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--- check_workload_cpu_gpu [2016/06/06 15:53] – hj
+++ check_workload_cpu_gpu [2017/07/10 20:05] – hj
@@ Line 1: / Line 1: @@
  === Check CPUs workload: ===
-     - We have a simple script for you to check the workload of all machines, you may run:  //cs/home/hj/bin/available_computers.pl//. Every time when you submit a new job, please use this command to look for a free or light-loaded machine. For a 6-core machine, we normally should not have its workload over 6.
+     - We have a simple script for you to check the workload of all machines, you may run:  **/cs/home/hj/bin/available_computers.pl**. Every time when you submit a new job, please use this command to look for a free or light-loaded machine. For a 6-core machine, we normally should not have its workload over 6.
-    - Run Linux 'top' command to check the CPU load, memory usage in each machines.
+    - Run Linux '**htop**' command to check the CPU load, memory usage in each machines.
     - If your program consumes lots of memory (over 10G), DON’T submit it more than once to a single machine.
+ === Check GPUs workload: ===
+  - We have a simple script for you to check the workload of all machines, you may run:  **/cs/home/hj/bin/AllGPUStat.sh**.
+  - To check one server equipped with GPU, the GPU summary can be retried by “**nvidia-smi**”. As long as the remaining memory meets your memory need, it’s runnable. However, it may not progress since the GPU utilization is high. If there are 2 programs executing on the same GPU and one of them allocates too much memory, BOTH programs crash. “nvidia-smi” is not available on OSX.
+In most machine learning framework, the first GPU is picked by default. Tensorflow, for example, will pre-allocate a chunk of memory on EVERY SINGLE GPU if you don’t explicitly mask the unneeded. Masking can be done by, for example “**setenv CUDA_VISIBLE_DEVICES 1**”, if you only want to expose the second GPU (GPU is 0-indexing).
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