dvc
spock
dvc | spock | |
---|---|---|
109 | 12 | |
13,139 | 115 | |
0.6% | 1.7% | |
9.6 | 7.0 | |
3 days ago | 6 months ago | |
Python | Python | |
Apache License 2.0 | Apache 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.
dvc
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My Favorite DevTools to Build AI/ML Applications!
Collaboration and version control are crucial in AI/ML development projects due to the iterative nature of model development and the need for reproducibility. GitHub is the leading platform for source code management, allowing teams to collaborate on code, track issues, and manage project milestones. DVC (Data Version Control) complements Git by handling large data files, data sets, and machine learning models that Git can't manage effectively, enabling version control for the data and model files used in AI projects.
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Why bad scientific code beats code following "best practices"
What you’re describing sounds like DVC (at a higher-ish—80%-solution level).
https://dvc.org/
See pachyderm too.
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First 15 Open Source Advent projects
10. DVC by Iterative | Github | tutorial
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Platforms such as MLflow monitor the development stages of machine learning models. In parallel, Data Version Control (DVC) brings version control system-like functions to the realm of data sets and models.
- ML Experiments Management with Git
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Git Version Controlled Datasets in S3
I was using DVC (https://dvc.org/) for some time to help solve this but it was getting hard to manage the storage connections and I would run into cache issues a lot, but this solves it using git-lfs itself.
- Ask HN: How do your ML teams version datasets and models?
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Exploring MLOps Tools and Frameworks: Enhancing Machine Learning Operations
DVC (Data Version Control):
- Evaluate and Track Your LLM Experiments: Introducing TruLens for LLMs
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[D] Is there a tool to keep track of my ML experiments?
I have been using DVC and MLflow since then DVC had only data tracking and MLflow only model tracking. I can say both are awesome now and maybe the only factor I would like to mention is that IMO, MLflow is a bit harder to learn while DVC is just a git practically.
spock
- Managing complex configurations any other way would be highly illogical
- [D] Alternatives to fb Hydra?
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Why you should use Data Classes in Python
(Note: I wrote a library called spock that was originally based on dataclasses and then shifted to attrs. In the end attrs was just the better and more fully fledged library for what I needed so I’ve always preferred attrs over dataclasses since then)
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Is Spock-Config the only tool that integrates object-oriented config files and command-line interfaces?
Spock-Config allows one to create OO configuration files. That's how I roll. I currently use PYdantic settings and it's great. But it does not offer command-line re-configuration of what you have in the OO config file.
- My first Python project: reference finder
- Python 3.11 will now have tomllib - Support for Parsing TOML in the Standard Library
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Spock - Managing complex configurations any other way would be highly illogical...
Check out more in the docs or on GitHub
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[D] I'm new and scrappy. What tips do you have for better logging and documentation when training or hyperparameter training?
We wrote Spock which actually sits in the middle ground between Hydra and OmegaConf (I’m of the same opinion that Hydra does a little too much feature wise). You can do hierarchical composition within the markdown of any JSON, YAML, or TOML files by simply using the config argument. No code needed to merge. Docs are here if you’re interested.
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[D] Tools to avoid writing tons of scripts
Spock
What are some alternatives?
MLflow - Open source platform for the machine learning lifecycle
gin-config - Gin provides a lightweight configuration framework for Python
lakeFS - lakeFS - Data version control for your data lake | Git for data
strictyaml - Type-safe YAML parser and validator.
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]
reference-finder - Matches PDFs to sentences in text or docx file
delta - An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
traitlets - A lightweight Traits like module
aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
tune-sklearn - A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.