Running a python Apache Beam Pipeline on Spark

I am giving apache beam (with python sdk) a try here so I created a simple pipeline and I tried to deploy it on a Spark cluster.

from apache_beam.options.pipeline_options import PipelineOptions
import apache_beam as beam

op = PipelineOptions([
        "--runner=DirectRunner"
    ]
)


with beam.Pipeline(options=op) as p:
    p | beam.Create([1, 2, 3]) | beam.Map(lambda x: x+1) | beam.Map(print)

This pipeline is working well with DirectRunner. So to deploy the same code on Spark (as the portability is a key concept in Beam)...

First I edited the PipelineOptions as mentioned here:

op = PipelineOptions([
        "--runner=PortableRunner",
        "--job_endpoint=localhost:8099",
        "--environment_type=LOOPBACK"
    ]
)

job_endpoint is the url to the docker container of the beam spark job server that I run using the command:

docker run --net=host apache/beam_spark_job_server:latest --spark-master-url=spark://SPARK_URL:SPARK_PORT

This is supposed to work well but the job fails on Spark with this error :

20/10/31 14:35:58 ERROR TransportRequestHandler: Error while invoking RpcHandler#receive() for one-way message.

java.io.InvalidClassException: org.apache.spark.deploy.ApplicationDescription; local class incompatible: stream classdesc serialVersionUID = 6543101073799644159, local class serialVersionUID = 1574364215946805297

Also, I have this WARN in the beam_spark_job_server logs:

WARN org.apache.beam.runners.spark.translation.SparkContextFactory: Creating a new Spark Context.

Any idea where is the problem here? Is there any other way to run python Beam Pipelines on spark without passing by a containerized service ?