cascade
guildai
| cascade | guildai | |
|---|---|---|
| 9 | 16 | |
| 16 | 858 | |
| - | 0.3% | |
| 9.3 | 8.8 | |
| 7 days ago | 9 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.
cascade
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modeldb VS cascade - a user suggested alternative
2 projects | 12 Dec 2023
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Sacred VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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keepsake VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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aim VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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guildai VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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metaflow VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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clearml VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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cascade alternatives - clearml and MLflow
3 projects | 1 Nov 2023
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Announcing Cascade
This is Cascade - very lightweight MLE solution for individuals and small teams
guildai
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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?
deeplake - Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai
MLflow - Open source platform for the machine learning lifecycle
NVTabular - NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.
aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
powershap - A power-full Shapley feature selection method.
dvc - 🦉 ML Experiments and Data Management with Git
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
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]
ds2 - Easiest way to use AI models without coding (Web UI & API support)
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
FeatureHub - The most comprehensive library of AI/ML features across multiple domains. Our goal is to create a dataset that serves as a valuable resource for researchers and data scientists worldwide
wandb - 🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.