TFserving job with prometheus
This guide walks through the steps required to deploy and serve a TensorFlow model with prometheus and Arena.
1. Setup
Before using Arena for TensorFlow serving with GPU, we need to setup the environment including Kubernetes cluster.
Make sure that your Kubernetes cluster is running and follow the Kubernetes instructions for enabling GPUs.
2. Create Persistent Volume for Model Files
Create /tfmodel in the NFS Server, and prepare mnist models by following the command:
$ mount -t nfs -o vers=4.0 NFS_SERVER_IP:/ /tfmodel/
$ wget https://github.com/osswangxining/tensorflow-sample-code/raw/master/models/tensorflow/mnist.tar.gz
$ tar xvf mnist.tar.gz
Create /tfmodel/config in the NFS Server，and create a monitoring config file named prometheus_config.txt with content
prometheus_config {
enable: true,
path: "/monitoring/prometheus/metrics"
}
Then create Persistent Volume and Persistent Volume Claim by following the command (using NFS as sample):
Create Persistent Volume:
apiVersion: v1
kind: PersistentVolume
metadata:
name: tfmodel
labels:
tfmodel: nas-mnist
spec:
persistentVolumeReclaimPolicy: Retain
capacity:
storage: 10Gi
accessModes:
- ReadWriteMany
nfs:
server: NFS_SERVER_IP
path: "/tfmodel"
Create Persistent Volume Claim:
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: tfmodel
annotations:
description: "this is tfmodel for mnist"
owner: tester
spec:
accessModes:
- ReadWriteMany
resources:
requests:
storage: 5Gi
selector:
matchLabels:
tfmodel: nas-mnist
Check the data volume:
$ arena data list
NAME ACCESSMODE DESCRIPTION OWNER AGE
tfmodel ReadWriteMany this is tfmodel for mnist tester 31s
3. Tensorflow serving with prometheus
You can deploy and serve a Tensorflow model with GPU.If you want to serve a Tensorflow model with GPUMemory,please look at GPUShare User Guide.
Submit tensorflow serving job to deploy and serve machine learning models using the following command.
$ arena serve tensorflow --help
Submit tensorflow serving job to deploy and serve machine learning models.
Usage:
arena serve tensorflow [flags]
Aliases:
tensorflow, tf
Flags:
-a, --annotation stringArray specify the annotations
--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>
--data-dir stringArray specify the trained models datasource on host to mount for serving, like <host_path>:<mount_point_on_job>
--enable-istio enable Istio for serving or not (disable Istio by default)
-e, --env stringArray the environment variables
--expose-service 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 serving job (default "tensorflow/serving:latest")
--image-pull-policy string the policy to pull the image, and the default policy is IfNotPresent (default "IfNotPresent")
-l, --label stringArray specify the labels
--memory string the request memory of each replica to run the serve.
--model-config-file string corresponding with --model_config_file in tensorflow serving
--model-name string the model name for serving
--model-path string the model path for serving in the container
--monitoring-config-file string corresponding with --monitoring_config_file in tensorflow serving
--name string the serving name
--port int the port of tensorflow gRPC listening port (default 8500)
--replicas int the replicas number of the serve job. (default 1)
--restful-port int the port of tensorflow RESTful listening port (default 8501)
--selector stringArray assigning jobs to some k8s particular nodes, usage: "--selector=key=value" or "--selector key=value"
--toleration stringArray tolerate some k8s nodes with taints,usage: "--toleration taint-key" or "--toleration all"
--version string the serving version
--version-policy string support latest, latest:N, specific:N, all
Global Flags:
--arena-namespace string The namespace of arena system service, like tf-operator (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")
-n, --namespace string the namespace of the job
--pprof enable cpu profile
--trace enable trace
3.1. View the GPU resource of your cluster
Before you submit the serving task,make sure you have GPU in your cluster and you have deployed k8s-device-plugin.
Using arena top node to see the GPU resource of your cluster.
$ arena top node
NAME IPADDRESS ROLE STATUS GPU(Total) GPU(Allocated)
cn-shanghai.i-uf61h64dz1tmlob9hmtb 192.168.0.71 <none> ready 1 0
cn-shanghai.i-uf61h64dz1tmlob9hmtc 192.168.0.70 <none> ready 1 0
cn-shanghai.i-uf6347ba9krw8hj5yvsy 192.168.0.67 master ready 0 0
cn-shanghai.i-uf662a07bhojl329pity 192.168.0.68 master ready 0 0
cn-shanghai.i-uf69zddmom136duk79qu 192.168.0.69 master ready 0 0
-------------------------------------------------------------------------------------------
Allocated/Total GPUs In Cluster:
0/2 (0%)
If your cluster have enough GPU resource,you can submit a serving task.
3.2. Submit tensorflow serving task
you can submit a Tensorflow-GPU model with specific version policy as below.
$ arena serve tensorflow \
--name=mymnist1 \
--model-name=mnist1 \
--gpus=1 \
--image=tensorflow/serving:latest-gpu \
--data=tfmodel:/tfmodel \
--model-path=/tfmodel/mnist \
----monitoring-config-file=/tfmodel/config/prometheus_config.txt \
--version-policy=specific:1
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 gpus as it request. After the command,using arena top node to see the gpu resource of the cluster.
