kitops
Activeloop Hub
kitops | Activeloop Hub | |
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6 | 31 | |
188 | 4,807 | |
82.9% | - | |
9.7 | 9.9 | |
3 days ago | over 1 year ago | |
Go | Python | |
Apache License 2.0 | Mozilla Public License 2.0 |
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.
kitops
- The Docker build – Docker run workflow missing from AI/ML?
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KitOps Release v0.2–Introducing Dev Mode and the ability to chain ModelKits
Currently dev mode is only available on MacOS, although we plan to expand it to additional platforms and include more inference runtimes and utilities for models, data, and code. File an issue in our GitHub repository telling us what platform we should tackle next, or how to improve the Kit dev command in general - we love community feedback!
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Streamlining AI/ML Deployment with ModelKits: Innovations and Future Directions
Yesterday, Brad Micklea, Jozu CEO and KitOps maintainer, was a guest on the Partially Redacted podcast hosted by Sean Falconer. The 45-minute conversation (which you can listen to here) covered a lot of ground. Specifically, they discussed the current state of the KitOps project, where the project is headed, and some of our early ideas for productizing and releasing Jozu, which builds on top of KitOps.
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Introducing the New GitHub Action for using Kit CLI on MLOps pipelines
As we continue to innovate and improve KitOps, your feedback is invaluable to us. Whether you're encountering challenges, have suggestions for new features, or simply want to share your success stories, we're all ears. You can provide feedback on our GitHub repo or our Discord channel.
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Say hello to Kit–An open source solution to MLOps complexity
You can learn more about Kit here: https://kitops.ml, and support us by [giving Kit a star on GitHub]!(https://github.com/jozu-ai/kitops)
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The transitory nature of MLOps: Advocating for DevOps/MLOps coalescence
And checkout the source code here: https://github.com/jozu-ai/kitops
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!
What are some alternatives?
distribution-spec - OCI Distribution Specification
dvc - 🦉 ML Experiments and Data Management with Git
aqueduct - Aqueduct is no longer being maintained. Aqueduct allows you to run LLM and ML workloads on any cloud infrastructure.
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.
glide - 🐦 A open blazing-fast simple model gateway for rapid development of production GenAI apps
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.
datasets - TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...
TileDB - The Universal Storage Engine
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.
caer - High-performance Vision library in Python. Scale your research, not boilerplate.
typedb-ml - TypeDB-ML is the Machine Learning integrations library for TypeDB
deepchecks - Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.