horovod
deep-significance
| horovod | deep-significance | |
|---|---|---|
| 1 | 6 | |
| 11,889 | 316 | |
| - | - | |
| 9.4 | 4.0 | |
| over 2 years ago | 7 months ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 only |
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.
horovod
-
[P] Cost of distributed deep learning on AWS
Code for https://arxiv.org/abs/1802.05799 found: https://github.com/uber/horovod
deep-significance
- [P] deep-significance: Enabling easy statistical significance testing for deep neural networks
-
[D] Statistical Significance in Deep RL Papers: What is going on?
Because I was so frustrated by this topics as well, I actually reimplemented and packaged a test specifically for NNs and gave it a lot of documentation in the hope of lowering the entry barrier as much as possible https://github.com/Kaleidophon/deep-significance
- deep-significance: Easy and Better Significance Testing for Deep Neural Networks
- [P] deep-significance: Easy and Better Significance Testing for Deep Neural Networks
- [Project] deep-significance: Easy and Better Significance Testing for Deep Neural Networks (link below)
- [P] deep-significance: Easy and Better Significance Testing for Deep Neural Networks (link below)
What are some alternatives?
tf-encrypted - A Framework for Encrypted Machine Learning in TensorFlow
nannyml - nannyml: post-deployment data science in python
rxray - Ray distributed computing integration for RxPY
Note - Easily implement parallel training and distributed training. Machine learning library. Note.neuralnetwork.tf package include Llama2, Llama3, CLIP, ViT, ConvNeXt, SwiftFormer, etc, these models built with Note are compatible with TensorFlow and can be trained with TensorFlow.
seq2seq - A general-purpose encoder-decoder framework for Tensorflow
ludwig - Low-code framework for building custom LLMs, neural networks, and other AI models
polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
openrec - OpenRec is an open-source and modular library for neural network-inspired recommendation algorithms
ray_snowflake - Ray Data Connector for Snowflake
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
client - DagsHub client libraries
clearml - ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution