pachyderm
scikit-image
pachyderm | scikit-image | |
---|---|---|
8 | 10 | |
6,077 | 5,880 | |
0.2% | 0.7% | |
9.8 | 9.6 | |
6 days ago | 5 days ago | |
Go | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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.
pachyderm
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Open Source Advent Fun Wraps Up!
20. Pachyderm | Github | tutorial
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Pachyderm specializes in creating compliance-focused pipelines that integrate with enterprise-level storage solutions.
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Show HN: We scaled Git to support 1 TB repos
There are a couple of other contenders in this space. DVC (https://dvc.org/) seems most similar.
If you're interested in something you can self-host... I work on Pachyderm (https://github.com/pachyderm/pachyderm), which doesn't have a Git-like interface, but also implements data versioning. Our approach de-duplicates between files (even very small files), and our storage algorithm doesn't create objects proportional to O(n) directory nesting depth as Xet appears to. (Xet is very much like Git in that respect.)
The data versioning system enables us to run pipelines based on changes to your data; the pipelines declare what files they read, and that allows us to schedule processing jobs that only reprocess new or changed data, while still giving you a full view of what "would" have happened if all the data had been reprocessed. This, to me, is the key advantage of data versioning; you can save hundreds of thousands of dollars on compute. Being able to undo an oopsie is just icing on the cake.
Xet's system for mounting a remote repo as a filesystem is a good idea. We do that too :)
- pachyderm: Data-Centric Pipelines and Data Versioning
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Awesome list of VCs investing in commercial open-source startups
Pachyderm - License prevents competition.
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Airflow's Problem
I was at Airbnb when we open-sourced Airflow, it was a great solution to the problems we had at the time. It's amazing how many more use cases people have found for it since then. At the time it was pretty focused on solving our problem of orchestrating a largely static DAG of SQL jobs. It could do other stuff even then, but that was mostly what we were using it for. Airflow has become a victim of its success as it's expanded to meet every problem which could ever be considered a data workflow. The flaws and horror stories in the post and comments here definitely resonate with me. Around the time Airflow was opensource I starting working on data-centric approach to workflow management called Pachyderm[0]. By data-centric I mean that it's focused around the data itself, and its storage, versioning, orchestration and lineage. This leads to a system that feels radically different from a job focused system like Airflow. In a data-centric system your spaghetti nest of DAGs is greatly simplified as the data itself is used to describe most of the complexity. The benefit is that data is a lot simpler to reason about, it's not a living thing that needs to run in a certain way, it just exists, and because it's versioned you have strong guarantees about how it can change.
[0] https://github.com/pachyderm/pachyderm
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One secret tip for first-time OSS contributors. Shh! 🤫 don't tell anyone else
Here is a demo run of lgtm on pachyderm
- Dud: a tool for versioning data alongside source code, written in Go
scikit-image
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How to Estimate Depth from a Single Image
We will use the Hugging Face transformers and diffusers libraries for inference, FiftyOne for data management and visualization, and scikit-image for evaluation metrics.
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.
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Converting Scikit-Learn Library Algorithms to C
scikit hog library: https://github.com/scikit-image/scikit-image/blob/main/skimage/feature/_hog.py#L302 , https://github.com/scikit-image/scikit-image/blob/main/skimage/feature/_hoghistogram.pyx
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Is it possible to add a noise to an image in python?
This is a good cv deep learning book with python examples https://www.manning.com/books/deep-learning-for-vision-systems. If you're pretty comfortable with the concepts of traditional image processing this is a good companion to cv2 (so you don't have to reinvent the wheel) https://scikit-image.org/
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A CLI that does simple image processing and also generates cool patterns
Also, don't know if you're familiar with Python, but if you need ideas for to implement for future directions : https://scikit-image.org/
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Color Matrices for scan correction
There's probably something in scikit-image to do what you want, or close enough to build on.
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Python: The Best Image Processing Libraries
Scikit-image The Scikit-image library is a collection of image processing algorithms that are designed to be easy to use and understand. It includes algorithms for common tasks like edge detection, feature extraction, and image restoration. If you are just starting out in image processing, then this is a good library to check out!
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Image Processing is Easier than you Thought! (Getting started with Python Pillow)
Python is a general-purpose programming language that provides many image processing libraries for adding image processing capabilities to digital images. Some of the most common image processing libraries in Python are OpenCV, Python Imaging Library (PIL), Scikit-image etc.
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Scikit-image for Image Processing
Then you would need to find what this plugin does for imshow. First thing you can see is that "interpolation" is not "bicubic" as you used, but "nearest"… but there are other settings here that are responsible for the difference of displays. (it's better that you look at the source code in your environment, as it might be slightly different)
- Patented algorithm removed from scikit-image shortly before merge accept
What are some alternatives?
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
pillow - Python Imaging Library (Fork)
trivy - Find vulnerabilities, misconfigurations, secrets, SBOM in containers, Kubernetes, code repositories, clouds and more
OpenCV - Open Source Computer Vision Library
dud - A lightweight CLI tool for versioning data alongside source code and building data pipelines.
nude.py - Nudity detection with Python
beneath - Beneath is a serverless real-time data platform ⚡️
python-qrcode - Python QR Code image generator
typhoon-orchestrator - Create elegant data pipelines and deploy to AWS Lambda or Airflow
thumbor - thumbor is an open-source photo thumbnail service by globo.com
tsuru - Open source and extensible Platform as a Service (PaaS).
wand - The ctypes-based simple ImageMagick binding for Python