$ arena top node
NAME IPADDRESS ROLE STATUS GPU(Total) GPU(Allocated)
cn-shanghai.i-uf61h64dz1tmlob9hmtb 192.168.0.71 <none> ready 1 0
cn-shanghai.i-uf61h64dz1tmlob9hmtc 192.168.0.70 <none> ready 1 1
cn-shanghai.i-uf6347ba9krw8hj5yvsy 192.168.0.67 master ready 0 0
cn-shanghai.i-uf662a07bhojl329pity 192.168.0.68 master ready 0 0
cn-shanghai.i-uf69zddmom136duk79qu 192.168.0.69 master ready 0 0
-------------------------------------------------------------------------------------------
Allocated/Total GPUs In Cluster:
1/2 (50%)
If you want to see the details of pod ,you can use arena top node -d.
$ arena top node -d
NAME: cn-shanghai.i-uf61h64dz1tmlob9hmtc
IPADDRESS: 192.168.0.70
ROLE: <none>
NAMESPACE NAME GPU REQUESTS
default mymnist1-tensorflow-serving-76d5c7c8fc-2kwpw 1
Total GPUs In Node cn-shanghai.i-uf61h64dz1tmlob9hmtc: 1
Allocated GPUs In Node cn-shanghai.i-uf61h64dz1tmlob9hmtc: 1 (100%)
4. List all the serving jobs
You can use the following command to list all the serving jobs.
$ arena serve list
NAME TYPE VERSION DESIRED AVAILABLE ENDPOINT_ADDRESS PORTS
mymnist1 TENSORFLOW 1 1 172.19.10.38 serving:8500,http-serving:8501
5. 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:
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-5dd9955c58-km59h -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]]
}
6. Get promethes metrics
$ curl -X POST http://ENDPOINT_ADDRESS:8501/monitoring/prometheus/metrics
# curl http://172.19.10.38:8501/monitoring/prometheus/metrics
# TYPE :tensorflow:cc:saved_model:load_attempt_count counter
:tensorflow:cc:saved_model:load_attempt_count{model_path="/tfmodel/mnist/1",status="success"} 1
# TYPE :tensorflow:cc:saved_model:load_latency counter
:tensorflow:cc:saved_model:load_latency{model_path="/tfmodel/mnist/1"} 29398960
# TYPE :tensorflow:cc:saved_model:load_latency_by_stage histogram
:tensorflow:cc:saved_model:load_latency_by_stage_bucket{model_path="/tfmodel/mnist/1",stage="init_graph",le="10"} 0
:tensorflow:cc:saved_model:load_latency_by_stage_bucket{model_path="/tfmodel/mnist/1",stage="init_graph",le="58.32"} 1
:tensorflow:cc:saved_model:load_latency_by_stage_bucket{model_path="/tfmodel/mnist/1",stage="init_graph",le="1.47476e+09"} 1
:tensorflow:cc:saved_model:load_latency_by_stage_bucket{model_path="/tfmodel/mnist/1",stage="init_graph",le="+Inf"} 1
:tensorflow:cc:saved_model:load_latency_by_stage_sum{model_path="/tfmodel/mnist/1",stage="init_graph"} 10
:tensorflow:cc:saved_model:load_latency_by_stage_count{model_path="/tfmodel/mnist/1",stage="init_graph"} 1
:tensorflow:cc:saved_model:load_latency_by_stage_bucket{model_path="/tfmodel/mnist/1",stage="restore_graph",le="1.27482e+06"} 0
:tensorflow:cc:saved_model:load_latency_by_stage_bucket{model_path="/tfmodel/mnist/1",stage="restore_graph",le="1.47476e+09"} 1
:tensorflow:cc:saved_model:load_latency_by_stage_bucket{model_path="/tfmodel/mnist/1",stage="restore_graph",le="+Inf"} 1
:tensorflow:cc:saved_model:load_latency_by_stage_sum{model_path="/tfmodel/mnist/1",stage="restore_graph"} 2.93989e+07
:tensorflow:cc:saved_model:load_latency_by_stage_count{model_path="/tfmodel/mnist/1",stage="restore_graph"} 1
# TYPE :tensorflow:contrib:session_bundle:load_attempt_count counter
# TYPE :tensorflow:contrib:session_bundle:load_latency counter
# TYPE :tensorflow:core:direct_session_runs counter
# TYPE :tensorflow:core:graph_build_calls counter
# TYPE :tensorflow:core:graph_build_time_usecs counter
# TYPE :tensorflow:core:graph_run_input_tensor_bytes histogram
# TYPE :tensorflow:core:graph_run_output_tensor_bytes histogram
# TYPE :tensorflow:core:graph_run_time_usecs counter
# TYPE :tensorflow:core:graph_run_time_usecs_histogram histogram
# TYPE :tensorflow:core:graph_runs counter
# TYPE :tensorflow:core:session_created gauge
:tensorflow:core:session_created{} 0
# TYPE :tensorflow:core:xla_compilation_time_usecs counter
# TYPE :tensorflow:core:xla_compilations counter
# TYPE :tensorflow:data:autotune counter
# TYPE :tensorflow:data:bytes_read counter
# TYPE :tensorflow:data:elements counter
# TYPE :tensorflow:data:optimization counter
# TYPE :tensorflow:serving:model_warmup_latency histogram
# TYPE :tensorflow:serving:request_example_count_total counter
# TYPE :tensorflow:serving:request_example_counts histogram
# TYPE :tensorflow:serving:request_log_count counter
7. Delete one serving job
You can use the following command to delete a tfserving job and its associated pods
$ arena serve delete mymnist1
configmap "mymnist1-tensorflow-serving-cm" deleted
service "mymnist1-tensorflow-serving" deleted
deployment.extensions "mymnist1-tensorflow-serving" deleted
configmap "mymnist1-tf-serving" deleted
INFO[0000] The Serving job mymnist1 has been deleted successfully