channel=anaconda (a.k.a the default location) These locations are called "channels", which can be thought of as the remote/cloud repository in which anaconda looks for the package. $ conda create -name my-virtenv-R-4d1 -c conda-forge r-base=4.1 -yes Explaining Anaconda "channels"Īn overwhelming majority of research software (including R, Python, Julia and more) is available via anaconda in one of three locations. The commands below will create an isolated environment in your HOME folder in the following location ~/.conda/envs/my-virtenv-R-4d1 with only R 4.1 installed. At this point, you can use either conda to install additional Python packages into the environment or Python's native package manager, PyPi, to install whatever Python package(s). In many ways, you are the system administrator of this Python installation as you have full read/write/execute privileges inside of that folder in your HOME directory. Once the environment is activated, you have full control over what Python packages are installed into the environment. $ conda create -name my-virtenv-p圓9 python=3.9 -yes The commands below will create an isolated environment in your HOME folder in the following location ~/.conda/envs/my-virtenv-p圓9 with only Python 3.9 installed. Once activated, you can then install packages into that environment: conda install īelow we demonstrate examples of using anaconda to install Python and R. Then activate the environment: conda activate Where is the name your want for your environment. To create a new Conda environment in your home directory, enter: conda create -name Install packages into the environment with conda install.Activate the environment with conda activate.Create an environment with conda create.The process for creating and using environments has a few basic steps: However, you can use the conda command, with various options, to install and inspect Conda environments. We recommend using the mamba command for faster package solving, downloading, and installing. Conda environments are isolated project environments designed to manage distinct package requirements and dependencies for different projects. You can create new Conda environments in one of your available directories. Creating Conda environments and installing packages Read more about Conda configuration here. If you want a newer version of Conda or Mamba than what is available in the module, you can also install them into your HOME directory as follows curl -output $HOME/miniconda.shīash ~/miniconda.sh -b -p $HOME/minicondaĬonda can also be configured with various options. This modifies your ~/.bashrc file so that conda are ready to use every time you log in ( without needing to load the module). The next step is to initialize your shell to use conda: conda init bash If you prefer to use Mamba, which is a drop-in replacement for most conda commands that enables faster package solving, downloading, and installing, you can load the corresponding mamba module: module purge Included in all versions of Anaconda, Conda is the package and environment manager that installs, runs, and updates packages and their dependencies. This module is based on the minimal Miniconda installer. To use Anaconda, first load the corresponding module: module purge It also supports other programming languages like C, C++, FORTRAN, Java, Scala, Ruby, and Lua. Anaconda is a package and environment manager primarily used for open-source data science packages for the Python and R programming languages.
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