HPRC supports three kinds of environment for Jupyter Notebooks: Conda, Module + Python virtualenv, and Singularity.
All three of these allow some customization by the user, to varying degrees. Broadly speaking:
- Conda: software built by external repository. Provides quick access to commonly-used python packages. Can be extended by the user. Version choices are limited. Recommended for novice users.
- Module + Python virtualenv: software built and maintained by HPRC, optimized for use on our cluster. Can be extended by the user. New software can be requested. Recommended for experienced users.
- Singularity: software built by anyone, anywhere. Fully customizable by the user. Recommended for research groups who collaborate on software builds across multiple clusters.
HPRC provides Jupyter Notebook installations for use with our Conda and Python modules. 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.
Your custom Notebook environment must be created on the command line for later use on the Jupyter Notebook portal app.
Notice that you will need to make sure you have enough available file quota (~10,000) since conda and pip creates thousands of files.
This table can help you decide when to use a Python module and when to use an Anaconda module for installing python packages.
|Example module||module load Python/3.6.6-intel-2018b||module load Anaconda/3-22.214.171.124|
|When to use||When only python packages are required||When C, C++ or R modules are required for installing a software package with an extensive dependency list (Example: qiime2)|
|Python version||only the same version as the module loaded||can install any version of Python 3 within Anaconda|
|Env location||virtual environment can be saved in any directory. It's up to the user to remember where environments are saved||Manages environments in a centralized location:|
|Env activation||Must provide full or relative path when activating
Command Line Example:
source activate /scratch/user/netid/my_envs/env_name/bin/activate
Terra Jupyter Notebook Portal App Example:
|Only need to provide environment name when activating|
Command Line Example:
source activate env_name
Jupyter Notebook Portal App Example:
|Available packages||PyPI||anaconda cloud (includes bioconda) and PyPI|
|Package install command||pip||conda (for Anaconda packages) |
pip (for PyPI packages)
|Installation type||wheel or source||precompiled binaries (using 'conda install pkg_name')|
wheel or source (using 'pip install --user pkg_name')
|software speed||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. However, the performance for GPU versions of the TensorFlow modules versus Anaconda environments are relatively similar.||precompiled binaries may be slower for some software packages that run on CPU|
|Dependency checks||yes but not completely (see link below)||yes|
|File usage||each virutal environment downloads its own packages||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')|
|Remove install cache||pip cache purge
for pip >=20.1b1
|conda clean -t |
to remove downloaded tar packages from shared pkgs directory
|Delete virtual environment||rm -rf env_name_directory||conda env remove --name env_name|
|possible issues||not all dependencies are resolved globally when installing multiple packages (see link below)||installing package dependencies from multiple channels (default vs conda-forge) may cause conflicts|
Note: you must activate the python virtualenv or anaconda environment before installing packages with 'pip install --user' or 'conda install'
A 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.
You can use a default Python virtual environment in the Jupyter Notebook portal app by leaving the "Optional Environment to be activated" field blank.
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 save your virtual environments in any $SCRATCH directory you want. In this example a directory called /scratch/user/mynetid/pip_envs is used but you can use another name instead of pip_envs
A good practice is to name your environment so that you can identify which Python version is in your virtualenv so that you know which module to load.
The next three lines will create your virtual environment. You can use any one of the modules listed on the Jupyter notebook portal app. Python/3.6.6-intel-2018b is used in this example.
module purge module load Python/3.6.6-intel-2018b virtualenv --system-site-packages /scratch/user/mynetid/pip_envs/my_notebook-python-3.6.6-intel-2018b
We recommend enabling --system-site-packages so that any modules you load will continue to function.
Then you can activate the virtual environment by using the full path to the activate command inside your virtual environment and install Python packages.
source /scratch/user/mynetid/pip_envs/my_notebook-python-3.6.6-intel-2018b/bin/activate pip install notebook pip install optional-python-package-name
You can use your Python/3.6.6-intel-2018b environment in the Jupyter Notebook portal app by selecting the Python/3.6.6-intel-2018b module in the portal app page and providing the name including full path to the activate command for your Python/3.6.6-intel-2018b virtual environment in the "Optional Environment to be activated" box (i.e. /scratch/user/mynetid/pip_envs/my_notebook-python-3.6.6-intel-2018b/bin/activate). The activate command is found inside the bin directory of your virtual env. An example of what to put in the "Optional Environment to be activated" box is the full path used in the source command above.
