TensorFlow Serving Cluster PPML

This solution presents a framework for developing a PPML (Privacy-Preserving Machine Learning) solution - TensorFlow Serving cluster with Intel SGX and Gramine.


Simply running a TensorFlow Serving system inside Gramine is not enough for a safe & secure end-user experience. Thus, there is a need to build a complete secure inference flow. This paper will present TensorFlow Serving with Intel SGX and Gramine and will provide end-to-end protection (from client to servers) and integrate various security ingredients such as the load balancer (Nginx Ingress) and elastic scheduler (Kubernetes). Please refer to What is Kubernetes for more details.

Figure: Nginx Ingress controller

In this solution, we focus on:

  • AI Service - TensorFlow Serving, a flexible, high-performance serving system for machine learning models.

  • Model protection - protecting the confidentiality and integrity of the model when the inference takes place on an untrusted platform such as a public cloud virtual machine.

  • Data protection - establishing a secure communication link from end-user to TensorFlow Serving when the user doesn’t trust the remote platform where the TensorFlow Serving system is executing.

  • Platform Integrity - providing a way for Intel SGX platform to attest itself to the remote user, so that she can gain trust in the remote SGX platform.

  • Elasticity - providing the Kubernetes service for automating deployment, scaling, and management of containerized TensorFlow Serving so that the cloud providers can setup the environment easily. We use Nginx for automatic load balancing.

The goal of this solution is to show how these applications - TensorFlow Serving and Kubernetes - can run in an untrusted environment (like a public cloud), automating deployment while still ensuring the confidentiality and integrity of sensitive input data and the model. To this end, we use Intel SGX enclaves to isolate TensorFlow Serving’s execution to protect data confidentiality and integrity, and to provide a cryptographic proof that the program is correctly initialized and running on legitimate hardware with the latest patches. We also use LibOS Gramine to simplify the task of porting TensorFlow Serving to SGX, without any changes.

Figure: TensorFlow Serving Flow

In this tutorial, we use three machines: client trusted machine, it can be a non-SGX platform or an SGX platform; SGX-enabled machine, treated as untrusted machine; remote client machine. In this solution, you can also deploy this solution in one SGX-enabled machine with below steps.

Here we will show the complete workflow for using Kubernetes to manage the TensorFlow Serving running inside an SGX enclave with Gramine and its features of Secret Provisioning and Protected Files. We rely on the new ECDSA/DCAP remote attestation scheme developed by Intel for untrusted cloud environments.

To run the TensorFlow Serving application on a particular SGX platform, the owner of the SGX platform must retrieve the corresponding SGX certificate from the Intel Provisioning Certification Service, along with Certificate Revocation Lists (CRLs) and other SGX-identifying information . Typically, this is a part of provisioning the SGX platform in a cloud or a data center environment, and the end user can access it as a service (in other words, the end user doesn’t need to deal with the details of this SGX platform provisioning but instead uses a simpler interface provided by the cloud/data center vendor).

As a second preliminary step, the user must encrypt model files with her cryptographic (wrap) key and send these protected files to the remote storage accessible from the SGX platform .

Next, the untrusted remote platform uses Kubernetes to start TensorFlow Serving inside the SGX enclave . Meanwhile, the user starts the secret provisioning application on her own machine. The three machines establish a TLS connection using RA-TLS , the user verifies that the untrusted remote platform has a genuine up-to-date SGX processor and that the application runs in a genuine SGX enclave , and finally provisions the cryptographic wrap key to this untrusted remote platform . Note that during build time, Gramine informs the user of the expected measurements of the SGX application.

After the cryptographic wrap key is provisioned, the untrusted remote platform may start executing the application. Gramine uses Protected FS to transparently decrypt the model files using the provisioned key when the TensorFlow Serving application starts . TensorFlow Serving then proceeds with execution on plaintext files . The client and the TensorFlow Serving will establish a TLS connection using gRPC TLS with the key and certificate generated by the client . The Nginx load balancer will monitor the requests from the client , and will forward external requests to TensorFlow Serving . When TensorFlow Serving completes the inference, it will send back the result to the client through gRPC TLS .


