Distributed Deep Learning on Hadoop Clusters
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This document discusses distributed deep learning on Hadoop clusters using CaffeOnSpark. CaffeOnSpark is an open source project that allows deep learning models defined in Caffe to be trained and run on large datasets distributed across a Spark cluster. It provides a scalable architecture that can reduce training time by up to 19x compared to single node training. CaffeOnSpark provides APIs in Scala and Python and can be easily deployed on both public and private clouds. It has been used in production at Yahoo since 2015 to power applications like Flickr and Yahoo Weather.
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Distributed Deep Learning on Hadoop Clusters
- 1. Distributed Deep Learning on Hadoop Clusters Andy Feng & Jun Shi Yahoo! Inc.
- 2. Our Talks @ Hadoop Summit 2 Storm on YARN (2013) › http://bit.ly/1W02tZy Spark on YARN (2014) › http://bit.ly/1W03dxE Machine Learning on Hadoop/Spark (2015) › http://bit.ly/1NW3GvO
- 3. Agenda • Why Deep Learning on Hadoop? • CaffeOnSpark – Architecture – API: Scala + Python • Demo – CaffeOnSpark + Python Notebook
- 5. Use Case: Flickr Magic View flickr.com/cameraroll
- 6. Yahoo Use Case: Yahoo Weather 6 Beauty › Computational assessed Relevant › Location › Time › Cloudy › Shower › … Weather App Yahoo Weather App
- 7. Yahoo Vision Kit: Demo 7
- 8. (4) Apply ML Model @ Scale Flickr DL/ML Pipeline (3) Non-deep Learning @ Scale * http://bit.ly/1KIDfof by Pierre Garrigues, Deep Learning Summit 2015 (2) Deep Learning @ Scale (1) Prepare Datasets @ Scale * 10 billion photos * 7.5 million per day
- 9. Deep Learning vs. Hadoop 9
- 10. 10 Machine Learning & Deep Learning on Hadoop
- 11. 11 Hadoop Cluster Enhanced GPU servers added › 4 Tesla K80 cards • 2 GK210 GPUs, 24GB memory Network interface enhanced › InfiniBand for direct access to GPU memory › Ethernet for external communication
- 12. Deep Learning Frameworks Caffe › Available since Sept, 2013, 6.3k forks › Popular in vision community & Yahoo TensorFlow › Released in Nov. 2015, 9.8k forks Theano, Torch, DL4J, etc.
- 13. Released in Feb. 2016 • Apache 2.0 license • Distributed deep learning – GPU or CPU – Ethernet or InfiniBand • Easily deployed on public cloud or private cloud 13 CaffeOnSpark Open Sourced github.com/yahoo/CaffeOnSpark
- 15. CaffeOnSpark: 19x Speedup (est.) Training latency (hours) Top-5ValidationError
- 16. CaffeOnSpark: Deployment Options 16 • Single node – Spark-submit –master local • Multiple nodes – Spark-submit –master URL –connection ethernet – Ex. EC2 – Spark-submit –master URL –connection infiniband – Ex., Yahoo Hadoop cluster
- 17. Spark CLI • spark-submit --num-executors #_Processes --class com.yahoo.ml.CaffeOnSpark caffe-on-spark.jar -devices #_gpus_per_proc -conf solver_config_file -model model_file -train | -test | -feature Caffe Configuration layer { name: "data" type: "MemoryData" source_class=“com.yahoo.ml.caffe.LMDB” memory_data_param { source: ”hdfs:///mnist/trainingdata/" batch_size: 64; channels: 1; height: 28; width: 28; } … } 17 CaffeOnSpark: DL Made Easy
- 18. CaffeOnSpark: One Program (Scala) http://bit.ly/21ZY1c2 18 cos = new CaffeOnSpark(ctx) conf = new Config(ctx, args).init() // (1) training DL model dl_train_source = DataSource.getSource(conf, true) cos.train(dl_train_source) // (2) extract features via DL lr_raw_source = DataSource.getSource(conf, false) ext_df = cos.features(lr_raw_source) // (3) apply ML lr_input=ext_df.withColumn(“L", cos.floats2doubleUDF(ext_df(conf.label))) .withColumn(“F", cos.floats2doublesUDF(ext_df(conf.features(0)))) lr = new LogisticRegression().setLabelCol(”L").setFeaturesCol(”F") lr_model = lr.fit(lr_input_df) Non-deep Learning DeepLearning
- 19. CaffeOnSpark: One Notebook (Python) http://bit.ly/1REZ0cN 19
- 20. 20 CaffeOnSpark: UI & Logs
- 21. Demo: CaffeOnSpark on EC2 https://github.com/yahoo/CaffeOnSpark/wiki › Get started on EC2 › Python for CaffeOnSpark
- 22. CaffeOnSpark: What’s Next? Validation within training Enhanced data layer RNN and LSTM Java API Asynchronous distributed training
- 23. Related Work: SparkNet & DL4J 1) [driver] sc.broadcast(model) to executors 2) [executor] apply DL training against a mini-batch of dataset to update models locally 3) [driver] aggregate(models) to produce a new model REPEAT Driver
- 24. Summary 24 Yahoo Hadoop clusters enhanced for deep learning › GPU nodes + CPU nodes › Infiniband network for fast communication CaffeOnSpark open sourced › Empower Flickr and other Yahoo services • In production since Q3 2015 • Reduced training latency, and improved accuracy › Scalable deep learning made easy • spark-submit on your Spark cluster
- 25. 25 Thank You! Repo: github.com/yahoo/CaffeOnSpark Email: caffeonspark-users@googlegroups.com
Notes de l'éditeur
- In 2013, I talked about Yahoo’s adoption of Storm for low-latency processing. Last year, I described Yahoo’s effort to bring Spark onto YARN cluster. Today, we should cover our progress on machine learning using YARN clusters. I will cover 3 areas: WHY does Yahoo apply machine learning WHAT challenges we try to address HOW we address them I will wrap the talk with key lessons learned from our experience.
