dephell
clearml
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| dephell | clearml | |
|---|---|---|
| 5 | 20 | |
| 1,668 | 5,217 | |
| - | 2.5% | |
| 7.6 | 8.1 | |
| over 3 years ago | 8 days ago | |
| Python | Python | |
| MIT License | 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.
dephell
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How to generate setup.py from pyproject.toml
I've found https://github.com/dephell/dephell but seems to be outdated.
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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 enjoy doing.
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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
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[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
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Whats The Latest On Pipenv Poetry Etc
(& also come across DepHell)
clearml
- FLaNK Stack Weekly 12 February 2024
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clearml VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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cascade alternatives - clearml and MLflow
3 projects | 1 Nov 2023
- Is there any workflow orchestrator that is Hydra friendly ?
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Show HN: Open-source infra for data scientists
It looks like Magniv is targeting Python in general. This is similar to ClearML. What are the differentiating points to Magniv compared to similar products?
It seems like the product also integrates with SCM systems. Are you using gitea and then containers to push code and data to execution like CodeOcean?
https://github.com/allegroai/clearml
https://codeocean.com/
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[D] Drop your best open source Deep learning related Project
Hi there. ClearML is our open-source solution which is part of the PyTorch ecosystem. We would really appreciate it if you read our README and starred us if you like what you see!
- Start with powerful experiment management and scale into full MLOps with only 2 lines of code.
- Everything you need to log, share, and version experiments, orchestrate pipelines, and scale within one open-source MLOps solution.
- Start with powerful experiment management and scale into full MLOps with only 2 lines of code
What are some alternatives?
PDM - A modern Python package and dependency manager supporting the latest PEP standards
MLflow - Open source platform for the machine learning lifecycle
conda - A system-level, binary package and environment manager running on all major operating systems and platforms.
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
pip-tools - A set of tools to keep your pinned Python dependencies fresh.
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
pip - The Python package installer
kedro-great - The easiest way to integrate Kedro and Great Expectations
wheel - Adoption analysis of Python Wheels: https://pythonwheels.com/
streamlit - Streamlit — A faster way to build and share data apps.
Curdling - Concurrent package manager for Python
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️