tensorflow与kubernetes/docker结合使用实践

tensorflow

tensorflow是谷歌基于DistBelief进行研发的第二代人工智能学习系统，其命名来源于本身的运行原理。Tensor（张量）意味着N维数组，Flow（流）意味着基于数据流图的计算，TensorFlow为张量从图象的一端流动到另一端计算过程。TensorFlow是将复杂的数据结构传输至人工智能神经网中进行分析和处理过程的系统。

tensorflow可在小到一部智能手机、大到数千台数据中心服务器的各种设备上运行。本文主要探讨的是tensorflow在大规模容器上运行的一种方案。

tensorflow作为深度学习的框架，其对于数据的处理可以分为训练、验证、测试、服务几种。一般来说，训练是用指来训练模型，验证主要用以检验所训练出来的模型的正确性和是否过拟合。测试是计算黑盒数据对于训练的模型进行测试，从而评判模型的准确率。服务是指利用已经完成的训练模型提供服务。这里为了简化，将处理分为了训练和服务两种。

训练主要是指从给定训练的程序和训练数据集，用以生成训练的模型。训练完成的模型可以通过存储形成为checkpoints文件。

验证、测试、服务统统归一到服务，其主要流程是使用已有的模型，对于数据集进行处理。

tensorflow训练 in kubernetes

对于tensorflow训练的支持，kubernetes可以通过创立多个pod来进行支持。tensorflow分布式可以通过制定parameters服务器(ps参数服务器)和worker服务器进行。

首先ps是整个训练集群的参数服务器，保存模型的Variable，worker是计算模型梯度的节点，得到的梯度向量会交付给ps更新模型。in-graph与between-graph对应，但两者都可以实现同步训练和异步训练，in-graph指整个集群由一个client来构建graph，并且由这个client来提交graph到集群中，其他worker只负责处理梯度计算的任务，而between-graph指的是一个集群中多个worker可以创建多个graph，但由于worker运行的代码相同因此构建的graph也相同，并且参数都保存到相同的ps中保证训练同一个模型，这样多个worker都可以构建graph和读取训练数据，适合大数据场景。同步训练和异步训练差异在于，同步训练每次更新梯度需要阻塞等待所有worker的结果，而异步训练不会有阻塞，训练的效率更高，在大数据和分布式的场景下一般使用异步训练。----TensorFlow深度学习

我使用rc创建多个ps和worker服务器。

gcr.io/tensorflow/tensorflow:latest镜像是tensorflow提供的官网镜像，使用CPU进行计算。使用GPU计算的版本下文再行介绍。

[root@A01-R06-I184-22 yaml]# cat ps.yaml apiVersion: v1kind: ReplicationControllermetadata:  name: tensorflow-ps-rcspec:  replicas: 2  selector:    name: tensorflow-ps  template:    metadata:      labels:        name: tensorflow-ps        role: ps    spec:      containers:        - name: ps          image: gcr.io/tensorflow/tensorflow:latest          ports:           - containerPort: 2222[root@A01-R06-I184-22 yaml]# cat worker.yaml apiVersion: v1kind: ReplicationControllermetadata:  name: tensorflow-worker-rcspec:  replicas: 2  selector:    name: tensorflow-worker  template:    metadata:      labels:        name: tensorflow-worker        role: worker    spec:      containers:        - name: worker          image: gcr.io/tensorflow/tensorflow:latest          ports:           - containerPort: 2222

之后为ps和worker分别创建服务。

[root@A01-R06-I184-22 yaml]# cat ps-srv.yamlapiVersion: v1kind: Servicemetadata:  labels:    name: tensorflow-ps    role: service  name: tensorflow-ps-servicespec:  ports:    - port: 2222      targetPort: 2222  selector:    name: tensorflow-ps    [root@A01-R06-I184-22 yaml]# cat worker-srv.yamlapiVersion: v1kind: Servicemetadata:  labels:    name: tensorflow-worker    role: service  name: tensorflow-wk-servicespec:  ports:    - port: 2222      targetPort: 2222  selector:    name: tensorflow-worker

我们可以通过查看service来查看对应的容器的ip。

[root@A01-R06-I184-22 yaml]# kubectl describe service tensorflow-ps-service Name:           tensorflow-ps-serviceNamespace:      defaultLabels:         name=tensorflow-ps,role=serviceSelector:       name=tensorflow-psType:           ClusterIPIP:         10.254.170.61Port:           <unset> 2222/TCPEndpoints:      4.0.84.3:2222,4.0.84.4:2222Session Affinity:   NoneNo events.[root@A01-R06-I184-22 yaml]# kubectl describe service tensorflow-wk-service Name:           tensorflow-wk-serviceNamespace:      defaultLabels:         name=tensorflow-worker,role=serviceSelector:       name=tensorflow-workerType:           ClusterIPIP:         10.254.70.9Port:           <unset> 2222/TCPEndpoints:      4.0.84.5:2222,4.0.84.6:2222Session Affinity:   NoneNo events.

