doddle-model is an in-memory machine learning library that can be summed up with three main characteristics:
Caveat emptor! doddle-model is in an early-stage development phase. Any kind of contributions are much appreciated.
You can chat with us on gitter.
Add the dependency to your SBT project definition (see the main GitHub repository for the latest release):
libraryDependencies += "io.github.picnicml" %% "doddle-model" % "<latest_version>"
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.
If you encounter
java.lang.OutOfMemoryError: Java heap space increase the maximum heap size with
-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.
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