DepHell VS Metaflow

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  I've found https://github.com/dephell/dephell but seems to be outdated.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 1 year 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 2 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 2 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 3 years ago

Metaflow mentions (12)

• 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: 12 months 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 1 year 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 1 year ago

• [OC] Gender diversity in Tech companies

  They had to figure out video compression that worked at the volume that they wanted to deliver. They had to build and maintain their own CDN to be able to have a always available and consistent viewing experience. Don’t even get me started on the resiliency tools like hystrix that they were kind enough to open source. I mean, they have their own fucking data science framework and they’re looking into using neural... Source: over 1 year ago

• Going to Production with Github Actions, Metaflow and AWS SageMaker

  Github Actions, Metaflow and AWS SageMaker are awesome technologies by themselves however they are seldom used together in the same sentence, even less so in the same Machine Learning project. - Source: dev.to / over 1 year ago