MPS use error: Failed to allocate device vector A (error code all CUDA-capable devices are busy or unavailable)!

[lengrongfu]

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1. Quick Debug Information

• OS/Version(e.g. RHEL8.6, Ubuntu22.04): Ubuntu22.04
• Kernel Version: 5.15.0-102-generic
• Container Runtime Type/Version(e.g. Containerd, CRI-O, Docker): Containerd
• K8s Flavor/Version(e.g. K8s, OCP, Rancher, GKE, EKS): K8s

2. Issue or feature description

Briefly explain the issue in terms of expected behavior and current behavior.

I use helm to deploy k8s-device-plugin, and config mps, but deploy a workload running error. mps-controller-daemon pod having running.

3. Information to attach (optional if deemed irrelevant)

I use gpu-operator to install gpu driver, use helm chart version is v23.9.1, and driver、toolkit having install success. and then i use followers helm command to install k8s-device-plugin:

$ helm upgrade -i nvdp nvdp/nvidia-device-plugin     --version=0.15.0-rc.2     --namespace nvidia-device-plugin     --create-namespace     --set config.name=nvidia-plugin-configs --set gfd.enabled=true

nvidia-plugin-configs config content is :

    version: v1
    sharing:
      mps:
        resources:
        - name: nvidia.com/gpu
          replicas: 10

deploy workload pod command is:

$ cat <<EOF | kubectl apply -f -
apiVersion: v1
kind: Pod
metadata:
  name: gpu-pod
spec:
  restartPolicy: Never
  containers:
    - name: cuda-container
      image: nvcr.io/nvidia/k8s/cuda-sample:vectoradd-cuda10.2
      resources:
        limits:
          nvidia.com/gpu: 1 # requesting 1 GPU
  tolerations:
  - key: nvidia.com/gpu
    operator: Exists
    effect: NoSchedule
EOF

and then pod status is Error, error log is:

Failed to allocate device vector A (error code all CUDA-capable devices are busy or unavailable)!
[Vector addition of 50000 elements]

• device-plugin pod log:

$ kubectl -n nvidia-device-plugin logs -f nvdp-nvidia-device-plugin-9dffl -c nvidia-device-plugin-ctr
I0415 09:32:35.712604      36 main.go:276]
Running with config:
{
  "version": "v1",
  "flags": {
    "migStrategy": "none",
    "failOnInitError": true,
    "mpsRoot": "/run/nvidia/mps",
    "nvidiaDriverRoot": "/",
    "gdsEnabled": false,
    "mofedEnabled": false,
    "useNodeFeatureAPI": null,
    "plugin": {
      "passDeviceSpecs": false,
      "deviceListStrategy": [
        "envvar"
      ],
      "deviceIDStrategy": "uuid",
      "cdiAnnotationPrefix": "cdi.k8s.io/",
      "nvidiaCTKPath": "/usr/bin/nvidia-ctk",
      "containerDriverRoot": "/driver-root"
    }
  },
  "resources": {
    "gpus": [
      {
        "pattern": "*",
        "name": "nvidia.com/gpu"
      }
    ]
  },
  "sharing": {
    "timeSlicing": {},
    "mps": {
      "failRequestsGreaterThanOne": true,
      "resources": [
        {
          "name": "nvidia.com/gpu",
          "devices": "all",
          "replicas": 10
        }
      ]
    }
  }
}
I0415 09:32:35.712615      36 main.go:279] Retrieving plugins.
I0415 09:32:35.712627      36 factory.go:104] Detected NVML platform: found NVML library
I0415 09:32:35.712647      36 factory.go:104] Detected non-Tegra platform: /sys/devices/soc0/family file not found
I0415 09:32:35.714289      36 server.go:176] "MPS daemon is healthy" resource="nvidia.com/gpu"
I0415 09:32:35.714450      36 server.go:216] Starting GRPC server for 'nvidia.com/gpu'
I0415 09:32:35.714822      36 server.go:147] Starting to serve 'nvidia.com/gpu' on /var/lib/kubelet/device-plugins/nvidia-gpu.sock
I0415 09:32:35.716273      36 server.go:154] Registered device plugin for 'nvidia.com/gpu' with Kubelet

