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Workshop Machine Learning at Scale with Google Cloud Platform
- 1. Workshop: Machine Learning at scale with GCP using ML Engine & Python Dataflow 25/01/2018 Robbe Sneyders & Juta Staes
- 2. 2 About ML6 We are a team of data scientists, machine learning experts, software engineers and mathematicians. Our mission is to provide tailor-made systems to help your organization get smart actionable insights from large data volumes. + Specialized Machine Learning partner of Google Cloud Robbe Sneyders ML engineer @ ML6 Juta Staes ML engineer @ ML6
- 3. 3 Outline GCP tools overview Workshop part 1: Dataflow Workshop part 2: ML Engine 1 2 3
- 4. 4 Machine Learning Pipeline Collect data Create model Train model with organized data Organize data Deploy trained model iterate
- 5. 5 Mapping to GCP Products Collect data Create model Train model with organized data Organize data Deploy trained model Cloud Machine Learning Engine Cloud Dataflow Cloud Machine Learning Engine Cloud Storage Tensorflow
- 6. 6 Google Cloud Products Compute Storage Data & Analytics Machine Learning Cloud Functions
- 7. 7 Google Cloud Platform: Open Cloud Philosophy ● Powerful open source frameworks that run everywhere ● Fully managed compute and storage services to run it more easily ● Free trial for 1 year with $300 worth in credits Cloud Machine Learning Engine TensorFlow Cloud Dataflow Apache Beam
- 8. 8 Outline GCP tools overview Workshop part 1: Dataflow Workshop part 2: ML Engine 1 2 3
- 9. 9 Workshop: overview ● Build ML model to classify flower images
- 10. 10 Workshop: overview Cloud Machine Learning Engine Cloud Dataflow Tensorflow Part 1: Transform images from Cloud Storage into TF records and split into train, test and validation set Part 2: Build ML model, deploy it and use it to make predictions
- 11. 11 Google Cloud Storage (GCS) ● Object Storage Service ● Your data lives here: ○ Raw input data ○ Cleaned examples for TF models ○ Serialized Tensorflow models ● Single interface/API, multiple offerings Name Access Frequency Multi-Regional Frequent, Cross-regional Regional Frequent, Single-region Nearline Less than once per month Coldline Less than once per year
- 12. 12 Apache Beam running on Cloud Dataflow ● Open source, unified model for defining both batch and streaming data-parallel processing pipelines. ● Using one of the open source Beam SDKs, you build a program that defines the pipeline. ● The pipeline is then executed by one of Beam’s supported distributed processing back-ends, which include Apache Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow. Beam Model: Fn Runners Apache Flink Apache Spark Beam Model: Pipeline Construction Other LanguagesBeam Java Beam Python Execution Execution Cloud Dataflow Execution Source: https://beam.apache.org
- 13. 13 Apache Beam key concepts ● Pipelines: data processing job made of a series of computations including input, processing, and output ● PCollections: bounded (or unbounded) datasets which represent the input, intermediate and output data in pipelines ● PTransforms: data processing step in a pipeline in which one or more PCollections are an input and output ● I/O Sources and Sinks: APIs for reading and writing data which are the roots and endpoints of the pipeline. Source: https://beam.apache.org
- 14. 14 Apache Beam running on Cloud Dataflow ● Fully-managed data processing service to run Apache Beam pipelines: ○ Automated and optimized work partitioning which can dynamically rebalance lagging work ○ Horizontal dynamic autoscaling of worker resources
- 15. 15 Input data Flowers sample Hosted publicly by google ● Csv file on Google Cloud Storage ○ One line per sample ○ Format: image uri, label ● Text file on Google Cloud Storage ○ All labels
- 16. 16 Collect & organize data with Cloud Dataflow Flowers sample steps ● ReadData: ○ Read metadata from 1 csv-file ○ Output one string per line ● Split: ○ Transform to string to tuple ■ (uri, label) ● ReadDictionary ○ Read labels from text file
- 17. 17 One hot encoding Daisy Dandelion Roses Sunflowers Tulips Daisy 1 0 0 0 0 Dandelion 0 1 0 0 0 Roses 0 0 1 0 0 Sunflowers 0 0 0 1 0 Tulips 0 0 0 0 1
- 18. 18 Collect & organize data with Cloud Dataflow Flowers sample steps ● OneHotEncoding: ○ Main input: (uri, label) ○ Side input: labels ○ Output: (uri, one hot encoding) ● ReadImage: ○ Read image from uri and convert to pixels ● BuildExamples ○ Build a dictionary for each sample to store as TFRecord
- 19. 19 Collect & organize data with Cloud Dataflow Flowers sample steps ● Partition ○ Partition data into train, validation and test set ● WriteExamples ○ Write TFRecords to GC Storage
- 20. 20 AstroHack apache_beam code example 1 1 2 2 3 3 4 4 5 5
- 21. 21 Starting from boilerplate code https://github.com/Fematich/mlengine-boilerplate
- 23. 23 Outline GCP tools overview Workshop part 1: Dataflow Workshop part 2: ML Engine 1 2 3
- 24. 24 Tensorflow ● Open-source library for machine learning ● Single API for multiple platforms/devices: cpu(s), gpu(s),tpu(s), mobile phones... ● 2 step approach: ○ Construct your model as a computational graph ○ Train your model by pushing data through the graph ● Big community with lots of SotA model implementations
- 25. 25 ML Engine Training ● Tensorflow Training As a Service ● Data needs to be available online ● No fancy interface (only logging + Tensorboard) ● Same code can run locally to test on small datasets ● Nice features: ○ Easy setup of (GPU) clusters for distributed Tensorflow models ○ Automatic parallel hyperparameter tuning with Hypertune
- 26. 26 ML Engine Predictions ● Deploy trained model: ○ model (container) ○ version (actual code) ● Predictions: ○ batch ○ online ● Autoscaling
- 28. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 28 Apache Beam Beam.apache.org Tensorflow tensorflow.org ML6 ml6.eu Developer codelabs codelabs.developers.google.com