Guide to FATE Model Publishing & Federated Online Inference

[中文]

1. Overview

1.1 Highly usable Federated Online Inference service is provided by FATE-Serving, a sub-project of FederatedAI, repository: https://github.com/FederatedAI/FATE-Serving

1.2 Use FATE-Flow command line to publish model to online inference service

1.3 Federated Online Inference service support HTTP/GRPC online inference API

2. Cluster Deploy

Offline Cluster for Training(FATE), please refer to: https://github.com/FederatedAI/FATE/tree/master/deploy/cluster-deploy

Online Cluster for Inference(FATE-Serving)，please refer to: https://github.com/FederatedAI/FATE-Serving/wiki

3. Configuration of Offline Cluster & Online Cluster with/without ZooKeeper (two different modes)

configuration: conf/service_conf.yaml

3.1 Online cluster without ZooKeeper mode

1) Modify Service Configuration

• Fill in servings:hosts with actual ip:port of serving-server service, for example:

servings:
  hosts:
    - 192.168.0.1:8000
    - 192.168.0.2:8000

2) Running Service

• Refer to the deploy document on offline cluster for training to restart FATE-Flow service

3.2 Online cluster with ZooKeeper mode:

1) Modify Service Configuration

Fill in zookeeper:hosts with actual ip:port of ZooKeeper of online inference cluster

• If ZooKeeper uses ACL, modify use_acl user password; otherwise, skip the following step:

use_registry: true
zookeeper:
  hosts:
    - 192.168.0.1:2181
    - 192.168.0.2:2181
  use_acl: true
  user: fate_dev
  password: fate_dev

2) Running Service

• Refer to the deploy document on offline cluster for training to restart FATE-Flow service

4. Load Model

Copy and modify configuration file fate_flow/examples/model/publish_load_model.json under deploy directory, which is used to generate load configuration for corresponding model Example of modified configuration:

{
    "initiator": {
        "party_id": "10000",
        "role": "guest"
    },
    "role": {
        "guest": ["10000"],
        "host": ["10000"],
        "arbiter": ["10000"]
    },
    "job_parameters": {
        "model_id": "arbiter-10000#guest-9999#host-10000#model",
        "model_version": "202006122116502527621"
    }
}

All parameters should be filled in according to actual setting. The serving server will load model from the fate flow service. By default, the address for serving server to load model is formatted as follows: http://{FATE_FLOW_IP}:{FATE_FLOW_HTTP_PORT}{FATE_FLOW_MODEL_TRANSFER_ENDPOINT}. To load model with model.transfer.url defined in serving-server.properties, a user can set job_parameters['use_transfer_url_on_serving'] to true.

Run command:

flow model load -c fate_flow/examples/model/publish_load_model.json

5. Publish Model

Copy and modify configuration file fate_flow/examples/model/bind_model_service.json under deploy directory, which is used to generate bind configuration for corresponding model Example of modified configuration:

{
    "service_id": "",
    "initiator": {
        "party_id": "10000",
        "role": "guest"
    },
    "role": {
        "guest": ["10000"],
        "host": ["10000"],
        "arbiter": ["10000"]
    },
    "job_parameters": {
        "model_id": "arbiter-10000#guest-10000#host-10000#model",
        "model_version": "2019081217340125761469"
    },
    "servings": [
    ]
}

Except for optional parameter servings, all parameters should be filled in according to actual setting.

If parameter servings is unfilled, model will be published to all serving-server instances

If servings is filled, model will only be published to specified serving-server instance(s)

Run command:

flow model bind -c fate_flow/examples/model/bind_model_service.json

6. Testing Online Inference

Please refer to FATE-Serving document Fill in parameter service_id according to step 5 above.

---

Last update: 2021-11-23