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#Anaconda for windows install

To remove a Conda environment, enter: mamba env remove -name Performance considerations To view a list of all your Conda environments, enter: mamba env list Then activate the environment: conda activate /project// Where is your project's account ID of the form _. For example: mamba create -prefix /project// You can also create a new environment in your project directory instead using the -prefix option. To deactivate an environment, enter: conda deactivate Please note that a version of the main application you are using (e.g., Python or R) is installed in the Conda environment, so the module versions of these should not be loaded when the Conda environment is activated.

#Anaconda for windows install

Once activated, you can then install packages into that environment: mamba install 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: mamba create -name

Install packages into the environment with mamba install.

Activate the environment with conda activate.

Create an environment with mamba create.

The process for creating and using environments has a few basic steps: Mamba is a drop-in replacement for conda. However, we recommend using the mamba command instead for faster package solving, downloading, and installing. You can use the conda command, with various options, to install and inspect Conda environments. 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. Installing Conda environments and packages Alternatively, you can install Miniconda. If you want a newer version of Conda than what is available on the cluster, we recommend installing mambaforge. Read more about Conda configuration here. To disable this, change the Conda config: conda config -set auto_activate_base false The base environment should now be activated, which will be reflected in your shell prompt: (base) ~]$īy default, the base environment will now automatically be activated every time you log in. This modifies your ~/.bashrc file so that Conda is ready to use every time you log in (without needing to load the module). To use these packages or to create your own Conda environment, initialize your shell to use Conda: conda init bash source ~/.bashrc Many Conda packages are pre-installed with Anaconda in the base Conda environment, including the popular data science packages for Python like pandas, NumPy, SciPy, matplotlib, and scikit-learn.

Included in all versions of Anaconda, Conda is the package and environment manager that installs, runs, and updates packages and their dependencies. To use Anaconda, first load the corresponding module: module load anaconda3 You can find instructions for this in the Getting Started with Discovery or Getting Started with Endeavour user guides. Using Anaconda on CARC systemsīegin by logging in. 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.