dropbox-sdk-python
pyxet
| dropbox-sdk-python | pyxet | |
|---|---|---|
| 4 | 4 | |
| 915 | 33 | |
| 1.3% | - | |
| 3.2 | 8.1 | |
| 5 months ago | 9 days ago | |
| Python | Python | |
| MIT License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
dropbox-sdk-python
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Python projects with best practices on Github?
The creation of the dropbox-sdk-python repo was almost certainly overseen by Guido van Rossum since he was working at Dropbox at the time. There is a note in the Smartsheet SDK repo that parts of it were developed by Dropbox as well.
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How do you read files that's in Dropbox using django-storage?
Or do you or should I just use this 'dropbox-sdk-python' (https://github.com/dropbox/dropbox-sdk-python)?
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A Quadrillion Mainframes on Your Lap
I'm not drawing conclusions from this, but I did some poking around..
I ran `git clone` on the repos of both rsync[0] and the official Dropbox SDK for Python[1]. I then ran sloccount on both of them.
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rsync came in at a total of 51,410 physical source lines of code.
The Dropbox SDK came in at a total of 74,140 physical source lines of code.
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[0] https://git.samba.org/rsync.git
[1] https://github.com/dropbox/dropbox-sdk-python
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Create an Appwrite File Backup Function Using the Dropbox API
In this example, we will demonstrate how we can integrate with a third-party storage provider like Dropbox to create backups of files uploaded to Appwrite. For the sake of this example, we will be using Dropbox’s Python SDK. A similar concept applies to other API providers like Box or Google Drive. So let’s get started.
pyxet
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Backing up datasets locally is crucial.
Have you looked into Xethub? It's like Github with ML-extensions. Their philosophy is you commit everything: code, assets, embeddings, the source docs, models, etc.
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LLM for Church Fathers texts
There are plenty of variations on this idea but one I found most enlightening is a workshop example for a commercial product called Xethub.
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On storing trained models (with sklearn and other libraries) - how do you do it?
I've been playing with Xethub lately. It looks and feels like git but deals with large models and embeddings pretty seamlessly.
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[P] pyxet: a Python library for ML teams to work with data like S3, while having the memory of Git.
Code: https://github.com/xetdata/pyxet
What are some alternatives?
solana-py - Solana Python SDK
telekinesis - Control Objects and Functions Remotely
terra.py - Python SDK for Terra
pycloudinary - Python package for cloudinary
auth0-python - Auth0 SDK for Python
fastapi-memory-leak
filestack-python - Official Python SDK for Filestack - API and content management system that makes it easy to add powerful file uploading and transformation capabilities to any web or mobile application.
streamlit - Streamlit — A faster way to build and share data apps.
interfax-python - Fax send and receive in Python with the InterFAX REST API
stone - The Official API Spec Language for Dropbox API V2
Dropbox.NET - The Official Dropbox API V2 SDK for .NET