  • Ubuntu 18.04. This solution should work on other Linux distributions as well, but for simplicity we provide the steps for Ubuntu 18.04 only.

  • Docker Engine. Docker Engine is an open source containerization technology for building and containerizing your applications. In this tutorial, applications, like Gramine, TensorFlow Serving, secret provisioning, will be built in Docker images. Then Kubernetes will manage these Docker images. Please follow this guide to install Docker engine.

  • CCZoo TensorFlow Serving cluster scripts package. You can download the source package tensorflow-serving-cluster:

    git clone https://github.com/intel/confidential-computing-zoo.git
  • Intel SGX Driver and SDK/PSW. You need a machine that supports Intel SGX and FLC/DCAP. Please follow this guide to install the Intel SGX driver and SDK/PSW on the machine/VM. Make sure to install the driver with ECDSA/DCAP attestation.

    For deployments on Microsoft Azure, a script is provided to install general dependencies, Intel SGX DCAP dependencies, and the Azure DCAP Client. To run this script:

    cd <tensorflow-serving-cluster dir>/tensorflow-serving
    sudo ./setup_azure_vm.sh

    After Intel SGX DCAP is setup, verify the Intel Architectural Enclave Service Manager is active (running):

    sudo systemctl status aesmd

Solution Ingredients

This solution uses the following ingredients, which are installed as described in the sections below.

  • TensorFlow Serving. TensorFlow Serving is a flexible, high-performance serving system for machine learning models

  • Gramine

  • Kubernetes. Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications. In this tutorial, we will provide a script (install_kubernetes.sh) to install Kubernetes in your machine.

We will start with the TensorFlow Serving service running in a container without the use of Kubernetes. The TensorFlow Serving service provides confidentiality of the model file using encryption (handled by Gramine) and remote attestation from a secret provisioning server (run from a separate container).

Then we will use Kubernetes to provide automated deployment, scaling and management of the containerized TensorFlow Serving application.

Executing Confidential TF Serving without Kubernetes

1. Client Preparation

Under client machine, please download source package:

git clone https://github.com/intel/confidential-computing-zoo.git

1.1 Download the Model

We use ResNet50 model with FP32 precision for TensorFlow Serving to the inference. First, use download_model.sh to download the pre-trained model file. It will generate the directory models/resnet50-v15-fp32 in current directory:

cd <tensorflow-serving-cluster dir>/tensorflow-serving/docker/client

The model file will be downloaded to models/resnet50-v15-fp32. Then use model_graph_to_saved_model.py to convert the pre-trained model to SavedModel:

pip3 install -r requirements.txt
python3 ./model_graph_to_saved_model.py --import_path `pwd -P`/models/resnet50-v15-fp32/resnet50-v15-fp32.pb --export_dir  `pwd -P`/models/resnet50-v15-fp32 --model_version 1 --inputs input --outputs  predict

Note: model_graph_to_saved_model.py has dependencies on tensorflow, please install tensorflow.

The converted model file will be under:


1.2 Create the SSL/TLS certificate

We choose gRPC SSL/TLS and create the SSL/TLS Keys and certificates by setting TensorFlow Serving domain name to establish a communication link between client and TensorFlow Serving.

For ensuring security of the data being transferred between a client and server, SSL/TLS can be implemented either one-way TLS authentication or two-way TLS authentication (mutual TLS authentication).

one-way SSL/TLS authentication(client verifies server):

./generate_oneway_ssl_config.sh ${service_domain_name}
tar -cvf ssl_configure.tar ssl_configure

generate_oneway_ssl_config.sh will generate the directory ssl_configure which includes server/*.pem and ssl.cfg. server/cert.pem will be used by the remote client and ssl.cfg will be used by TensorFlow Serving.

two-way SSL/TLS authentication(server and client verify each other):

./generate_twoway_ssl_config.sh ${service_domain_name} ${client_domain_name}
tar -cvf ssl_configure.tar ssl_configure

generate_twoway_ssl_config.sh will generate the directory ssl_configure which includes server/*.pem, client/*.pem, ca_*.pem and ssl.cfg. client/*.pem and ca_cert.pem will be used by the remote client and ssl.cfg will be used by TensorFlow Serving.

