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Spark summit 2019 infrastructure for deep learning in apache spark 0425
The document discusses Microsoft's infrastructure for deep learning using Apache Spark, covering topics such as data preparation, model training, and the challenges of distributed deep learning. It highlights performance improvements in model training times with advanced GPU setups and outlines design considerations for machine learning workflows at scale. The presentation also addresses job execution, resource management, and collaboration among data scientists and engineers in the context of machine learning pipelines.
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Spark summit 2019 infrastructure for deep learning in apache spark 0425
- 2. Kaarthik Sivashanmugam, WeeHyong Tok Microsoft Infrastructure for Deep Learning in Apache Spark #UnifiedAnalytics #SparkAISummit
- 3. Agenda • Evolution ofdata infrastructure • ML workflow: Data prep & DNN training • Intro to deep learning and computing needs • Distributed deep learning and challenges • Unified platform using Spark – Infra considerations, challenges • ML Pipelines 3#UnifiedAnalytics #SparkAISummit
- 4. Video Feeds Call Logs Data Web logs Products Images …… Organization’sData Database / Data Warehouse Organization’s data
- 5. Machine Learning Typical E2EProcess … Prepare Experiment Deploy Orchestrate
- 6. + Machine Learningand Deep Learning workloads 6#UnifiedAnalytics #SparkAISummit
- 7. How long doesit take to train Resnet-50 on ImageNet? 7#UnifiedAnalytics #SparkAISummit 14 daysBefore 2017 NVIDIA M40 GPU
- 8. Training Resnet-50 onImagenet 8#UnifiedAnalytics #SparkAISummit 1 hour 31 mins 15 mins Apr Sept Nov Tesla P100 x 256 1,600 CPUs Tesla P100 x 1,024 Facebook Caffe2 UC Berkeley, TACC, UC Davis Tensorflow Preferred Network ChainerMN 2017 6.6 mins Tesla P40 x 2,048 Tencent TensorFlow July Nov 2.0 mins Sony Neural Network Library (NNL) Tesla V100 x 3,456 2018 2019 Fujitsu MXNet 1.2 mins Tesla V100 x 2,048 Apr
- 9. Considerations for DeepLearning @ Scale • CPU vs. GPU • Single vs. multi-GPU • MPI vs. non-MPI • Infiniband vs. Ethernet 9#UnifiedAnalytics #SparkAISummit Credits: Mathew Salvaris https://azure.microsoft.com/en-us/blog/gpus-vs-cpus-for-deployment-of-deep-learning-models/
- 10. “Things” you needto deal with when training machine learning/deep learning models Gather results Secure Access Scale resources Schedule jobs Dependencies and Containers Provision VM clusters Distribute data Handling failures
- 11. Machine Learning Typical E2EProcess … Prepare Experiment Deploy Orchestrate
- 12. Machine Learning andDeep Learning 12#UnifiedAnalytics #SparkAISummit Top figure source; Bottom figure from NVIDIA ML DL
- 13. Lots of ML Frameworks…. 13#UnifiedAnalytics #SparkAISummit TensorFlow PyTorch Scikit-Learn MXNet Chainer Keras
- 14. Design Choices forBig Data and Machine Learning/Deep Learning 14#UnifiedAnalytics #SparkAISummit Laptop Spark + Separate infrastructure for ML/DL training/inference Cloud Spark
- 15. Execution Models forSpark and Deep Learning 15#UnifiedAnalytics #SparkAISummit Task 1 • Independent Tasks • Embarrassingly Parallel and Massively Scalable Task 2 Task 3 Spark Data Parallelism Model Parallelism • Non-Independent Tasks • Some parallel processing • Optimizing communication between nodes Distributed Learning Credits – Reynold Xin, Project Hydrogen – State of Art Deep Learning on Apache Spark
- 16. Execution Models forSpark and Deep Learning 16#UnifiedAnalytics #SparkAISummit Task 1 • Independent Tasks • Embarrassingly Parallel and Massively Scalable Task 2 Task 3 Spark • Non-Independent Tasks • Some parallel processing • Optimizing communication between nodes Distributed Learning Task 3 Task 2 Task 1 Credits – Reynold Xin, Project Hydrogen – State of Art Deep Learning on Apache Spark
