The guide is designed to be followed from start to finish, as certain steps must be completed in the correct order before some commands work properly. For those that just want a quick-reference list of common conda commands, see the Quick-Reference Commands section.
This guide introduces a user to the basic workflow of using conda environments, as well as providing an example of how to create a conda environment that uses a different version of Python than the base environment uses on Summit. Although Summit is being used in this guide, all of the concepts still apply to other OLCF systems.
Inspecting and setting up an environment
First, load the python module and the gnu compiler module on Summit (most Python packages assume use of GCC)
$ module load gcc $ module load python
This puts you in the “base” conda environment, which is the default Python environment after loading the module.
To see a list of environments, use the command
conda env list:
$ conda env list # conda environments: # base * /sw/summit/python/3.8/anaconda3/2020.07-rhel8
This also is a great way to keep track of the locations and names of all other environments that have been created.
The current environment is indicated by
To see what packages are installed in the active environment, use
$ conda list # packages in environment at /sw/summit/python/3.8/anaconda3/2020.07-rhel8: # # Name Version Build Channel _ipyw_jlab_nb_ext_conf 0.1.0 py38_0 _libgcc_mutex 0.1 main alabaster 0.7.12 py_0 anaconda 2020.07 py38_0 anaconda-client 1.7.2 py38_0 anaconda-project 0.8.4 py_0 asn1crypto 1.3.0 py38_0 astroid 2.4.2 py38_0 astropy 4.0.1.post1 py38h7b6447c_1 . . .
You can find the version of Python that exists in this base environment by executing:
$ python --version Python 3.8.3
Creating a new environment
For this guide, you are going to install a different version of Python.
To do so, create a new environment using the
conda create command:
$ conda create -p /ccs/proj/<YOUR_PROJECT_ID>/<YOUR_USER_ID>/conda_envs/summit/py3711-summit python=3.7.11
-p flag specifies the desired path and name of your new virtual environment.
The directory structure is case sensitive, so be sure to insert
<YOUR_PROJECT_ID> as lowercase.
Directories will be created if they do not exist already (provided you have write-access in that location).
Instead, one can solely use the
--name <your_env_name> flag which will automatically use your
It is highly recommended to create new environments in the “Project Home” directory (on Summit, this is
This space avoids purges, allows for potential collaboration within your project, and works better with the compute nodes.
It is also recommended, for convenience, that you use environment names that indicate the hostname, as virtual environments created on one system will not necessarily work on others.
After executing the
conda create command, you will be prompted to install “the following NEW packages” – type “y” then hit Enter/Return.
Downloads of the fresh packages will start and eventually you should see something similar to:
Preparing transaction: done Verifying transaction: done Executing transaction: done # # To activate this environment, use # # $ conda activate /ccs/proj/<YOUR_PROJECT_ID>/<YOUR_USER_ID>/conda_envs/summit/py3711-summit # # To deactivate an active environment, use # # $ conda deactivate
Due to the specific nature of conda on Summit, you must use
source activate and
source deactivate instead of
conda activate and
Let’s activate the new environment:
$ source activate /ccs/proj/<YOUR_PROJECT_ID>/<YOUR_USER_ID>/conda_envs/summit/py3711-summit
The path to the environment should now be displayed in “( )” at the beginning of your terminal lines, which indicate that you are currently using that specific conda environment.
And if you check with
conda env list again, you should see that the
* marker has moved to your newly activated environment:
$ conda env list # conda environments: # * /ccs/proj/<YOUR_PROJECT_ID>/<YOUR_USER_ID>/conda_envs/summit/py3711-summit base /sw/summit/python/3.8/anaconda3/2020.07-rhel8
Next, let’s install a package (NumPy). There are a few different approaches.
Installing with pip
One way to install packages into your conda environment is to build packages from source using pip. This approach is useful if a specific package or package version is not available in the conda repository, or if the pre-compiled binaries don’t work on the HPC resources (which is common). However, building from source means you need to take care of some of the dependencies yourself, especially for optimization. Pip is available to use after installing Python into your conda environment, which you have already done.
Because issues can arise when using conda and pip together (see link in Additional Resources), it is recommended to do this only if absolutely necessary.