1.3 Create encrypted model file

Starting from Intel SGX SDK v1.9, SGX SDK provides the function of secure file I/O operations. This function is provided by a component of the SGX SDK called Protect File System Library, which enables safely I/O operations in the Enclave.

It guarantees below items.

  • Integrity of user data. All user data are read from disk and then decrypted with MAC (Message Authentication Code) verified to detect any data tampering.

  • Matching of file name. When opening an existing file, the metadata of the to-be-opened file will be checked to ensure that the name of the file when created is the same as the name given to the open operation.

  • Confidentiality of user data. All user data is encrypted and then written to disk to prevent any data leakage.

For more details, please refer to Understanding SGX Protected File System.

In our solution, we use a tool named gramine-sgx-pf-crypt provided by the LibOS Gramine for secure file I/O operations based on the SGX SDK, which can be used to encrypt and decrypt files. In the template configuration file provided by Gramine, the configuration option “sgx.protected_files.file_mode=file_name” is given, which specifies the files to be protected by encryption.

When TensorFlow Serving loads the model, the path to load the model is models/resnet50-v15-fp32/1/saved_model.pb, and the encryption key is located in files/wrap-key. You can also customize the 128-bit password. According to the file path matching principle, the file path must be consistent with the one used during encryption.

Use the gramine-sgx-pf-crypt tool to encrypt the model file command as follow:

mkdir plaintext/
mv models/resnet50-v15-fp32/1/saved_model.pb plaintext/
LD_LIBRARY_PATH=./libs ./gramine-sgx-pf-crypt encrypt -w files/wrap-key -i  plaintext/saved_model.pb -o  models/resnet50-v15-fp32/1/saved_model.pb
tar -cvf models.tar models

For more information about gramine-sgx-pf-crypt, please refer to pf_crypt.

1.4 Start Secret Provision Service

In order to deploy this service easily, we build and run this service in container. Basically, we use secret_prov_server_dcap as the remote SGX Enclave Quote authentication service and relies on the Quote-related authentication library provided by SGX DCAP. The certification service will obtain Quote certification related data from Intel PCCS, such as TCB related information and CRL information. After successful verification of SGX Enclave Quote, the key stored in files/wrap-key will be sent to the remote application. The remote application here is Gramine in the SGX environment. After remote Gramine gets the key, it will decrypt the encrypted model file.

Build and run the secret provisioning service container. For deployments on Microsoft Azure:

cd <tensorflow-serving-cluster dir>/tensorflow-serving/docker/secret_prov
sudo AZURE=1 ./build_secret_prov_image.sh
sudo ./run_secret_prov.sh -i secret_prov_server:latest

For other cloud deployments:

cd <tensorflow-serving-cluster dir>/tensorflow-serving/docker/secret_prov
./run_secret_prov.sh -i secret_prov_server:latest -a pccs.service.com:ip_addr

For Anolisos cloud deployments:

cd <tensorflow-serving-cluster dir>/tensorflow-serving/docker/secret_prov
./build_secret_prov_image.sh anolisos
./run_secret_prov.sh -i anolisos_secret_prov_server:latest -a pccs.service.com:ip_addr
  1. ip_addr is the host machine where your PCCS service is installed.

  2. secret provision service will start port 4433 and monitor request. Under public cloud instance, please make sure the port 4433 is enabled to access.

