Show HN: Mandala – Automatically save, query and version Python computations

1. mandala

   1 10 530 8.7 Jupyter Notebook

   A simple & elegant experiment tracking framework that integrates persistence logic & best practices directly into Python

   

   - If an `@op` call was memoized, the underlying Python function call succeeded, so in this sense it can't be "broken"; it's however possible that there was a bug. In this case, you can delete the affected calls and all values that depend on them (if you keep these values, you're left with "zombie" values that don't have a proper computational history). The `ComputationFrame` supports declarative deletion - you build a ComputationFrame that captures the calls you want to delete, and call `.delete_calls()` - though there's still no example of this in the tutorial :)

   - How the cache is invalidated is detailed here: https://github.com/amakelov/mandala?tab=readme-ov-file#how-i...

2. InfluxDB

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   InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.

   

3. funes

   2 1 3 - Python

   Funes the Memorious decorates functions to memoize their computations permanently to disk.

   

   Cool! Looks pretty professional.

   I explored a similar idea once (also implemented in Python, via decorators) to help speed up some neuroscience research that involved a lot of hyperparameter sweeps. It's named after a Borges story about a man cursed to remember everything: https://github.com/taliesinb/funes

   Maybe one day we'll have a global version of this, where all non-private computations are cached on a global distributed store somehow via content-based hashing.

4. python-notebook-simulator

   3 1 0 7.9 Python

   The Python Notebook Simulator is a project designed to help learn how deterministic and reproducible execution works in Python-like notebook environments.

   

   Very cool! looking forward to trying it out - the graphs reminded me of a toy project I'd done a while back to better understand deterministic and reproducible execution in python as seen in marimo notebooks https://github.com/vrtnis/python-notebook-simulator

5. OCP

   4 2 127 6.0 C++

   

6. provenance

   5 1 37 - Python

   Provenance and caching library for python functions, built for creating lightweight machine learning pipelines (by bmabey)

   

   Used something similar to this in the past: https://github.com/bmabey/provenance. Curious to see similarities/differences. Also reminds me of Unison at a conceptual level: https://github.com/unisonweb/unison

7. unison

   6 18 6,178 9.9 Haskell

   A friendly programming language from the future

   

   Used something similar to this in the past: https://github.com/bmabey/provenance. Curious to see similarities/differences. Also reminds me of Unison at a conceptual level: https://github.com/unisonweb/unison

8. mltrace

   7 1 469 - Python

   Coarse-grained lineage and tracing for machine learning pipelines.

   

   This is a very innovative take on infra for ML observability. Shreya Shankar and collaborators at Berkeley came up with https://github.com/loglabs/mltrace, which treads the same ground - perhaps you've looked at it already?

9. Sevalla

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   Deploy and host your apps and databases, now with $50 credit! Sevalla is the PaaS you have been looking for! Advanced deployment pipelines, usage-based pricing, preview apps, templates, human support by developers, and much more!

   

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