- At least year’s Hadoop Summit, we discussed how Hadoop clusters have become the preferred platform for large-scale machine learning at Yahoo. Recently, we introduced distributed deep learning as a new capability of Hadoop clusters. These new clusters augment our existing CPU nodes and Ethernet connectivity with GPU nodes and Infiniband connectivity. We developed a distributed deep learning solution, CaffeOnSpark, based on Apache Spark and Caffe from UC Berkeley. CaffeOnSpark enables deep learning tasks to be launched via spark-submit command, as in any Spark application. Given a partition of HDFS-based training data, each Spark executors launches Caffe-based training threads to train deep neural network models. After back-propagation processing of a batch of training examples, CaffeOnSpark training threads exchange the gradients of model parameters across all GPUs on multiple servers. In this talk, we will provide a technical overview of CaffeOnSpark, and explain how CaffeOnSpark conducts deep learning in a private cloud or public cloud (such as AWS EC2). We will share our experience at Yahoo through use cases (including photo auto tagging), and discuss the areas of collaboration with open source communities for Hadoop-based deep learning.
- Deep learning is a branch of a branch of artificial intelligence. It attempts to model high-level abstractions in data by using multiple processing layers. A deep neural network has multiple hidden layers of units between the input and output layers. * ImageNet competition 2014 … GoogleNet w/ 22 layers. * ILSVRC competition 2015 … Microsoft w/ 152 layers. Many of these deep networks have millions or even billions of parameters. To learn these parameters from data, we go through many iterations of forward prediction and back propagation over these networks.
- We released the magic view as part of the Flickr 4.0 release last April, and this is the most visible user-facing feature that exposes our image recognition capabilities. Our users can switch from the traditional timeline view of their photo to an experience where their photos are arranged according to 70 categories. For example, you can see here that landscape photos are sub-categorized into different types such as mountain, rock, or shore. This is a great feature for serendipitous photo discovery. Most of us have thousands of photos that we don’t get to see very often but are emotionally very attached to, and these types of groupings help us re-discover photos.
- To enable approximate computing, we are build machine learning on top of Hadoop, Spark and our machine learning servers. These servers are a YARN application, specfically design for machine learning. All data are stored in memory with customized stores. These stores enables lockless concurrency, and could handle millions operations per second. Our servers were implemented in Java, but creates zero garbage. This enables us to run training consistently with high throughput, without worry about garbage collection. Our API supports asynchronous machine learning and mini-batch. This ensures very fast training by many learners. To minimize data movement, we enable clients to move computing logic to servers. For example, we enable MapReduce operations on servers. As an example, you may want to perform statistic analysis of large models using MapReduce operations. Our servers provides built-in support of Hadoop file systems. You could store your models after each training, and load previoud trained models from HDFS.
- (A) yinst i ycaffe -r ${YROOT} –nosudo (B) spark-submit --queue ${QUEUE} … -train\ -conf ${SOLVER_CONFIG_FILE} \ -input ${TRAIN_DATA_ON_HDFS} \ -model ${MODEL_ON_HDFS} (C) spark-submit --queue ${QUEUE} … -test\ -conf ${SOLVER_CONFIG}, ${EXTRA_CONFIG_FILES} \ -input ${TEST_DATA_ON_HDFS} \ -model ${MODEL_ON_HDFS}
- In summary, Yahoo has made significant progress on scalable machine learning. We conduct daily training w/ billions of signals for our critical business such as search and advertisement. Hadoop and YARN are playing a central role for this evolution. In YARN cluster, we built a framework for approximate computing. We are currently exploring both GPU and CPU in a single cluster.