这里我使用deep_recommend_system来进行分布式的实验。

在pod中先下载对应的deep_recommend_system的代码。

curl https://codeload.github.com/tobegit3hub/deep_recommend_system/zip/master -o drs.zipunzip drs.zipcd deep_recommend_system-master/distributed/

在ps的其中一个容器(4.0.84.3)中执行启动ps服务器的任务：

root@tensorflow-ps-rc-b5d6g:/notebooks/deep_recommend_system-master/distributed# nohup python cancer_classifier.py --ps_hosts=4.0.84.3:2222,4.0.84.3:2223 --worker_hosts=4.0.84.5:2222,4.0.84.6:2222 --job_name=ps --task_index=0 >log1 &[1] 502root@tensorflow-ps-rc-b5d6g:/notebooks/deep_recommend_system-master/distributed# nohup: ignoring input and redirecting stderr to stdout root@tensorflow-ps-rc-b5d6g:/notebooks/deep_recommend_system-master/distributed# nohup python cancer_classifier.py --ps_hosts=4.0.84.3:2222,4.0.84.4:2223 --worker_hosts=4.0.84.5:2222,4.0.84.6:2222 --job_name=ps --task_index=1 >log2 &[2] 603root@tensorflow-ps-rc-b5d6g:/notebooks/deep_recommend_system-master/distributed# nohup: ignoring input and redirecting stderr to stdout

这里我尝试使用两个pod分别做ps服务器，但是总是报core dump的错误。官网也有类似错误，未能解决，推测原因可能是复用了某个设备的缘故(两个pod都在同一个宿主机上)。使用一个pod作为两个ps服务器即无问题。

在worker两个容器中分别执行：

root@tensorflow-worker-rc-vznvt:/notebooks/deep_recommend_system-master/distributed# nohup python cancer_classifier.py --ps_hosts=4.0.84.3:2222,4.0.84.3:2223 --worker_hosts=4.0.84.5:2222,4.0.84.6:2222 --job_name=worker --task_index=0 >log &***********************root@tensorflow-worker-rc-cpnt7:/notebooks/deep_recommend_system-master/distributed# nohup python cancer_classifier.py --ps_hosts=4.0.84.3:2222,4.0.84.3:2223 --worker_hosts=4.0.84.5:2222,4.0.84.6:2222 --job_name=worker --task_index=1 >log &

之后在worker服务器上的checkpoint文件夹中可以查看计算模型的中间保存结果。

root@tensorflow-worker-rc-vznvt:/notebooks/deep_recommend_system-master/distributed# ll checkpoint/total 840drwxr-xr-x 2 root root   4096 Oct 10 15:45 ./drwxr-xr-x 3 root root     76 Oct 10 15:18 ../-rw-r--r-- 1 root root      0 Sep 23 14:27 .gitkeeper-rw-r--r-- 1 root root    270 Oct 10 15:45 checkpoint-rw-r--r-- 1 root root  86469 Oct 10 15:45 events.out.tfevents.1476113854.tensorflow-worker-rc-vznvt-rw-r--r-- 1 root root 248875 Oct 10 15:37 graph.pbtxt-rw-r--r-- 1 root root   2229 Oct 10 15:42 model.ckpt-1172-rw-r--r-- 1 root root  94464 Oct 10 15:42 model.ckpt-1172.meta-rw-r--r-- 1 root root   2229 Oct 10 15:43 model.ckpt-1422-rw-r--r-- 1 root root  94464 Oct 10 15:43 model.ckpt-1422.meta-rw-r--r-- 1 root root   2229 Oct 10 15:44 model.ckpt-1670-rw-r--r-- 1 root root  94464 Oct 10 15:44 model.ckpt-1670.meta-rw-r--r-- 1 root root   2229 Oct 10 15:45 model.ckpt-1921-rw-r--r-- 1 root root  94464 Oct 10 15:45 model.ckpt-1921.meta-rw-r--r-- 1 root root   2229 Oct 10 15:41 model.ckpt-921-rw-r--r-- 1 root root  94464 Oct 10 15:41 model.ckpt-921.meta