• mps-controller-daemon pod log:

$ kubectl -n nvidia-device-plugin logs -f nvdp-nvidia-device-plugin-mps-control-daemon-tgsfc -c mps-control-daemon-ctr
I0415 09:21:27.340107      50 main.go:183]
Running with config:
{
  "version": "v1",
  "flags": {
    "migStrategy": "none",
    "failOnInitError": null,
    "gdsEnabled": null,
    "mofedEnabled": null,
    "useNodeFeatureAPI": null,
    "plugin": {
      "passDeviceSpecs": null,
      "deviceListStrategy": null,
      "deviceIDStrategy": null,
      "cdiAnnotationPrefix": null,
      "nvidiaCTKPath": null,
      "containerDriverRoot": null
    }
  },
  "resources": {
    "gpus": [
      {
        "pattern": "*",
        "name": "nvidia.com/gpu"
      }
    ]
  },
  "sharing": {
    "timeSlicing": {},
    "mps": {
      "failRequestsGreaterThanOne": true,
      "resources": [
        {
          "name": "nvidia.com/gpu",
          "devices": "all",
          "replicas": 10
        }
      ]
    }
  }
}
I0415 09:21:27.340116      50 main.go:187] Retrieving MPS daemons.
I0415 09:21:27.375915      50 daemon.go:93] "Staring MPS daemon" resource="nvidia.com/gpu"
I0415 09:21:27.379471      50 daemon.go:131] "Starting log tailer" resource="nvidia.com/gpu"
[2024-04-15 09:21:27.377 Control    65] Starting control daemon using socket /mps/nvidia.com/gpu/pipe/control
[2024-04-15 09:21:27.377 Control    65] To connect CUDA applications to this daemon, set env CUDA_MPS_PIPE_DIRECTORY=/mps/nvidia.com/gpu/pipe
[2024-04-15 09:21:27.378 Control    65] Accepting connection...
[2024-04-15 09:21:27.378 Control    65] NEW UI
[2024-04-15 09:21:27.378 Control    65] Cmd:set_default_device_pinned_mem_limit 0 2304M
[2024-04-15 09:21:27.378 Control    65] UI closed
[2024-04-15 09:21:27.379 Control    65] Accepting connection...
[2024-04-15 09:21:27.379 Control    65] NEW UI
[2024-04-15 09:21:27.379 Control    65] Cmd:set_default_active_thread_percentage 10
[2024-04-15 09:21:27.379 Control    65] 10.0
[2024-04-15 09:21:27.379 Control    65] UI closed
[2024-04-15 09:22:24.978 Control    65] Accepting connection...
[2024-04-15 09:22:24.978 Control    65] User did not send valid credentials
[2024-04-15 09:22:24.978 Control    65] Accepting connection...
[2024-04-15 09:22:24.978 Control    65] NEW CLIENT 0 from user 0: Server is not ready, push client to pending list
[2024-04-15 09:22:24.978 Control    65] Starting new server 75 for user 0
[2024-04-15 09:22:24.980 Control    65] Accepting connection...
[2024-04-15 09:22:25.207 Control    65] NEW SERVER 75: Ready
[2024-04-15 09:32:35.713 Control    65] Accepting connection...
[2024-04-15 09:32:35.713 Control    65] NEW UI
[2024-04-15 09:32:35.714 Control    65] Cmd:get_default_active_thread_percentage
[2024-04-15 09:32:35.714 Control    65] 10.0
[2024-04-15 09:32:35.714 Control    65] UI closed
[2024-04-15 09:33:08.579 Control    65] Accepting connection...
[2024-04-15 09:33:08.579 Control    65] User did not send valid credentials
[2024-04-15 09:33:08.579 Control    65] Accepting connection...
[2024-04-15 09:33:08.579 Control    65] NEW CLIENT 0 from user 0: Server already exists

GPU info :

root@nvidia-driver-daemonset-4p4qs:/drivers# nvidia-smi -L
GPU 0: Tesla P40 (UUID: GPU-70a7e30d-99a5-1117-8e85-759a592fb582)

[lengrongfu]

@elezar Can you help me look into this issue?

