标签：make 实现 利用 接口 runtime -- 文章 释放 aries
这篇文章涉及mesos如何在原生的mesoscontainerizer和docker containerizer上支持gpu的，以及如果自己实现一个mesos之上的framework capos支持gpu调度的实现原理，(capos是hulu内部的资源调度平台 refer to https://www.cnblogs.com/yanghuahui/p/9304302.html）。
mesos slave在启动的时候需要初始化containerizer的resource，包含cpu/mem/gpu等，这对于mesos containerizer和docker containerizer都是通用的
void Slave::initialize() { ... Try<Resources> resources = Containerizer::resources(flags); ... }
然后到了src/slave/containerizer/containerizer.cpp 代码块中, 根据mesos-slave/agent的启动参数flags，调用allocator逻辑
Try<Resources> Containerizer::resources(const Flags& flags) { ... // GPU resource. Try<Resources> gpus = NvidiaGpuAllocator::resources(flags); if (gpus.isError()) { return Error("Failed to obtain GPU resources: " + gpus.error()); } // When adding in the GPU resources, make sure that we filter out // the existing GPU resources (if any) so that we do not double // allocate GPUs. resources = gpus.get() + resources.filter( [](const Resource& resource) { return resource.name() != "gpus"; }); ... }
src/slave/containerizer/mesos/isolators/gpu/allocator.cpp 会用nvidia的管理gpu的命令nvml以及根据启动参数，返回这台机器上gpu的资源，供之后的调度使用。
// To determine the proper number of GPU resources to return, we // need to check both --resources and --nvidia_gpu_devices. // There are two cases to consider: // // (1) --resources includes "gpus" and --nvidia_gpu_devices is set. // The number of GPUs in --resources must equal the number of // GPUs within --nvidia_gpu_resources. // // (2) --resources does not include "gpus" and --nvidia_gpu_devices // is not specified. Here we auto-discover GPUs using the // NVIDIA management Library (NVML). We special case specifying // `gpus:0` explicitly to not perform auto-discovery. // static Try<Resources> enumerateGpuResources(const Flags& flags) { ... }
因为gpu资源是需要绑定gpu卡number的，gpu资源在调度的数据结构中，是一个set<Gpu>, allocator.go提供allocate和deallocate接口的实现
Future<Nothing> allocate(const set<Gpu>& gpus) { set<Gpu> allocation = available & gpus; if (allocation.size() < gpus.size()) { return Failure(stringify(gpus - allocation) + " are not available"); } available = available - allocation; allocated = allocated | allocation; return Nothing(); } Future<Nothing> deallocate(const set<Gpu>& gpus) { set<Gpu> deallocation = allocated & gpus; if (deallocation.size() < gpus.size()) { return Failure(stringify(gpus - deallocation) + " are not allocated"); } allocated = allocated - deallocation; available = available | deallocation; return Nothing(); }
但是封装到上层，供containerizer调用的时候，指定需要allocate的gpu number就可以
Future<set<Gpu>> NvidiaGpuAllocator::allocate(size_t count) { // Need to disambiguate for the compiler. Future<set<Gpu>> (NvidiaGpuAllocatorProcess::*allocate)(size_t) = &NvidiaGpuAllocatorProcess::allocate; return process::dispatch(data->process, allocate, count); }
但是deallocate仍然需要显示指定需要释放哪些gpu
Future<Nothing> NvidiaGpuAllocator::deallocate(const set<Gpu>& gpus) { return process::dispatch( data->process, &NvidiaGpuAllocatorProcess::deallocate, gpus); }
然后如果作业是用docker containerizer，可以看到src/slave/containerizer/docker.cpp中调用gpu的逻辑
Future<Nothing> DockerContainerizerProcess::allocateNvidiaGpus( const ContainerID& containerId, const size_t count) { if (!nvidia.isSome()) { return Failure("Attempted to allocate GPUs" " without Nvidia libraries available"); } if (!containers_.contains(containerId)) { return Failure("Container is already destroyed"); } return nvidia->allocator.allocate(count) .then(defer( self(), &Self::_allocateNvidiaGpus, containerId, lambda::_1)); }
所以之上，就是在slave中启动的时候加载确认gpu资源，然后在启动containerizer的时候，可以利用slave中维护的gpu set资源池，去拿到资源，之后启动作业。
那capos是如何实现的呢，capos是hulu内部的资源调度平台（refer to https://www.cnblogs.com/yanghuahui/p/9304302.html），因为自己实现了mesos的capos containerizer，我们的做法是，在mesos slave注册的时候显示的通过参数或者自动探测的机制，发现gpu资源，然后用--resources=gpu range的形式启动mesos agent，这样offer资源的gpu在capos看来就是一个range，可以类似使用port资源的方式，来调度gpu，在capos containerizer中，根据调度器指定的gpu range，去绑定一个或者多个gpu资源到docker nvidia runtime中。完成gpu调度功能。
标签：make 实现 利用 接口 runtime -- 文章 释放 aries
原文地址：https://www.cnblogs.com/yanghuahui/p/9381857.html
