使用Bitfusion在K8s上共享GPU资源

Henry Zhang

发布于 2021-08-05 09:40:14

1.5K1

发布于 2021-08-05 09:40:14

文章被收录于专栏：亨利笔记亨利笔记

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（本文作者系VMware中国研发云原生实验室架构师，负责云原生、机器学习等方面的创新项目。）

GPU 的虚拟化解决方案通常存在一些不足:

GPU计算力未得到充分利用
无法较好的隔离GPU资源或无法动态的调整资源隔离粒度
只能使用本地的GPU资源
应用程序调度困难

Bitfusion 是 vSphere 的一个强大功能，通过网络提供远程 GPU 池来解决以上这些问题。Bitfusion 使得 GPU 可以像计算资源一样被抽象、分区、自动化和共享。

另一方面，Kubernetes 已经成为事实上的部署和管理机器学习工作负载的平台，但 Kubernetes 没有提供一种原生方式来使用 Bitfusion 的远程 GPU 池。这一限制成为 Kubernetes 上的作业使用 Bitfusion GPU 的关键挑战。Kubernetes 需要一种友好的方式来使用 Bitfusion GPU 资源:

支持资源管理
支持GPU池管理

我们开发了一个 Bitfusion 的 device plugin，并且开源了相关代码。该项目通过在 Kubernetes 使用 Bitfusion 的方式来实现 GPU 共享能力。

开源代码地址：https://github.com/vmware/bitfusion-with-kubernetes-integration

1.架构

我们通过以下两个组件来实现允许 Kubernetes 使用 Bitfusion 的目的。

1. bitfusion-device-plugin
2. bitfusion-webhook

组件1和组件2分别内置在独立的 Docker 镜像中。 bitfusion-device-plugin 作为DaemonSet 运行在kubelet所在的每个工作节点上。 bitfusion-webhook 作为Deployment 运行在Kubernetes主节点上。

2. 前置条件

安装机器的操作系统为 Ubuntu Linux
OpenSSL需要安装在 Ubuntu上
Kubernetes 1.17+
Bitfusion 2.5+
可以正常使用 kubectl 和 docker 命令

如果需要启用配额功能，需要更新以下的yaml文件。

修改 bitfusion-injector.yaml 文件。

vim bitfusion-with-kubernetes-integration/bitfusion_device_plugin/webhook/deployment/bitfusion-injector.yaml

为TOTAL_GPU_MEMORY 设置合适的值，TOTAL_GPU_MEMORY 代表 Bitfusion Server 所管理的 GPU 物理设备的显存大小，单位是MB。

apiVersion: apps/v1
...
          env:
            - name: TOTAL_GPU_MEMORY
              value: 16000
...

2.1 获取Bitfusion的token文件

为了启用Bitfusion，用户必须生成一个用于授权的Bitfusion Token，并将相关的tar文件下载到安装机器上。按照以下步骤从vCenter获取Token:

Step 1. 登录 vCenter Step 2. 在插件部分点击Bitfusion

Step 3. 选择Tokens 标签，然后选择要下载的适当的token

Step 4. 点击 DOWNLOAD 按钮, 在此之前需要确保token是可用的

如果列表中没有可用的 token, 需要点击 NEW TOKEN 来创建 Token. 更多详情，

请参阅: https://docs.vmware.com/en/VMware-vSphere-Bitfusion/2.5/Install-Guide/GUID-361A9C59-BB22-4BF0-9B05-8E80DE70BE5B.html

2.2 使用Bitfusion Baremetal token 创建 Kubernetes secret

将 Bitfusion Baremetal Token 文件上传到安装机器。使用以下命令解压缩文件:

$ mkdir tokens   
$ tar -xvf ./2BgkZdN.tar -C tokens

现在我们得到了三个文件在目录 tokens/ 中，分别是: ca.crt, client.yaml 和 services.conf :

tokens
├── ca.crt
├── client.yaml
└── servers.conf

然后使用以下命令在Kubernetes中的kube-system 命名空间中创建一个secret :

$ kubectl create secret generic bitfusion-secret --from-file=tokens -n kube-system

更多关于kubectl 信息，请参阅： https://kubernetes.io/docs/reference/kubectl/overview/

3.快速开始

这里有两种部署的方式，可以选择其中任意一种:

使用已经构建好的容器镜像部署
使用代码构建容器镜像，并用生成的镜像部署

3.1 部署方式1: 使用已经构建好的容器镜像部署 (推荐)

使用以下命令克隆源代码:

