Clarification on isolation. As Ben and we (Nvidia) working to introduce GPU as first class resource into Mesos.
By default there is no isolation. But there will be isolation module for Nvidia GPU devices which can be linked at build time and provide isolation for GPU tasks among GPU devices. Initially device level isolation will be there assuming all tasks using same device libraries (hence no file system isolation). Our initial proposal is not exposing details of GPU but subsequently more detail of GPU resources like (topology, memory, core, bandwidth etc.) will be exposed to do better job scheduling. As Ben indicated very soon we will send out design proposal to community for comments. Regards -- Vikram From: Benjamin Mahler [mailto:[email protected]] Sent: Saturday, January 16, 2016 4:31 PM To: [email protected] Subject: Re: Share GPU resources via attributes or as custom resources (INTERNAL) There is a design proposal coming that will include guidance around using GPUs and better GPU support in mesos, so stay tuned. Mesos supports adding arbitrary resources, e.g. --resources=cpus(*):4;gpus(*):4 Mesos will then manage a scalar "gpu" resource with a value of 4. This means "gpu" scalars will be offered to the framework and the framework may launch tasks / executors that are allocated a "gpu" scalar. Of course, you'll need support from Marathon for custom resources when you define your job, not sure if that exists currently. Now, by default no isolation is going to take place. That may be ok for you if you have tight control over the fact that tasks/executors only try to consume the number of gpus that have been allocated to them. If not, you may run an isolator module for gpus (e.g. using the device whitelist controller cgroup). At the current time you would have to write one, as I'm not sure whether one has been written / published. You'll need to make sure your containers have access to the necessary gpu libraries. If you are running without filesystem isolation then tasks can just reach out of the sandbox to use the necessary libraries. Hope that helps, Ben On Thu, Jan 14, 2016 at 9:02 AM, <[email protected]<mailto:[email protected]>> wrote: I have a machine with 4 GPUs and want to use Mesos+Marathon to schedule the jobs to be run in the machine. Each job will use maximum 1 GPU and sharing 1 GPU between small jobs would be ok. I know Mesos does not directly support GPUs, but it seems I might use custom resources or attributes to do what I want. But how exactly should this be done? If I use --attributes="hasGpu:true", would a job be sent to the machine when another job is already running in the machine (and only using 1 GPU)? I would say all jobs requesting a machine with a hasGpu attribute would be sent to the machine (as long as it has free CPU and memory resources). Then, if a job is sent to the machine when the 4 GPUs are already busy, the job will fail to start, right? Could then Marathon be used to re-send the job after some time, until it is accepted by the machine? If I specify --resources="gpu(*):4", it is my understanding that once a job is sent to the machine, all 4 GPUs will become busy to the eyes of Mesos (even if this is not really true). If that is right, would this work-around work: specify 4 different resources: gpu:A, gpu:B, gpu:C and gpu:D; and use constraints in Marathon like this "constraints": [["gpu", "LIKE", " [A-D]"]]? Cheers ----------------------------------------------------------------------------------- This email message is for the sole use of the intended recipient(s) and may contain confidential information. Any unauthorized review, use, disclosure or distribution is prohibited. If you are not the intended recipient, please contact the sender by reply email and destroy all copies of the original message. -----------------------------------------------------------------------------------