tensorflow gpu支持

tensorflow gpu in docker

docker可以通过提供gpu设备到容器中。nvidia官方提供了nvidia-docker的一种方式，其用nvidia-docker的命令行代替了docker的命令行来使用GPU。

nvidia-docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow:latest-gpu

这种方式对于docker侵入较多，因此nvidia还提供了一种nvidia-docker-plugin的方式。其使用流程如下：

首先在宿主机启动nvidia-docker-plugin：

[root@A01-R06-I184-22 nvidia-docker]# ./nvidia-docker-plugin ./nvidia-docker-plugin | 2016/10/10 00:01:12 Loading NVIDIA unified memory./nvidia-docker-plugin | 2016/10/10 00:01:12 Loading NVIDIA management library./nvidia-docker-plugin | 2016/10/10 00:01:17 Discovering GPU devices./nvidia-docker-plugin | 2016/10/10 00:01:18 Provisioning volumes at /var/lib/nvidia-docker/volumes./nvidia-docker-plugin | 2016/10/10 00:01:18 Serving plugin API at /run/docker/plugins./nvidia-docker-plugin | 2016/10/10 00:01:18 Serving remote API at localhost:3476

可以看到nvidia-docker-plugin监听了3486端口。然后在宿主机上运行docker run -ti curl -s http://localhost:3476/v1.0/docker/cli -p 8890:8888 gcr.io/tensorflow/tensorflow:latest-gpu /bin/bash命令以创建tensorflow的GPU容器。并可以在容器中验证是否能正常import tensorflow。

[root@A01-R06-I184-22 ~]# docker run -ti `curl -s http://localhost:3476/v1.0/docker/cli` -p 8890:8888 gcr.io/tensorflow/tensorflow:latest-gpu /bin/bashroot@7087e1f99062:/notebooks# pythonPython 2.7.6 (default, Jun 22 2015, 17:58:13) [GCC 4.8.2] on linux2Type "help", "copyright", "credits" or "license" for more information.>>> import tensorflowI tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcublas.so locallyI tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcudnn.so locallyI tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcufft.so locallyI tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcuda.so.1 locallyI tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcurand.so locally>>> 

可以看到tensorflow已经能够正确加载了。

这里同样使用deep_recommend_system进行测试。在pod中先下载对应的deep_recommend_system的代码。

curl https://codeload.github.com/tobegit3hub/deep_recommend_system/zip/master -o drs.zipunzip drs.zipcd deep_recommend_system-master/

然后使用GPU0和1进行计算。

root@087e1f99062:/notebooks/deep_recommend_system-master# export CUDA_VISIBLE_DEVICES='0,1'    //用以指定使用的GPU的编号root@087e1f99062:/notebooks/deep_recommend_system-master# python cancer_classifier.py I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcublas.so locallyI tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcudnn.so locallyI tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcufft.so locallyI tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcuda.so.1 locallyI tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcurand.so locallyUse the model: wide_and_deepUse the optimizer: adagradUse the model: wide_and_deepUse the model: wide_and_deepI tensorflow/core/common_runtime/gpu/gpu_device.cc:951] Found device 0 with properties: name: Tesla K20cmajor: 3 minor: 5 memoryClockRate (GHz) 0.7055pciBusID 0000:02:00.0Total memory: 4.9GiBFree memory: 4.1GiBW tensorflow/stream_executor/cuda/cuda_driver.cc:572] creating context when one is currently active; existing: x24402e0I tensorflow/core/common_runtime/gpu/gpu_device.cc:951] Found device 1 with properties: name: Tesla K20cmajor: 3 minor: 5 memoryClockRate (GHz) 0.7055pciBusID 0000:04:00.0Total memory: 4.9GiBFree memory: 4.1GiBI tensorflow/core/common_runtime/gpu/gpu_device.cc:972] DMA: 0 1 I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] 0:   Y Y I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] 1:   Y Y I tensorflow/core/common_runtime/gpu/gpu_device.cc:1041] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla K20c, pci bus id: 0000:02:00.0)I tensorflow/core/common_runtime/gpu/gpu_device.cc:1041] Creating TensorFlow device (/gpu:1) -> (device: 1, name: Tesla K20c, pci bus id: 0000:04:00.0)[0:00:34.437041] Step: 100, loss: 2.97578692436, accuracy: 0.77734375, auc: 0.763736724854[0:00:32.162310] Step: 200, loss: 1.81753754616, accuracy: 0.7890625, auc: 0.788772583008[0:00:37.559177] Step: 300, loss: 1.26066374779, accuracy: 0.865234375, auc: 0.811861813068[0:00:36.082163] Step: 400, loss: 0.920016527176, accuracy: 0.8359375, auc: 0.820605039597

