guildai
aim
Our great sponsors
guildai | aim | |
---|---|---|
16 | 70 | |
856 | 4,782 | |
0.6% | 3.1% | |
8.8 | 8.0 | |
9 months ago | 1 day 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.
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.
aim
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aim VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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End-to-end observability for LlamaIndex environment
LlamaIndex Observer is one of the logging apps built in AimOS (aimstack.io).
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Data Registry suggestions for ML projects
I've been working with Aim for a while, and it's been solid. What stands out for me is its open-source nature. https://aimstack.io/
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Building and debugging LLMs with Aim: self-hosted and open-source AI metadata tracking tool
If you haven't yet, drop a star to support open-source project! ⭐️ https://github.com/aimhubio/aim
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Any tools that offer In-depth tracking of model runtime performance?
Here is the GitHub repository: https://github.com/aimhubio/aim
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Using MLflow(Machine Learning experimentation tracking tool) in Kaggle notebooks with the help of DagsHub
You can also check out Aim, which has an integration with MLflow, called aimlflow.
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Visualize metadata with Aim on Hugging Face Spaces and seamlessly share training results with anyone
Hope you enjoyed reading and thanks for your time! Feel free to share your thoughts, would love to read them. Support Aim by dropping a star on GitHub: https://github.com/aimhubio/aim
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Effortless image tracking and analysis for 3D segmentation task with Aim
Aim: An easy-to-use & supercharged open-source AI metadata tracker aimstack.io
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Evaluate Different Vector Databases
Seems useful: https://github.com/aimhubio/aim
- Metadata visualization via Aim Explorers
What are some alternatives?
MLflow - Open source platform for the machine learning lifecycle
tensorboard - TensorFlow's Visualization Toolkit
dvc - 🦉 ML Experiments and Data Management with Git
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]
wandb - 🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!