[elezar]

Could you try to update your workload to use the following container instead:

nvcr.io/nvidia/k8s/cuda-sample:vectoradd-cuda11.7.1

Also, is the nvidia runtime configured as your default runtime, or are you using a runtime class? If it is the latter, you would also need to specify a runtime class in your workload.

[lengrongfu]

I use nvcr.io/nvidia/k8s/cuda-sample:vectoradd-cuda11.7.1 this image to deploy workload, error then having.

Failed to allocate device vector A (error code CUDA-capable device(s) is/are busy or unavailable)!
[Vector addition of 50000 elements]

nvidia runtime is configured default.

$ cat /etc/containerd/config.toml
disabled_plugins = []
imports = []
oom_score = 0
plugin_dir = ""
required_plugins = []
root = "/var/lib/containerd"
state = "/run/containerd"
temp = ""
version = 2

[cgroup]
  path = ""

[debug]
  address = ""
  format = ""
  gid = 0
  level = ""
  uid = 0

[grpc]
  address = "/run/containerd/containerd.sock"
  gid = 0
  max_recv_message_size = 16777216
  max_send_message_size = 16777216
  tcp_address = ""
  tcp_tls_ca = ""
  tcp_tls_cert = ""
  tcp_tls_key = ""
  uid = 0

[metrics]
  address = ""
  grpc_histogram = false

[plugins]

  [plugins."io.containerd.gc.v1.scheduler"]
    deletion_threshold = 0
    mutation_threshold = 100
    pause_threshold = 0.02
    schedule_delay = "0s"
    startup_delay = "100ms"

  [plugins."io.containerd.grpc.v1.cri"]
    cdi_spec_dirs = ["/etc/cdi", "/var/run/cdi"]
    device_ownership_from_security_context = false
    disable_apparmor = false
    disable_cgroup = false
    disable_hugetlb_controller = true
    disable_proc_mount = false
    disable_tcp_service = true
    enable_cdi = false
    enable_selinux = false
    enable_tls_streaming = false
    enable_unprivileged_icmp = false
    enable_unprivileged_ports = false
    ignore_image_defined_volumes = false
    max_concurrent_downloads = 3
    max_container_log_line_size = 16384
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    restrict_oom_score_adj = false
    sandbox_image = "easzlab.io.local:5000/easzlab/pause:3.9"
    selinux_category_range = 1024
    stats_collect_period = 10
    stream_idle_timeout = "4h0m0s"
    stream_server_address = "127.0.0.1"
    stream_server_port = "0"
    systemd_cgroup = false
    tolerate_missing_hugetlb_controller = true
    unset_seccomp_profile = ""

    [plugins."io.containerd.grpc.v1.cri".cni]
      bin_dir = "/opt/cni/bin"
      conf_dir = "/etc/cni/net.d"
      conf_template = "/etc/cni/net.d/10-default.conf"
      max_conf_num = 1

    [plugins."io.containerd.grpc.v1.cri".containerd]
      default_runtime_name = "nvidia"
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      discard_unpacked_layers = false
      ignore_rdt_not_enabled_errors = false
      no_pivot = false
      snapshotter = "overlayfs"

      [plugins."io.containerd.grpc.v1.cri".containerd.default_runtime]
        base_runtime_spec = ""
        container_annotations = []
        pod_annotations = []
        privileged_without_host_devices = false
        runtime_engine = ""
        runtime_root = ""
        runtime_type = ""

