Installing Packages
Once you have an environment created, you are ready to install packages into it.
There are a few ways to do this. You can install things one-by-one with either conda install ____
or pip install ____
.
If you have an environment.yml
file and want to import that, check out this section of the Conda page.
conda install
packages¶
If you plan on using a GPU
To make sure your code will run on GPUs install a recent cuda
package that works with the NVIDIA GPUs on HiPerGator. The L4 GPUs require cuda >= 12.0 and B200 GPUs require cuda >= 12.8.1
See the UFIT-RC provided pytorch
installs for examples if needed.
Conda can detect if there is a GPU available on the computer, so the easiest approach is to run the conda install
command in a GPU session. Alternatively, you can run conda install
on any node, or if a cpu-only pytorch
package was already installed, by explicitly requiring a GPU version of pytorch
when running conda install
.
Load the conda
module¶
To get started, be sure to load the conda
module and activate your environment.
module load conda
conda activate my_env
module load conda
conda activate /blue/mygroup/share/conda/envs/my_env
Example of installing PyTorch
Start with installing Python:
conda install python cuda=12.8
Pytorch no longer supports conda. From the PyTorch Installation page, use:
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
Install Additional Packages¶
Use conda install package-name
to install any additional packages you need.
Add Packages to Your Environment with pip install
¶
Not everything is available in a conda
channel, and you may need to use pip
to install some packages.
As long as your environment is active (look for the environment name in the prompt (my_env) [username@login8 ~]$
), pip
will install packages within the environment, not your home directory.
pip install package-name
It is recommended to only use pip install
after you have installed as many of your packages as possible via conda. conda install
will not always recognize and appropriately deal with dependencies that have been installed by pip.