Our great sponsors
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mlf-core
CPU and GPU deterministic and therefore fully reproducible machine learning pipelines using MLflow.
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.
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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.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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