Loading additional Lmod modules
The default Python/3.7.4-GCCcore-8.3.0 virtualenv for JupyterLab on Terra has Jupyterlmod installed which allows the user to load any module built with the GCCcore-8.3.0 or 2019b toolchains after starting jupyter notebook in JupyterLab.
You can install the jupyterlmod package in your python virtual environment on Terra which will allow you to load additional system modules that you may need or may have used during the creation of your virtual environment on Terra for use with the Terra Jupyter Notebook portal app.
To add this feature to your existing Terra virtual environment, do the following on the command line prior to launching Jupyter Notebook on the portal (you can use Python/3.6.6-foss-2018b if the additional module(s) you need are not available with the intel-2018b toolchain):
module purge module load Python/3.6.6-intel-2018b source /scratch/user/mynetid/pip_envs/my_notebook-python-3.6.6-intel-2018b/bin/activate pip install jupyter jupyterlmod
Then launch the Terra Jupyter Notebook portal app using your optional environment and click the 'Softwares' tab in your notebook and search for system modules.
Select a module or multiple modules that match the toolchain and python version that you used in creating your virtual environment and then click enter to load the module.
The 'Loaded Modules' list will update in a few seconds to reflect the additional module(s) loaded.
You can save your modules loaded using the 'collection' button at the right side of the notebook 'softwares' page so that you just have to select the collection instead of searching for modules each time you want to use your python virtual environment.
Anaconda is different than Python's virtualenv in that you can install other types of software such as R and R packages in your environment.
Anaconda also manages the installation path and installs in your $SCRATCH/.conda directory so you don't have to create a directory prior to creating an environment.
The recommended Anaconda module to use for Python 3 is Anaconda/3-126.96.36.199
To to create an Anaconda conda environment called my_notebook (you can name it whatever you like), do the following on the command line:
module purge module load Anaconda/3-188.8.131.52 conda create -n my_notebook
After your my_notebook environment is created, you will see output on how to activate and use your my_notebook environment
# # To activate this environment, use: # > source activate my_notebook # # To deactivate an active environment, use: # > source deactivate #
Then you need to install notebook and then you can add optional packages to your my_notebook environment
source activate my_notebook conda install -c conda-forge notebook conda install -c conda-forge optional-package-name
You can use your Anaconda/3-184.108.40.206 environment in the Jupyter Notebook portal app by selecting the Anaconda/3-220.127.116.11 module in the portal app page and providing just the name (without the full path) of your Anaconda/3-18.104.22.168 environment in the "Optional Environment to be activated" box. In the example above, the value to enter is: my_notebook
Errors importing a python package
Sometimes the latest or specific versions of a python package does not work in a specific Anaconda environment due to incompatibility with the Python version or with other packages.
Default Python version
You can try installing an older version of a python package by searching available versions on anaconda.org and specify a specific version to install.
For example, if you have Python 3.6.11 and numpy 1.19.2 and see the following error:
import numpy ModuleNotFoundError: No module named 'numpy.core._multiarray_umath'
Install an older version of numpy (the following command automatically overwrites the currently installed numpy version)
conda install -c conda-forge numpy=1.15.4
In the above example, this resolved an 'import numpy' error in an environment with Python 3.6.11 with numpy 1.19.2 by downgrading numpy which also downgraded other packages:
The following packages will be DOWNGRADED: matplotlib: 3.3.2-0 conda-forge --> 3.2.0-1 conda-forge matplotlib-base: 3.3.2-py36h2451756_0 conda-forge --> 3.2.0-py36h250f245_1 conda-forge numpy: 1.19.2-py36he0f5f23_1 conda-forge --> 1.15.4-py36h8b7e671_1002 conda-forge scipy: 1.5.2-py36h832618f_0 conda-forge --> 1.4.1-py36h921218d_0 conda-forge
Latest Python version
The default Python version for a new conda environment isn't always the latest available. If you want to use the latest version of numpy then you may have to specify a newer version of python than the default version when installing the latest version of packages in the environment:
conda install -c conda-forge python=3.8.5 notebook numpy=1.19.2 scipy matplotlib
Jupyter Notebook runs on the compute nodes which do not have internet access.
If you need internet access for your notebook then enable the proxy using the following.
Run the following lines in your notebook:
import os os.environ['http_proxy'] = '10.76.5.24:8080' os.environ['https_proxy'] = '10.76.5.24:8080'
Run the following lines in your notebook:
import os os.environ['http_proxy'] = '10.73.132.63:8080' os.environ['https_proxy'] = '10.73.132.63:8080'