同样我可以使用nvidia-smi查看GPU使用情况

[root@A01-R06-I184-22 ~]# nvidia-smi Tue Oct 11 00:10:28 2016       +------------------------------------------------------+                       | NVIDIA-SMI 352.39     Driver Version: 352.39         |                       |-------------------------------+----------------------+----------------------+| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC || Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. ||===============================+======================+======================||   0  Tesla K20c          Off  | 0000:02:00.0     Off |                    0 || 30%   26C    P0    48W / 225W |   4540MiB /  4799MiB |      2%      Default |+-------------------------------+----------------------+----------------------+|   1  Tesla K20c          Off  | 0000:04:00.0     Off |                    0 || 30%   31C    P0    48W / 225W |   4499MiB /  4799MiB |      0%      Default |+-------------------------------+----------------------+----------------------+|   2  Tesla K20c          Off  | 0000:83:00.0     Off |                    0 || 30%   25C    P8    26W / 225W |     11MiB /  4799MiB |      0%      Default |+-------------------------------+----------------------+----------------------+|   3  Tesla K20c          Off  | 0000:84:00.0     Off |                    0 || 30%   24C    P8    25W / 225W |     11MiB /  4799MiB |      0%      Default |+-------------------------------+----------------------+----------------------+                                                                               +-----------------------------------------------------------------------------+| Processes:                                                       GPU Memory ||  GPU       PID  Type  Process name                               Usage      ||=============================================================================||    0    132460    C   python                                        4524MiB ||    1    132460    C   python                                        4484MiB |+-----------------------------------------------------------------------------+

nvidia-docker-plugin工作原理是是其提供了一个API

[root@A01-R06-I184-22 ~]# curl -s http://localhost:3476/v1.0/docker/cli--volume-driver=nvidia-docker --volume=nvidia_driver_352.39:/usr/local/nvidia:ro --device=/dev/nvidiactl --device=/dev/nvidia-uvm --device=/dev/nvidia0 --device=/dev/nvidia1 --device=/dev/nvidia2 --device=/dev/nvidia3

可以看到curl -s http://localhost:3476/v1.0/docker/cli命令实际是提供了docker run时候的一些必要参数。其中包括把gpu设备映射进入容器中的部分(--device=/dev/nvidiactl --device=/dev/nvidia-uvm --device=/dev/nvidia0 --device=/dev/nvidia1 --device=/dev/nvidia2 --device=/dev/nvidia3)，还包括了将nvidia_driver_352.39存储映射进入容器的部分。

接下来我们对于nvidia_driver_352.39进行分析

[root@A01-R06-I184-22 ~]# docker volume lsDRIVER              VOLUME NAMEnvidia-docker       nvidia_driver_352.39[root@A01-R06-I184-22 ~]# docker volume inspect nvidia_driver_352.39 [    {        "Name": "nvidia_driver_352.39",        "Driver": "nvidia-docker",        "Mountpoint": "/var/lib/nvidia-docker/volumes/nvidia_driver/352.39"    }]