  3. Under cloud SGX environment (except for Microsoft Azure), if CSP provides their own PCCS server, please replace the PCCS URL in sgx_default_qcnl.conf with the one provided by CSP. You can start the secret provision service:

    ./run_secret_prov.sh -i <secret_prov_service_image_id>

To check the secret provision service logs:

sudo docker ps -a
sudo docker logs <secret_prov_service_container_id>

Get the container’s IP address, which will be used when starting the TensorFlow Serving Service in the next step:

sudo docker ps -a
sudo docker inspect -f '{{range .NetworkSettings.Networks}}{{.IPAddress}}{{end}}' <secret_prov_service_container_id>

2. Run TensorFlow Serving w/ Gramine in SGX-enabled machine

Under SGX-enabled machine, please download source package:

git clone https://github.com/intel/confidential-computing-zoo.git

2.1 Preparation

Recall that we’ve created encrypted model and TLS certificate in client machine, we need to copy them to this machine. For example:

cd <tensorflow-serving-cluster dir>/tensorflow-serving/docker/tf_serving
cp ../client/models.tar .
cp ../client/ssl_configure.tar .
tar -xvf models.tar
tar -xvf ssl_configure.tar

2.2 Build TensorFlow Serving Docker image

Build the TensorFlow Serving container. For deployments on Microsoft Azure:

cd <tensorflow-serving-cluster dir>/tensorflow-serving/docker/tf_serving
sudo AZURE=1 ./build_gramine_tf_serving_image.sh

For other cloud deployments:

cd <tensorflow-serving-cluster dir>/tensorflow-serving/docker/tf_serving

For Anolisos cloud deployments:

cd <tensorflow-serving-cluster dir>/tensorflow-serving/docker/tf_serving
./build_gramine_tf_serving_image.sh anolisos

The dockerfile used is gramine_tf_serving.dockerfile, which includes the following install items:

  • Install basic dependencies for source code build.

  • Install TensorFlow Serving.

  • Install LibOS - Gramine.

  • Copy files from host to built container.

The files copied from host to container include:

  • Makefile. It is used to compile TensorFlow with Gramine.

  • sgx_default_qcnl.conf. Please replace the PCCS url provided by CSP when under public cloud instance.

  • tf_serving_entrypoint.sh. The execution script when container is launched.

  • tensorflow_model_server.manifest.template. The TensorFlow Serving configuration template used by Gramine.

Gramine supports SGX RA-TLS function, it can be enabled by configurations in the template.Key parameters used in current template as below:

sgx.remote_attestation = 1
loader.env.LD_PRELOAD = "libsecret_prov_attest.so"
loader.env.SECRET_PROVISION_CA_CHAIN_PATH ="certs/test-ca-sha256.crt"
loader.env.SECRET_PROVISION_SERVERS ="attestation.service.com:4433"
sgx.trusted_files.libsecretprovattest ="file:libsecret_prov_attest.so"
sgx.trusted_files.cachain= "file:certs/test-ca-sha256.crt"
sgx.protected_files.model= "file:models/resnet50-v15-fp32/1/saved_model.pb"

SECRET_PROVISION_SERVERS is the remote secret provision server address in client. attestation.service.com is the Domain name, 4433 is the port used by secret provision server.

SECRET_PROVISION_SET_PF_KEY presents if application need secret provision server sends secret key back to it when attestation verification pass in secret provision server.

sgx.protected_files shows self-defined encrypted files. Files is encrypted with key stored in secret provision server. For more syntax used in the manifest template, please refer to Gramine Manifest syntax.

2.3 Execute TensorFlow Serving w/ Gramine in SGX

Run the TensorFlow Serving container:

cd <tensorflow-serving-cluster dir>/tensorflow-serving/docker/tf_serving
cp ssl_configure/ssl.cfg .
sudo ./run_gramine_tf_serving.sh -i gramine_tf_serving:latest -p 8500-8501 -m resnet50-v15-fp32 -s ssl.cfg -a attestation.service.com:<secret_prov_service_container_ip_addr>

Run the TensorFlow Serving container:

cd <tensorflow-serving-cluster dir>/tensorflow-serving/docker/tf_serving
cp ssl_configure/ssl.cfg .
sudo ./run_gramine_tf_serving.sh -i anolisos_gramine_tf_serving:latest -p 8500-8501 -m resnet50-v15-fp32 -s ssl.cfg -a attestation.service.com:<secret_prov_service_container_ip_addr>
  1. 8500-8501 are the ports created on (bound to) the host, you can change them if you need.

