Differences

This shows you the differences between two versions of the page.

--- spark:running-spark-on-single-machine-or-on-cluster [2022/12/14 12:58]
straka [Examples]
+++ spark:running-spark-on-single-machine-or-on-cluster [2023/11/07 12:48] (current)
straka [Starting Spark Cluster]
@@ Line 13: / Line 13: @@
 Spark computations can be started both on desktop machines and on cluster machines, either by specifying ''MASTER'' to one of ''local'' modes, or by not specifying MASTER at all (''local[*]'' is used then).
-Note that when you use ''qrsh'' or ''qsub'', your job is usually expected to use one core, so you should specify ''MASTER=local''. If you do not, Spark will use all cores on the machine, even though SGE gave you only one.
+Note that when you use ''sbatch'' or ''srun'' to run a cluster locally, your job is by default expected to use just a single core, so you should specify ''MASTER=local''. If you do not, Spark will use all cores on the machine, even though Slurm gave you only one.
 ===== Starting Spark Cluster  =====
@@ Line 20: / Line 20: @@
 The Spark cluster can be started using one of the following two commands:
-  * ''spark-sbatch'': start a Spark cluster via an ''sbatch'' <file>spark-srun [sbatch args] workers memory_per_workerG[:python_memoryG] command [arguments...]</file>
+  * ''spark-sbatch'': start a Spark cluster via an ''sbatch'' <file>spark-sbatch [sbatch args] workers memory_per_workerG[:python_memoryG] command [arguments...]</file>
   * ''spark-srun'': start a Spark cluster via an ''srun'' <file>spark-srun [salloc args] workers memory_per_workerG[:python_memoryG] [command arguments...]</file>

[ Back to the navigation ] [ Back to the content ]

Institute of Formal and Applied Linguistics Wiki

Differences