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HpBandSter: A distributed Hyperband implementation on Steroids
HpBandSter
a distributed Hyperband implementation on Steroids
This python 3 package is a framework for distributed hyperparameter optimization. It started out as a simple implementation of Hyperband (Li et al. 2017), and contains an implementation of BOHB (Falkner et al. 2018)
How to install
We try to keep the package on PyPI up to date. So you should be able to install it via:
pip install hpbandster
If you want to develop on the code you could install it via:
python3 setup.py develop --user
Documentation
The documentation is hosted on github pages: https://automl.github.io/HpBandSter/ It contains a quickstart guide with worked out examples to get you started in different circumstances. Check it out if you are interest in applying one of the implemented optimizers to your problem.
We have also written a blogpost showcasing the results from our ICML paper.
Author: automl
Source Code: https://github.com/automl/HpBandSter
License: BSD-3-Clause License
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Sampling Distributions with Python
College Statistics with Python
Introduction
In a series of weekly articles, I will be covering some important topics of statistics with a twist.
The goal is to use Python to help us get intuition on complex concepts, empirically test theoretical proofs, or build algorithms from scratch. In this series, you will find articles covering topics such as random variables, sampling distributions, confidence intervals, significance tests, and more.
At the end of each article, you can find exercises to test your knowledge. The solutions will be shared in the article of the following week.
Articles published so far:
- Bernoulli and Binomial Random Variables with Python
- From Binomial to Geometric and Poisson Random Variables with Python
- Sampling Distributions with Python
As usual, the code is available on my GitHub.
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1641650400
HpBandSter: A distributed Hyperband implementation on Steroids
HpBandSter
a distributed Hyperband implementation on Steroids
This python 3 package is a framework for distributed hyperparameter optimization. It started out as a simple implementation of Hyperband (Li et al. 2017), and contains an implementation of BOHB (Falkner et al. 2018)
How to install
We try to keep the package on PyPI up to date. So you should be able to install it via:
pip install hpbandster
If you want to develop on the code you could install it via:
python3 setup.py develop --user
Documentation
The documentation is hosted on github pages: https://automl.github.io/HpBandSter/ It contains a quickstart guide with worked out examples to get you started in different circumstances. Check it out if you are interest in applying one of the implemented optimizers to your problem.
We have also written a blogpost showcasing the results from our ICML paper.
Author: automl
Source Code: https://github.com/automl/HpBandSter
License: BSD-3-Clause License
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Data Distribution in Apache Ignite
This blog is an abridged version of the talk that I gave at the Apache Ignite community meetup. You can download the slides that I presented at the meetup here. In the talk, I explain how data in Apache Ignite is distributed.
Why Do You Need to Distribute Anything at all?
Inevitably, the evolution of a system that requires data storage and processing reaches a threshold. Either too much data is accumulated, so the data simply does not fit into the storage device, or the load increases so rapidly that a single server cannot manage the number of queries. Both scenarios happen frequently.
Usually, in such situations, two solutions come in handy—sharding the data storage or migrating to a distributed database. The solutions have features in common. The most frequently used feature uses a set of nodes to manage data. Throughout this post, I will refer to the set of nodes as “topology.”
The problem of data distribution among the nodes of the topology can be described in regard to the set of requirements that the distribution must comply with:
- Algorithm. The algorithm allows the topology nodes and front-end applications to discover unambiguously on which node or nodes an object (or key) is located.
- Distribution uniformity. The more uniform the data distribution is among the nodes, the more uniform the workloads on the nodes is. Here, I assume that the nodes have approximately equal resources.
- Minimal disruption. If the topology is changed because of a node failure, the changes in distribution should affect only the data that is on the failed node. It should also be noted that, if a node is added to the topology, no data swap should occur among the nodes that are already present in the topology.
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