PaddlePaddle
bodywork
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| PaddlePaddle | bodywork | |
|---|---|---|
| 6 | 8 | |
| 21,584 | 430 | |
| 0.8% | - | |
| 10.0 | 0.0 | |
| 7 days ago | 8 months ago | |
| C++ | Python | |
| Apache License 2.0 | GNU Affero General Public License v3.0 |
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.
PaddlePaddle
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List of AI-Models
Click to Learn more...
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Ask HN: Are there any notable Chinese FLOSS projects?
PaddlePaddle?
https://github.com/PaddlePaddle/Paddle
Also, Baidu have quite a few OSS projects out there in general.
https://github.com/baidu
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Volcano vs Yunikorn vs Knative
Volcano is a batch scheduler on top of Kube-batch targetting spark-operator, plain old MPI, chinesium paddlepaddle, and Kromwell HPC.
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Baidu AI Researchers Introduce SE-MoE That Proposes Elastic MoE Training With 2D Prefetch And Fusion Communication Over Hierarchical Storage
Continue reading | Check out the paper, and Github
- I have issue with only __habs for half datatype? Please help!
- Alternatives to google collab?
bodywork
- Deployment automation for ML projects of all shapes and sizes
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A tutorial on how to handle prediction uncertainty in production systems, by using Bayesian inference and probabilistic programs
how to deploy it to Kuberentes using Bodywork.
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[P] [D] How are you approaching prediction uncertainty in ML systems?
I usually turn to generative models - e.g. probabilistic programs and Bayesian inference. I’ve written-up my thoughts on how to engineer these into a ‘production system’ deployed to Kubernetes, using PyMC and Bodywork (an open-source ML deployment tool that I contribute to).
- Bodywork: MLOps tool for deploying ML projects to Kubernetes
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Tool for mapping executable Python modules to Kubernetes deployments
I’m one of the core contributors to Bodywork, an open-source tool for deploying machine learning projects developed in Python, to Kubernetes.
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[P] [D] The benefits of training the simplest model you can think of and deploying it to production, as soon as you can.
I’ve had many successes with this approach. With this in mind, I’ve put together an example of how to make this Agile approach to developing machine learning systems a reality, by demonstrating that it takes under 15 minutes to deploy a Scikit-Learn model, using FastAPI with Bodywork (an open-source MLOps tool that I have built).
- bodywork - MLOps for Python and K8S
- bodywork-ml/bodywork-core - MLOps automation for Python and Kubernetes
What are some alternatives?
tensorflow - An Open Source Machine Learning Framework for Everyone
NuPIC - Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex.
PyTorch-NLP - Basic Utilities for PyTorch Natural Language Processing (NLP)
gensim - Topic Modelling for Humans
Keras - Deep Learning for humans
Crab - Crab is a flexible, fast recommender engine for Python that integrates classic information filtering recommendation algorithms in the world of scientific Python packages (numpy, scipy, matplotlib).
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
TFLearn - Deep learning library featuring a higher-level API for TensorFlow.
MLflow - Open source platform for the machine learning lifecycle
PyBrain
gym - A toolkit for developing and comparing reinforcement learning algorithms.
neptune-contrib - This library is a location of the LegacyLogger for PyTorch Lightning.