[Research] protocols and structures for inference a res tful api for machine learning - James Montgomery
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Diversity in machine learning APIs works against realising machine learning's full potential by making it difficult to compose multiple algorithms. This paper introduces the Protocols and Structures for Inference (PSI) service architecture and specification for presenting learning algorithms and data as RESTful web resources that are accessible via a common but flexible and extensible interface. This is joint work with Dr. Mark Reid of the Australian National University and NICTA and Dr. Barry Drake of Canon Information Systems Research Australia.
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[Research] protocols and structures for inference a res tful api for machine learning - James Montgomery
- 1. psikit.net github.com/psi-project Protocols and Structures for Inference A RESTful API for Machine Learning work conducted at ANU in collaboration with CISRA and additional support from an Amazon AWS Education Grant James Montgomery james.montgomery@utas.edu.au @jamesatbond Mark Reid mark.reid@anu.edu.au @mdreid Barry Drake barry.drake@cisra.canon.com.au http://... http://... http://...
- 2. psikit.net github.com/psi-project An ‘ecosystem’ of ML services Prediction API Microsoft Azure Machine Learning Amazon Machine Learning …and many others The problem is not that these are bad (they’re all very good) nor that there is competition (also good) But this ecosystem doesn’t encourage service composition or provide a way for ML practitioners of all sizes to share their data and algorithms
- 3. psikit.net github.com/psi-project Goals of PSI A web service API • that is standardised, yet • sufficiently flexible to support a wide range of ML techniques Select your ML du jour Flexible Federated http://... http://... http://...
- 4. psikit.net github.com/psi-project Protocols and Structures for Inference An API specification for ML web services Communication via JSON Set of common ML-related resources that describe their differences using a schema language (based on JSON schema) Support for data transformation, training, prediction and updating/online learning with points for extension and customisation and support for data formats beyond JSON Iris Versicolor by Danielle Langlois / CC-BY-SA-3.0
- 5. psikit.net github.com/psi-project Catalogue of PSI resources Relations (datasets) of instances @ Attributes Learners Predictors f(x) Transformers Collections of PSI resources and their
- 6. psikit.net github.com/psi-project PSI start Schema collection integer string ... schema ... Relations collection relation attribute sub-attribute ... ... ... Learners collection learner ... Predictors collection predictor update ... Transformers collection transformer ... Structure of a PSI service Optional resources Resource instances Required resources L L f(x) f(x) Structured attributes (arrays, objects) can be decomposed and new attributes created
- 7. psikit.net github.com/psi-project PSI start Schema collection integer string ... schema ... Relations collection relation attribute sub-attribute ... ... ... Learners collection learner ... Predictors collection predictor update ... Transformers collection transformer ... This is also a PSI service Optional resources Resource instances Required resources L L f(x) f(x) An organisation or individual could choose to provide access to one or more datasets The ‘root’ and collection resources are very lightweight
- 8. psikit.net github.com/psi-project PSI start Schema collection integer string ... schema ... Relations collection relation attribute sub-attribute ... ... ... Learners collection learner ... Predictors collection predictor update ... Transformers collection transformer ... …and so is this Optional resources Resource instances Required resources L L f(x) f(x) ML researchers could present their just-published learning algorithm as a resource
- 9. psikit.net github.com/psi-project PSI start Schema collection integer string ... schema ... Relations collection relation attribute sub-attribute ... ... ... Learners collection learner ... Predictors collection predictor update ... Transformers collection transformer ... …and this, etc. Optional resources Resource instances Required resources L L f(x) f(x) Or even a single predictor
- 10. psikit.net github.com/psi-project Is it RESTful? Is that important? 100% RESTful is not a reasonable aim But can improve interoperability and development of clients • Discoverable namespace • Extensible through links entry in resource representations (similar to HTML link element & part of JSON Hyper-schema standard) Client must still know it’s using a PSI service… • but each resource does provide information about how to use it through schema (no PSI media types)
