@Debasish I see that the spark version being used in the project that you mentioned is 1.6.0. I would suggest that you take a look at some blogs related to Spark 2.0 Pipelines, Models in new ml package. The new ml package's API as of latest Spark 2.1.0 release has no way to call predict on single vector. There is no API exposed. It is WIP but not yet released.
On Sat, Feb 4, 2017 at 11:07 PM, Debasish Das <debasish.da...@gmail.com> wrote: > If we expose an API to access the raw models out of PipelineModel can't we > call predict directly on it from an API ? Is there a task open to expose > the model out of PipelineModel so that predict can be called on it....there > is no dependency of spark context in ml model... > On Feb 4, 2017 9:11 AM, "Aseem Bansal" <asmbans...@gmail.com> wrote: > >> >> - In Spark 2.0 there is a class called PipelineModel. I know that the >> title says pipeline but it is actually talking about PipelineModel trained >> via using a Pipeline. >> - Why PipelineModel instead of pipeline? Because usually there is a >> series of stuff that needs to be done when doing ML which warrants an >> ordered sequence of operations. Read the new spark ml docs or one of the >> databricks blogs related to spark pipelines. If you have used python's >> sklearn library the concept is inspired from there. >> - "once model is deserialized as ml model from the store of choice >> within ms" - The timing of loading the model was not what I was >> referring to when I was talking about timing. >> - "it can be used on incoming features to score through >> spark.ml.Model predict API". The predict API is in the old mllib package >> not the new ml package. >> - "why r we using dataframe and not the ML model directly from API" - >> Because as of now the new ml package does not have the direct API. >> >> >> On Sat, Feb 4, 2017 at 10:24 PM, Debasish Das <debasish.da...@gmail.com> >> wrote: >> >>> I am not sure why I will use pipeline to do scoring...idea is to build a >>> model, use model ser/deser feature to put it in the row or column store of >>> choice and provide a api access to the model...we support these primitives >>> in github.com/Verizon/trapezium...the api has access to spark context >>> in local or distributed mode...once model is deserialized as ml model from >>> the store of choice within ms, it can be used on incoming features to score >>> through spark.ml.Model predict API...I am not clear on 2200x speedup...why >>> r we using dataframe and not the ML model directly from API ? >>> On Feb 4, 2017 7:52 AM, "Aseem Bansal" <asmbans...@gmail.com> wrote: >>> >>>> Does this support Java 7? >>>> What is your timezone in case someone wanted to talk? >>>> >>>> On Fri, Feb 3, 2017 at 10:23 PM, Hollin Wilkins <hol...@combust.ml> >>>> wrote: >>>> >>>>> Hey Aseem, >>>>> >>>>> We have built pipelines that execute several string indexers, one hot >>>>> encoders, scaling, and a random forest or linear regression at the end. >>>>> Execution time for the linear regression was on the order of 11 >>>>> microseconds, a bit longer for random forest. This can be further >>>>> optimized >>>>> by using row-based transformations if your pipeline is simple to around >>>>> 2-3 >>>>> microseconds. The pipeline operated on roughly 12 input features, and by >>>>> the time all the processing was done, we had somewhere around 1000 >>>>> features >>>>> or so going into the linear regression after one hot encoding and >>>>> everything else. >>>>> >>>>> Hope this helps, >>>>> Hollin >>>>> >>>>> On Fri, Feb 3, 2017 at 4:05 AM, Aseem Bansal <asmbans...@gmail.com> >>>>> wrote: >>>>> >>>>>> Does this support Java 7? >>>>>> >>>>>> On Fri, Feb 3, 2017 at 5:30 PM, Aseem Bansal <asmbans...@gmail.com> >>>>>> wrote: >>>>>> >>>>>>> Is computational time for predictions on the order of few >>>>>>> milliseconds (< 10 ms) like the old mllib library? >>>>>>> >>>>>>> On Thu, Feb 2, 2017 at 10:12 PM, Hollin Wilkins <hol...@combust.ml> >>>>>>> wrote: >>>>>>> >>>>>>>> Hey everyone, >>>>>>>> >>>>>>>> >>>>>>>> Some of you may have seen Mikhail and I talk at Spark/Hadoop >>>>>>>> Summits about MLeap and how you can use it to build production services >>>>>>>> from your Spark-trained ML pipelines. MLeap is an open-source >>>>>>>> technology >>>>>>>> that allows Data Scientists and Engineers to deploy Spark-trained ML >>>>>>>> Pipelines and Models to a scoring engine instantly. The MLeap execution >>>>>>>> engine has no dependencies on a Spark context and the serialization >>>>>>>> format >>>>>>>> is entirely based on Protobuf 3 and JSON. >>>>>>>> >>>>>>>> >>>>>>>> The recent 0.5.0 release provides serialization and inference >>>>>>>> support for close to 100% of Spark transformers (we don’t yet support >>>>>>>> ALS >>>>>>>> and LDA). >>>>>>>> >>>>>>>> >>>>>>>> MLeap is open-source, take a look at our Github page: >>>>>>>> >>>>>>>> https://github.com/combust/mleap >>>>>>>> >>>>>>>> >>>>>>>> Or join the conversation on Gitter: >>>>>>>> >>>>>>>> https://gitter.im/combust/mleap >>>>>>>> >>>>>>>> >>>>>>>> We have a set of documentation to help get you started here: >>>>>>>> >>>>>>>> http://mleap-docs.combust.ml/ >>>>>>>> >>>>>>>> >>>>>>>> We even have a set of demos, for training ML Pipelines and linear, >>>>>>>> logistic and random forest models: >>>>>>>> >>>>>>>> https://github.com/combust/mleap-demo >>>>>>>> >>>>>>>> >>>>>>>> Check out our latest MLeap-serving Docker image, which allows you >>>>>>>> to expose a REST interface to your Spark ML pipeline models: >>>>>>>> >>>>>>>> http://mleap-docs.combust.ml/mleap-serving/ >>>>>>>> >>>>>>>> >>>>>>>> Several companies are using MLeap in production and even more are >>>>>>>> currently evaluating it. Take a look and tell us what you think! We >>>>>>>> hope to >>>>>>>> talk with you soon and welcome feedback/suggestions! >>>>>>>> >>>>>>>> >>>>>>>> Sincerely, >>>>>>>> >>>>>>>> Hollin and Mikhail >>>>>>>> >>>>>>> >>>>>>> >>>>>> >>>>> >>>> >>
