TaskSchedulerImpl: Initial job has not accepted any resources;

I faced similar issue and after some online research and trial-n-error, I narrowed down to 3 causes for this (except for the first the other two are not even close to the error message):

1. As indicated by the error, probably you are allocating the resources more than that is available. => This was not my issue
2. Hostname & IP Address mishaps: I took care of this by specifying the SPARK_MASTER_IP and SPARK_LOCAL_IP in spark-env.sh
3. Disable Firewall on the client : This was the solution that worked for me. Since I was working on a prototype in-house code, I disabled the firewall on the client node. For some reason the worker nodes, were not able to talk back to the client for me. For production purposes, you would want to open-up certain number of ports required.

My problem was that I was assigning too much memory than my slaves had available. Try reducing the memory size of the spark submit. Something like the following:

~/spark-1.5.0/bin/spark-submit --master spark://my-pc:7077 --total-executor-cores 2 --executor-memory 512m

with my ~/spark-1.5.0/conf/spark-env.sh being:

SPARK_WORKER_INSTANCES=4
SPARK_WORKER_MEMORY=1000m
SPARK_WORKER_CORES=2