  2. secret_prov_service_container_ip_addr is the ip address of the container running the secret provisioning service.

Check the TensorFlow Serving container logs:

sudo docker ps -a
sudo docker logs <tf_serving_container_id>

Now, the TensorFlow Serving is running in SGX and waiting for remote requests.

Figure: TensorFlow Serving

Get the container’s IP address, which will be used when starting the Client container in the next step:

sudo docker ps -a
sudo docker inspect -f '{{range .NetworkSettings.Networks}}{{.IPAddress}}{{end}}' <tf_serving_container_id>

3. Remote Inference Request

In this section, the files in the ssl_configure directory will be reused.

3.1 Build Client Docker Image

Build the Client container:

cd <tensorflow-serving-cluster dir>/tensorflow-serving/docker/client
sudo docker build -f client.dockerfile . -t client:latest

Build the Client container in Anolisos:

cd <tensorflow-serving-cluster dir>/tensorflow-serving/docker/client
sudo docker build -f anolisos_client.dockerfile . -t anolisos_client:latest

Run the Client container:

sudo docker run -it --add-host="grpc.tf-serving.service.com:<tf_serving_service_ip_addr>" client:latest bash

Run the Client container in Anolisos:

sudo docker run -it --add-host="grpc.tf-serving.service.com:<tf_serving_service_ip_addr>" anolisos_client:latest bash

3.2 Send remote inference request

Send the remote inference request (with a dummy image) to demonstrate a single TensorFlow serving node with remote attestation:

one-way SSL/TLS authentication::

   cd /client
   python3 ./resnet_client_grpc.py -batch 1 -cnum 1 -loop 50 -url grpc.tf-serving.service.com:8500 -crt `pwd -P`/ssl_configure/server/cert.pem

two-way SSL/TLS authentication::

   cd /client
   python3 ./resnet_client_grpc.py -batch 1 -cnum 1 -loop 50 -url grpc.tf-serving.service.com:8500 -ca `pwd -P`/ssl_configure/ca_cert.pem -crt `pwd -P`/ssl_configure/client/cert.pem -key `pwd -P`/ssl_configure/client/key.pem

The inference result is printed in the terminal window.

Executing Confidential TF Serving with Kubernetes

In this section, we will setup Kubernetes on the SGX-enabled machine. Then we will use Kubernetes to start multiple TensorFlow Serving containers. The following sections will reuse the machine/VM Intel SGX DCAP setup and containers built from the previous sections. Stop and remove the client and tf-serving containers. Start the secret provisioning container if it isn’t running:

sudo docker ps -a
sudo docker stop <client_container_id> <tf_serving_container_id>
sudo docker rm <client_container_id> <tf_serving_container_id>
sudo docker start <secret_prov_service_container_id>

1. Setup Kubernetes

First, please make sure the system time on your machine is updated.

1.1 Install Kubernetes

Refer to https://kubernetes.io/docs/setup/production-environment/ or use install_kubernetes.sh to install Kubernetes:

cd <tensorflow-serving-cluster dir>/kubernetes
sudo ./install_kubernetes.sh

Create the control plane / master node and allow pods to be scheduled onto this node:

unset http_proxy && unset https_proxy
swapoff -a && free -m
sudo rm /etc/containerd/config.toml
containerd config default | sudo tee /etc/containerd/config.toml
sudo systemctl restart containerd
sudo kubeadm init --v=5 --node-name=master-node --pod-network-cidr= --kubernetes-version=v1.23.9 --cri-socket /run/containerd/containerd.sock

mkdir -p $HOME/.kube
sudo cp -i /etc/kubernetes/admin.conf $HOME/.kube/config
sudo chown $(id -u):$(id -g) $HOME/.kube/config

kubectl taint nodes --all node-role.kubernetes.io/master-

1.2 Setup Flannel in Kubernetes

Setup Flannel in Kubernetes.

Flannel is focused on networking and responsible for providing a layer 3 IPv4 network between multiple nodes in a cluster. Flannel does not control how containers are networked to the host, only how the traffic is transported between hosts.

