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Getting Started:

The first step is to identify a folder location where you would like to work in a development environment. We suggest a location that will be able to easily access streamflow predictions to make for easy evaluation of your model. Using the command prompt, change your working directory to this folder and git clone Snow-Extrapolation

git clone https://github.com/whitelightning450/LSTM-Tutorials

Virtual Environment

It is a best practice to create a virtual environment when starting a new project, as a virtual environment essentially creates an isolated working copy of Python for a particular project. I.e., each environment can have its own dependencies or even its own Python versions. Creating a Python virtual environment is useful if you need different versions of Python or packages for different projects. Lastly, a virtual environment keeps things tidy, makes sure your main Python installation stays healthy and supports reproducible and open science.

Creating Stable CONDA Environment on CIROH Cloud or other 2i2c Cloud Computing Platform

Go to home directory

cd ~

Create a envs directory

mkdir envs

Create .condarc file and link it to a text file

touch .condarc

ln -s .condarc condarc.txt

Add the below lines to the condarc.txt file

# .condarc
envs_dirs:
 - ~/envs

Restart your server

Creating your Python Virtual Environment: GPU

Since we will be using Jupyter Notebooks for this exercise, we will use the Anaconda command prompt to create our virtual environment. In the command line type:

mamba env create --name PyTorch_env -f pytorch.yml

mamba activate cuda_env

You should now be working in your new PyTorch_env within the command prompt. However, we will want to work in this environment within our Jupyter Notebook and need to create a kernel to connect them. We begin by installing the ipykernel python package:

pip install --user ipykernel

With the package installed, we can connect the NSM_env to our Python Notebook

python -m ipykernel install --user --name=PyTorch_env

Creating your Python Virtual Environment: CPU

Since we will be using Jupyter Notebooks for this exercise, we will use the Anaconda command prompt to create our virtual environment. In the command line type:

conda create -n LSTM python=3.11.7

For this example, we will be using Python version 3.11.7, specify this version when setting up your new virtual environment. After Anaconda finishes setting up your LSTM_env , activate it using the activate function.

conda activate LSTM_env

You should now be working in your new LSTM_env within the command prompt. However, we will want to work in this environment within our Jupyter Notebook and need to create a kernel to connect them. We begin by installing the ipykernel python package:

pip install --user ipykernel

With the package installed, we can connect the LSTM_env to our Python Notebook

python -m ipykernel install --user --name=LSTM_env

We will now be installing the packages needed to use NSM_env, as well as other tools to accomplish data science tasks. Enter the following code block in your Anaconda Command Prompt to get the required dependencies with the appropriate versions, note, you must be in the correct working directory:

pip install -r requirements.txt

Connect to AWS

All of the data for the project is on a publicly accessible AWS S3 bucket (streamflow-app-data), however, some methods require credentials. Please request credentials as an issue and put the credentials in the head of the repo (e.g., SWEML) as AWSaccessKeys.csv.