[Required badges consist of 1) programming language + version 2) Jupyter, 3) main ML/data-science framework and 4) license. We do not require authors to create a badge for every dependency. We encourage authors to keep the top 3-5 significant dependencies. Badge urls can be found on shields.io/badges]
[Provide an abstract describing the tutorial.]
Provide a bulleted list of all author names, affiliations, and contact links.
- [author 1 fullname], [Affiliation/Institution] , [contact email]
- [author 2 fullname], [Affiliation/Institution] , [contact email]
Reminder for Round II reviews please keep all tutorial materials anonymized to support the double-blind review process.
Originally presented at the [insert CCAI event or workshop full name with the year].
After completing this tutorial, participants should be able to:
- [Learning objective]
- [Learning objective]
- [Learning Objective]
[Describe the intended audience and please be as specific as possible, especially with respect to their expected background.]
Participants should be familiar with:
- [Prerequisite]
- [Prerequisite]
- [Prerequisite]
➡️ Video URL Duration: [XX minutes]
| Resource | Description |
|---|---|
notebook/ |
Jupyter notebook |
model-card/ |
Model documentation (if applicable) |
datasheet/ |
Dataset documentation (if applicable) |
emissions-reporting/ |
Energy/carbon reporting |
figures/ |
Images and figures (optional) |
data/ |
Dataset sample (optional) |
Primary language: [Python / R / Julia / Other] Language version: [e.g., Python 3.12]
- [package] [version]
- [package] [version]
- [package] [version]
See requirements.txt for the complete environment.
We recommend executing this notebook in a Colab environment to gain access to GPUs and to manage all necessary dependencies.
To run locally, see:
➡️ notebook/[final_notebook_name_without_version.ipynb]
Estimated time to execute end-to-end: [insert runtime here].
Last successfully tested: [YYYY-MM-DD]
This submission [does/does not] involve a machine-learning model.
[If the tutorial trains, fine-tunes, adapts, evaluates, or demonstrates an AI/ML model please provide a model-card. A description of model-cards is provided here.]
If applicable, see:
➡️ model-card/MODEL_CARD.md
This submission [does/does not] use a dataset.
[If the tutorial utilizes a dataset, please provide a datasheet. A description of datasheets is provided here.]
If applicable, see:
➡️ datasheet/DATASHEET.md
Carbon emissions associated with the computational experiments were measured using [CodeCarbon or emissions tracking tool of choice]. For more details, go to:
➡️ emissions-reporting/CARBON_EMISSIONS.md
Please refer to these GitHub instructions to open a pull request via the "fork and pull request" workflow.
Pull requests will be reviewed by members of the Climate Change AI Tutorials team for relevance, accuracy, and conciseness.
Check out the tutorials page on our website for a full list of tutorials demonstrating how AI can be used to tackle problems related to climate change.
Usage of this tutorial is subject to the MIT License.
[Insert plain text citation of accepted submission here.]
[Insert BibTex citation of accepted submission here.]