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From Vadim Semenov <>
Subject Re: Loading objects only once
Date Thu, 28 Sep 2017 19:49:24 GMT
as an alternative
spark-submit --files <file you want>

the files will be put on each executor in the working directory, so you can
then load it alongside your `map` function

Behind the scene it uses `SparkContext.addFile` method that you can use too✓#L1508-L1558

On Wed, Sep 27, 2017 at 10:08 PM, Naveen Swamy <> wrote:

> Hello all,
> I am a new user to Spark, please bear with me if this has been discussed
> earlier.
> I am trying to run batch inference using DL frameworks pre-trained models
> and Spark. Basically, I want to download a model(which is usually ~500 MB)
> onto the workers and load the model and run inference on images fetched
> from the source like S3 something like this
> rdd = sc.parallelize(load_from_s3)
> I was able to get it running in local mode on Jupyter, However, I would
> like to load the model only once and not every map operation. A setup hook
> would have nice which loads the model once into the JVM, I came across this
> JIRA  which suggests that
> I can use Singleton and static initialization. I tried to do this using
> a Singleton metaclass following the thread here
> Following this failed miserably complaining that Spark cannot serialize
> ctype objects with pointer references.
> After a lot of trial and error, I moved the code to a separate file by
> creating a static method for predict that checks if a class variable is set
> or not and loads the model if not set. This approach does not sound thread
> safe to me, So I wanted to reach out and see if there are established
> patterns on how to achieve something like this.
> Also, I would like to understand the executor->tasks->python process
> mapping, Does each task gets mapped to a separate python process?  The
> reason I ask is I want to be to use mapPartition method to load a batch of
> files and run inference on them separately for which I need to load the
> object once per task. Any
> Thanks for your time in answering my question.
> Cheers, Naveen

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