mlf-core
CPU and GPU deterministic and therefore fully reproducible machine learning pipelines using MLflow. (by mlf-core)
system-intelligence
Query your system for all hardware and software related information. (by mlf-core)
Our great sponsors
| mlf-core | system-intelligence | |
|---|---|---|
| 3 | 1 | |
| 45 | 7 | |
| - | - | |
| 0.0 | 0.0 | |
| about 1 year ago | about 1 year ago | |
| Python | Python | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
mlf-core
Posts with mentions or reviews of mlf-core.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-05-31.
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[D] “Please Commit More Blatant Academic Fraud” (Blog post on problems in ML research by Jacob Buckman)
Link: https://github.com/mlf-core/mlf-core
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your ML workflow?
I am using mlf-core (Github: https://github.com/mlf-core/mlf-core) to make all of my projects fully CPU and GPU deterministic and reproducible. MLflow and Tensorboard allow me to explore my generated results interactively. Conda and Docker ensure a quick and reproducible runtime environment.
- Building an End-to-End Machine Learning Application From Idea to Deployment
system-intelligence
Posts with mentions or reviews of system-intelligence.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-02-23.
-
your ML workflow?
mlf-core provides CPU and GPU deterministic project templates. Hence, when you are using a mlf-core template you can be sure that you always get the bit exact same results given the same hardware. mlf-core ensures this by tracking all parameters and metrics with MLflow, tracking all hardware with system-intelligence, containerizing the environment with Conda and MLflow and the final spicy ingredient: mlf-core lint. This custom static code analyzer evaluates your code for two things: 1. You are forcing deterministic algorithms. Pytorch and Tensorflow use non-deterministic algorithms by default, but there ways to force part of them to behave 2. You are NOT using non-deterministic algorithms. If any of those are found mlf-core lint will alert you and tell you which function in which file and line violates determinism. You can then replace this method with a deterministic workaround.
What are some alternatives?
When comparing mlf-core and system-intelligence you can also consider the following projects:
ImageStackAlignator - Implementation of Google's Handheld Multi-Frame Super-Resolution algorithm (from Pixel 3 and Pixel 4 camera)
python-semver - Python package to work with Semantic Versioning (https://semver.org/)
fake-news - Building a fake news detector from initial ideation to model deployment
terminusdb-client-python - TerminusDB Python Client
Preql - An interpreted relational query language that compiles to SQL.