To build a package from source, use
pip install --no-binary=<package_name> <package_name>:
$ CC=gcc pip install --no-binary=numpy numpy
CC=gcc flag will ensure that you are using the proper compiler and wrapper.
Building from source results in a longer installation time for packages, so you may need to wait a few minutes for the install to finish.
You have successfully built NumPy from source in your conda environment; however, you did not link in any additional linear algebra packages, so this version of NumPy is not optimized. Let’s install a more optimized version using a different method instead, but first you must uninstall the pip-installed NumPy:
$ pip uninstall numpy
Installing with conda commands
The traditional, and more basic, approach to installing/uninstalling packages into a conda environment is to use the commands
conda install and
Installing packages with this method checks the Anaconda Distribution Repository for pre-built binary packages to install.
Let’s do this to install NumPy:
$ conda install numpy
Because NumPy depends on other packages for optimization, this will also install all of its dependencies. You have just installed an optimized version of NumPy, now let’s test it.
Testing your new environment
Let’s run a test to make sure everything installed properly. Since you are running a small test, you can do this without having to run on a compute node.
Remember, at larger scales both your performance and your fellow users’ performance will suffer if you do not run on the compute nodes. It is always highly recommended to run on the compute nodes (through the use of a batch job or interactive batch job).
Make sure you’re in a Python shell first, then print out the versions of Python and NumPy:
>>> import platform >>> import numpy >>> py_vers = platform.python_version() >>> np_vers = numpy.__version__ >>> print("Hello from Python", py_vers) Hello from Python 3.7.11 >>> print("You are using NumPy", np_vers) You are using NumPy 1.20.3
Cloning the base environment:
It is not recommended to try to install new packages into the base environment. Instead, you can clone the base environment for yourself and install packages into the clone. To clone an environment, you must use the
--clone <env_to_clone>flag when creating a new conda environment. An example for cloning the base environment into your Project Home directory on Summit is provided below:
$ conda create -p /ccs/proj/<YOUR_PROJECT_ID>/<YOUR_USER_ID>/conda_envs/summit/baseclone-summit --clone base $ source activate /ccs/proj/<YOUR_PROJECT_ID>/<YOUR_USER_ID>/conda_envs/summit/baseclone-summit
Adding known environment locations:
For a conda environment to be callable by a “name”, it must be installed in one of the
envs_dirsdirectories. The list of known directories can be seen by executing:
$ conda config --show envs_dirs
On OLCF systems, the default location is your
$HOMEdirectory. If you plan to frequently create environments in a different location other than the default (such as
/ccs/proj/...), then there is an option to add directories to the
For example, to track conda environments in a subdirectory called
summitin Project Home you would execute:
$ conda config --append envs_dirs /ccs/proj/<YOUR_PROJECT_ID>/<YOUR_USER_ID>/conda_envs/summit
This will create a
.condarcfile in your
$HOMEdirectory if you do not have one already, which will now contain this new envs_dirs location. This will now enable you to use the
--name env_nameflag when using conda commands for environments stored in the
summitdirectory, instead of having to use the
-p /ccs/proj/<YOUR_PROJECT_ID>/<YOUR_USER_ID>/conda_envs/summit/env_nameflag and specifying the full path to the environment. For example, you can do
source activate py3711-summitinstead of
source activate /ccs/proj/<YOUR_PROJECT_ID>/<YOUR_USER_ID>/conda_envs/summit/py3711-summit.
$ conda env list
List installed packages in current environment:
$ conda list
Creating an environment with Python version X.Y:
For a specific path:
$ conda create -p /path/to/your/my_env python=X.Y
For a specific name:
$ conda create -n my_env python=X.Y
Deleting an environment:
For a specific path:
$ conda env remove -p /path/to/your/my_env
For a specific name:
$ conda env remove -n my_env
Copying an environment:
For a specific path:
$ conda create -p /path/to/new_env --clone old_env
For a specific name:
$ conda create -n new_env --clone old_env
Activating/Deactivating an environment:
$ source activate my_env $ source deactivate # deactivates the current environment
$ conda install package_name $ conda remove package_name
$ pip install package_name $ pip uninstall package_name $ pip install --no-binary=package_name package_name # builds from source