mandala
aim
mandala | aim | |
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8 | 70 | |
228 | 4,797 | |
- | 1.8% | |
6.3 | 8.0 | |
about 2 months ago | 4 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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mandala
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Mandala: A little plaground for testing pixel logic patterns
I was so confused, expecting this to be some trickery related to the computational-graph-memoization-and-exploration tool "mandala" https://github.com/amakelov/mandala
- Mandala: Notebook memoization on steroids, used by Anthropic
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Improve Jupyter Notebook Reruns by Caching Cells
This is neat and self-contained! But as someone running experiments with a high degree of interactivity, I often have an orthogonal requirement: add more computations to the same cell without recomputing previous computations done in the cell (or in other cells).
For a concrete example, often in an ML project you want to study how several quantities vary across several parameters. A straightforward workflow for this is: write some nested loops, collect results in python dictionaries, finally put everything together in a dataframe and compare (by plotting or otherwise).
However, after looking at the results, maybe you spot some trend and wonder if it will continue if you tweak one of the parameters by using a new value for it; of course, you also want to look at the previous values and bring everything together in the same plot(s). You now have a problem: either re-run the cell (thus losing previous work, which is annoying even if you have to wait 1 minute - you know it's a wasted minute!), or write the new computation in a new cell, possibly with a lot of redundancy (which over time makes the notebook hard to navigate and keep consistent).
So, this and other considerations eventually convinced me that the function is more natural than the cell as an interface/boundary at which caching should be implemented, at least for my use cases (coming from ML research). I wrote a framework based on this idea, with lots of other features (some quite experimental/unusual) to turn this into a feasible experiment management tool - check it out at https://github.com/amakelov/mandala
P.S.: I notice you use `pickle` for the hashing - `joblib.dump` is faster with objects containing numpy arrays, which covers a lot of useful ML things
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ML Experiments Management with Git
Another option, that manages versioning of your computational graph and its results and provides extremely elegant query-able memoization is Mandala https://github.com/amakelov/mandala
It is a much simpler and much more magical piece of software that truly expanded how I think about writing, exploring, and experimenting with code. Even if you never use it, you probably would really enjoy reading the blog posts the author wrote about the design of the tool https://amakelov.github.io/blog/pl/
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Snakemake – A framework for reproducible data analysis
You might like mandala (https://github.com/amakelov/mandala) - it is not a build recipe tool, rather it is a tool that tracks the history of how your builds / computational graph has changed, and ties it to how the data looked like at each such step.
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Piper: A proposal for a graphy pipe-based build system
u/rust4yy: I've been building mandala, a Python framework for (among other things) incremental computing. One way to think of it is "a build system for Python objects", except the units of computation are Python functions.
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?
oxen-release - Lightning fast data version control system for structured and unstructured machine learning datasets. We aim to make versioning datasets as easy as versioning code.
tensorboard - TensorFlow's Visualization Toolkit
snakemake-wrappers - This is the development home of the Snakemake wrapper repository, see
dvc - 🦉 ML Experiments and Data Management with Git
beaver - Simple, but capable build system and command runner for any project
guildai - Experiment tracking, ML developer tools
sdk - Metadata store for Production ML
wandb - 🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
make-booster - Utility routines to simplify using GNU make and Python
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
curio - Good Curio!
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]