These tutorials are written as interactive Jupyter Notebooks if you're new to notebooks we've included some information on how to get these running on your computer.
After you have installed the necessary python packages
All instructions are for running in your terminal (Mac or Linux) or you
launch the notebook server
jupyter notebook --generate-config
jupyter-notebook
The notebook should launch in your browsers if not go to the address given in the terminal e.g.
- A quick overview of jupyter notebooks can be found here
- If you want a clean copy and one to "play with" select
file
-->make a copy
and rename your copy as a working copy.
- most things will be fixed by a fresh install
# List environments installed
conda env list
# Remove the broken environment by name
conda env remove -n <environment name>
-
strange widget errors can be fixed with
jupyter nbextension enable --py --sys-prefix widgetsnbextension
-
missing output:
jupyter notebook --NotebookApp.iopub_data_rate_limit=1.0e10
If you wish to run these notebooks on a remote machine with accelerated hardware then please follow these instructions
Installation Anaconda (recomended instaltation method)
-
Install anaconda or miniconda on your computer via the appropriate installer found here
-
Machine Learning libraries are large and have many compatiblity issues so install the tutorial specific yml file using conda or mamba for each notebook.
cd RandomForests
conda env create -f RF.yml
conda activate RF
cd ConvolutionalNeuralNetworks
conda env create -f CNN.yml
conda activate CNN
cd GaussianProcesses
conda env create -f GP.yml
conda activate
cd Physics_Informed_Neural_Networks
conda env create -f PINN.yml
conda activate PINNs
Mamba is a faster version of conda installed by running conda install -c conda-forge mamba
and then used in place of conda in commands e.g. conda install
becomes mamba install
conda env create
becomes mamba env create
and so on.
This may be useful if the conda environment is taking a long time to solve.