dslinter
mllint
dslinter | mllint | |
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2 | 3 | |
17 | 72 | |
- | - | |
4.2 | 3.8 | |
almost 2 years ago | almost 2 years ago | |
Python | Go | |
GNU General Public License v3.0 only | GNU General Public License v3.0 only |
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.
dslinter
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[P][R] Announcing `dslinter` -- a Pylint plugin for assessing Python-written ML project code quality
Check out our repo for more information: https://github.com/SERG-Delft/dslinter
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Announcing `dslinter` -- a Pylint plugin for assessing Python-written ML project code quality
It would be a massive help if you could run `dslinter` on your machine learning project in the industry setting and share the text and the json output with us. You can simply use pip to install dslinter and run dslinter. The steps and commands can be found here: https://github.com/SERG-Delft/dslinter/blob/main/STEPS_TO_FOLLOW.md . The running time of the dslinter should be approximately 1 minute for a project with 10000 lines. The whole process should take no longer than 10 minutes. The process is anonymous and we will remove any sensitive information before the results are published. We can also arrange a meeting and go through the process together if you prefer. If you have time to check whether the output linting message can help you with the project development, we are more than happy to hear the feedback! Please don't hesitate to contact me if you have any questions : )
mllint
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[D] How to maintain ML models?
Finally, there is the mllint tool that I have been developing during my MSc thesis on Software Quality in ML projects. While still a research prototype, it can already analyse your project and may be able to provide you with practical recommendations on what tools & techniques to employ for several aspects of your ML project's development. Feel free to try it out on your project and let me know what you think of it!
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Last week fluff-free AI, ML, and data-related original articles summary
- OpenAI released an improved version of Codex - Command-line utility to evaluate the technical quality of ML projects written in Python - It takes a whole convolutional neural network with five to eight layers to approximate a single cortical neuron - MLOps Monitoring Market Review
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[P][R] Announcing `mllint` — a linter for ML project software quality.
Source: https://github.com/bvobart/mllint
What are some alternatives?
pinferencia - Python + Inference - Model Deployment library in Python. Simplest model inference server ever.
mlnotify - 🔔 No need to keep checking your training - just one import line and you'll know the second it's done.
ck - Collective Mind (CM) is a small, modular, cross-platform and decentralized workflow automation framework with a human-friendly interface and reusable automation recipes to make it easier to build, run, benchmark and optimize AI, ML and other applications and systems across diverse and continuously changing models, data, software and hardware
MLOps - MLOps examples
readsql - Convert SQL to most human readable format
CortexTheseus - Cortex - AI on Blockchain, Official Golang implementation
libsa4py - LibSA4Py: Light-weight static analysis for extracting type hints and features
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
gorse - Gorse open source recommender system engine
awesome-seml - A curated list of articles that cover the software engineering best practices for building machine learning applications.
Lossless - Lossless is a Machine Learning library built for Golang, capable of handling MLPs, CNNs, and more soon.