doddle-model
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doddle-model is an in-memory machine learning library that can be summed up with three main characteristics:
- it is built on top of Breeze
- it provides immutable objects that are a doddle to use in parallel code
- it exposes its functionality through a scikit-learn-like API [2]
Caveat emptor! doddle-model is in an early-stage development phase. Any kind of contributions are much appreciated.
Installation
Add the dependency to your SBT project definition:
libraryDependencies += "io.github.picnicml" %% "doddle-model" % "<latest_version>"Getting Started
For a complete list of code examples see doddle-model-examples. For an example of how to serve a trained doddle-model in a pipeline implemented with Apache Beam see doddle-beam-example.
Performance
doddle-model is developed with performance in mind, for benchmarks see the doddle-benchmark repository.
Native Linear Algebra Libraries
Breeze utilizes netlib-java for accessing hardware optimised linear algebra libraries. TL;DR seeing something like
INFO: successfully loaded /var/folders/9h/w52f2svd3jb750h890q1x4j80000gn/T/jniloader3358656786070405996netlib-native_system-osx-x86_64.jnilib
means that BLAS/LAPACK/ARPACK implementations are used. For more information see the Breeze documentation.
Memory
If you encounter java.lang.OutOfMemoryError: Java heap space increase the maximum heap size with -Xms and -Xmx JVM properties. E.g. use -Xms8192m -Xmx8192m for initial and maximum heap space of 8Gb. Note that the maximum heap limit for the 32-bit JVM is 4Gb (at least in theory) so make sure to use 64-bit JVM if more memory is needed. If the error still occurs and you are using hyperparameter search or cross validation, see the next section.
Parallelism
To limit the number of threads running at one time (and thus memory consumption) when doing cross validation and hyperparameter search, a FixedThreadPool executor is used. By default maximum number of threads is set to the number of system's cores. Set the -DmaxNumThreads JVM property to change that, e.g. to allow for 16 threads use -DmaxNumThreads=16.
Development
Run the tests with sbt test. Concerning the code style, PayPal Scala Style and Databricks Scala Guide are roughly followed. Note that a maximum line length of 120 characters is used.
Resources
- [1] Pattern Recognition and Machine Learning, Christopher Bishop
- [2] API design for machine learning software: experiences from the scikit-learn project, L. Buitinck et al.
- [3] UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science, Dua, D. and Karra Taniskidou, E.