$ git clone https://github.com/vmware/bitfusion-with-kubernetes-integration.git

使用以下命令部署 Bitfusion device plugin 和其他相关组件，需要确保Kubernetes 集群可以连接到 Internet。

$ cd bitfusion-with-kubernetes-integration-main/bitfusion_device_plugin
$ make deploy

3.2 部署方式2: 使用代码构建容器镜像，并用生成的镜像部署

用户可以选择从源代码构建容器镜像，而不是使用预先构建的容器镜像。在镜像构建完成后，它们也可以被推送到镜像仓库 (Docker Hub或本地的镜像仓库)。

使用以下命令克隆源代码:

$ git clone https://github.com/vmware/bitfusion-with-kubernetes-integration.git

在开始构建过程之前，需要修改Makefile中一些变量的值:

$ cd bitfusion-with-kubernetes-integration-main/bitfusion_device_plugin
$ vim Makefile

大多数变量的值不需要更改。如果要将镜像推送到镜像仓库，请确保IMAGE_REPO变量的值被设置为所选择的正确镜像仓库地址 (它的默认值是 docker.io/bitfusiondeviceplugin ):

# Variables below are the configuration of Docker images and repo for this project.
# Update these variable values with your own configuration if necessary.

IMAGE_REPO ?= docker.io/bitfusiondeviceplugin
DEVICE_IMAGE_NAME ?= bitfusion-device-plugin
WEBHOOK_IMAGE_NAME ?= bitfusion-webhook
PKG_IMAGE_NAME ?= bitfusion-client
IMAGE_TAG  ?= 0.1

现在可以使用下面的命令构建容器镜像了:

$ make build-image

可以用下面的命令检查容器镜像的构建结果:

$ docker images
REPOSITORY                                                                         TAG
docker.io/bitfusiondeviceplugin/bitfusion-device-plugin                            0.1                
docker.io/bitfusiondeviceplugin/bitfusion-webhook                                  0.1                
docker.io/bitfusiondeviceplugin/bitfusion-client                                   0.1

(推荐使用的可选项)如果需要将容器镜像推送到容器仓库，请使用以下命令推送它们。如果需要，可以使用“docker login”命令登录镜像仓库。

$ make push-image

注意: 如果没有可用的镜像仓库，可以将容器镜像导出到文件，然后拷贝到 Kubernetes 集群的每个工作节点。使用docker命令将容器镜像文件保存为 tar 文件，并手动分发到 Kubernetes 节点。然后从每个节点上的 tar 文件加载容器镜像，详见 docker 命令文档。

使用以下命令部署 Bitfusion device plugin 和其他相关组件:

$ make deploy

3.3 部署的环境的验证

通过"部署方式1"或"部署方式2"完成安装后，使用以下命令查看命名空间"bwki"中是否正确启动了所有组件:

检查 device plugin 是否正在运行:

$ kubectl get pods -n kube-system

NAME                            READY   STATUS    RESTARTS   AGE
bitfusion-device-plugin-cfr87   1/1     Running   0          6m13s

检查webhook 是否正在运行:

$ kubectl  get pod -n bwki

NAME                                            READY   STATUS    RESTARTS   AGE
bitfusion-webhook-deployment-6dbc6df664-td6t7   1/1     Running   0          7m49s

检查其他部署的组件状态

$ kubectl get configmap -n bwki

NAME                                DATA   AGE
bwki-webhook-configmap              1      71m

$ kubectl get serviceaccount  -n bwki

NAME                           SECRETS   AGE
bitfusion-webhook-deployment   1         72m

$ kubectl get ValidatingWebhookConfiguration  -n bwki

NAME                          CREATED AT
validation.bitfusion.io-cfg   2021-03-25T05:29:17Z

$ kubectl get MutatingWebhookConfiguration   -n bwki

NAME                          CREATED AT
bwki-webhook-cfg              2021-03-25T05:29:17Z

$ kubectl get svc   -n bwki

NAME                          TYPE        CLUSTER-IP    EXTERNAL-IP   PORT(S)   AGE
bwki-webhook-svc              ClusterIP   10.101.39.4   <none>        443/TCP   76m

4. 在 Kubernetes 中使用 Bitfusion 资源创建任务

完成安装后，用户可以编写 Kubernetes 的 YAML 文件来使用 Bitfusion 资源。在YAML 文件中有以下几个与 Bitfusion 资源相关的参数:

4.1. 方式 1：通过 gpu-percent 参数指定Bitfusion资源

编辑任务的YAML文件example/pod.yaml

apiVersion: v1
kind: Pod
metadata:
  annotations:
    auto-management/bitfusion: "all"
    bitfusion-client/os: "ubuntu18"
    bitfusion-client/version: "250"
  name: bf-pkgs
  # You can specify any namespace
  namespace: tensorflow-benchmark
spec:
  containers:
    - image: nvcr.io/nvidia/tensorflow:19.07-py3
      imagePullPolicy: IfNotPresent
      name: bf-pkgs
      command: ["python /benchmark/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py --local_parameter_device=gpu --batch_size=32 --model=inception3"]
      resources:
        limits:
          # Request one GPU for this Pod from the Bitfusion cluster
          bitfusion.io/gpu-amount: 1
          # 50 percent of each GPU to be consumed
          bitfusion.io/gpu-percent: 50
      volumeMounts:
        - name: code
          mountPath: /benchmark
    volumes:
        - name: code
          # The Benchmarks used for the test came from: https://github.com/tensorflow/benchmarks/tree/tf_benchmark_stage 
          # Please make sure you have the corresponding content in /home/benchmarks directory on your node
          hostPath:
            path: /home/benchmarks

运行以下命令提交任务：

$ kubectl create namespace tensorflow-benchmark
$ kubectl create -f example/pod.yaml

4.2 方式 2：通过 gpu-memory 参数指定Bitfusion资源

编辑任务的 YAML 文件 example/pod-memory.yaml

apiVersion: v1
kind: Pod
metadata:
  annotations:
    auto-management/bitfusion: "all"
    bitfusion-client/os: "ubuntu18"
    bitfusion-client/version: "250"
  name: bf-pkgs
  # You can specify any namespace
  namespace: tensorflow-benchmark
spec:
  containers:
    - image: nvcr.io/nvidia/tensorflow:19.07-py3
      imagePullPolicy: IfNotPresent
      name: bf-pkgs
      command: ["python /benchmark/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py --local_parameter_device=gpu --batch_size=32 --model=inception3"]
      resources:
        limits:
          bitfusion.io/gpu-amount: 1
          bitfusion.io/gpu-memory: 8000M
      volumeMounts:
        - name: code
          mountPath: /benchmark
    volumes:
        - name: code
          # The Benchmarks used for the test came from: https://github.com/tensorflow/benchmarks/tree/tf_benchmark_stage 
          # Please make sure you have the corresponding content in /home/benchmarks directory on your node
          hostPath:
            path: /home/benchmarks

运行以下命令提交任务：

$ kubectl create namespace tensorflow-benchmark
$ kubectl create -f example/pod-memory.yaml

如果任务成功运行，会得到以下日志输出:

[INFO] 2021-03-27T04:26:40Z Query server 192.168.1.100:56001 gpu availability
[INFO] 2021-03-27T04:26:41Z Choosing GPUs from server list [192.168.1.100:56001]
[INFO] 2021-03-27T04:26:41Z Requesting GPUs [0] with 8080 MiB of memory from server 0, with version 2.5.0-fd3e4839...
Requested resources:
Server List: 192.168.1.100:56001
Client idle timeout: 0 min
[INFO] 2021-03-27T04:26:42Z Locked 1 GPUs with partial memory 0.5, configuration saved to '/tmp/bitfusion125236687'
[INFO] 2021-03-27T04:26:42Z Running client command 'python /benchmark/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py --local_parameter_device=gpu --batch_size=32 --model=inception3' on 1 GPUs, with the following servers:
[INFO] 2021-03-27T04:26:42Z 192.168.1.100 55001 ab4a56d5-8df4-4c93-891d-1c5814cf83f6 56001 2.5.0-fd3e4839

2021-03-27 04:26:43.511803: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.1

......

Instructions for updating:
non-resource variables are not supported in the long term
2021-03-27 04:26:48.173243: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2394455000 Hz
2021-03-27 04:26:48.174378: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4c8ad60 executing computations on platform Host. Devices:
2021-03-27 04:26:48.174426: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>
2021-03-27 04:26:48.184024: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1
2021-03-27 04:26:54.831820: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-03-27 04:26:55.195722: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4c927b0 executing computations on platform CUDA. Devices:
2021-03-27 04:26:55.195825: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Tesla V100-PCIE-16GB, Compute Capability 7.0
2021-03-27 04:26:56.476786: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-03-27 04:26:56.846965: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: Tesla V100-PCIE-16GB major: 7 minor: 0 memoryClockRate(GHz): 1.38
pciBusID: 0000:00:00.0
2021-03-27 04:26:56.847095: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.1
2021-03-27 04:26:56.858148: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10
2021-03-27 04:26:56.870662: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10
2021-03-27 04:26:56.872082: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10
2021-03-27 04:26:56.884804: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10
2021-03-27 04:26:56.891062: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10
2021-03-27 04:26:56.916430: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2021-03-27 04:26:57.108177: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-03-27 04:26:57.699172: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-03-27 04:26:58.487127: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2021-03-27 04:26:58.487327: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.1
2021-03-27 04:53:53.568256: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-03-27 04:53:53.568703: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0
2021-03-27 04:53:53.569011: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N
2021-03-27 04:53:53.939681: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-03-27 04:53:54.482940: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-03-27 04:53:54.846537: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7010 MB memory) -> physical GPU (device: 0, name: Tesla V100-PCIE-16GB, pci bus id: 0000:00:00.0, compute capability: 7.0)

......

