Analytics + AI Platform for Apache Spark and BigDL.

Analytics Zoo makes it easy to build deep learning application on Spark and BigDL, by providing an end-to-end Analytics + AI Platform (including high level pipeline APIs, built-in deep learning models, reference use cases, etc.).

• High level pipeline APIs

  • nnframes: native deep learning support in Spark DataFrames and ML Pipelines
  • autograd: build custom layer/loss using auto differentiation operations
  • Transfer learning: customize pretained model for feature extraction or fine-tuning

• Built-in deep learning models

  • Object detection API: high-level API and pretrained models (e.g., SSD and Faster-RCNN) for object detection
  • Image classification API: high-level API and pretrained models (e.g., VGG, Inception, ResNet, MobileNet, etc.) for image classification
  • Text classification API: high-level API and pre-defined models (using CNN, LSTM, etc.) for text classification
  • Recommedation API: high-level API and pre-defined models (e.g., Neural Collaborative Filtering, Wide and Deep Learning, etc.) for recommendation

• Reference use cases: a collection of end-to-end reference use cases (e.g., anomaly detection, sentiment analysis, fraud detection, image augmentation, object detection, variational autoencoder, etc.)

• To get started, please refer to the Python install guide or Scala install guide.

• For more information, You may refer to the Analytis Zoo document website

• For additional questions and discussions, you can join the Google User Group (or subscribe to the Mail List)

Analytics Zoo provides a set of easy-to-use, high level pipeline APIs that natively support Spark DataFrames and ML Pipelines, autograd and custom layer/loss, trasnfer learning, etc.

nnframes provides native deep learning support in Spark DataFrames and ML Pipelines, so that you can easily build complex deep learning pipelines in just a few lines, as illustracted below. (See more details here)

1. Initialize NNContext and load images into DataFrames using NNImageReader

   from zoo.common.nncontext import *
   from zoo.pipeline.nnframes import *
   sc = init_nncontext()
   imageDF = NNImageReader.readImages(image_path, sc)
   

2. Process loaded data using DataFrames transformations

   getName = udf(lambda row: ...)
   getLabel = udf(lambda name: ...)
   df = imageDF.withColumn("name", getName(col("image"))).withColumn("label", getLabel(col('name')))
   

3. Processing image using built-in feature engineering operations

   from zoo.feature.image import *
   transformer = RowToImageFeature() -> ImageResize(64, 64) -> ImageChannelNormalize(123.0, 117.0, 104.0) \
                 -> ImageMatToTensor() -> ImageFeatureToTensor())
   

4. Define model using Keras-style APIs

   from zoo.pipeline.api.keras.layers import *
   from zoo.pipeline.api.keras.models import *
   model = Sequential().add(Convolution2D(32, 3, 3, activation='relu', input_shape=(1, 28, 28))) \
                   .add(MaxPooling2D(pool_size=(2, 2))).add(Flatten()).add(Dense(10, activation='softmax')))
   

5. Train model using Spark ML Pipelines

   classifier = NNClassifier(model, CrossEntropyCriterion(),transformer).setLearningRate(0.003) \
                   .setBatchSize(40).setMaxEpoch(1).setFeaturesCol("image").setCachingSample(False)
   nnModel = classifier.fit(df)
   

autograd provides automatic differentiation for math operations, so that you can easily build your own custom loss and layer (in both Python and Scala), as illustracted below. (See more details here)

1. Define custom functions using autograd

   from zoo.pipeline.api.autograd import *
   
   def mean_absolute_error(y_true, y_pred):
       return mean(abs(y_true - y_pred), axis=1)
   
   def add_one_func(x):
       return x + 1.0
   

2. Define model using Keras-style API and custom Lambda layer

   from zoo.pipeline.api.keras.layers import *
   from zoo.pipeline.api.keras.models import *
   model = Sequential().add(Dense(1, input_shape=(2,))) \
                       .add(Lambda(function=add_one_func))
   

3. Train model with custom loss function

   model.compile(optimizer = SGD(), loss = mean_absolute_error)
   model.fit(x = ..., y = ...)
   

Using the high level transfer learning APIs, you can easily customize pretrained models for feature extraction or fine-tuning. (See more details here)

1. Load an existing model (pretrained in Caffe)

   from zoo.pipeline.api.net import *
   full_model = Net.load_caffe(def_path, model_path)
   

2. Remove last few layers

   # create a new model by remove layers after pool5/drop_7x7_s1
   model = full_model.new_graph(["pool5/drop_7x7_s1"])
   

3. Freeze first few layers

   # freeze layers from input to pool4/3x3_s2 inclusive
   model.freeze_up_to(["pool4/3x3_s2"])
   

4. Add a few new layers

   from zoo.pipeline.api.keras.layers import *
   from zoo.pipeline.api.keras.models import *
   inputs = Input(name="input", shape=(3, 224, 224))
   inception = model.to_keras()(inputs)
   flatten = Flatten()(inception)
   logits = Dense(2)(flatten)
   newModel = Model(inputs, logits)
   

Analytics Zoo provides several built-in deep learning models that you can use for a variety of problem types, such as object detection, image classification, text classification, recommendation, etc.

Using Analytics Zoo Object Detection API (including a set of pretrained detection models such as SSD and Faster-RCNN), you can easily build your object detection applications (e.g., localizing and identifying multiple objects in images and videos), as illustrated below. (See more details here)

1. Download object detection models in Analytics Zoo

   You can download a collection of detection models (pretrained on the PSCAL VOC dataset and COCO dataset) from detection model zoo.

2. Use Object Detection API for off-the-shell inference

   from zoo.models.image.objectdetection import *
   model = ObjectDetector.load_model(model_path)
   image_set = ImageSet.read(img_path, sc)
   output = model.predict_image_set(image_set)
   

Using Analytics Zoo Image Classification API (including a set of pretrained detection models such as VGG, Inception, ResNet, MobileNet, etc.), you can easily build your image classification applications, as illustrated below. (See more details here)

1. Download image classification models in Analytics Zoo

   You can download a collection of image classification models (pretrained on the ImageNet dataset) from image classification model zoo.

2. Use Image classification API for off-the-shell inference

   from zoo.models.image.imageclassification import *
   model = ImageClassifier.load_model(model_path)
   image_set = ImageSet.read(img_path, sc)
   output = model.predict_image_set(image_set)
   

Analytics Zoo Text Classification API provides a set of pre-defined models (using CNN, LSTM, etc.) for text classifications. (See more details here)

Analytics Zoo Recommendation API provides a set of pre-defined models (such as Neural Collaborative Filtering, Wide and Deep Learning, etc.) for recommendations. (See more details here)

Analytics Zoo provides a collection of end-to-end reference use cases, including anomaly detection (for time series data), sentiment analysis, fraud detection, image augmentation, object detection, variational autoencoder, etc. (See more details here)