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Paddle_Spark_Summit
- 1. Scalable Deep Learningin Baidu Weide Zhang, Kyle Tsai, Jiang Wang Baidu USDC
- 2. Background • Spark – Batch/Streamingprocessing, Spark SQL, MLLib • Deep learning has many use cases in Baidu and showed significant improvement in quality – Image retrieval ranking – Ads CTR prediction – Machine translation – Speech recognition • Goal: able to use distributed deep learning in Spark
- 3. Typical Training Datain Baidu • Image Recognition: 100 millions • OCR: 100 millions • Speech: 10 billions • CTR: 100 billions • Grows every year since 2012
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- 5. Paddle • Parallel AsynchronousDistributed Deep Learning Library – Support distributed parameter servers to do synchronous/asynchronous parameter update – Support Multi GPU / CPU training – Support sparse model – Easy to understand API for user to add new layers – Support rich features for deep learning use cases, especially for NLP
- 6. Deep learning optionscomparison Caffe Tensor Flow Torch Paddle Distributed Training Yes Yes No Yes Communication Cost Medium High N/A Medium to Low Easy to customize and coding Yes More Learning Curve More Learning Curve Yes Sparse Model Support No Yes Yes Yes Area of Focus Vision All All All Integration with Spark Yes No No Yes
- 7. High Level Goals •Implement Spark ML abstractions to let user train deep learning models with minimal code change • Leverage paddle’s native training and parameter server mechanisms to be scheduled in spark deep learning jobs • Handle multi-tenancy and heterogeneity • Parallelize hyper parameter selection • Batch and Streaming learning
- 8. Paddle on Spark •Training Communication •DeepLearning Algorithm •Resource Management • Training Environment Spark Yarn Parameter Server Paddle
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- 11. Spark ML’s Abstraction •Train • Predict
- 12. Simple Parameter IsNot Enough Image Label Bird Cat Convolution Pooling Full Connection Cost Parameter For CNN
- 13. Use Paddle AsEstimator
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- 15. Example of caffeprototxt
- 16. Design decisions • SparkML Compatible API – Compatible with Spark is more important than implemented under Spark • Code level configuration – Easy and flexible – Manual is prone to error
- 17. PADDLE Scalable DeepLearning Platform at Baidu
- 18. Sharded Parameter Server •One parameter an one trainer co-locate in a machine. • Parameters are shared, but not replicated. • All-to-all communication. • Our environments • 4 GPUs per machine. • 4-10 machines. • all machines in one switch • reliable data center.
- 19. GPU Ring Synchronization •Each parameter only needs to go through slow connection two times. • One for reduce. • Another for scatter.
- 20. ImageNet Scale Experiments 0 10 20 30 40 12 3 4 5 Time(s) Number of machines Time per 100 batches TCP RDMA • AlexNet on ImageNet • batch size = 64 • 1 Machine has 4 K10 GPUs.
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- 22. Sparse Training Experiment 0 75 150 225 12 4 8 16 Time(s) Number of nodes Time per 100 batches Non Sparse Sparse • 1451594 dimensional sparse feature. • Embedded to 128d, 96d, 96d, and 128d. • Using a ranking cost on the top. • batch size = 128.
- 23. Flexible and EfficientRNN Implementation
- 24. RNN Performance Comparisonwith TensorFlow 0 125 250 375 500 625 200 650 1500 Time(ms) RNN Hidden Size Time per Batch PADDLE TensorFlow • Machine Translation • batch size = 64 • embedding size = hidden_size • dictionary size = 10000
- 25. Distributed Training PerformanceCharacter Neural Machine Translation • 8 Machines, each with 4 K40 GPUs • number of RNN encoder layers: 9, number of RNN decoder layers: 7 • Word embedding size: 256, RNN size: 512 • batch size: 25000 character • Speed: •attention: 25 minutes / 100 batches . •encoder-decoder: 9 minutes / 100 batches.
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Editor's Notes
- #3 Pros Distributed Comprehensive algorithm support Fast Strong NLP support Cons Difficult to use Training resource management Wasteful data copying Reliability Cons 还包含manually model serving
- #9 Spark take care of everthing outside of training algorithm , includes data preparation(maybe also preprocess by spark, model serving in the same app) Yarn take care of RM, to support hyper parameter training and multi tennancy. Yarn enable job level heterogeneous computing and provide better resource utilization.
- #10 We wrap raw training data inside DF. The source could be a DB table or a HDFS folder. Partition based on the size of paddle trainer and push training input data through RPC interface into paddle trainer.
- #11 Our work got some inspiration from caffe on spark, but we took a different design choice. Leave DP as a black box to keep it’s training performance and use spark/yarn to make engineers’ life easier. Keep deep learning training scheduling intact inside paddle because scheduling during training is a heavily tuned result involved deep learning background(trainer speed equal) … Here shows an ideal case of a training which colocate trainer and executor in a 1 to 1 ratio. We can have a spark executor running on a cpu machine and push training input to a gpu machine for trainer with Yarn RM.
- #12 API design starts here: Encapsulation paddle inside spark ML API, accomplish Deep learning support in spark ML. Our work demonstrate how DP platform could be fit into spark ML seamlessly.
- #13 Current Spark ML parameter abstraction is not enough for deep learning, so we extend it with a neural network parameter to support DP.
- #17 Hyper parameter is easier to achieve here through code level configuration. You can code your own rule of tuning process and have spark take care of the rest. Code level config can do sanity check like spelling, correct unreasonable configs.
- #27 训练过程动态增减trainer 数量 资源抢占 资源混布 cpu gpu 混合使用 Serving model service could be deployed through yarn after training automatically.