dask kubernetes aks（天蓝色）虚拟节点

Question

使用下面的代码可以在 azure aks 中创建一个 dask kubernetes 集群。

它使用远程调度程序（ dask.config.set({"kubernetes.scheduler-service-type": "LoadBalancer"}) ）并且运行良好。

要使用虚拟节点，请取消注释extra_pod_config=virtual_config行（遵循这个官方示例）。

它不起作用，出现以下错误：

ACI does not support providing args without specifying the command. Please supply both command and args to the pod spec.

这与传递containers: args: [dask-scheduler]

哪些containers: command:我应该提供哪些来解决这个问题？

谢谢

import dask
from dask.distributed import Client
from dask_kubernetes import KubeCluster, KubeConfig, make_pod_spec

image = "daskdev/dask"
cluster = "aks-cluster1"
dask.config.set({"kubernetes.scheduler-service-type": "LoadBalancer"})
dask.config.set({"distributed.comm.timeouts.connect": 180})
virtual_config = {
    "nodeSelector": {
        "kubernetes.io/role": "agent",
        "beta.kubernetes.io/os": "linux",
        "type": "virtual-kubelet",
    },
    "tolerations": [
        {"key": "virtual-kubelet.io/provider", "operator": "Exists"},
    ],
}

pod_spec = make_pod_spec(
    image=image,
    # extra_pod_config=virtual_config,
    memory_limit="2G",
    memory_request="2G",
    cpu_limit=1,
    cpu_request=1,
    threads_per_worker=1,  # same as cpu
)

# az aks get-credentials --name aks-cluster1 --resource-group resource_group1
# cp ~/.kube/config ./aksconfig.yaml
auth = KubeConfig(config_file="./aksconfig.yaml", context=cluster,)
cluster = KubeCluster(
    pod_spec, auth=auth, deploy_mode="remote", scheduler_service_wait_timeout=180
)
client = Client(cluster)

Answer 1

原因来自这个虚拟 kubelet 保护：在 pod 配置中，dask 使用args来启动调度程序或工作程序，但没有提供任何command 。

所以我明确地添加了入口点命令command_entrypoint_explicit并且它工作：成功创建了 pod。

第二个问题：网络名称解析。 工作人员无法通过网络名称连接到调度程序： tcp://{name}.{namespace}:{port}

虽然tcp://{name}.{namespace}.svc.cluster.local:{port}有效。 我在dask_kubernetes.core.Scheduler.start中编辑了它，它可以工作。

另一个选项是下面的virtual_config 。 下面的代码是一个完整的解决方案。

import dask
from dask.distributed import Client
from dask_kubernetes import KubeCluster, KubeConfig, make_pod_spec

dask.config.set({"kubernetes.scheduler-service-type": "LoadBalancer"})
dask.config.set({"distributed.comm.timeouts.connect": 180})
image = "daskdev/dask"
cluster = "aks-cluster-prod3"
virtual_config = {
    "nodeSelector": {
        "kubernetes.io/role": "agent",
        "beta.kubernetes.io/os": "linux",
        "type": "virtual-kubelet",
    },
    "tolerations": [
        {"key": "virtual-kubelet.io/provider", "operator": "Exists"},
        {"key": "azure.com/aci", "effect": "NoSchedule"},
    ],
    "dnsConfig": {
        "options": [{"name": "ndots", "value": "5"}],
        "searches": [
            "default.svc.cluster.local",
            "svc.cluster.local",
            "cluster.local",
        ],
    },
}

# copied from: https://github.com/dask/dask-docker/blob/master/base/Dockerfile#L25
command_entrypoint_explicit = {
    "command": ["tini", "-g", "--", "/usr/bin/prepare.sh"],
}

pod_spec = make_pod_spec(
    image=image,
    extra_pod_config=virtual_config,
    extra_container_config=command_entrypoint_explicit,
    memory_limit="2G",
    memory_request="2G",
    cpu_limit=1,
    cpu_request=1,
    threads_per_worker=1,  # same as cpu
)

# az aks get-credentials --name aks-cluster1 --resource-group resource_group1
# cp ~/.kube/config ./aksconfig.yaml
auth = KubeConfig(config_file="./aksconfig.yaml", context=cluster,)
cluster = KubeCluster(
    pod_spec,
    auth=auth,
    deploy_mode="remote",
    scheduler_service_wait_timeout=180,
    n_workers=1,
)
client = Client(cluster)