        [plugins."io.containerd.grpc.v1.cri".containerd.default_runtime.options]

      [plugins."io.containerd.grpc.v1.cri".containerd.runtimes]

        [plugins."io.containerd.grpc.v1.cri".containerd.runtimes.nvidia]
          base_runtime_spec = ""
          container_annotations = []
          pod_annotations = []
          privileged_without_host_devices = false
          runtime_engine = ""
          runtime_root = ""
          runtime_type = "io.containerd.runc.v2"

          [plugins."io.containerd.grpc.v1.cri".containerd.runtimes.nvidia.options]
            BinaryName = "/usr/local/nvidia/toolkit/nvidia-container-runtime"
            CriuImagePath = ""
            CriuPath = ""
            CriuWorkPath = ""
            IoGid = 0
            IoUid = 0
            NoNewKeyring = false
            NoPivotRoot = false
            Root = ""
            ShimCgroup = ""
            SystemdCgroup = true

        [plugins."io.containerd.grpc.v1.cri".containerd.runtimes.nvidia-cdi]
          base_runtime_spec = ""
          container_annotations = []
          pod_annotations = []
          privileged_without_host_devices = false
          runtime_engine = ""
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          runtime_type = "io.containerd.runc.v2"

          [plugins."io.containerd.grpc.v1.cri".containerd.runtimes.nvidia-cdi.options]
            BinaryName = "/usr/local/nvidia/toolkit/nvidia-container-runtime.cdi"
            CriuImagePath = ""
            CriuPath = ""
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            IoGid = 0
            IoUid = 0
            NoNewKeyring = false
            NoPivotRoot = false
            Root = ""
            ShimCgroup = ""
            SystemdCgroup = true

        [plugins."io.containerd.grpc.v1.cri".containerd.runtimes.nvidia-legacy]
          base_runtime_spec = ""
          container_annotations = []
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          privileged_without_host_devices = false
          runtime_engine = ""
          runtime_root = ""
          runtime_type = "io.containerd.runc.v2"

          [plugins."io.containerd.grpc.v1.cri".containerd.runtimes.nvidia-legacy.options]
            BinaryName = "/usr/local/nvidia/toolkit/nvidia-container-runtime.legacy"
            CriuImagePath = ""
            CriuPath = ""
            CriuWorkPath = ""
            IoGid = 0
            IoUid = 0
            NoNewKeyring = false
            NoPivotRoot = false
            Root = ""
            ShimCgroup = ""
            SystemdCgroup = true

        [plugins."io.containerd.grpc.v1.cri".containerd.runtimes.runc]
          base_runtime_spec = ""
          container_annotations = []
          pod_annotations = []
          privileged_without_host_devices = false
          runtime_engine = ""
          runtime_root = ""
          runtime_type = "io.containerd.runc.v2"

          [plugins."io.containerd.grpc.v1.cri".containerd.runtimes.runc.options]
            BinaryName = ""
            CriuImagePath = ""
            CriuPath = ""
            CriuWorkPath = ""
            IoGid = 0
            IoUid = 0
            NoNewKeyring = false
            NoPivotRoot = false
            Root = ""
            ShimCgroup = ""
            SystemdCgroup = true

      [plugins."io.containerd.grpc.v1.cri".containerd.untrusted_workload_runtime]
        base_runtime_spec = ""
        container_annotations = []
        pod_annotations = []
        privileged_without_host_devices = false
        runtime_engine = ""
        runtime_root = ""
        runtime_type = ""

        [plugins."io.containerd.grpc.v1.cri".containerd.untrusted_workload_runtime.options]

    [plugins."io.containerd.grpc.v1.cri".image_decryption]
      key_model = "node"

    [plugins."io.containerd.grpc.v1.cri".registry]

      [plugins."io.containerd.grpc.v1.cri".registry.auths]