Deploy the Flannel service:

kubectl apply -f flannel/deploy.yaml

1.3 Setup Ingress-Nginx in Kubernetes

Setup Ingress-Nginx in Kubernetes. Please refer to the Introduction part for more information about Nginx.

Deploy the Nginx service:

kubectl apply -f ingress-nginx/deploy-nodeport.yaml

1.4 Verify Node Status

Get node info to verify that the node status is Ready:

kubectl get node

1.5 Config Kubernetes cluster DNS

Configure the cluster DNS in Kubernetes so that all the TensorFlow Serving pods can communicate with the secret provisioning server:

kubectl edit configmap -n kube-system coredns

The config file will open in an editor. Add the following hosts section:

# new added
hosts {
       ${secret_prov_service_container_ip_addr} attestation.service.com
# end
prometheus :9153
forward . /etc/resolv.conf {
          max_concurrent 1000

${secret_prov_service_container_ip_addr} is the IP address of the Secret Provisioning Service container.

1.6 Setup Docker Registry

Setup a local Docker registry to serve the TensorFlow Serving container image to the Kubernetes cluster:

sudo docker run -d -p 5000:5000 --restart=always --name registry registry:2
sudo docker tag gramine_tf_serving:latest localhost:5000/gramine_tf_serving
sudo docker push localhost:5000/gramine_tf_serving

1.7 Start TensorFlow Serving Deployment

Let’s take a look at the configuration for the elastic deployment of TensorFlow Serving under the directory:

<tensorflow-serving-cluster dir>/tensorflow-serving/kubernetes

There are two Yaml files: deploy.yaml and ingress.yaml.

You can look at this for more information about Yaml.

Customize the deploy.yaml TensorFlow Serving container information, if needed:

- name: gramine-tf-serving-container
  image: localhost:5000/gramine_tf_serving
  imagePullPolicy: IfNotPresent

Customize the deploy.yaml model and ssl host paths:

- name: model-path
    path: <Your confidential-computing-zoo path>/cczoo/tensorflow-serving-cluster/tensorflow-serving/docker/tf_serving/models
- name: ssl-path
    path: <Your confidential-computing-zoo path>/cczoo/tensorflow-serving-cluster/tensorflow-serving/docker/tf_serving/ssl_configure/ssl.cfg

ingress.yaml mainly configures the networking options. Use the default domain name, or use a custom domain name:

  - host: grpc.tf-serving.service.com

Apply the two yaml files:

cd <tensorflow-serving-cluster dir>/tensorflow-serving/kubernetes
kubectl apply -f deploy.yaml
kubectl apply -f ingress.yaml

1.8 Verify TensorFlow Serving Deployment

Verify one pod of the TensorFlow Serving container is running and that the service is ready (look for log “Entering the event loop”):

$ kubectl get pods -n gramine-tf-serving
NAME                                             READY   STATUS    RESTARTS   AGE
gramine-tf-serving-deployment-548f95f46d-rx4w2   1/1     Running   0          5m1s
$ kubectl logs -n gramine-tf-serving gramine-tf-serving-deployment-548f95f46d-rx4w2

Check pod info if the pod is not running:

$ kubectl describe pod -n gramine-tf-serving gramine-tf-serving-deployment-548f95f46d-rx4w2

Check the coredns setup if the TensorFlow Serving service is not ready. This can be caused when the TensorFlow Serving service is unable to obtain the wrap-key (used to decrypt the model file) from the secret provisioning container.

1.9 Scale the TensorFlow Serving Service

Scale the TensorFlow Serving service to two replicas:

$ kubectl scale -n gramine-tf-serving deployment.apps/gramine-tf-serving-deployment --replicas 2

This starts two TensorFlow Serving containers, each with its own TensorFlow Serving service running on its own SGX enclave.

