This repository has been archived by the owner on Sep 25, 2024. It is now read-only.

Forest Fire Prediction

Learning Objectives:

Apply Intel Extension for PyTorch to convert model and data to xpu
Apply fine tuning to satellite images to predict fire fires 2 years in advance
Generate synthetic satellite images using Stable Diffusion
Optimize a four-stage Stable Diffusion pipeline using Intel(r) Extensions for PyTorch*

First - a No Code approach: Stable Diffusion

Run all cells
When for an image url try: https://github.com/intelsoftware/ForestFirePrediction/blob/main/data/real_USGS_NAIP/train/Fire/m_3912105_sw_10_h_20160713.png?raw=true
For text prompt, try: similar aerial photo just translate location

To Access via Intel(R) Developer Cloud:

Go to the Intel Developer Cloud by scanning the QR code or by visiting https://cloud.intel.com/

Click “Get Started”
Subscribe to the “Standard – Free” service tier and complete your cloud registration.

To start up a free and quick Jupyter notebook session with the latest 4th Gen Intel Xeon CPU and Intel Data Center GPU Max 1100, click the “Training and Workshops” icon and then “Launch JupyterLab”, or one of the specific training materials launches.

Navigate using the Jupyter Hub folder in the left pane and Launch a terminal

Git Clone the repo

git clone https://github.com/IntelSoftware/ForestFirePrediction.git

cd to ForestFirePrediction
conda activate pytorch-gpu
pip install -r requirements.txt
Open and run each Jupyter Notebook in sequence

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