      [plugins."io.containerd.grpc.v1.cri".registry.configs]

        [plugins."io.containerd.grpc.v1.cri".registry.configs."10.6.194.8"]

          [plugins."io.containerd.grpc.v1.cri".registry.configs."10.6.194.8".tls]
            insecure_skip_verify = true

        [plugins."io.containerd.grpc.v1.cri".registry.configs."easzlab.io.local:5000"]

          [plugins."io.containerd.grpc.v1.cri".registry.configs."easzlab.io.local:5000".tls]
            insecure_skip_verify = true

        [plugins."io.containerd.grpc.v1.cri".registry.configs."harbor.easzlab.io.local:8443"]

          [plugins."io.containerd.grpc.v1.cri".registry.configs."harbor.easzlab.io.local:8443".tls]
            insecure_skip_verify = true

      [plugins."io.containerd.grpc.v1.cri".registry.headers]

      [plugins."io.containerd.grpc.v1.cri".registry.mirrors]

        [plugins."io.containerd.grpc.v1.cri".registry.mirrors."docker.io"]
          endpoint = ["https://docker.nju.edu.cn/", "https://kuamavit.mirror.aliyuncs.com"]

        [plugins."io.containerd.grpc.v1.cri".registry.mirrors."easzlab.io.local:5000"]
          endpoint = ["http://easzlab.io.local:5000"]

        [plugins."io.containerd.grpc.v1.cri".registry.mirrors."gcr.io"]
          endpoint = ["https://gcr.nju.edu.cn"]

        [plugins."io.containerd.grpc.v1.cri".registry.mirrors."ghcr.io"]
          endpoint = ["https://ghcr.nju.edu.cn"]

        [plugins."io.containerd.grpc.v1.cri".registry.mirrors."harbor.easzlab.io.local:8443"]
          endpoint = ["https://harbor.easzlab.io.local:8443"]

        [plugins."io.containerd.grpc.v1.cri".registry.mirrors."k8s.gcr.io"]
          endpoint = ["https://gcr.nju.edu.cn/google-containers/"]

        [plugins."io.containerd.grpc.v1.cri".registry.mirrors."nvcr.io"]
          endpoint = ["https://ngc.nju.edu.cn"]

        [plugins."io.containerd.grpc.v1.cri".registry.mirrors."quay.io"]
          endpoint = ["https://quay.nju.edu.cn"]

    [plugins."io.containerd.grpc.v1.cri".x509_key_pair_streaming]
      tls_cert_file = ""
      tls_key_file = ""

  [plugins."io.containerd.internal.v1.opt"]
    path = "/opt/containerd"

  [plugins."io.containerd.internal.v1.restart"]
    interval = "10s"

  [plugins."io.containerd.metadata.v1.bolt"]
    content_sharing_policy = "shared"

  [plugins."io.containerd.monitor.v1.cgroups"]
    no_prometheus = false

  [plugins."io.containerd.nri.v1.nri"]
    disable = false
    disable_connections = false
    plugin_config_path = "/etc/nri/conf.d"
    plugin_path = "/opt/nri/plugins1"
    plugin_registration_timeout = "5s"
    plugin_request_timeout = "2s"
    socket_path = "/var/run/nri/nri.sock"

  [plugins."io.containerd.runtime.v1.linux"]
    no_shim = false
    runtime = "runc"
    runtime_root = ""
    shim = "containerd-shim"
    shim_debug = false

  [plugins."io.containerd.service.v1.diff-service"]
    default = ["walking"]

  [plugins."io.containerd.snapshotter.v1.aufs"]
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  [plugins."io.containerd.snapshotter.v1.btrfs"]
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  [plugins."io.containerd.snapshotter.v1.devmapper"]
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  [plugins."io.containerd.snapshotter.v1.native"]
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  [plugins."io.containerd.snapshotter.v1.overlayfs"]
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[proxy_plugins]

[stream_processors]

