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From Vadim Semenov <vadim.seme...@datadoghq.com>
Subject Re: Loading objects only once
Date Thu, 28 Sep 2017 20:00:45 GMT
Something like this:

```
object Model {
   @transient lazy val modelObject = new ModelLoader("model-filename")

   def get() = modelObject
}

object SparkJob {
  def main(args: Array[String]) = {
     sc.addFile("s3://bucket/path/model-filename")

     sc.parallelize(…).map(test => {
         Model.get().use(…)
     })
  }
}
```

On Thu, Sep 28, 2017 at 3:49 PM, Vadim Semenov <vadim.semenov@datadoghq.com>
wrote:

> 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
> https://github.com/apache/spark/blob/master/core/src/
> main/scala/org/apache/spark/SparkContext.scala?utf8=✓#L1508-L1558
>
> On Wed, Sep 27, 2017 at 10:08 PM, Naveen Swamy <mnnaveen@gmail.com> 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)
>> rdd.map(fetch_from_s3).map(read_file).map(predict)
>>
>> 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 https://issues.apache.org/jira/browse/SPARK-650  which suggests
>> that I can use Singleton and static initialization. I tried to do this
>> using a Singleton metaclass following the thread here
>> https://stackoverflow.com/questions/6760685/creating-a-singl
>> eton-in-python. 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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