You can save your virtual environments in any $SCRATCH directory you want. To to create a Python virtual environment called my_notebook-python-3.6.6-intel-2018b (you can name it whatever you like), do the following on the command line. You can use a default Python virtual environment in the Jupyter Notebook portal app by leaving the "Optional Environment to be activated" field blank. Note: you must activate the python virtualenv or anaconda environment before installing packages with 'pip install -user' or 'conda install'Ī Python module can be used to create a virtual environment to be used in the portal Jupyter Notebook app when all you need is Python packages. Installing package dependencies from multiple channels (default vs conda-forge) may cause conflicts Not all dependencies are resolved globally when installing multiple packages (see link below) To remove downloaded tar packages from shared pkgs directory Multiple conda environments share a common directory for downloaded packages so if a package has been previously installed in a conda environment, it doesn't have to be downloaded again when used in a new conda environment (unless you did 'conda clean -t') Precompiled binaries may be slower for some software packages that run on CPUĮach virutal environment downloads its own packages However, the performance for GPU versions of the TensorFlow modules versus Anaconda environments are relatively similar. Specific software packages such as TensorFlow non-GPU are much faster when configured correctly than Anaconda binaries since they are compiled from source and can take advantage of CPU features. Wheel or source (using 'pip install -user pkg_name') Precompiled binaries (using 'conda install pkg_name') Only need to provide environment name when activatingĪnaconda cloud (includes bioconda) and PyPI scratch/user/netid/my_envs/env_name/bin/activate Terra Jupyter Notebook Portal App Example: Source activate /scratch/user/netid/my_envs/env_name/bin/activate Must provide full or relative path when activating Manages environments in a centralized location: It's up to the user to remember where environments are saved Virtual environment can be saved in any directory. Only the same version as the module loadedĬan install any version of Python 3 within Anaconda When C, C++ or R modules are required for installing a software package with an extensive dependency list (Example: qiime2)Ĭan also install programming languages with specific versions such as Python, R, Ruby, Lua, Scala, Java, JavaScript, C/ C++, FORTRAN, Julia and more within a conda environment This table can help you decide when to use a Python module and when to use an Anaconda module for installing python packages. Notice that you will need to make sure you have enough available file quota (~10,000) since conda and pip creates thousands of files. Your custom Notebook environment must be created on the command line for later use on the Jupyter Notebook portal app. You can also create your own Jupyter Notebook environment using either a Python environment or Anaconda environment for use on the HPRC Portal, but you must use one of the Module versions that are available on the Jupyter Notebook HPRC portal web page. HPRC provides Jupyter Notebook installations for use with our Conda and Python modules. Recommended for research groups who collaborate on software builds across multiple clusters. Singularity: software built by anyone, anywhere.Module + Python virtualenv: software built and maintained by HPRC, optimized for use on our cluster.Provides quick access to commonly-used python packages. Conda: software built by external repository.HPRC supports three kinds of environment for Jupyter Notebooks: Conda, Module + Python virtualenv, and Singularity.Īll three of these allow some customization by the user, to varying degrees. 1.2.1 Errors importing a python package.
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