It is available as a pip package called "numexpr", and also as a conda package called "numexpr". To give an example of installing a Python package, the "numexpr" library is a numerical expression evaluator for NumPy. To take an example of the size, a new Python virtual environment without additional packages occupies about 10MB and contains under 1000 files (maybe approximately twice this if using the "-system-site-packages" option explained in more detail elsewhere), whereas as a new conda base environment occupies about 400MB and contains over 20,000 files. (It is also possible to use the pip installer when working with conda environments.) When you run the conda installer, similarly it will install whatever additional packages are required in order to satisfy dependencies. As with python virtual environments, you can have any number of these environments and activate the required one. Where packages contain compiled libraries, these are generally available as pre-compiled binaries. This enables the installation of packages from conda channels, typically conda-forge, which are not restricted to being Python packages. By contrast, a Conda environment is a much bulkier, more fully featured environment, using the conda package manager.Depending on the package being installed, if it requires compiled libraries to accompany it, it may try to compile these locally, but depending what development libraries are available, occasionally this might not succeed. When you run "pip" to install a Python package, additional Python packages may be installed automatically in order to satisfy dependencies. This enables you to install packages in your home directory without writing to the underlying Python installation itself (for example when you do not have write permission), and you can have any number of separate virtual environments and "activate" the relevant one when needed. A Python virtual environment is a relatively lightweight environment, which is used for running Python packages, typically installed using the "pip" installer from the Python Package Index ( pypi) or locally from Python source containing a "setup.py" file.Description of Python virtual environments and Conda environments This page gives an overview of what they are, and how to choose which one is most suitable for your needs. Separate pages explain the details of how to create and install Python virtual environments and Conda environments. Typical examples why you may wish to do this is if you have asked us to add packages to Jaspy but wish to make use of them before the next release, or if they are not likely to be relevant to other users. This article describes two types of software environments that you can create in order to install packages for your own use on JASMIN. condarc for the miniconda module.Conda environments and Python virtual environments Introduction If you would like to override these defaults, see the Conda docs on managing channels. Starting with minconda module version 4.8.3 we set the default channels (the sources to find packages) to conda-forge and bioconda, which provide a wider array of packages than the default channels do. Conda will install to and search in these directories for environments and cached packages. On all clusters, we set the CONDA_ENVS_PATH and CONDA_PKGS_DIRS environment variables to conda_envs and conda_pkgs in your project directory where there is more quota available. Note: If you are on Milgram and run out of space in your home directory for Conda, you can either reinstall your environment in your project space (see below) or contact us for help with your home quota. If you are using Conda-installed packages, this should be the only module you load in your jobs. We set some defaults for you in this module, and we keep it relatively up-to-date so you don't have to. This is a read-only environment from which you can create your own. The Miniconda Moduleįor your convenience, we provide a relatively recent version of Miniconda as a module. If you get stuck, you can always ask us for help. When constructing an environment for your work you should load either modules or a conda environment. Mixing modules and conda-managed software is almost never a good idea. Conda version doesn't match the module loaded
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