PyNeuraLogic
hatch
PyNeuraLogic | hatch | |
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7 | 20 | |
267 | 5,356 | |
- | 2.9% | |
8.0 | 9.5 | |
6 days ago | 1 day ago | |
Python | Python | |
MIT License | MIT License |
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PyNeuraLogic
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[P] PyNeuraLogic - a framework for writing differentiable logic programs
Hi, sure. With this framework, you can write and train deep learning models similarly to PyTorch or TensorFlow. Although the main aim of PyNeuraLogic is on deep relational learning and it uses custom declarative language (implemented in Python). Best fitting use cases are everything where you can utilize relations. One of those use-cases that we are promoting right now is on Graph Neural Networks (GNNs), where you have relations between nodes (such as social networks, molecules). You can then utilize those relations and do regular tasks on graphs, such as link prediction, graph classification, node classification, etc. GNNs quite nicely fit the framework and its language and can be expressed just in one line (as shown in the README). The concrete use-case of PyNeuraLogic on GNNs could then be a molecule classification (example). Other use-cases could be for NLP (we have todo to write an example for it) or knowledge base completion. You could also use it like a regular framework without utilizing relations, but in that case, it might be more efficient to go with PyTorch or TensorFlow.
- Show HN: Evaluate Deep Learning models directly in a database with PyNeuraLogic
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Why Hypergraphs? (2013)
For an original proposal that do logic inference on Hypergraphs I am using NeuraLogic, through a Python frontend (https://github.com/LukasZahradnik/PyNeuraLogic)
I wonder if this is something the author would have enjoyed…
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This Week in Python
PyNeuraLogic – PyNeuraLogic lets you use Python to create Differentiable Logic Programs
- GitHub - LukasZahradnik/PyNeuraLogic: PyNeuraLogic lets you use Python to create Differentiable Logic Programs
- Show HN: PyNeuraLogic: Python Differentiable Logic Programs
hatch
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Uv: Python Packaging in Rust
Exciting stuff! I view Hatch [1] as becoming the Cargo for Python because it's already close and has an existing (and growing) user base but I can definitely see depending on this for resolution and potentially not even using pip after it becomes more stable.
[1]: https://hatch.pypa.io/latest/
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lockfiles for hatch projects
I was inspired enough by the hatch sync idea that I created a PR to add that functionality to hatch: https://github.com/pypa/hatch/pull/1094
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Building and Releasing a Python CLI
Another concept I learned was about build backends, an import step which is used to initialize and install any dependencies of the app you're packaging. Since the tutorial went with using Hatch that is also what I went with, though it didn't provide a lot of useful details especially because it didn't show how to add any dependencies, so I took a look at the docs which were very nice and simple to follow.
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Is there an up-to-date python package template?
Try using hatch: https://hatch.pypa.io/latest/
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How do I install dependencies in Hatch?
I'm trying to learn Hatch, I currently use [Poetry](python-poetry.org/) to manage my dependencies, and while I'm overall happy with it, I really like the features I'm reading about with Hatch. I'm also working on learning CI pipelines & Dockerizing Python applications, and Hatch seems like a really useful tool to learn for this (and just as a general use tool).
- pipenv or virtualenv ?
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Call for questions for Guido van Rossum from Lex Fridman
Poetry 1.2 has been a pain. Which was the dev's fault though. Switching to something new while deprecating a related feature is just plain bad. I've been looking into modern alternatives like PDM and Hatch, but haven't used them (yet).
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So how do you actually deploy code/scripts?
For example, when it comes to Python, one option is to use the same packaging system that a huge number of open-source libraries and tools are published with. You can use setuptools or Hatch to build a "packaged" version of your code, and publish it to either the public PyPi repository or an internal one that you set up. Then your users can use pip to install your package, automatically fetch its dependencies, and keep it up to date, just like any other Python module.
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Scala isn't fun anymore
Don't forget the new PyPa tool on the block: Hatch.
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How to create a Python package in 2022
See also: https://github.com/pypa/hatch
What are some alternatives?
reloadium - Hot Reloading and Profiling for Python
Poetry - Python packaging and dependency management made easy
chemicalx - A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)
setuptools - Official project repository for the Setuptools build system
pytorch_geometric - Graph Neural Network Library for PyTorch [Moved to: https://github.com/pyg-team/pytorch_geometric]
pip-tools - A set of tools to keep your pinned Python dependencies fresh.
pygod - A Python Library for Graph Outlier Detection (Anomaly Detection)
poetry-dynamic-versioning - Plugin for Poetry to enable dynamic versioning based on VCS tags
gradio - Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
typedb-ml - TypeDB-ML is the Machine Learning integrations library for TypeDB
pypyr automation task runner - pypyr task-runner cli & api for automation pipelines. Automate anything by combining commands, different scripts in different languages & applications into one pipeline process.