This guide walks through the steps required to deploy and serve a TensorFlow model with GPUMemory using Kubernetes (K8s)、GPUShare and Arena.
1. Deploy GPUShare. Make sure GPUShare is deployed.
$ kubectl get po -n kube-system|grep gpu
gpushare-device-plugin-ds-4src6 1/1 Running
gpushare-schd-extender-6866868cf5-bb9fc 1/1 Running
2. Tensorflow serving with GPUMemory
You can deploy and serve a Tensorflow model with GPUMemory. Submit tensorflow serving job to deploy and serve machine learning models using the following command.
$ arena serve tensorflow [flags]
options:
--command string the command will inject to container's command.
--cpu string the request cpu of each replica to run the serve.
-d, --data stringArray specify the trained models datasource to mount for serving, like <name_of_datasource>:<mount_point_on_job>
--enableIstio enable Istio for serving or not (disable Istio by default)
-e, --envs stringArray the environment variables
--exposeService expose service using Istio gateway for external access or not (not expose by default)
--gpumemory int the limit GPU memory of each replica to run the serve.
--gpus int the limit GPU count of each replica to run the serve.
-h, --help help for tensorflow
--image string the docker image name of serve job, default image is tensorflow/serving:latest (default "tensorflow/serving:latest")
--imagePullPolicy string the policy to pull the image, and the default policy is IfNotPresent (default "IfNotPresent")
--memory string the request memory of each replica to run the serve.
--modelConfigFile string Corresponding with --model_config_file in tensorflow serving
--modelName string the model name for serving
--modelPath string the model path for serving in the container
--port int the port of tensorflow gRPC listening port (default 8500)
--replicas int the replicas number of the serve job. (default 1)
--restfulPort int the port of tensorflow RESTful listening port (default 8501)
--servingName string the serving name
--servingVersion string the serving version
--versionPolicy string support latest, latest:N, specific:N, all
Options inherited from parent commands
--arenaNamespace string The namespace of arena system service, like TFJob (default "arena-system")
--config string Path to a kube config. Only required if out-of-cluster
--loglevel string Set the logging level. One of: debug|info|warn|error (default "info")
--namespace string the namespace of the job (default "default")
--pprof enable cpu profile
Before you submit the serving task,make sure you have enough GPUMemory in your GPUShare nodes.
Using arena top node -m s to see the GPUMemory resource of your GPUShare nodes.
$ arena top node -m s
NAME IPADDRESS GPU0(Allocated/Total)(GiB)
cn-zhangjiakou.10.0.0.86 10.0.0.86 0/15
cn-zhangjiakou.10.0.0.87 10.0.0.87 0/15
cn-zhangjiakou.10.0.0.88 10.0.0.88 0/15
-------------------------------------------------------------
Allocated/Total GPU Memory In GPUShare Node:
0/45 (GiB) (0%)
If your cluster have enough gpu memory resource ,you can submit a task as below.
$ arena serve tensorflow \
--name=mymnist2 \
--model-name=mnist2 \
--gpumemory=3 \
--image=tensorflow/serving:latest-gpu \
--data=tfmodel:/tfmodel \
--model-path=/tfmodel/mnist \
--version-policy=specific:2
configmap/mymnist2-202007161051-tf-serving created
configmap/mymnist2-202007161051-tf-serving labeled
configmap/mymnist2-202007161051-tensorflow-serving-cm created
service/mymnist2-202007161051-tensorflow-serving created
deployment.apps/mymnist2-202007161051-tensorflow-serving created
Once this command is triggered, one Kubernetes service will be created to expose gRPC and RESTful APIs of mnist model.The task will assume the same gpu memory as it request.
$ arena top node -m s
NAME IPADDRESS GPU0(Allocated/Total)(GiB)
cn-zhangjiakou.10.0.0.86 10.0.0.86 3/15
cn-zhangjiakou.10.0.0.87 10.0.0.87 0/15
cn-zhangjiakou.10.0.0.88 10.0.0.88 0/15
-------------------------------------------------------------
Allocated/Total GPU Memory In GPUShare Node:
3/45 (GiB) (6%)
If you want to see the details of pod ,you can use arena top node -m s -d.
$ arena top node -s -d
NAME: cn-zhangjiakou.10.0.0.88
IPADDRESS: 10.0.0.88
NAME NAMESPACE GPU0(Allocated)
Allocated(GiB) : 0 (0%)
Total(GiB) : 15
----------
NAME: cn-zhangjiakou.10.0.0.86
IPADDRESS: 10.0.0.86
NAME NAMESPACE GPU0(Allocated)
mymnist2-202007161051-tensorflow-serving-86446d46d8-dbsfw default 3
Allocated(GiB) : 3 (20%)
Total(GiB) : 15
----------
NAME: cn-zhangjiakou.10.0.0.87
IPADDRESS: 10.0.0.87
NAME NAMESPACE GPU0(Allocated)
Allocated(GiB) : 0 (0%)
Total(GiB) : 15
----------
Allocated/Total GPU Memory In GPUShare Node:
3/45 (GiB) (6%)
3. List all the serving jobs
$ arena serve list
NAME TYPE VERSION DESIRED AVAILABLE ENDPOINT_ADDRESS PORTS
mymnist2 TENSORFLOW 202007161051 1 0 192.168.75.28 serving:8500,http-serving:8501
4. Test RESTful APIs of serving models
Deploy the sleep pod so you can use curl to test above serving models via RESTful APIs.
$ cat <<EOF | kubectl create -f -
apiVersion: apps/v1
kind: Deployment
metadata:
name: sleep
spec:
selector:
matchLabels:
app: sleep
replicas: 1
template:
metadata:
labels:
app: sleep
spec:
containers:
- name: sleep
image: tutum/curl
command: ["/bin/sleep","infinity"]
imagePullPolicy: IfNotPresent
EOF
Find the name of sleep pod and enter into this pod, for example:
$ kubectl exec -it sleep-bb596f69d-bcmtp -c sleep bash
In this pod, use curl to call the exposed Tensorflow serving RESTful API:
$ curl -X POST http://ENDPOINT_ADDRESS:8501/v1/models/mnist:predict -d '{"signature_name": "predict_images", "instances": [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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You should update request url with your model service ENDPOINT_ADDRESS and modelname accordingly.
The value of "instances" is actually a list of numeric pixels of the first image (which is a hand-written digit "7") in MNIST test dataset.
So you may get response as below. It means the model predicts the input data as "7" with the highest probability among all 10 digits.
{
"predictions": [[2.04608277e-05, 1.72721537e-09, 7.74099826e-05, 0.00364777888, 1.25222812e-06, 2.27521778e-05, 1.14668968e-08, 0.99597472, 3.68833353e-05, 0.000218785644]]
}
5.View the actual memory usage of tf-serving task. Log in your node.
$ nvidia-smi
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 410.72 Driver Version: 410.72 CUDA Version: 10.0 |
|-------------------------------+----------------------+----------------------+
| 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 V100-SXM2... Off | 00000000:00:08.0 Off | 0 |
| N/A 34C P0 51W / 300W | 3753MiB / 16130MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 20840 C /usr/bin/tensorflow_model_server 3743MiB |
+-----------------------------------------------------------------------------+
6. Delete one serving job
You can use the following command to delete a tfserving job and its associated pods
$ arena serve delete mymnist2
configmap "mymnist2-tensorflow-serving-cm" deleted
service "mymnist2-tensorflow-serving" deleted
deployment.extensions "mymnist2-tensorflow-serving" deleted
configmap "mymnist2-tf-serving" deleted
INFO[0000] The Serving job mymnist2 has been deleted successfully