Using the command $XLEARNING_HOME/bin/xl-submit to submit the application to Cluster at the XLearning client. Please see the example in the part of README Quick Start. The following is more details of the parameter.
| Property Name | Meaning |
|---|---|
| app-name | application name |
| app-type | application type, default as the "XLearning", can set as "TensorFlow", "Caffe" according to the deeplearning framework |
| input | input file path in the format of "the HDFS path"#"local path" |
| output | output file path in the format of "the HDFS path"#"local path" |
| files | the required local files of the application |
| cacheArchive | the required compressed files in the HDFS path |
| cacheFile | the required files in the HDFS path |
| launch-cmd | execute command |
| user-path | the append for the environment variable $PATH |
| jars | the required jar files |
| user-classpath-first | whether user's job jar should be the first one on class path or not, default as the configure of xlearning.user.classpath.first |
| conf | set the configuration |
| am-cores | number of cores to use for the AM process, default as the configure of xlearning.am.cores |
| am-memory | amount of memory to use for the AM process (in MB)，default as the configure of xlearning.am.memory |
| ps-num | number of ps containers to use for the application, default as the configure of xlearning.ps.num |
| ps-cores | number of cores to use for the ps process, default as the configure of xlearning.ps.cores |
| ps-memory | amount of memory to use for the ps process (in MB), default as the configure of xlearning.ps.memory |
| worker-num | number of worker containers to use for the application, default as the configure of xlearning.worker.num |
| worker-cores | number of cores to use for the worker process, default as the configure of xlearning.worker.cores |
| worker-gpus | number of gpus to use for the worker process, default as the configure of xlearning.worker.gpus |
| worker-memory | amount of memory to use for the worker process(in MB), default as the configure of xlearning.worker.memory |
| queue | the queue of application submitted to, default as the configure of xlearning.app.queue |
| priority | the priority of application, default as the configure of xlearning.app.priority |
| board-enable | whether to start the service of TensorBoard, default as the configure of xlearning.tf.board.enable |
| board-index | specify the index of worker which start the TensorBoard, default as the configure of xlearning.tf.board.worker.index |
| board-logdir | the directory save TensorBoard event log, default as the configure of xlearning.tf.board.log.dir |
| board-reloadinterval | how often the backend should load more data of event log, default as the configure of xlearning.tf.board.reload.interval |
| board-historydir | specify the HDFS path which the TensorBoard event log upload to, default as the configure of xlearning.tf.board.history.dir |
| input-strategy | the strategy of the input file, default as the configure of xlearning.input.strategy |
| inRenameInputFile | whether to rename the download file when input-strategy is "DOWNLOAD", default as the configure of xlearning.inputfile.rename |
| stream-epoch | specify the epoch num of the input file read when input-strategy is "STREAM", default as the configure of xlearning.stream.epoch |
| inputformat | specify the class of the inputformat when input-strategy is "STREAM", default as the configure of xlearning.inputformat.class |
| inputformat-shuffle | whether to shuffle the input splits when input-strategy is "STREAM", default as the configure of xlearning.input.stream.shuffle |
| output-strategy | the strategy of the output file, default as the configure of xlearning.output.strategy |
| outputformat | specify the class of outputformat when output-strategy is "STREAM", default as the configure of xlearning.outputformat.class |