Re: Loading objects only once
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
wrote:
> as an alternative
> ```
> spark-submit --files
> ```
>
> 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 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
>>
>>
>>
>
Re: Loading objects only once
as an alternative ``` spark-submit --files ``` 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 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-singleton-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 > > >
RE: Loading objects only once
Maybe load the model on each executor’s disk and load it from there? Depending on how you use the data/model, using something like Livy and sharing the same connection may help? From: Naveen Swamy [mailto:mnnav...@gmail.com] Sent: Wednesday, September 27, 2017 9:08 PM To: user@spark.apache.org Subject: Loading objects only once 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-singleton-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
Re: Loading objects only once
Hello, maybe broadcast can help you here. [1] You can load the model once on the driver and then broadcast it to the workers with `bc_model = sc.broadcast(model)`? You can access the model in the map function with `bc_model.value()`. Best Eike [1] https://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.SparkContext.broadcast Naveen Swamy schrieb am Do., 28. Sep. 2017 um 04:09 Uhr: > 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-singleton-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 > > >
