Description
This library is a collection of helpers and extensions that make working with https://neptune.ml more effective and better. It is build on top of neptune-client and gives you option to do things like:
- interactive visualizations of experiment runs or hyperparameters
- running hyper parameter sweeps in scikit-optimize, hyperopt or any other tool you like
- monitor training of the lightGBM or fastai models with a single callback
- much more
neptune-contrib alternatives and similar packages
Based on the "Machine Learning" category.
Alternatively, view neptune-contrib alternatives based on common mentions on social networks and blogs.
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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 -
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PaddlePaddle
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Prophet
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NuPIC
DISCONTINUED. 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. -
H2O
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc. -
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Sacred
Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA. -
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Clairvoyant
Software designed to identify and monitor social/historical cues for short term stock movement -
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garak, LLM vulnerability scanner
DISCONTINUED. the LLM vulnerability scanner [Moved to: https://github.com/NVIDIA/garak] -
karateclub
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020) -
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awesome-embedding-models
A curated list of awesome embedding models tutorials, projects and communities. -
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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). -
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seqeval
A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...) -
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SciKit-Learn Laboratory
SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments. -
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Robocorp Action Server
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Feature Forge
A set of tools for creating and testing machine learning features, with a scikit-learn compatible API -
Data Flow Facilitator for Machine Learning (dffml)
DISCONTINUED. The easiest way to use Machine Learning. Mix and match underlying ML libraries and data set sources. Generate new datasets or modify existing ones with ease. -
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Judoscale - Save 47% on cloud hosting with autoscaling that just works
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
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README
neptune-contrib
Documentation
See neptune-contrib documentation site
Installation
Get prerequisites
- python versions
3.5.6/3.6are supported
Install lib
pip install neptune-contrib
Install additional packages depending on which submodule you want to use. For example:
pip install neptune-contrib[monitoring]
Installation options are: [bots], [hpo], [monitoring], [versioning], [viz] and [all].
Getting help
If you get stuck, don't worry we are here to help. The best order of communication is:
Contributing
If you see something that you don't like you are more than welcome to contribute! There are many options:
- Participate in discussions on neptune community forum or neptune community Spectrum
- Submit a feature request or a bug here, on Github
- Submit a pull request that deals with an open feature-request or bug
- Spread a word about neptune-contrib in your community