  [stream_processors."io.containerd.ocicrypt.decoder.v1.tar"]
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    args = ["--decryption-keys-path", "/etc/containerd/ocicrypt/keys"]
    env = ["OCICRYPT_KEYPROVIDER_CONFIG=/etc/containerd/ocicrypt/ocicrypt_keyprovider.conf"]
    path = "ctd-decoder"
    returns = "application/vnd.oci.image.layer.v1.tar"

  [stream_processors."io.containerd.ocicrypt.decoder.v1.tar.gzip"]
    accepts = ["application/vnd.oci.image.layer.v1.tar+gzip+encrypted"]
    args = ["--decryption-keys-path", "/etc/containerd/ocicrypt/keys"]
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    path = "ctd-decoder"
    returns = "application/vnd.oci.image.layer.v1.tar+gzip"

[timeouts]
  "io.containerd.timeout.shim.cleanup" = "5s"
  "io.containerd.timeout.shim.load" = "5s"
  "io.containerd.timeout.shim.shutdown" = "3s"
  "io.containerd.timeout.task.state" = "2s"

[ttrpc]
  address = ""
  gid = 0
  uid = 0

[lengrongfu]

I found that it is possible to run the mps program directly on the host, but in the container it will prompt that device(s) is/are busy or unavailable

[elezar]

I found that it is possible to run the mps program directly on the host, but in the container it will prompt that device(s) is/are busy or unavailable

Could you provide more information on how you achieved this. Note that one of the key communication mechanism between the MPS processes is the /dev/shm that we create for the containerized daemon. How are you injecting this into the container?

[lengrongfu]

I found that it is possible to run the mps program directly on the host, but in the container it will prompt that device(s) is/are busy or unavailable

Could you provide more information on how you achieved this. Note that one of the key communication mechanism between the MPS processes is the /dev/shm that we create for the containerized daemon. How are you injecting this into the container?

First thanks for the quick answer.

About me in container to use MPS step:

1. i use gpu-operatpr to install driver and toolkit.
2. i use k8s-device-plugin to deploy mps controller and device-plugin

helm upgrade -i nvdp nvdp/nvidia-device-plugin     --version=0.15.0-rc.2     --namespace nvidia-device-plugin     --create-namespace     --set config.name=nvidia-plugin-configs --set gfd.enabled=true

3. then i to deploy a workload use follow command

$ cat <<EOF | kubectl apply -f -
apiVersion: v1
kind: Pod
metadata:
  name: gpu-pod
spec:
  restartPolicy: Never
  containers:
    - name: cuda-container
      image: nvcr.io/nvidia/k8s/cuda-sample:vectoradd-cuda11.7.1
      resources:
        limits:
          nvidia.com/gpu: 1 # requesting 1 GPU
  tolerations:
  - key: nvidia.com/gpu
    operator: Exists
    effect: NoSchedule
EOF

About you tips MPS processes is /dev/shm to communication, what do I need to do with this?

[lengrongfu]

@elezar Need any more information?

[elezar]

Sorry for the delay, @lengrongfu. Since you're using the GPU Operator to install the other components of the NVIDIA Container Stack. Can you confirm that it isn't managing the device plugin? Which pods are running in the GPU Operator namespace?

Also, to rule out any issues in the rc.2, could you deploy the v0.15.0 version of the device plugin that was released last week.

It would also be good to confirm that the workload container can properly access the MPS control daemon with the correct settings. Here, I would recommend updating the command to sleep 9999 and then exec into the container and run:

echo get_default_active_thread_percentage | mps-control-daemon

This should give 10 in your case.

[lengrongfu]

Thank you for your reply.

• "Can you confirm that it isn't managing the device plugin? Which pods are running in the GPU Operator namespace?" Confirm that it does not exist.

• "Also, to rule out any issues in the rc.2, could you deploy the v0.15.0 version of the device plugin that was released last week." I use 0.15.0 version to deploy, this issue still exist.

