way viper is configured it will see python system packages because of the PYTHONPATH environment variable. So it is advised if you would like a clean environment (meaning no system packages being included) to set this environment variable to empty as follows: export PYTHONPATH=
[user@c001 ~ ]$ module load python/anaconda/4.6/miniconda/3.7
[user@c001 ~ ]$ conda create –n tensorflow1

The above command creates a new virtual environment called tensorflow1.

To activate this virtual environment you would issue the following command: 

[user@c001 ~ ]$ conda activate tensorflow1

On successful activation of this virtual environment you should the name of your environment in front of your login prompt like so: 

(tensorflow1)  [user@c001 ~ ]$

To exit the virtual environment use the key combination ctrl + d or 'conda deactivate'.

Adding packages

Once you have installed Miniconda and setup your environment to access it, you can then add whatever packages you wish to the installation using the conda install ... command. For example: 

(tensorflow1) user@c001:~> conda install numpy
Fetching package metadata ...............
Solving package specifications: .

Package plan for installation in environment /home/t01/t01/user/miniconda3:

The following NEW packages will be INSTALLED:

    blas:        1.1-openblas                  conda-forge
    libgfortran: 3.0.0-1                                  
    numpy:       1.14.0-py36_blas_openblas_200 conda-forge [blas_openblas]
    openblas:    0.2.20-7                      conda-forge

The following packages will be UPDATED:

    conda:       4.3.31-py36_0                             --> 4.3.33-py36_0 conda-forge

The following packages will be SUPERSEDED by a higher-priority channel:

    conda-env:   2.6.0-h36134e3_1                          --> 2.6.0-0       conda-forge

Proceed ([y]/n)? y
  • Please note, for some package installations it may also be necessary to specify a channel such as conda-forge. For example, the following command installs the pygobject module.


(tensorflow1) [user@c001]$ conda install -c conda-forge pygobject
  • To create an environment with a specific version of a package:
[user@c001]$ conda create -n myenv scipy=0.15.0
  • or even defining the python version at 3.4
[user@c001]$ conda create -n myenv python=3.4 scipy=0.15.0 astroid babel

Clone an environment

[user@c001]$ conda create -n OriginalENV --clone NewENV

Removing an environment

To delete a conda environment, enter the following, where yourenvname is the name of the environment you wish to delete. 

[user@c001]$ conda remove --name EnvironmentNAME --all


Using SLURM with a virtual environment

Here is two examples of SLURM scripts which are using a Python virtual environment

  • substitute /home/<user> for your own path

Compute Node Example

#!/bin/bash
#SBATCH -J BUILDCPU
#SBATCH -N 1
#SBATCH --ntasks-per-node 12
#SBATCH -D /home/<user>/
#SBATCH -o debug.out
#SBATCH -e debug.err
#SBATCH -p compute
#SBATCH -t 00:10:00

echo $SLURM_JOB_NODELIST

module purge
module load python/anaconda/4.6/miniconda/3.7

source activate /home/<user>/.conda/envs/bioinformatics1
export PATH=/home/<user>/.conda/envs/bioinformatics/bin:${PATH}

python /home/user/TATT-CPU.py


GPU Node Example

#!/bin/bash
#SBATCH -J BIDGPU
#SBATCH -N 1
#SBATCH --ntasks-per-node 1
#SBATCH -D /home/<user>/
#SBATCH -o debug.out
#SBATCH -e debug.err
#SBATCH --gres=gpu:tesla
#SBATCH -p gpu
#SBATCH -t 00:10:00

echo $SLURM_JOB_NODELIST

module purge
module load gcc/5.2.0
module load python/anaconda/4.6/miniconda/3.7
module load cuda/10.1.168

source activate /home/<user>/.conda/envs/bioinformatics1
export PATH=/home/<user>/.conda/envs/bioinformatics/bin:${PATH}

python /home/user/TATT-GPU.py


Creation of a Virtual Environment in Anaconda Using a YAML File

To create a virtual environment from a YAML file you would issue the following command: 

[user@c001 ~ ]$ conda env create -f myenv.yml

This above command is creating a virtual environment from the YAML called myenv.yml. Below is a copy of the mark-up contained within the file called "myenv.yml". 

name: ytenv
channels:
- defaults
dependencies:
- ca-certificates=2017.08.26=h1d4fec5_0
- certifi=2018.1.18=py27_0
- intel-openmp=2018.0.0=hc7b2577_8
- libedit=3.1=heed3624_0
- libffi=3.2.1=hd88cf55_4
- libgcc-ng=7.2.0=h7cc24e2_2
- libgfortran-ng=7.2.0=h9f7466a_2
- libstdcxx-ng=7.2.0=h7a57d05_2
- mkl=2018.0.1=h19d6760_4
- ncurses=6.0=h9df7e31_2
- numpy=1.14.0=py27h3dfced4_1
- openssl=1.0.2n=hb7f436b_0
- pip=9.0.1=py27ha730c48_4
- python=2.7.14=h1571d57_29
- readline=7.0=ha6073c6_4
- setuptools=38.4.0=py27_0
- sqlite=3.22.0=h1bed415_0
- tk=8.6.7=hc745277_3
- wheel=0.30.0=py27h2bc6bb2_1
- zlib=1.2.11=ha838bed_2
- pip:
  - backports.functools-lru-cache==1.5
  - backports.shutil-get-terminal-size==1.0.0
  - cycler==0.10.0
  - decorator==4.2.1
  - enum34==1.1.6
  - h5py==2.7.1
  - ipython==5.5.0
  - ipython-genutils==0.2.0
  - matplotlib==2.1.2
  - mpmath==1.0.0
  - pathlib2==2.3.0
  - pexpect==4.4.0
  - pickleshare==0.7.4
  - prompt-toolkit==1.0.15
  - ptyprocess==0.5.2
  - pygments==2.2.0
  - pyparsing==2.2.0
  - python-dateutil==2.6.1
  - pytz==2018.3
  - scandir==1.7
  - simplegeneric==0.8.1
  - six==1.11.0
  - subprocess32==3.2.7
  - sympy==1.1.1
  - traitlets==4.3.2
  - wcwidth==0.1.7
  - yt==3.4.1

Exporting a Virtual Environment in Anaconda to a YAML File

To export a virtual environment to a YAML file so that you or another researcher can replicate your environment using Anaconda can be done using the following steps:

  • Activate the Virtual environment you wish to export:
[user@c001 ~ ]$ source activate tensorflow1
  • Export your active virtual environment using the following command:
{tensorflow1} [user@c001 ~ ]$ conda env export > tensorflow1.yml

Virtual Environment Tips

  • Avoid using pip by itself. Using python -m pip will always guarantee you are using the pip associated with that specific python being called, instead of potentially calling a pip associated with a different python.
  • I recommend using a separate virtual environment for each project.
  • You should never copy or move around virtual environments. Always create new ones, or use YAML exports.
  • Ignore the virtual environment directories from repositories (eg GitHub, GitLab). For example, .gitignore them.

Further Information

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