# Login to Cannon
ssh <username>@login.rc.fas.harvard.edu

# Check current location & change if desired for this training:
pwd
cd <desired-location>

# Clone FASRC User Codes repository: https://github.com/fasrc/User_Codes/tree/master
SSH - git clone git@github.com:fasrc/User_Codes.git  
HTTPS - git clone https://github.com/fasrc/User_Codes.git

# Create a training folder & go to that folder:
mkdir python-training
cd python-training

# Copy Python folders from the User Codes directory: 
cp -r ../User_Codes/Languages/Python .
cp -r ../User_Codes/Parallel_Computing/Python/Python-Multiprocessing-Tutorial .

# Go to a compute node
salloc -p test --nodes=1 --cpus-per-task=2 --mem=12GB --time=01:00:00

# Create a vanilla mamba environment on a desired location
module load python
mamba create --prefix=/n/holylabs/LABS/<desired-folder>/multiproc_env python=3.11 -y

# Create a vanilla mamba environment on default $HOME
module load python
mamba create --name multiproc_env python=3.11 -y

# Activate the environment based on the location
mamba activate /n/holylabs/LABS/<desired-folder>/multiproc_env

#OR
mamba activate multiproc_env

# Install relevant packages
mamba install numpy pandas matplotlib -y
pip install jupyterlab swifter

# Deactivate environment
mamba deactivate

# Go to the Multiprocessing Tutorial folder
cd Python-Multiprocessing-Tutorial

# Submit job
sbatch run_multiproc.sbatch