diff --git a/bin/pyspark b/bin/pyspark
index ed6f8da73035a..5a12516d70740 100755
--- a/bin/pyspark
+++ b/bin/pyspark
@@ -58,7 +58,8 @@ if [ -n "$IPYTHON_OPTS" ]; then
   IPYTHON=1
 fi
 
-if [[ "$IPYTHON" = "1" ]] ; then
+# Only use ipython if no command line arguments were provided [SPARK-1134]
+if [[ "$IPYTHON" = "1" && $# = 0 ]] ; then
   exec ipython $IPYTHON_OPTS
 else
   exec "$PYSPARK_PYTHON" "$@"
diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index ee1d892a3b630..9a170f1b1ff11 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -99,16 +99,16 @@ With this mode, your application is actually run on the remote machine where the
 
 ## Launch spark application with yarn-client mode.
 
-With yarn-client mode, the application will be launched locally. Just like running application or spark-shell on Local / Mesos / Standalone mode. The launch method is also the similar with them, just make sure that when you need to specify a master url, use "yarn-client" instead. And you also need to export the env value for SPARK_JAR.
+With yarn-client mode, the application will be launched locally, as when running the application or spark-shell on Local / Mesos / Standalone mode. The method to launch is similar as with those modes, except you should specify "yarn-client" as the master URL. You also need to export the env value for SPARK_JAR.
 
 Configuration in yarn-client mode:
 
-In order to tune worker core/number/memory etc. You need to export environment variables or add them to the spark configuration file (./conf/spark_env.sh). The following are the list of options.
+In order to tune worker core/number/memory etc. you need to export environment variables or add them to the spark configuration file (./conf/spark_env.sh). The following are the list of options.
 
 * `SPARK_WORKER_INSTANCES`, Number of workers to start (Default: 2)
-* `SPARK_WORKER_CORES`, Number of cores for the workers (Default: 1).
+* `SPARK_WORKER_CORES`, Number of cores for the workers (Default: 1)
 * `SPARK_WORKER_MEMORY`, Memory per Worker (e.g. 1000M, 2G) (Default: 1G)
-* `SPARK_MASTER_MEMORY`, Memory for Master (e.g. 1000M, 2G) (Default: 512 Mb)
+* `SPARK_MASTER_MEMORY`, Memory for Master (e.g. 1000M, 2G) (Default: 512M)
 * `SPARK_YARN_APP_NAME`, The name of your application (Default: Spark)
 * `SPARK_YARN_QUEUE`, The hadoop queue to use for allocation requests (Default: 'default')
 * `SPARK_YARN_DIST_FILES`, Comma separated list of files to be distributed with the job.