DepHell Reviews

Metaflow Reviews

DepHell mentions (4)

• How to generate setup.py from pyproject.toml

  I've found https://github.com/dephell/dephell but seems to be outdated. Source: over 2 years ago

• Should i Continue this Project or Abandon it? ; https://github.com/iamDyeus/KnickAI

  I had a few relatively famous projects (like dephell), and at some point I lost my sleep because I was "fixing bugs" in it in my head in the middle of the night. Archiving it, closing issues in everything else, and starting to just write projects for my own fun only was the best decision I ever made. Don't make my mistakes. Don't ask random people on the internet what you should do. Do what you want to do and... Source: almost 3 years ago

• PDM: A Modern Python Package Manager

  You jest and yet... https://github.com/dephell/dephell Dephell is a converter for python packaging systems. It can turn poetry files into requirements.txt, or setuptools' setup.py into pipenv's Pipfile etc. Python Packaging: There is More Than One Way to Do It. - Source: Hacker News / over 3 years ago

• [D] What’s the simplest, most lightweight but complete and 100% open source MLOps toolkit? -> MY OWN CONCLUSIONS

  Not necessarily. You can use Dephell (https://github.com/dephell/dephell) to convert from poetry to the old-fashioned requirements.txt. Source: about 4 years ago

Metaflow mentions (14)

• 20 Open Source Tools I Recommend to Build, Share, and Run AI Projects

  Metaflow is an open source framework developed at Netflix for building and managing ML, AI, and data science projects. This tool addresses the issue of deploying large data science applications in production by allowing developers to build workflows using their Python API, explore with notebooks, test, and quickly scale out to the cloud. ML experiments and workflows can also be tracked and stored on the platform. - Source: dev.to / 6 months ago

• Recapping the AI, Machine Learning and Computer Meetup — August 15, 2024

  As a data scientist/ML practitioner, how would you feel if you can independently iterate on your data science projects without ever worrying about operational overheads like deployment or containerization? Let’s find out by walking you through a sample project that helps you do so! We’ll combine Python, AWS, Metaflow and BentoML into a template/scaffolding project with sample code to train, serve, and deploy ML... - Source: dev.to / 9 months ago

• What are some open-source ML pipeline managers that are easy to use?

  I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home. Source: almost 2 years ago

• Needs advice for choosing tools for my team. We use AWS.

  1) I've been looking into [Metaflow](https://metaflow.org/), which connects nicely to AWS, does a lot of heavy lifting for you, including scheduling. Source: about 2 years ago

• Selfhosted chatGPT with local contente

  Even for people who don't have an ML background there's now a lot of very fully-featured model deployment environments that allow self-hosting (kubeflow has a good self-hosting option, as do mlflow and metaflow), handle most of the complicated stuff involved in just deploying an individual model, and work pretty well off the shelf. Source: about 2 years ago