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[Hadoop Meetup] Tensorflow on Apache Hadoop YARN - Sunil Govindan
- 1. Running Tensorflow on Apache YARN – A sneak peak into GPU Scheduling Sunil Govindan Apache Hadoop PMC member YARN Team @ Hortonworks
- 2. 2 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Agenda Overview to Machine Learning on Big Data Platform GPU support in Apache Hadoop YARN Tensorflow on YARN – example and demo
- 3. 3 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Overview: Machine Learning on Big Data Platform
- 4. 4 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Machine learning workflow Feature Selection Data Feature Transform Feature Encoding Feature Evaluation Model Training Feature Model Evaluation Model Validation Model Staging Experiment Online Feature Model Database Exper- iment Model as Service Real-time Feature Calibration Data Preprocessing Feature Engineering Model Training Online Service
- 5. 5 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Machine learning (BigData) – Data Preprocessing Feature Selection Data Feature Transform Feature Encoding Feature Evaluation Feature Engineering Import data – HDFS – AWS – RDBMS Join data Data exploration Data sample Training/Test random split
- 6. 6 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Machine learning (BigData) – Feature Engineering Feature Selection Data Feature Transform Feature Encoding Feature Evaluation Feature Engineering Feature transform/selection Feature embedding
- 7. 7 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Machine learning (BigData) – Model Training Model Training Feature Model Evaluation Model Validation Model Staging Model Training Traditional machine learning models – Logistic Regression – Gradient boosting tree – Recommendation/ALS – LDA Libraries – Apache Spark MLlib – XGBoost Deep learning models – DNN – CNN – RNN – LSTM Libraries – TensorFlow – Apache MXNet – BigDL
- 8. 8 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Machine learning (BigData) – Model Serving Experiment Online Feature Model Database Exper- iment Model as Service Real-time Feature Calibration Online Service Model deploy Model serving – Batch – Streaming Experiment – offline – online (A/B test)
- 9. 9 © Hortonworks Inc. 2011 – 2016. All Rights Reserved GPU support in Apache Hadoop YARN
- 10. 10 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Machine learning platform on YARN CPU GPU SSD YARN: Data Operating System (Cluster Resource Management) Spark MLlib XGBoost Hive/LLAP Spark SQLTensorFlow Zeppelin HDFS AWS S3 RDBMS
- 11. 11 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Why GPU? GPU can speed up following computation- intensive applications 10x - 300x times Gene Analysis Deep learningSelf-Driving Car Scientific Computation Without GPU speed up, you will almost impossible to do these computations. (If job runs for weeks).
- 12. 12 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Why GPU? GPU: Many cores to handle massive (but simple) computation tasks simultaneously: GPU CPU Computation Intensive Other Nvidia Tesla K40: 2880 CUDA cores. $2200.00 => $0.76 / core Intel Xeon E5-2697 14 cores $2295.00 => $163 / core
- 13. 13 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Why all under YARN SLA! Monitoring!A normal YARN user Quotas! Isolation! Capacity Planning, Preemption, Reservation System. Time line services, Grafana, etc. CPU / Memory, (WIP) GPU, FPGA, Network
- 14. 14 © Hortonworks Inc. 2011 – 2016. All Rights Reserved All running on the same YARN platform LLAP 128 G 128 G 128 G 128 G 128 G LLAP LLAP 128 G 128 G GPUs
- 15. 15 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Current status of GPU support on YARN Using node label (YARN-796), since Apache Hadoop 2.6.0 – Use node label to partition one big cluster to smaller disjoint clusters, and assign shares/acls to queues. – Issues: 1) GPU is not a countable resource in scheduling. 2) No proper isolation for GPU. Rest part of GPU support is WIP, umbrella JIRA: YARN-6223
- 16. 16 © Hortonworks Inc. 2011 – 2016. All Rights Reserved GPU support: Challenges GPU isolation – Different from memory / cpu, computations affinity to per-GPU-device. – And multiple processes use the single GPU will be serialized. (MPS is an exception). – And multiple process share the same GPU cause OOM easily. • Even though TF provide options to use GPU memory less than whole device provided. But we cannot enforce this from external.
- 17. 17 © Hortonworks Inc. 2011 – 2016. All Rights Reserved GPU support: Challenges Hierarchy of GPUs matters: – Topology of GPU really matters: affect communication latency a lot! (Von Neumann bottleneck) Picture credit to: https://opus.nci.org.au
- 18. 18 © Hortonworks Inc. 2011 – 2016. All Rights Reserved GPU support: Challenges GPU on Docker: Build once and run anywhere is not simple: For a regular app: It can run on Centos 6/7, or any different hosts as well as CPU arch is same. However, GPU application needs driver to talk to hardware: Nginx App Nginx AppUbuntu 14:04 Tensorflow 1.2 Nginx AppUbuntu 14:04 GPU Base Lib v2 Nginx AppHost OS GPU Base Lib v1 X Fails CUDA Library 5.0
- 19. 19 © Hortonworks Inc. 2011 – 2016. All Rights Reserved GPU Support : Solutions GPU isolation: – With general resource types feature: • detect & report number of GPUs to YARN scheduler, and scheduler make central decision. – For normal processes: use cgroups: device submodule. (Same as cpu/memory isolation mechanism) – For docker processes: use --device command line before launch docker container.