TensorFlow:  1.14
Model:       inception3
Dataset:     imagenet (synthetic)
Mode:        training
SingleSess:  False
Batch size:  32 global
             32 per device
Num batches: 100
Num epochs:  0.00
Devices:     ['/gpu:0']
NUMA bind:   False
Data format: NCHW
Optimizer:   sgd
Variables:   parameter_server
==========
Generating training model
Initializing graph
Running warm up
Done warm up
Step    Img/sec total_loss
1       images/sec: 199.4 +/- 0.0 (jitter = 0.0)        7.312
10      images/sec: 196.6 +/- 2.1 (jitter = 5.7)        7.290
20      images/sec: 198.3 +/- 1.3 (jitter = 4.5)        7.351
30      images/sec: 198.4 +/- 0.9 (jitter = 3.8)        7.300
40      images/sec: 199.4 +/- 0.8 (jitter = 4.1)        7.250
50      images/sec: 199.8 +/- 0.7 (jitter = 4.6)        7.283
60      images/sec: 200.1 +/- 0.6 (jitter = 4.2)        7.301
70      images/sec: 199.8 +/- 0.6 (jitter = 4.2)        7.266
80      images/sec: 200.1 +/- 0.6 (jitter = 4.4)        7.286
90      images/sec: 199.9 +/- 0.5 (jitter = 4.4)        7.334
100     images/sec: 199.9 +/- 0.5 (jitter = 4.0)        7.380
----------------------------------------------------------------
total images/sec: 199.65
----------------------------------------------------------------

4.3 auto-management/bitfusion的设置

当设置auto-management/bitfusion的值为all时, 经过webhook的处理之后，通过以下方式可以查看改写后的容器命令

$ kubectl edit pod -n tensorflow-benchmark bf-pkgs

apiVersion: v1
kind: Pod
metadata:
  annotations:
    # update annotations
    auto-management/status: injected
  name: bf-pkgs
  namespace: tensorflow-benchmark
spec:
  containers:
  # update command
  - command:
    - /bin/bash
    - -c
    - /bitfusion/bitfusion-client-ubuntu1804_2.5.1-13/usr/bin/bitfusion run -n 1 -p
      0.500000 python /benchmark/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py --local_parameter_device=gpu
      --batch_size=32 --model=inception3
    env:
    # add LD_LIBRARY_PATH
    - name: LD_LIBRARY_PATH
      value: /bitfusion/bitfusion-client-ubuntu1804_2.5.1-13/opt/bitfusion/lib/x86_64-linux-gnu/lib/:/usr/local/nvidia/lib:/usr/local/nvidia/lib64
    image: nvcr.io/nvidia/tensorflow:19.07-py3
    imagePullPolicy: IfNotPresent
    name: bf-pkgs
    resources:
      limits:
        # update resource name
        bitfusion.io/gpu: "50"
      requests:
        bitfusion.io/gpu: "50"
    volumeMounts:
    - mountPath: /benchmark
      name: code
    # add some volume
    - mountPath: /etc/bitfusion
    .......
  # add initContainer
  initContainers:
  - command:
    - /bin/bash
    - -c
    - ' cp -ra /bitfusion/* /bitfusion-distro/ && cp /root/.bitfusion/client.yaml
      /client && cp -r /bitfusion/bitfusion-client-centos7-2.5.0-10/opt/bitfusion/*
      /workload-container-opt '
    image: docker.io/bitfusiondeviceplugin/bitfusion-client:0.1.1
    imagePullPolicy: IfNotPresent
    name: populate
    ......

