Activeloop Hub
django-ninja
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Activeloop Hub | django-ninja | |
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31 | 70 | |
4,807 | 6,166 | |
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9.9 | 9.1 | |
over 1 year ago | 7 days ago | |
Python | Python | |
Mozilla Public License 2.0 | MIT 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.
Activeloop Hub
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[Q] where to host 50GB dataset (for free?)
Hey u/platoTheSloth, as u/gopietz mentioned (thanks a lot for the shout-out!!!), you can share them with the general public through uploading to Activeloop Platform (for researchers, we offer special terms, but even as a general public member you get up to 300GBs of free storage!). Thanks to our open source dataset format for AI, Hub, anyone can load the dataset in under 3seconds with one line of code, and stream it while training in PyTorch/TensorFlow.
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[D] NLP has HuggingFace, what does Computer Vision have?
u/Remote_Cancel_7977 we just launched 100+ computer vision datasets via Activeloop Hub yesterday on r/ML (#1 post for the day!). Note: we do not intend to compete with HuggingFace (we're building the database for AI). Accessing computer vision datasets via Hub is much faster than via HuggingFace though, according to some third-party benchmarks. :)
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[N] [P] Access 100+ image, video & audio datasets in seconds with one line of code & stream them while training ML models with Activeloop Hub (more at docs.activeloop.ai, description & links in the comments below)
u/gopietz good question. htype="class_label" will work, but querying doesn't support multi-dimensional labels yet. Would you mind opening an issue requesting that feature?
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Easy way to load, create, version, query and visualize computer vision datasets
Hi HN,
In machine learning, we are faced with tensor-based computations (that's the language that ML models think in). I've recently discovered a project that helps you make it much easier to set up and conduct machine learning projects, and enables you to create and store datasets in deep learning-native format.
Hub by Activeloop (https://github.com/activeloopai/Hub) is an open-source Python package that arranges data in Numpy-like arrays. It integrates smoothly with deep learning frameworks such as TensorFlow and PyTorch for faster GPU processing and training. In addition, one can update the data stored in the cloud, create machine learning pipelines using Hub API and interact with datasets (e.g. visualize) in Activeloop platform (https://app.activeloop.ai). The real benefit for me is that, I can stream my datasets without the need to store them on my machine (my datasets can be up to 10GB+ big, but it works just as well with 100GB+ datasets like ImageNet (https://docs.activeloop.ai/datasets/imagenet-dataset), for instance).
Hub allows us to store images, audio, video data in a way that can be accessed at lightning speed. The data can be stored on GCS/S3 buckets, local storage, or on Activeloop cloud. The data can directly be used in the training TensorFlow/ PyTorch models so that you don't need to set up data pipelines. The package also comes with data version control, dataset search queries, and distributed workloads.
For me, personally the simplicity of the API stands out, for instance:
Loading datasets in seconds
import hub ds = hub.load("hub://activeloop/cifar10-train")
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Easy way to load, create, version, query & visualize machine learning datasets
Hub by Activeloop (https://github.com/activeloopai/Hub) is an open-source Python package that arranges data in Numpy-like arrays. It integrates smoothly with deep learning frameworks such as Tensorflow and PyTorch for faster GPU processing and training. In addition, one can update the data stored in the cloud, create machine learning pipelines using Hub API and interact with datasets (e.g. visualize) in Activeloop platform (https://app.activeloop.ai/3)
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Datasets and model creation flow
Consider this
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[P] Database for AI: Visualize, version-control & explore image, video and audio datasets
Please take a look at our open-source dataset format https://github.com/activeloopai/hub and a tutorial on htypes https://docs.activeloop.ai/how-hub-works/visualization-and-htype
I'm Davit from Activeloop (activeloop.ai).
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The hand-picked selection of the best Python libraries released in 2021
Hub.
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What are good alternatives to zip files when working with large online image datasets?
What solution have you used that you like as a data scientist when working with large datasets? Any standard python API to access the data? Other solution? If anyone has used https://github.com/activeloopai/Hub or other similar API I'd be interested to hear your experience working with it!
django-ninja
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Ask HN: What Underrated Open Source Project Deserves More Recognition?
Django Ninja [1], it forever changed how I write Django project, in a way so elegant and productive.
- Django Ninja is a web framework for building APIs with Django
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UtilMeta Python Framework VS django-ninja - a user suggested alternative
2 projects | 3 Feb 2024
Django Ninja is a RESTful wrapper for Django, while UtilMeta Python Framework uses a more concise declarative ORM Schema for Django and other future-supporting ORMs like sqlachemy and Peewee to build RESTful APIs more efficiently, and supports not only Django but all Python mainstream frameworks like Django, Flask, Starlette, FastAPI, Sanic, Tornado, etc.
- Django Ninja
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Ask HN: What Python libraries do you wish more people knew about?
I can't recommend [django-ninja](https://github.com/vitalik/django-ninja) enough. It's an easy to use, extremely fast, typed API for django. I've found it to be better in almost all aspects when compared to djangorestframework.
It's gaining popularity but is still widely unknown.
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Building a Blog in Django
> The only place I really see Django at large companies is as an api using DRF or something.
This is not a bad thing. Using Django as an API backend is amazingly fast in terms of development time, especially with modern frameworks such as django-ninja [1].
Just use the built-in ORM to create models, write your endpoints, and use the built-in admin interface to play with the database if you don't have endpoints for everything.
There is also a less known feature of Django called admindocs [2], which automatically generates a human readable, hyperlinked documentation for your models and relations between them.
[1] https://django-ninja.rest-framework.com/
[2] https://docs.djangoproject.com/en/4.2/ref/contrib/admin/admi...
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Learning Django
Personally, I also prefer django-ninja to DRF.
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Why I chose django-ninja instead of django-rest-framework to build my project
Actually that's not fully true. If you mix async and sync codes in django-ninja there will be some errors. Where's the proof ? django-ninja doesn't support async auth
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Built This GPT-Powered Document Search and Question Answering App with Django
Subscribe to this issue :D
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Django 4.2 released
Also recommend Django-Ninja. It basically reimplements fastapi's type and decorator-based API construction, but embedded directly in django so you have access to django's ORM and middleware library.
What are some alternatives?
dvc - 🦉 ML Experiments and Data Management with Git
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
petastorm - Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.
django-rest-framework - Web APIs for Django. 🎸
CKAN - CKAN is an open-source DMS (data management system) for powering data hubs and data portals. CKAN makes it easy to publish, share and use data. It powers catalog.data.gov, open.canada.ca/data, data.humdata.org among many other sites.
fastapi-admin - A fast admin dashboard based on FastAPI and TortoiseORM with tabler ui, inspired by Django admin
datasets - TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...
drf-spectacular - Sane and flexible OpenAPI 3 schema generation for Django REST framework.
TileDB - The Universal Storage Engine
openapi-generator - OpenAPI Generator allows generation of API client libraries (SDK generation), server stubs, documentation and configuration automatically given an OpenAPI Spec (v2, v3)
postgresml - The GPU-powered AI application database. Get your app to market faster using the simplicity of SQL and the latest NLP, ML + LLM models.
cookiecutter-django - Cookiecutter Django is a framework for jumpstarting production-ready Django projects quickly.