notebooks VS jupyterlab-gitplus

Yeah, inference[1] is our open source package for running locally (either directly in Python or via a Docker container). It works with all the models on Universe, models you train yourself (assuming we support the architecture; we have a bunch of notebooks available[2]), or train in our platform, plus several more general foundation models[3] (for things like embeddings, zero-shot detection, question answering, OCR, etc).

We also have a hosted API[4] you can hit for most models we support (except some of the large vision models that are really GPU-heavy) if you prefer.

[1] https://github.com/roboflow/inference

[2] https://github.com/roboflow/notebooks

[3] https://inference.roboflow.com/foundation/about/

[4] https://docs.roboflow.com/deploy/hosted-api

We (the Roboflow open source team) actively write open source Google Colab notebooks showing how to use new SOTA models. Our library covers SAM, CLIP, Detectron2, YOLOv8, RTMDet, DINOv2, and more. These notebooks helped me cross the chasm from "how do I use X model?" to being able to both write and understand inference code.

Here's an open source SegFormer notebook and guide: https://github.com/roboflow/notebooks/blob/main/notebooks/train-segformer-segmentation-on-custom-data.ipynb

Great ways to get some experience in general ML: * https://kaggle.com/learn to up your skill-set, practice a bit, and improve breadth of knowledge in topics like deep learning and computer vision * https://huggingface.co/learn free NLP courses that will really beef up your skillset * https://madewithml.com - robust tutorials for the end-to-end deep learning MLOps process * https://roboflow.com/learn - intro course material and some advanced topics in computer vision; tutorial walkthroughs for model training: https://github.com/roboflow/notebooks

Repo: https://github.com/roboflow/notebooks/blob/main/notebooks/sa...

Here are the notebooks I spend day and night refining: https://github.com/roboflow/notebooks

You’ll just need to have labeled solar panel images, and pick a model architecture and tutorial to train with: https://github.com/roboflow/notebooks

dataset: https://universe.roboflow.com/roboflow-jvuqo/football-players-detection-3zvbc/dataset/4 code: https://github.com/roboflow/notebooks/blob/main/notebooks/how-to-track-football-players.ipynb video: https://youtu.be/QCG8QMhga9k

Have fun! Great field. Just also try out the first few OpenCV tutorials, and train a few custom model to deploy to see what you think. Here’s a ton of free open source notebooks: https://github.com/roboflow/notebooks

If you are in need of a diff tool for jupter notebooks use https://www.reviewnb.com/ and for word documents use https://www.simuldocs.com/

- GitHub PR code reviews with ReviewNB[4]

Alternatively, if you don't care about cell outputs then Jupytext[5]

Disclaimer: I built ReviewNB. It's a completely bootstrapped business, 5 years in the making and now used by leading DS teams at Meta, AWS, NASA JPL, AirBnB, Lyft, Affirm, AMD, Microsoft & more (https://www.reviewnb.com/#customers)

[1] https://github.com/jupyterlab/jupyterlab-git

Already a GitHub plugin that does this very nicely: ReviewNB

For version control: https://www.reviewnb.com/ helps. Agree with the rest but some experimental notebooks are useful to track/version control.

It's not focused on collaboration, but it does add some critical pieces that otherwise make Jupyter development frustrating when working with a team. Specifically: `nbdev_prepare` ensures that diffs are as small as possible, by removing and standardising notebook metadata; and `nbdev_fix` fixes merge conflicts so that they are cell-level, rather than line level, so they can be opened and fixed in notebooks.

Something else we've found helpful for collaboration (not associated - just happy users) is this: https://www.reviewnb.com/ . It means we can get a nice notebook-based PR workflow.

Real-time collaboration is available in Jupyter nowadays: https://jupyterlab.readthedocs.io/en/stable/user/rtc.html . nbdev doesn't have any extra functionality for it, however -- but it should work fine in this environment.