- 17. Execution Models forSpark and Deep Learning 17#UnifiedAnalytics #SparkAISummit Task 1 • Independent Tasks • Embarrassingly Parallel and Massively Scalable • Re-run crashed task Task 2 Task 3 Spark • Non-Independent Tasks • Some parallel processing • Optimizing communication between nodes • Re-run all tasks Distributed Learning Task 3 Task 2 Task 1 Credits – Reynold Xin, Project Hydrogen – State of Art Deep Learning on Apache Spark
- 18. Spark + ML/DL 18#UnifiedAnalytics#SparkAISummit www.aka.ms/spark Sparkflow TensorFlowOnSpark Project Hydrogen HorovodRunner
- 19. 19#UnifiedAnalytics #SparkAISummit Microsoft MachineLearning for Apache Spark v0.16 Microsoft’s Open Source Contributions to Apache Spark www.aka.ms/spark Azure/mmlspark Cognitive Services Spark Serving Model Interpretability LightGBM Gradient Boosting Deep Networks with CNTK HTTP on Spark
- 20. Demo - AzureDatabricks and Deep Learning 20#UnifiedAnalytics #SparkAISummit
- 21. Demo – DistributedDeep Learning using Tensorflow with HorovodRunner 21#UnifiedAnalytics #SparkAISummit
- 22. What do you needfor training / distributed training? CPU GPU Network Storage Deep Learning Framework Memory Physics of Machine Learning and Deep Learning
- 23. GPU Device Interconnect •NVLink • GPUDirect P2P • GPUDirect RDMA Interconnect topology sample Credits:CUDA-MPI Blog (https://bit.ly/2KnmN58)
- 24. From CUDA toNCCL1 to NCCL2 Multi-Core CPU GPU Multi-GPU Multi-GPU Multi-Node NCCL 2NCCL 1CUDA Multi-GPU Communication Library Credits: NCCL Tutorial (https://bit.ly/2KpPP44)
- 25. NCCL 2.x (multi-node) Credits:NCCL Tutorial (https://bit.ly/2KpPP44)
- 26. NCCL 2.x (multi- node) Credits: NCCLTutorial (https://bit.ly/2KpPP44)
- 27. Spark & GPU •Using GPU with Spark options: 1. Native support (cluster manager, GPU tasks): SPARK- 24615 2. Use cores/memory as proxy for GPU resources and allow GPU-enabled code execution 3. Code implementation/generation for GPU offload • Considerations – Flexibility – Data management – Multi-GPU execution 27#UnifiedAnalytics #SparkAISummit
- 28. Infrastructure Considerations • Dataformat, storage and reuse – Co-locate Data Engineering storage infrastructure (cluster-local) – DL Framework support for HDFS (reading from HDFS does not mean data-locality-aware computation) – Sharing data between Spark and Deep Learning (HDFS, Spark-TF connector, Parquet/Petastorm) • Job execution – Gang scheduling – Refer to SPARK-24374 – Support for GPU (and other accelerators) – Refer to SPARK-24615 – Cluster sharing with other types of jobs (CPU-only cluster vs. CPU+GPU cluster) – Quota management – Support for Docker containers – MPI vs. non-MPI – Difference GPU generations • Node, GPU connectivity – Infiniband, RDMA – GPU Interconnect options – Interconnect-aware scheduling, minimize distribution, repacking
- 29. ML Pipelines • Usingmachine learning pipelines, data scientists, data engineers, and IT professionals can collaborate on different steps/phases • Enable use of best tech for different phases in ML/DL workflow 29#UnifiedAnalytics #SparkAISummit
- 30. Demo – AzureML Pipelines & Databricks 30#UnifiedAnalytics #SparkAISummit
- 31. What do you needfor training / distributed training? CPU GPU Network Storage Deep Learning Framework Memory Physics of Machine Learning and Deep Learning
- 32. Kaarthik Sivashanmugam, WeeHyong Tok Microsoft Infrastructure for Deep Learning in Apache Spark #UnifiedAnalytics #SparkAISummit
- 33. DON’T FORGET TORATE AND REVIEW THE SESSIONS SEARCH SPARK + AI SUMMIT