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Customizing Jupyterlab with Anaconda environments
Step one is to load an anaconda_<cpu, gpu, mi100> that you want to use as your base installation and initialize your default login shell to use conda environments.
Select an anaconda3:
| Code Block | ||
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[arnoldg@dt-login03 scripts]$ module load anaconda3_cpu
[arnoldg@dt-login03 scripts]$ conda init bash
...
[arnoldg@dt-login03 scripts]$
[arnoldg@dt-login03 scripts]$ bash
(base) |
After you have run "conda init bash" you will not need to load anaconda3_cpu (or gpu ) modules again. Just use your new custom environment.
| Info | ||
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You may see error messages from conda init bash above. Just control-c through them and continue. As long as conda added code to the end of your .bashrc (or similar for other shells), things will work properly. |
...
To create a new custom environment, you have 2 options.
Create a new empty environment:
| Info | ||
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If you will be making custom environments for more than one partition type (cpu, gpu, mi100), it may be helpful to include that metadata in the name of your environment. |
Install jupyter into the environment in order to use it with OpenOnDemand. This option adds about 150 python modules to your environment and requires about 1.3 GB in your $HOME. Setup time: about 10 minutes.
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(base) conda create --name mynewenv
Collecting package metadata (current_repodata.json): done
Solving environment: done
## Package Plan ##
environment location: /u/arnoldg/.conda/envs/mynewenv
Proceed ([y]/n)? y
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
# $ conda activate mynewenv
#
# To deactivate an active environment, use
#
# $ conda deactivate
Retrieving notices: ...working... done
(base) conda activate mynewenv
(mynewenv) conda install jupyter
Collecting package metadata (current_repodata.json): done
Solving environment: done
## Package Plan ##
environment location: /u/arnoldg/.conda/envs/mynewenv
added / updated specs:
- jupyter
The following NEW packages will be INSTALLED:
_libgcc_mutex pkgs/main/linux-64::_libgcc_mutex-0.1-main None
_openmp_mutex pkgs/main/linux-64::_openmp_mutex-5.1-1_gnu None
anyio pkgs/main/linux-64::anyio-3.5.0-py310h06a4308_0 None
argon2-cffi pkgs/main/noarch::argon2-cffi-21.3.0-pyhd3eb1b0_0 None
...
(mynewenv) conda list | grep jupyter
jupyter 1.0.0 py310h06a4308_8
jupyter_client 7.3.5 py310h06a4308_0
jupyter_console 6.4.3 pyhd3eb1b0_0
jupyter_core 4.11.1 py310h06a4308_0
jupyter_server 1.18.1 py310h06a4308_0
jupyterlab 3.4.4 py310h06a4308_0
jupyterlab_pygments 0.1.2 py_0
jupyterlab_server 2.15.2 py310h06a4308_0
jupyterlab_widgets 1.0.0 pyhd3eb1b0_1
(mynewenv) conda list | wc -l
152
(mynewenv) du -sh $HOME/.conda/envs/mynewenv
1.3G /u/arnoldg/.conda/envs/mynewenv
|
...or create a new clone of your chosen anaconda3_<cpu, gpu, mi100> module:
Jupyter (and everything else from your loaded anaconda3_ module will be copied into this environment). This option adds about 500 python modules to your environment and requires about 6.3 GB in your $HOME. Install time can be up to 1/2 hr.
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(base) time conda create --name myclone --clone base Source: /sw/external/python/anaconda3_cpu Destination: /u/arnoldg/.conda/envs/myclone The following packages cannot be cloned out of the root environment: - defaults/linux-64::conda-env-2.6.0-1 - defaults/linux-64::conda-22.9.0-py39h06a4308_0 - defaults/linux-64::conda-build-3.21.8-py39h06a4308_2 - defaults/noarch::conda-token-0.4.0-pyhd3eb1b0_0 - defaults/linux-64::_anaconda_depends-2022.05-py39_0 - defaults/linux-64::anaconda-navigator-2.1.4-py39h06a4308_0 - defaults/linux-64::anaconda-custom-py39_1 Packages: 447 Files: 24174 Preparing transaction: done Verifying transaction: done Executing transaction: \ ... Retrieving notices: ...working... done real 24m10.605s user 0m54.353s sys 1m56.843s (base) conda activate myclone (myclone) conda list | wc -l 501 (myclone) du -sh $HOME/.conda/envs/myclone 6.3G /u/arnoldg/.conda/envs/myclone |
Start an OpenOnDemand Jupyterlab session and access one of your environments (remember to match your partition and account types for gpu, cpu), then select the matching kernel for your Jupyter work:
Launch Jupyterlab
After filling in the OpenOnDemand form and submitting your job, it will start in a few minutes showing the "Connect to Jupyter" button when ready.
...
Change your kernel to match if you are opening a notebook from a different environment.
R
R is available via in Jupyterlab by activating the environment for it from your preferred conda module. Your bash shell should be setup with "conda init bash" as above. Then just append to your .bashrc, the line to activate the R environment. We've provided one at /sw/external/python/anaconda3_Rcpu that you should be able to use with any of anaconda3_<cpu,gpu,mi100>. It will run R via the module anaconda_Rcpu. Append the module load line to your .bashrc. R will run on the cpu cores (not gpu enabled).
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$ tail(/sw/external/python/anaconda3_Rcpu) tail -4 .bashrc # <<< conda initialize <<< conda activate /sw/external/python/ module load anaconda3_Rcpu (/sw/external/python/anaconda3_Rcpu) $ |
After modifying your default login environment.bashrc and getting a new shell, your login prompt should reflect that you are in an R python environment, and staring within the anaconda3_Rcpu environment, R will be in your $PATH, and starting Jupyterlab from the OpenOnDemand interface will automatically offer you the R options with the Launcher.
Proceed to use R:
Debugging OpenOnDemand problems:
For internal staff debugging (also useful for new OOD users: debugging jupyterlab , OpenOnDemand)