DataFrame and ML Pipeline
NNFrames is a package in Analytics Zoo aiming to provide DataFrame-based high level API to facilitate Spark users and speed-up development. It supports native integration with Spark ML Pipeline, which allows user to combine the power of Analytics Zoo, BigDL and Apache Spark MLlib. NNFrames provides both Python and Scala interfaces, and is compatible with both Spark 1.6 and Spark 2.x.
Easy-to-use DataFrame(DataSet)-based API for training, prediction and evaluation with deep learning models.
Effortless integration with Spark ML pipeline and compatibility with other feature transformers and algorithms in Spark ML.
In a few lines, run large scale inference or transfer learning from pre-trained models of Caffe, Keras, Tensorflow or BigDL.
Training of customized model or BigDL built-in neural models (e.g. Inception, ResNet, Wide And Deep).
Rich toolset for feature extraction and processing, including image, audio and texts.
The examples are included in the Analytics Zoo source code.
- image classification: model inference using pre-trained Inception v1 model. Scala version Python version
- image classification: transfer learning from pre-trained Inception v1 model. Scala version Python version
NNEstimator and NNModel
Analytics Zoo provides
NNEstimator for model training with Spark DataFrame, which
provides high level API for training a BigDL Model with the Apache Spark
pattern, thus users can conveniently fit Analytics Zoo into a ML pipeline. The fit result of
NNEstimator is a NNModel, which is a Spark ML Transformer.
please check our NNEstimator API for detailed usage.
NNClassifier and NNClassifierModel
NNModel and focus on
classification tasks, where both label column and prediction column are of Double type.
NNImageReader NNImageReader loads image into Spark DataFrame.
please check our ImageProcessing for detailed usage.