• use echo get_default_active_thread_percentage | mps-control-daemon exec log

• test pods error info

[lengrongfu]

I watch mps-control-daemon having a log is User did not send valid credentials, will this have any impact?

in nvidia-driver-daemonset pod exec nvidia-smi command can see nvidia-cuda-mps-server process is in use GPU device.

GPU compute is Exclusive_Process:

[elezar]

Could you run:

echo get_default_active_thread_percentage | nvidia-cuda-mps-control

in a workload conatiner: For example, the following one:

$ cat <<EOF | kubectl apply -f -
apiVersion: v1
kind: Pod
metadata:
  name: gpu-pod
spec:
  restartPolicy: Never
  containers:
    - name: cuda-container
      image: nvcr.io/nvidia/k8s/cuda-sample:vectoradd-cuda11.7.1
      command: ["bash", "-c"]
      args: ["nvidia-smi -L; sleep 9999"]
      resources:
        limits:
          nvidia.com/gpu: 1 # requesting 1 GPU
  tolerations:
  - key: nvidia.com/gpu
    operator: Exists
    effect: NoSchedule
EOF

[lengrongfu]

echo get_default_active_thread_percentage | mps-control-daemon

mps-control-daemon: command not found

[elezar]

Sorry it should be echo get_default_active_thread_percentage | nvidia-cuda-mps-control. A typo from my side.

[lengrongfu]

It return value is 10.0.

[elezar]

Just as a sanity check, could you confirm that running nvidia-smi produces the same output as in the driver container?

Looking through the configs again, since the GPU Operator is being used configure the toolkit and the driver, I would expect the nvidiaDriverRoot for the device plugin to be set to /run/nvidia/driver and not:

    "nvidiaDriverRoot": "/",

as is shown in your config.

Could you update the device plugin deployment with --set nvidiaDriverRoot=/run/nvidia/driver?

[lengrongfu]

Use helm update nvidiaDriverRoot field, and add a volume todevice-plugin pod, then pod start success, but gpu-pod run error.

[lengrongfu]

Maybe it has something to do with Tesla P40.

[robrakaric]

Maybe it has something to do with Tesla P40.

I'm running into the same issue on a GTX1070 w/ the same driver version as you. I wonder if a driver update would help.

[lengrongfu]

Maybe it has something to do with Tesla P40.

I'm running into the same issue on a GTX1070 w/ the same driver version as you. I wonder if a driver update would help.

I don't now. but i use Nvidia T4 device can success running.

[aphrodite1028]

Maybe it has something to do with Tesla P40.

I'm running into the same issue on a GTX1070 w/ the same driver version as you. I wonder if a driver update would help.

I don't now. but i use Nvidia T4 device can success running.

maybe pascal arch has problems using mps。if has correct client pod yaml for pascal arch gpu using mps?

[elezar]

There were significant improvements made to MPS with the release of Volta. It could be that our current implementation does not support pre-Volta devices. At present we have onl qualified volta devices.

[aphrodite1028]

There were significant improvements made to MPS with the release of Volta. It could be that our current implementation does not support pre-Volta devices. At present we have onl qualified volta devices. thanks for your reply if has any planning to support pre-Volta gpu like Pascal P40,P100,P4，or if has tested on pre-Volta using MPS, but not support? Could you tell us how to support pre-Volta Arch GPU, like P-series, and what changes need to be made based on the v0.15.0 version of k8s-device-plugin?
We want to using mps on Pascal Arch GPU.Looking forward to your reply, thank you

[lengrongfu]

There were significant improvements made to MPS with the release of Volta. It could be that our current implementation does not support pre-Volta devices. At present we have onl qualified volta devices.