Verify that two pods are now running. Also verify that the second pod of the TensorFlow Serving container is running and that the service is ready (look for log “Entering the event loop”):

$ kubectl get pods -n gramine-tf-serving
NAME                                             READY   STATUS    RESTARTS   AGE
gramine-tf-serving-deployment-548f95f46d-q4bcg   1/1     Running   0          2m28s
gramine-tf-serving-deployment-548f95f46d-rx4w2   1/1     Running   0          4m10s
$ kubectl logs -n gramine-tf-serving gramine-tf-serving-deployment-548f95f46d-q4bcg

These TensorFlow Serving containers perform remote attestation with the Secret Provisioning service to get the secret key. With the secret key, the TensorFlow Serving containers can decrypted the model file.

1.10 Send remote inference request

Send the remote inference request (with a dummy image) to demonstrate an elastic TensorFlow Serving deployment through Kubernetes.

First, get the CLUSTER-IP of the load balanced TensorFlow Serving service:

$ kubectl get service -n gramine-tf-serving
NAME                         TYPE       CLUSTER-IP      EXTERNAL-IP   PORT(S)          AGE
gramine-tf-serving-service   NodePort   <none>        8500:30500/TCP   13m

Run the Client container using the load balanced TensorFlow Serving IP address:

$ sudo docker run -it --add-host="grpc.tf-serving.service.com:<tf_serving_CLUSTER-IP>" client:latest bash

For one-way SSL/TLS authentication:

$ cd /client
$ python3 ./resnet_client_grpc.py -batch 1 -cnum 1 -loop 50 -url grpc.tf-serving.service.com:8500 -crt `pwd -P`/ssl_configure/server/cert.pem

For two-way SSL/TLS authentication:

$ cd /client
$ python3 ./resnet_client_grpc.py -batch 1 -cnum 1 -loop 50 -url grpc.tf-serving.service.com:8500 -ca `pwd -P`/ssl_configure/ca_cert.pem -crt `pwd -P`/ssl_configure/client/cert.pem -key `pwd -P`/ssl_configure/client/key.pem

The inference result is printed in the terminal window.

2. Cleaning Up

To stop the TensorFlow Serving deployment:

$ cd <tensorflow-serving-cluster dir>/tensorflow-serving/kubernetes
$ kubectl delete -f deploy.yaml

Cloud Deployment

  1. Except for Microsoft Azure, please replace server link in sgx_default_qcnl.conf included in the dockerfile with public cloud PCCS server address.

  2. If you choose to run this solution in separated public cloud instance, please make sure the ports 4433 and 8500-8501 are enabled to access.

1. Alibaba Cloud

Aliyun ECS (Elastic Compute Service) is an IaaS (Infrastructure as a Service) level cloud computing service provided by Alibaba Cloud. It builds security-enhanced instance families ( g7t, c7t, r7t ) based on Intel® SGX technology to provide a trusted and confidential environment with a higher security level.

The configuration of the ECS instance as blow:

This solution is also published in Ali Cloud as the best practice - Deploy TensorFlow Serving in Aliyun ECS security-enhanced instance.

2. Tencent Cloud

Tencent Cloud Virtual Machine (CVM) provides one instance named M6ce, which supports Intel® SGX encrypted computing technology.

The configuration of the M6ce instance as blow:

3. ByteDance Cloud

ByteDance Cloud (Volcengine SGX Instances) provides the instance named ebmg2t, which supports Intel® SGX encrypted computing technology.

The configuration of the ebmg2t instance as blow:

  • Instance Type : ecs.ebmg2t.32xlarge.

  • Instance Kernel: kernel-5.15

  • Instance OS : ubuntu-20.04

  • Instance Encrypted Memory: 256G

  • Instance vCPU : 16

  • Instance SGX PCCS Server: sgx-dcap-server.bytedance.com.

4. Microsoft Azure

Microsoft Azure DCsv3-series instances support Intel® SGX encrypted computing technology.

The following is the configuration of the DCsv3-series instance used:

  • Instance Type : Standard_DC16s_v3

  • Instance Kernel: 5.15.0-1017-azure

  • Instance OS : Ubuntu Server 20.04 LTS - Gen2

  • Instance Encrypted Memory: 64G

  • Instance vCPU : 16