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If you are not sure about how to make your choice, but you know you need more than 1 CPU core, we recommend using QuarterNode_cpu Resource Profile:

var rp = ResourceProfile.upsert({'id': 'QuarterNode_cpu', 'name': 'QuarterNode_cpu', 'cpuCount': 4, 'memoryMb': 16*1024, 'diskGb': 512, 'gpuCount': 0, 'gpuMemoryMb': 0});

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Jupyter.startWithConfig({'resourceProfile': 'QuarterNode_cpu'});


If you are using GPUs, we recommend using the entire node, since each node has only one GPU. You can do that by creating this FullNode_gpu Resource Profile:


var rp = ResourceProfile.upsert({'id': 'FullNode_gpu', 'name': 'FullNode_gpu', 'cpuCount': 5, 'memoryMb': 48*1024, 'diskGb': 512, 'gpuCount': 1, 'gpuMemoryMb': 12*1024});


Jupyter.setMemoryOverride(12*1024, ConfigOverride.USER);


Jupyter.startWithConfig({'resourceProfile': 'FullNode_gpu'});



If you have additional requirements, please contact DTI DevOps.


Access Through IDS

Through IDS, clear controls regarding Jupyter are exposed under the 'ML Studio' section for Application. When you've selected a project, you need to start the Jupyter service to access notebooks on that project. In the creation process, you're able to select the container profile to use for your Jupyter service. By default, the 'BasicGpu' profile offers a single K80 GPU.

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