The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning. Learn more →
Framework-reproducibility Alternatives
Similar projects and alternatives to framework-reproducibility based on common topics and language
-
einops
Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
-
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.
-
-
wandb
🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
-
RealESRScaler
Discontinued RealScaler - image/video AI upscaler app (Real-ESRGAN) [Moved to: https://github.com/Djdefrag/RealScaler]
-
InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better framework-reproducibility alternative or higher similarity.
framework-reproducibility reviews and mentions
Posts with mentions or reviews of framework-reproducibility.
We have used some of these posts to build our list of alternatives
and similar projects.
-
Tensorflow: I'm getting different results from the same code depending on where I run it. [D]
Even with a fixed seed there's no guarantee that you'll get the exact same results due to the fact that most floating operations are not deterministic when parallelized. You can enable determinism flags in your framework to try and mitigate that, but results may still vary depending on your model and how you're running it.
- Same seed, different images
-
Dealing with non-deterministic result
Setting the seed alone is not enough because there will be a randomness resulted from GPU operations (there is some way to eliminate randomness due to GPU operations like https://github.com/NVIDIA/framework-determinism, but I cannot make it work with the current latest version of TF). Another workaround is not using GPU, but the training time does not make sense as I need to iterate fast, trying new idea.
- No Bee, it's you...
-
[D] Do you yourself write 100% reproducible ML code?
check out https://github.com/NVIDIA/framework-determinism, which should allow you to make fully reproducible to the bit code that runs on GPU. i've contributed to this repo and the author is extremely helpful.
-
A note from our sponsor - WorkOS
workos.com | 28 Apr 2024
Stats
Basic framework-reproducibility repo stats
5
417
5.8
6 months ago
NVIDIA/framework-reproducibility is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of framework-reproducibility is Python.
Sponsored
SaaSHub - Software Alternatives and Reviews
SaaSHub helps you find the best software and product alternatives
www.saashub.com