...
https://jupyter.storrs.hpc.uconn.edu
Log in with your NetIDand NetID and password:
...
Select from the following quick options for time limit and memory allocation:
...
Custom Conda Environments in Jupyter
For information on how to create custom conda enviroments, please visit our guide:
Miniconda Environment Set Up - Storrs HPC - UConn Knowledge Base
Begin by activating/creating your desired environment:
| Code Block |
|---|
conda create -n myenv |
| Info |
|---|
The python version can be specified as well if necessary: |
| Code Block |
|---|
conda activate myenv |
Once you are in the enirvonment, install ipykernel with the following command:
| Code Block |
|---|
conda install ipykernel |
Add the environment to your jupyter kernels:
| Code Block |
|---|
python -m ipykernel install --user --name=myenv --display-name "myenv" |
Follow the previous instructions to launch jupyter. You should now be able to select the environment we just added:
...
partition, number of cores, and time limit:
...
The following advanced options are also available:
| Info |
|---|
To access the priority or priority-gpu partitions, make sure to include the proper “account” and “qos” in “Extra options”. |
...
Accessing Shared Storage
Due to limitations from jupyter the file browser and initial working directory are limited to the users home directory.
There are several methods to access shared storage.
Within a Notebook:
| Code Block |
|---|
import os
os.chdir('/shared')
os.chdir('/scratch')
or
%cd /shared
%cd /scratch |
In the File Browser:
Create a symlink in the users home directory.
| Code Block |
|---|
ln -s /path/to/directory <directoryName>
e.g.
ln -s /shared/ ~/shared |
Custom Environments
If you require a non-default python environment we provide setup examples here: custom environments.