发现容器的command中的命令被自动做了相应的改写。

5. 配额的设置和使用(可选)

5.1 设置配额

device plugin使用的资源bitfusion.io/gpu，使用以下命令来创建配额。requests.bitfusion.io/gpu: 100代表在指定的namespace下的作业，最多可以使用Bitfusion的一张GPU卡的100%的能力。

cat <<EOF | kubectl create -f -
apiVersion: v1
kind: List
items:
- apiVersion: v1
  kind: ResourceQuota
  metadata:
    name: bitfusion-quota
    namespace: tensorflow-benchmark
  spec:
    hard:
        requests.bitfusion.io/gpu: 100
EOF

分别通过指定bitfusion.io/gpu-memory参数和bitfusion.io/gpu-percent参数来创建任务。

5.2 指定bitfusion.io/gpu-memory参数创建pod：

使用以下命令创建pod

cat <<EOF | kubectl create -f -
apiVersion: v1
kind: Pod
metadata:
  annotations:
    auto-management/bitfusion: "all"
    bitfusion-client/os: "ubuntu18"
    bitfusion-client/version: "250"
  name: bf-pkgs
  namespace: tensorflow-benchmark
spec:
  containers:
    - image: nvcr.io/nvidia/tensorflow:19.07-py3
      imagePullPolicy: IfNotPresent
      name: bf-pkgs
      command: ["python /benchmark/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py --local_parameter_device=gpu --batch_size=32 --model=inception3"]
      resources:
        limits:
          bitfusion.io/gpu-amount: 1
          bitfusion.io/gpu-memory: 8000M
      volumeMounts:
        - name: code
          mountPath: /benchmark
  volumes:
    - name: code
      hostPath:
        path: /home/benchmarks
EOF

由于 TOTAL_GPU_MEMORY 的值被设置为 16000，代表 Bitfusion 集群中的GPU卡的显存是16GB。所以当这个 pod 请求 1张 GPU 设备的 8000MB 显存时，相当于申请了1张 GPU 卡的 50% 的算力，因此对于 bitfusion.io/gpu 资源的配额的占用是50。

配额占用的计算公式：bitfusion.io/gpu配额占用 = 申请的GPU显存大小 / TOTAL_GPU_MEMORY * 申请的GPU数量 * 100；结果会向上取整。

通过以下命令可以查看配额的占用情况。

$ kubectl describe quota -n tensorflow-benchmark bitfusion-quota

5.3 指定bitfusion.io/gpu-percent参数创建pod

使用以下命令创建pod

cat <<EOF | kubectl create -f -
apiVersion: v1
kind: Pod
metadata:
  annotations:
    auto-management/bitfusion: "all"
    bitfusion-client/os: "ubuntu18"
    bitfusion-client/version: "250"
  name: bf-pkgs
  namespace: tensorflow-benchmark
spec:
  containers:
    - image: nvcr.io/nvidia/tensorflow:19.07-py3
      imagePullPolicy: IfNotPresent
      name: bf-pkgs
      command: ["python /benchmark/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py --local_parameter_device=gpu --batch_size=32 --model=inception3"]
      resources:
        limits:
          bitfusion.io/gpu-amount: 1
          bitfusion.io/gpu-percent: 50
      volumeMounts:
        - name: code
          mountPath: /benchmark
  volumes:
    - name: code
      hostPath:
        path: /home/benchmarks
EOF

配额占用的计算公式：bitfusion.io/gpu 配额占用 = gpu 占用百分比 * 申请的GPU数量 * 100；结果会向上取整。

通过以下命令可以查看配额的占用情况。

$ kubectl describe quota -n tensorflow-benchmark bitfusion-quota

6.Troubleshooting

如果 pod 没有成功运行，使用下面的命令检查日志中的详细信息

$ kubectl logs -n tensorflow-benchmark   bf-pkgs

“tensorflow-benchmark”是 pod 的命名空间。“bf-pkgs”是 pod 的名称

下面的日志显示了一些连接 Bitfusion server 的错误

检查vCenter Bitfusion插件中Bitfusion token的有效性。重新下载一个新的token，并使用以下命令更新Kubernetes中的secret :(确保在Kubernetes的每个命名空间中删除所有旧的bitfusion-secret之后再新建secret)

$ kubectl delete secret -n kube-system bitfusion-secret
$ kubectl delete secret -n tensorflow-benchmark  bitfusion-secret
$ kubectl create secret generic bitfusion-secret --from-file=tokens -n kube-system
$ kubectl create secret generic bitfusion-secret --from-file=tokens -n kube-system
$ make deploy

7. 备注

Bitfusion device plugin 会更新 LD_LIBRARY_PATH 这个环境变量的值，如果用户在构建 docker 镜像时指定了这个值，建议在使用这个镜像时，在 yaml文件中通过以下方式来设置这个值。否则 LD_LIBRARY_PATH 的值可能会被覆盖。
如果要在 TKGi(Tanzu Kubernetes Grid Integrated)上部署 Bitfusion device plugin，需要安装 CFSSL，同时需要修改 bitfusion-with-kubernetes-integration-main/bitfusion_device_plugin/Makefile文件中的K8S_PLATFORM的值为tkgi。