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Support multi trial jobs on same GPU #1109

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merged 93 commits into from
May 28, 2019

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SparkSnail
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private designatedGpuIndices!: Set<number>;
private log: Logger;
private localTrailConfig?: TrialConfig;
private localConfig?: LocalConfig;
private isMultiPhase: boolean = false;
private jobStreamMap: Map<string, ts.Stream>;
private maxTrialNumPerGPU: number = 1;
private useActiveGPU: boolean = false;
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how about init them in constructor?

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fixed, moved to constructor.

if(trialJobDetail === undefined) {
throw new Error(`could not get trialJobDetail by id ${trialJobId}`);
}
if (trialJobDetail.rmMeta !== undefined && trialJobDetail.rmMeta.occupiedGpuIndexMap !== undefined && trialJobDetail.gpuIndices !== undefined && trialJobDetail.gpuIndices.length > 0) {
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better to split this line to four lines

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fixed, split to four lines.

if(rmMeta.occupiedGpuIndexMap !== undefined) {
let num = rmMeta.occupiedGpuIndexMap.get(gpuInfo.index);
let maxTrialNumPerGPU: number = rmMeta.maxTrialNumPerGPU? rmMeta.maxTrialNumPerGPU: 1;
if((num === undefined && (!rmMeta.useActiveGPU && gpuInfo.activeProcessNum === 0 || !rmMeta.useActiveGPU)) || (num !== undefined && num < maxTrialNumPerGPU)) {
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the same, better to use multiple lines

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suggest 2 lines for this case

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fixed, split to 2 lines.

}
rmMeta.occupiedGpuIndexMap.set(gpuInfo.index, num + 1);
}else {
rmMeta.occupiedGpuIndexMap = new Map<number, number>();
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also reconsider this logic

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fixed, initialize in constructor.

if request_data['local_config']:
if request_data['local_config'].get('gpuIndices') and isinstance(request_data['local_config'].get('gpuIndices'), int):
request_data['local_config']['gpuIndices'] = str(request_data['local_config'].get('gpuIndices'))
if request_data['local_config'].get('maxTrialNumOnEachGPU'):
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GPU?

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fixed.

if request_data['local_config'].get('maxTrialNumOnEachGPU'):
request_data['local_config']['maxTrialNumOnEachGPU'] = request_data['local_config'].get('maxTrialNumOnEachGPU')
if request_data['local_config'].get('useActiveGPU'):
request_data['local_config']['useActiveGPU'] = request_data['local_config'].get('useActiveGPU')
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GPU? -> Gpu

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fixed.


* __useActiveGpu__

__useActiveGpu__ is used to specify whether to use a GPU if there is another process. By default, NNI will use the GPU only if there is no another active process in the GPU, if __useActiveGpu__ is set to true, NNI will use the GPU regardless of another processes.
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need to state that this option is not applicable for NNI on Windows.

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Fixed.

* __useActiveGpu__

__useActiveGpu__ is used to specify whether to use a GPU if there is another process. By default, NNI will use the GPU only if there is no another active process in the GPU, if __useActiveGpu__ is set to true, NNI will use the GPU regardless of another processes. This field is not applicable for NNI on Windows.

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Nice Job!
I test it on my local machine and it works successfully.
It seems that it also works in remote mode when I catch a sight of the code.

I suggest put one example code here.

localConfig:
  maxTrialNumPerGpu:  5

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good suggestion, I've added an configuration in cifar10-pytorch-example

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4 participants