Machine Learning Lab

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 === 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.
  - 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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