We should check the device arch in the start mps-control-daemon pod if the current does not support pre-Volta devices.? @elezar

[PrakChandra]

@elezar Could you please help here. I am not able to configure the MPS sharing option

here is the output kubectl logs nvidia-device-plugin-daemonset-4p742 -c mps-control-daemon-ctr -n kube-system I0517 04:07:02.596152 1 main.go:78] Starting NVIDIA MPS Control Daemon 435bfb70 commit: 435bfb70a44b74daca23fe957a0f256afaa3c51e I0517 04:07:02.596300 1 main.go:55] "Starting NVIDIA MPS Control Daemon" version=< 435bfb70 commit: 435bfb70a44b74daca23fe957a0f256afaa3c51e

I0517 04:07:02.596318 1 main.go:107] Starting OS watcher. I0517 04:07:02.596523 1 main.go:121] Starting Daemons. I0517 04:07:02.596553 1 main.go:164] Loading configuration. I0517 04:07:02.596616 1 main.go:172] Updating config with default resource matching patterns. I0517 04:07:02.596875 1 main.go:183] Running with config: { "version": "v1", "flags": { "migStrategy": "none", "failOnInitError": null, "gdsEnabled": null, "mofedEnabled": null, "useNodeFeatureAPI": null, "plugin": { "passDeviceSpecs": null, "deviceListStrategy": null, "deviceIDStrategy": null, "cdiAnnotationPrefix": null, "nvidiaCTKPath": null, "containerDriverRoot": null } }, "resources": { "gpus": [ { "pattern": "*", "name": "nvidia.com/gpu" } ] }, "sharing": { "timeSlicing": {} } } I0517 04:07:02.596892 1 main.go:187] Retrieving MPS daemons. I0517 04:07:02.596938 1 manager.go:58] "Sharing strategy is not MPS; skipping MPS manager creation" strategy="none" I0517 04:07:02.596966 1 main.go:196] No devices are configured for MPS sharing; Waiting indefinitely.

[PrakChandra]

@channel

Could anyone please give some advice here?

[quanguachong]

I met the similar problem.

MPS works well in GPU A100-PCIE-40GB.

MPS not works in GPU TITAN X (Pascal), Pod vectoradd's image is nvcr.io/nvidia/k8s/cuda-sample:vectoradd-cuda10.2

$ k get pod
NAME                                         READY   STATUS    RESTARTS      AGE
vectoradd                                    0/1     Error     2 (26s ago)   30s
$ k logs vectoradd
[Vector addition of 50000 elements]
Failed to allocate device vector A (error code all CUDA-capable devices are busy or unavailable)!

[aphrodite1028]

I met the similar problem.

MPS works well in GPU A100-PCIE-40GB.

MPS not works in GPU TITAN X (Pascal), Pod vectoradd's image is nvcr.io/nvidia/k8s/cuda-sample:vectoradd-cuda10.2

$ k get pod
NAME                                         READY   STATUS    RESTARTS      AGE
vectoradd                                    0/1     Error     2 (26s ago)   30s
$ k logs vectoradd
[Vector addition of 50000 elements]
Failed to allocate device vector A (error code all CUDA-capable devices are busy or unavailable)!

your TITAN X GPU nvidia-smi how mush card can see? if your pod using one gpu card mps-control-daemon need to configed one gpu using NVIDIA-VISIBLE-DEVICES

[klueska]

@quanguachong

As @elezar mentioned above, the support we added for MPS implicitly only works for Volta+ GPUs. In pre-volta GPUs there was no ability to limit the memory of each MPS client, and our code assumes this functionality is available.

We should probably make this assumption explicit rather than implicit (or otherwise relax this constraint for pre-volta GPUs with a warning printed in the log).

[quanguachong]

Is following action causes NVIDIA Device Plugin's MPS only works for Volta+ GPU?

nvidia-device-plugin-mps-control-daemon send command set_default_device_pinned_mem_limit&set_default_active_thread_percentage(Commands available to Volta MPS control) to mps control daemon(reference)?

[bcc829]

Are there any plans to support MPS for pre-volta GPUs?