Analytics Zoo： 统一的大数据分析 + AI 平台

1 . - ++ Analytics Zoo: A Unified Data Analytics + AI Platform 

2 .Why Analytics-Zoo 

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4 .Real-World ML/DL Applications Are Complex Data Analytics Pipelines “Hidden Technical Debt in Machine Learning Systems”, Sculley et al., Google, NIPS 2015 Paper 

5 .Unified Big Data Analytics Platform 

6 .Chasm b/w Deep Learning and Big Data Communities The Chasm Deep learning experts Real-world users (big data users, data scientists, analysts, etc.) 

7 . Large-Scale Image Recognition https://software.intel.com/en-us/articles/building-large-scale-image-feature-extraction- with-bigdl-at-jdcom 

8 .Standard Spark jobs • No changes to the Spark or Hadoop clusters needed Data parallel • Each Spark task runs the same model on a subset of the data (batch) “Zero” code change • Directly support TensorFlow, Keras and Caffe Model Seamlessly deployed on production big data clusters • Only need to install on driver node. 

9 .What’s Analytics-Zoo 

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11 .Analytics-Zoo: Unified Analytics + AI Platform for BigData 

12 .Analytics-Zoo: Run as Standard Spark Programs 

13 . Training Set Partition 1 Worker Training samples Sample cached in worker memory 2 1 Partition 2 3 Worker Driver Sample 1 Broadcast W (>800MB) Each task computes to each worker in each 2 4 G (>800MB) in each iteration iteration 3 … 1 Partition n Worker Each task sends G Sample (>800MB) for aggregation in each iteration 3 2 13 

14 .Distributed Training in Analytics-Zoo Peer-2-Peer All-Reduce synchronization 

15 . Distributed TF & Keras on Spark Write TensorFlow code inline in PySpark program •Data wrangling and #pyspark code train_rdd = spark.hadoopFile(…).map(…) analysis using PySpark dataset = TFDataset.from_rdd(train_rdd,…) #tensorflow code •Deep learning model import tensorflow as tf development using slim = tf.contrib.slim images, labels = dataset.tensors TensorFlow or Keras with slim.arg_scope(lenet.lenet_arg_scope()): logits, end_points = lenet.lenet(images, …) loss = tf.reduce_mean( \ tf.losses.sparse_softmax_cross_entropy( \ logits=logits, labels=labels)) •Distributed training / #distributed training on Spark inference on Spark optimizer = TFOptimizer.from_loss(loss, Adam(…)) \ optimizer.optimize(end_trigger=MaxEpoch(5)) 

16 . Spark Dataframe & ML Pipeline for DL #Spark dataframe transformations parquetfile = spark.read.parquet(…) train_df = parquetfile.withColumn(…) #Keras API model = Sequential() .add(Convolution2D(32, 3, 3, activation='relu', input_shape=…)) \ .add(MaxPooling2D(pool_size=(2, 2))) \ .add(Flatten()).add(Dense(10, activation='softmax'))) #Spark ML pipeline Estimater = NNEstimater(model, CrossEntropyCriterion()) \ .setLearningRate(0.003).setBatchSize(40).setMaxEpoch(5) \ .setFeaturesCol("image") nnModel = estimater.fit(train_df) 

17 . Distributed Model Serving Analytics Zoo Model Bolt Kafka Flume Bolt HDFS/S3 Spout Kinesis Twitter Bolt Analytics Zoo Spout Model Bolt Bolt Distributed model serving in Web Service, Flink, Kafka, Storm, etc. • Plain Java or Python API, with OpenVINO and DL Boost (VNNI) support 

18 .Analytics-Zoo use cases 

19 .Computer vision Based Product Defect Detection in Midea https://software.intel.com/en-us/articles/industrial-inspection-platform-in-midea-and-kuka-using-distributed-tensorflow-on-analytics 

20 . Recommender AI Service in MasterCard https://software.intel.com/en-us/articles/deep-learning-with-analytic-zoo-optimizes-mastercard-recommender-ai-service 

21 .Deep Learning Made Easy for BigData 

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23 .Code Up 

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