- 11. psikit.net github.com/psi-project Schema describes… @ Form of learning tasks that learners can process The domain and range of transformers f(x) The domain and range of transformers Service-specific queries supported by relations The data format of attributes
- 12. psikit.net github.com/psi-project Common workflows Predict Train Predicted value Relation resource instance Attribute 1 emits resource Attribute 2 emits Attribute n emits instance representations Learnerrequires resource Predictoraccepts update emits resource Attribute emits resource Update Transformer emitsaccepts resource Relation resource instance schema other data source Attribute emits resourceRelation resource instance a resource resource Legend: JSON
- 13. psikit.net github.com/psi-project Common workflows Predict Train Predicted value Relation resource instance Attribute 1 emits resource Attribute 2 emits Attribute n emits instance representations Learnerrequires resource Predictoraccepts update emits resource Attribute emits resource Update Transformer emitsaccepts resource Relation resource instance schema other data source Attribute emits resourceRelation resource instance a resource resource Legend: JSON
- 14. psikit.net github.com/psi-project Training GET representation includes task schema resources any other vector attribute training parameters nominal attribute @ @ @ POST a task resources n = 5 a vector attribute l = 0.5 a nominal attribute JSON representations of attributes (not their values) or URI references discover attributes; reshape as needed; compose with transformers 201 Created/202 Accepted URI of 1 2 3 4
- 15. psikit.net github.com/psi-project What’s in the schema Algorithm requires an attribute that produces JSON schema required to describe PSI attribute’s representation and enforce it produces correct values Feature vectors of numbers {"type":"object","properties":{"responseTy pe":{"enum":["attribute#description"],"req uired":true},"uri":{"type":"string","requi red":true},"schema":{"type":"object","prop erties":{"type":{"enum":["array"],"require d":true},"items":{"type":"array","items":{ "type":"object","properties":{"type":{"enu m":["integer","number"],"required":true}}} ,"required":true}},"required":true},"descr iption":{"type":"string"},"provenance":{"t ype":["string","object"]},"relation":{"typ e":"string"},"subattributes":{"type":"arra y","items":{"type":"string"}}}} 𝑋 ∈ 𝑛 This is really the only change, but this is still very complicated
- 16. psikit.net github.com/psi-project 𝑋 ∈ ( ∪ ∗ ) 𝑛 Pre-defined PSI schema eases burden Algorithm requires an attribute that produces PSI schema Feature vectors of numbers "$arrayAttribute": { "allItems" : "$numberSchema" } Feature vectors of real- numbers, integers or strings "$arrayAttribute": { "allItems" : "$atomicValueSchema" } 𝑋 ∈ 𝑛
- 17. psikit.net github.com/psi-project Common workflows Predict Train Predicted value Relation resource instance Attribute 1 emits resource Attribute 2 emits Attribute n emits instance representations Learnerrequires resource Predictoraccepts update emits resource Attribute emits resource Update Transformer emitsaccepts resource Relation resource instance schema other data source Attribute emits resourceRelation resource instance a resource resource Legend: JSON
- 18. psikit.net github.com/psi-project Prediction GET?value=[5.1,3.5,1.4,0.2] @setosa join request with URI of 201 Created, URI of @ Simple Prediction Or join predictor with attribute to predict on whole relation GET?instance=all [setosa,setosa,setosa, …,virginica] @
- 19. psikit.net github.com/psi-project Beyond JSON data types • PSI rich values support data of any media type (PSI schema can still be used for data type validation) • Rich value is either an HTTP URI or Data URI Iris Versicolor by Danielle Langlois / CC-BY-SA-3.0 data:image/png;base64/9j/4AAQSkZJRgABAQEASABIAAD/2wBDAAYEBQYFBAYGBQYHBwYIChAKCgkJChQODww
- 20. psikit.net github.com/psi-project Proofs of concept Demonstration PSI service at poseidon.cecs.anu.edu.au Demonstration Javascript client at psi.cecs.anu.edu.au/demo • HTML forms generated from PSI schema • Predictor evaluation and comparison Play 1.2-based service, that exposes some classification and regression algorithms from scikit-learn plus a simple ranking algorithm using scikit-learn An HTML to bag-of-words transformer in Python will be on GitHub soon
- 21. psikit.net github.com/psi-project Querying relations Schema defines the elements of the query, their data type and can even include descriptions (which become hints here)
- 22. psikit.net github.com/psi-project Constructing a learning task
- 23. psikit.net github.com/psi-project Evaluation via client-service interactions
- 24. psikit.net github.com/psi-project Future Amazon AWS AMI of play-based service planned thanks to an Amazon AWS Education Grant PSI provides the core of a flexible ML API that can be freely implemented Security & authentication can be built on top Can be offered as alternative interface to existing ML web services