There's waffles
http://waffles.sourceforge.net/
and vowpal wabbit
https://github.com/JohnLangford/vowpal_wabbit/wiki
In PredictionIO, a machine learning server for software developers & data engineersVowpalWabbit
vowpal_wabbit
C++2009–2017
VowpalWabbit / vowpal_wabbit
vowpal_wabbit
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.
20Distinct people
22Mentions
1Explicit recommenders
8.7KGitHub stars
What to know before exploring
What it is
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.
GitHub
Where it is used
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What people are saying
This is a interesting take and why I love machine learning and its intersection with HPC. First the seemingly blind decision to implement an SVM for improved performance. An…
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This week on Hacker News
Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 10:56:20 AM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by year22 dated mentions
Observed mentions: 2009-12-30 06:00:28 UTC to 2017-10-28 14:39:13 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2009 | 2009-01-01 00:00:00 UTC | 2010-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2010 | 2010-01-01 00:00:00 UTC | 2011-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2011 | 2011-01-01 00:00:00 UTC | 2012-01-01 00:00:00 UTC | 2 | 1 | 1 |
| 2012 | 2012-01-01 00:00:00 UTC | 2013-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2013 | 2013-01-01 00:00:00 UTC | 2014-01-01 00:00:00 UTC | 4 | 0 | 4 |
| 2014 | 2014-01-01 00:00:00 UTC | 2015-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2015 | 2015-01-01 00:00:00 UTC | 2016-01-01 00:00:00 UTC | 4 | 1 | 3 |
| 2016 | 2016-01-01 00:00:00 UTC | 2017-01-01 00:00:00 UTC | 7 | 0 | 7 |
| 2017 | 2017-01-01 00:00:00 UTC | 2018-01-01 00:00:00 UTC | 2 | 0 | 2 |
From the project README
An excerpt from the repository, fetched Nov 13, 2026.
Read the project’s introduction
This is the Vowpal Wabbit fast online learning code.
Why Vowpal Wabbit?
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning. There is a specific focus on reinforcement learning with several contextual bandit algorithms implemented and the online nature lending to the problem well. Vowpal Wabbit is a destination for implementing and maturing state of the art algorithms with performance in mind.
Input Format. The input format for the learning algorithm is substantially more flexible than might be expected. Examples can have features consisting of free form text, which is interpreted in a bag-of-words way. There can even be multiple sets of free form text in different namespaces.
Speed. The learning algorithm is fast -- similar to the few other online algorithm implementations out there. There are several optimization algorithms available with the baseline being sparse gradient descent (GD) on a loss function.
Scalability. This is not the same as fast. Instead, the important characteristic here is that the memory footprint of the program is bounded independent of data. This means the training set is not loaded into main memory before learning starts. In addition, the size of the set of features is bounded independent of the amount of training data using the hashing trick.
Feature Interaction. Subsets of features can be internally paired so that the algorithm is linear in the cross-product of the subsets. This is useful for ranking problems. The alternative of explicitly expanding the features before feeding them into the learning algorithm can be both computation and space intensive, depending on how it's handled.
Visit the wiki to learn more.
Getting Started
For the most up to date instructions for getting started on Windows, MacOS or Linux please see the wiki. This includes:
Installing with a package manager
Building
Tutorial
Examples and Demos
demo/ - Command-line demos and experiments organized by VW feature
python/docs/source/examples/ - Python Jupyter notebook examples
python/docs/source/tutorials/ - Python and CLI tutorials
Read the full README on GitHubWhat Hacker News says
4 sourcesThis is a interesting take and why I love machine learning and its intersection with HPC.
First the seemingly blind decision to implement an SVM for improved performance. An SVM isn't magical in fact an SVM and neural network are equivalent with the SVM being the general case. SVMs suffer from the the same problems as neural networks in that your # Hidden Nodes/Activation function is the same as trying to choose your kernel function. When looking at your problem you have to ask yourself.
1) Is the model time varying? -> NN
2) Very large N dimensional search space -> SVM
Secondly even without changing algorithms you can get a significant accuracy improvement by examining your features. Features are the most important part of machine learning (garbage-in/garbage-out). Even simple classifiers such as Naive Bayes can do well if given the righ
Read the rest
t feature set. There are multiple methods to examine your features such as ReliefF another is ANOVA. If you find your features are not good enough try unsupervised feature detection and learn more about the problem domain and coming up with your own features.
The final issue specifically with HPC and machine learning is even given 100 cores your algorithms may not speedup. Many machine learning algorithms are built to be iterative in nature and do not lend themselves to the becoming parallel. This means that MapReduce must be invoked at each iteration. As you scale up the number of available cores the overhead of startup and shutdown of your cluster at each iteration overrides your gain in performance, as many nodes will finish faster than others and just sit and wait.
The solution to all of this is simple
1) Get your features correct
2) Try new algorithms
- Try online learning algorithm first like vowpal wabbit
here https://github.com/JohnLangford/vowpal_wabbit/
3) HPC
- Apache Spark http://spark.incubator.apache.org/
or
GraphLab http://graphlab.org/
or (if personal computer only)
GraphChi http://graphlab.org/graphchi/
- Both support HPC with graph centric framework
- Orders of magnitude faster than Hadoop
- Both built on top of Hadoop HDFS so connect and go
Hope this helps Everyone out there.
Have fun and try to solve some cool problems.
Anyone interested in the article should give VW a try. You can get good baseline performance in a fraction of time compared to other traditional approaches like liblinear et. al.
https://github.com/JohnLangford/vowpal_wabbit/wiki
In Abusing hash kernels for wildly unprincipled machine learningA few other "command-line" tools for machine learning include:
http://scikit-learn.org
http://www.r-project.org
https://github.com/JohnLangford/vowpal_wabbit/wiki
These all happen to be open source, but are probably not as pretty-looking as bigml's trees.
In A command-line tool for Machine Learning