可以看到该存储其实只是一个文件夹。对文件夹/var/lib/nvidia-docker/volumes/nvidia_driver/352.39/进行分析

[root@A01-R06-I184-22 ~]# tree -L 3 /var/lib/nvidia-docker/volumes/nvidia_driver/352.39//var/lib/nvidia-docker/volumes/nvidia_driver/352.39/├── bin│   ├── nvidia-cuda-mps-control│   ├── nvidia-cuda-mps-server│   ├── nvidia-debugdump│   ├── nvidia-persistenced│   └── nvidia-smi├── lib│   ├── libcuda.so -> libcuda.so.352.39│   ├── libcuda.so.1 -> libcuda.so.352.39│   ├── libcuda.so.352.39│   ├── libGL.so.1 -> libGL.so.352.39│   ├── libGL.so.352.39│   ├── libnvcuvid.so.1 -> libnvcuvid.so.352.39│   ├── libnvcuvid.so.352.39│   ├── libnvidia-compiler.so.352.39│   ├── libnvidia-eglcore.so.352.39│   ├── libnvidia-encode.so.1 -> libnvidia-encode.so.352.39│   ├── libnvidia-encode.so.352.39│   ├── libnvidia-fbc.so.1 -> libnvidia-fbc.so.352.39│   ├── libnvidia-fbc.so.352.39│   ├── libnvidia-glcore.so.352.39│   ├── libnvidia-glsi.so.352.39│   ├── libnvidia-ifr.so.1 -> libnvidia-ifr.so.352.39│   ├── libnvidia-ifr.so.352.39│   ├── libnvidia-ml.so.1 -> libnvidia-ml.so.352.39│   ├── libnvidia-ml.so.352.39│   ├── libnvidia-opencl.so.1 -> libnvidia-opencl.so.352.39│   ├── libnvidia-opencl.so.352.39│   ├── libvdpau_nvidia.so.1 -> libvdpau_nvidia.so.352.39│   └── libvdpau_nvidia.so.352.39└── lib64    ├── libcuda.so -> libcuda.so.352.39    ├── libcuda.so.1 -> libcuda.so.352.39    ├── libcuda.so.352.39    ├── libGL.so.1 -> libGL.so.352.39    ├── libGL.so.352.39    ├── libnvcuvid.so.1 -> libnvcuvid.so.352.39    ├── libnvcuvid.so.352.39    ├── libnvidia-compiler.so.352.39    ├── libnvidia-eglcore.so.352.39    ├── libnvidia-encode.so.1 -> libnvidia-encode.so.352.39    ├── libnvidia-encode.so.352.39    ├── libnvidia-fbc.so.1 -> libnvidia-fbc.so.352.39    ├── libnvidia-fbc.so.352.39    ├── libnvidia-glcore.so.352.39    ├── libnvidia-glsi.so.352.39    ├── libnvidia-ifr.so.1 -> libnvidia-ifr.so.352.39    ├── libnvidia-ifr.so.352.39    ├── libnvidia-ml.so.1 -> libnvidia-ml.so.352.39    ├── libnvidia-ml.so.352.39    ├── libnvidia-opencl.so.1 -> libnvidia-opencl.so.352.39    ├── libnvidia-opencl.so.352.39    ├── libnvidia-tls.so.352.39    ├── libvdpau_nvidia.so.1 -> libvdpau_nvidia.so.352.39    └── libvdpau_nvidia.so.352.393 directories, 52 files

可以看到这个文件夹其实主要包含的是关于GPU显卡的一些库、包和一些必要的可执行文件。这些文件实际上也是从宿主机上由nvidia-docker-plugin收集拷贝到该文件夹中的，用以提供给容器，方便容器对于GPU的使用。

kubernetes与GPU

kubernetes1.3已经引入了GPU调度支持，但是目前是实验性质。

tensorflow服务

Serving Inception Model with TensorFlow Serving and Kubernetes中对于tensorflow服务与kubernetes结合使用的方式进行了介绍。

其基本的工作方式是首先根据已经训练好的模型，制作成可以对外提供服务的镜像inception_serving。而后使用该镜像创建rc，并对应建立service。

$ kubectl get rcCONTROLLER             CONTAINER(S)          IMAGE(S)                              SELECTOR               REPLICAS   AGEinception-controller   inception-container   gcr.io/tensorflow-serving/inception   worker=inception-pod   3          20s$ kubectl get svcNAME                CLUSTER_IP      EXTERNAL_IP      PORT(S)    SELECTOR               AGEinception-service   10.15.242.244   146.148.88.232   9000/TCP   worker=inception-pod   3m$ kubectl describe svc inception-serviceName:     inception-serviceNamespace:    defaultLabels:     <none>Selector:   worker=inception-podType:     LoadBalancerIP:     10.15.242.244LoadBalancer Ingress: 146.148.88.232Port:     <unnamed> 9000/TCPNodePort:   <unnamed> 32006/TCPEndpoints:    10.12.2.4:9000,10.12.4.4:9000,10.12.4.5:9000Session Affinity: NoneEvents:  FirstSeen LastSeen  Count From      SubobjectPath Reason      Message  ───────── ────────  ───── ────      ───────────── ──────      ───────  4m    3m    2 {service-controller }     CreatingLoadBalancer  Creating load balancer  3m    2m    2 {service-controller }     CreatedLoadBalancer   Created load balancer

用户请求直接通过EXTERNAL_IP(146.148.88.232:9000)进行服务访问。当用户有请求到来时，kubernetes将请求分发给10.12.2.4:9000,10.12.4.4:9000,10.12.4.5:9000之一的pod，然后由该pod上提供实际的服务，从而返回结果。

这一过程本质上来说同提供web服务(如tomcat的服务)等是没有多大区别的。kubernetes可以很好的支持。