- 20. 20 © Hortonworks Inc. 2011 – 2016. All Rights Reserved GPU Support : Solutions GPU on Docker support – By using nvidia-docker-plugin. Tensorflow 1.2 Nginx AppUbuntu 14:04 Nginx AppHost OS GPU Base Lib v1 Volume Mount CUDA Library 5.0
- 21. 21 © Hortonworks Inc. 2011 – 2016. All Rights Reserved How rest of YARN helps GPU support. Node partition – Without node partition, cannot guarantee best GPU utilizations, let’s look at an example: – Two hosts in the cluster, only host1 has GPUs. At the beginning, cluster is empty. – At time T1, user submit a Spark job, which need 10G mem, 4 CPUs. Without node partition, it could be placed to Host1 – If we have another job, which needs 15G memory, 6 CPUs and 3 GPUs, it won’t possible to get allocated. 20G 8 4 Mem CPU GPU 20G 8 Host1 (GPU) Host2 10G 4 4 Mem CPU GPU 20G 8 Host1 (GPU) Host2 Task1 ?
- 22. 22 © Hortonworks Inc. 2011 – 2016. All Rights Reserved How rest of YARN helps GPU support. Resource Profiles – A generalized vector – Admins can create custom Resource Types! – Ease of resource requesting model using profiles NodeManager Memory CPU GPU NodeManager Memory CPU GPU ResourceManager Small Medium Large Profile Memory CPU GPU Small 2 GB 4 Cores 1 Cores Medium 4 GB 8 Cores 1 Cores Large 16 GB 16 Cores 4 Cores Application Master Small
- 23. 23 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Current development status (YARN-6223) Apache Hadoop 3.1.0 release (Jan 15, 2018) – GPU auto detection (Merged) – GPU scheduling in RM (Merged) – GPU isolation using Cgroups. (Merged) – GPU on docker isolation & volume. (Merged) – UI / Metrics (Merged). – Documentation (Open) – Ambari changes (Open)
- 24. 24 © Hortonworks Inc. 2011 – 2016. All Rights Reserved TensorFlow on Apache Hadoop YARN
- 25. 25 © Hortonworks Inc. 2011 – 2016. All Rights Reserved YARN assembly: Makes everything easier! Forget about writing an application master, this is how you can run app on YARN .. Write assembly spec in JSON (we call it Yarnfile) Post the JSON as REST request to YARN server. YARN to figure out rest of it. An example:
- 26. 26 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Demo….
- 27. 27 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Questions?
Editor's Notes
- This is a typical machine learning, which involves three steps: feature engineering, model training and online service. Not surprisingly, the most important thing is to have the right features: those capturing historical information dominate other types of features. Once we have the right features and the right model, other factors play small roles. We first get feature representation from raw data, and then feed these features into machine learning model, and then evaluate the model and choose the best one to push into online service. The machine learning workflow is complicated, usually involves several steps under the help of several infrastructure components.
- Machine learning workflow starts with loading data from different data sources, like HDFS, AWS S3 or database system. After that, we usually join data from different source to generate a wide table. Apache Hive or Apache Spark is the most appropriate tools to handle this workload. And then, data scientists starts data exploration via Zeppelin. The most common issue is unbalanced label for the dataset, for example, the number of positive label is far more than the negative label. To get more accurate model, we need to subsample data from the group which has more instances to make it balanced. After that, we random split the dataset for training and test under the help of Spark. Once we get training data, we can start feature engineering.
- Feature engineering technology has made great progress over the past decade, from hand-designed features to automating feature discovery by deep learning. In many cases, hand-designed features can leverage the understanding of the domain knowledge which will lead to optimal results, Spark MLlib provides lots of feature transform/selection operators to make it simple and easily. But it will involves heavy physical work and need hire experienced engineers. DNNs has been successful applied in computer vision, speech recognition and natural language processing during recent years. More and more scientists and engineers applied deep neural network in computer vision, speech recognition and natural language and it has achieved good results. DNN can learn features automatically via embedding, the most famous embedding trick is word2vec which can produce a vector space, with each unique word in the corpus being assigned a corresponding vector in the space.
- Model training is the most important step of the whole pipeline.
- Deploy the model distributed for parallel model serving on batch mode or streaming mode. Evaluate the model offline or online by different metrics.