cleaned up demos

examples/multi_node_examples/demo_script.sh

@@ -0,0 +1,66 @@
+#!/bin/bash
+#
+# Auto-generated by test-tube (https://github.com/williamFalcon/test-tube)
+#################
+
+# set a job name
+#SBATCH --job-name=lightning_test
+#################
+
+# a file for job output, you can check job progress
+#SBATCH --output=/slurm_output_%j.out
+#################
+
+# a file for errors
+#SBATCH --error=/slurm_output_%j.err
+#################
+
+# time needed for job
+#SBATCH --time=01:00:00
+#################
+
+# gpus per node
+#SBATCH --gres=gpu:8
+#################
+
+# cpus per job
+#SBATCH --cpus-per-task=10
+#################
+
+# number of requested nodes
+#SBATCH --nodes=2
+#################
+
+# memory per node (0 means all)
+#SBATCH --mem=0
+#################
+
+# slurm will send a signal this far out before it kills the job
+#SBATCH --signal=USR1@300
+#################
+
+# comment
+#SBATCH --comment=lightning_demo
+#################
+
+# 1 task per gpu
+#SBATCH --ntasks-per-node=8
+#################
+
+source activate YourEnv
+
+# debugging flags (optional)
+export NCCL_DEBUG=INFO
+export PYTHONFAULTHANDLER=1
+
+# on your cluster you might need these:
+# set the network interface
+export NCCL_SOCKET_IFNAME=^docker0,lo
+
+# might need the latest cuda
+module load NCCL/2.4.7-1-cuda.10.0
+
+# random port between 12k and 20k
+export MASTER_PORT=$((12000 + RANDOM % 20000))$
+
+srun python multi_node_own_slurm_script.py