distributed-computing

问题 I have JSON data that I am reading into a data frame with several fields, repartitioning it based on two columns, and converting to Pandas. This job keeps failing on EMR on just 600,000 rows of data with some obscure errors. I have also increased memory settings of the spark driver, and still don't see any resolution. Here is my pyspark code: enhDataDf = ( sqlContext .read.json(sys.argv[1]) ) enhDataDf = ( enhDataDf .repartition('column1', 'column2') .toPandas() ) enhDataDf = sqlContext

问题 I have JSON data that I am reading into a data frame with several fields, repartitioning it based on two columns, and converting to Pandas. This job keeps failing on EMR on just 600,000 rows of data with some obscure errors. I have also increased memory settings of the spark driver, and still don't see any resolution. Here is my pyspark code: enhDataDf = ( sqlContext .read.json(sys.argv[1]) ) enhDataDf = ( enhDataDf .repartition('column1', 'column2') .toPandas() ) enhDataDf = sqlContext

问题 I am finding it incredibly difficult to follow rays guidelines to running a docker image on a ray cluster in order to execute a python script. I am finding a lack of simple working examples. So I have the simplest docker file: FROM rayproject/ray WORKDIR /usr/src/app COPY . . CMD ["step_1.py"] ENTRYPOINT ["python3"] I use this to create can image and push this to docker hub. ("myimage" is just an example) docker build -t myimage . docker push myimage "step_1.py" just prints hello every second

问题 Could you please advice an ZeroMQ socket(s) architecture for the following scenario: 1) there is server listening on port 2) there are several clients connecting server simultaneously 3) server accept all connections from clients and provide bi-directional queue for each client, means both party (client N or server) can send or consume messages, i.e. both party can be INITIATOR of the communication and other party should have a callback to process the message. Should we create additional

问题 Could you please advice an ZeroMQ socket(s) architecture for the following scenario: 1) there is server listening on port 2) there are several clients connecting server simultaneously 3) server accept all connections from clients and provide bi-directional queue for each client, means both party (client N or server) can send or consume messages, i.e. both party can be INITIATOR of the communication and other party should have a callback to process the message. Should we create additional

问题 According to the Apache Flink documentation, KeyBy transformation logically partitions a stream into disjoint partitions. All records with the same key are assigned to the same partition. Is KeyBy 100% logical transformation? Doesn't it include physical data partitioning for distribution across the cluster nodes? If so, then how it can guarantee that all the records with the same key are assigned to the same partition? For instance, assuming that we are getting a distributed data stream from

问题 When training a neural network across multiple servers and GPUs, I can't think of a scenario where the ParameterServerStrategy would be preferable to the MultiWorkerMirroredStrategy . What are the ParameterServerStrategy 's main use cases and why would it be better than using MultiWorkerMirroredStrategy ? 回答1: MultiWorkerMirroredStrategy is intended for synchronous distributed training across multiple workers, each of which can have multiple GPUs ParameterServerStrategy : Supports parameter

来源： https://stackoverflow.com/questions/43717563/best-way-to-search-multiple-solr-core

来源： https://stackoverflow.com/questions/43216514/how-to-run-a-python-program-on-condor

来源： https://stackoverflow.com/questions/43216514/how-to-run-a-python-program-on-condor