dvc
🦉 ML Experiments and Data Management with Git (by iterative)
guildai
Experiment tracking, ML developer tools (by guildai)
| dvc | guildai | |
|---|---|---|
| 109 | 16 | |
| 13,166 | 858 | |
| 1.0% | 0.3% | |
| 9.6 | 8.8 | |
| 4 days ago | 9 months ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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
Posts with mentions or reviews of dvc.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-04-23.
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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.
guildai
Posts with mentions or reviews of guildai.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-05.
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guildai VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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[D] Who here are convinced that they have a really good setup that keeps track of their ML experiments?
Experiment tracking in DvC is implemented using git to store snapshots of a project and related artifacts. You might take a look at Guild AI's support for DvC, which is tightly integrated with DvC stages. You can run any of the stages defined for a project and you get a properly isolated run (each run is a project copy to ensure that you're not corrupting the run if you modify files while it's running - as well as properly supporting concurrent runs). Once you have runs in Guild, you can use any number of tools to study, compare, export, etc.
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[D] Deploying SOTA models into my own projects
I built an experiment tracking tool (Guild AI) that focuses on code/model reuse and so this question is dear to my heart :) Best of luck!
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[P] I reviewed 50+ open-source MLOps tools. Here’s the result
I'm not aware of experiment tracking in Jupyter notebooks themselves. Guild AI is able to run notebooks as experiments however.
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[D] What MLOps platform do you use, and how helpful are they?
Disclosure - I'm the author of Guild AI so take this for the biased opinion that it is.
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[N] Experiment tracking with DvC and Guild AI
I'm the author of Guild AI (open source experiment tracking). For some time now Guild users have asked for DvC support. This is now available as a pre-release.
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[D] Why doesn’t your team use an experiment tracking tool?
Guild AI now has support for running DvC stages as experiments. DvC uses git under the covers to manage project state for each experiment, along with the experiment results. Guild doesn't touch your git repo and instead copies your project source to a new run directory. This ensures that you have a correct record of your experiment without churning your project state.
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Data Science toolset summary from 2021
Guild.ai - https://guild.ai/
- [D] How do you ensure reproducibility?
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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?
Use guild and pytorch-lightning. Make it easy for new contributors to get your data by using dvc as a data access tool.
What are some alternatives?
When comparing dvc and guildai you can also consider the following projects:
MLflow - Open source platform for the machine learning lifecycle
lakeFS - lakeFS - Data version control for your data lake | Git for data
aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
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]
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
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. ☁️
wandb - 🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.