Skip to content

Latest commit

 

History

History
217 lines (166 loc) · 7.63 KB

management_api.md

File metadata and controls

217 lines (166 loc) · 7.63 KB
Raw

Management API

MMS provides a set of API allow user to manage models at runtime:

  1. Register a model
  2. Increase/decrease number of workers for specific model
  3. Describe a model's status
  4. Unregister a model
  5. List registered models

Management API is listening on port 8081 and only accessible from localhost by default. To change the default setting, see MMS Configuration.

Similar as Inference API, Management API also provide a API description to describe management APIs with OpenAPI 3.0 specification.

Management APIs

Register a model

POST /models

  • url - Model archive download url. Supports the following locations:
    • a local model archive (.mar); the file must be directly in model_store folder.
    • a local model directory; the directory must be directly in model_store folder. This option can avoid MMS extracting .mar file to temporary folder, which will improve load time and reduce disk space usage.
    • a URI using the HTTP(s) protocol. MMS can download .mar files from the Internet.
  • model_name - the name of the model; this name will be used as {model_name} in other API as path. If this parameter is not present, modelName in MANIFEST.json will be used.
  • handler - the inference handler entry-point. This value will override handler in MANIFEST.json if present. NOTE: Make sure that the given handler is in the PYTHONPATH. The format of handler is module_name:method_name.
  • runtime - the runtime for the model custom service code. This value will override runtime in MANIFEST.json if present. The default value is PYTHON.
  • batch_size - the inference batch size. The default value is 1.
  • max_batch_delay - the maximum delay for batch aggregation. The default value is 100 milliseconds.
  • initial_workers - the number of initial workers to create. The default value is 0. MMS will not run inference until there is at least one work assigned.
  • synchronous - whether or not the creation of worker is synchronous. The default value is false. MMS will create new workers without waiting for acknowledgement that the previous worker is online.
  • response_timeout - If the model's backend worker doesn't respond with inference response within this timeout period, the worker will be deemed unresponsive and rebooted. The units is seconds. The default value is 120 seconds.
curl -X POST "http://localhost:8081/models?url=https%3A%2F%2Fs3.amazonaws.com%2Fmodel-server%2Fmodel_archive_1.0%2Fsqueezenet_v1.1.mar"

{
  "status": "Model \"squeezenet_v1.1\" registered"
}

User may want to create workers while register, creating initial workers may take some time, user can choose between synchronous or synchronous call to make sure initial workers are created properly.

The asynchronous call will return before trying to create workers with HTTP code 202:

curl -v -X POST "http://localhost:8081/models?initial_workers=1&synchronous=false&url=https%3A%2F%2Fs3.amazonaws.com%2Fmodel-server%2Fmodel_archive_1.0%2Fsqueezenet_v1.1.mar"

< HTTP/1.1 202 Accepted
< content-type: application/json
< x-request-id: 29cde8a4-898e-48df-afef-f1a827a3cbc2
< content-length: 33
< connection: keep-alive
< 
{
  "status": "Worker updated"
}

The synchronous call will return after all workers has be adjusted with HTTP code 200.

curl -v -X POST "http://localhost:8081/models?initial_workers=1&synchronous=true&url=https%3A%2F%2Fs3.amazonaws.com%2Fmodel-server%2Fmodel_archive_1.0%2Fsqueezenet_v1.1.mar"

< HTTP/1.1 200 OK
< content-type: application/json
< x-request-id: c4b2804e-42b1-4d6f-9e8f-1e8901fc2c6c
< content-length: 32
< connection: keep-alive
< 
{
  "status": "Worker scaled"
}

Scale workers

PUT /models/{model_name}

  • min_worker - (optional) the minimum number of worker processes. MMS will try to maintain this minimum for specified model. The default value is 1.
  • max_worker - (optional) the maximum number of worker processes. MMS will make no more that this number of workers for the specified model. The default is the same as the setting for min_worker.
  • number_gpu - (optional) the number of GPU worker processes to create. The default value is 0. If number_gpu exceeds the number of available GPUs, the rest of workers will run on CPU.
  • synchronous - whether or not the call is synchronous. The default value is false.
  • timeout - the specified wait time for a worker to complete all pending requests. If exceeded, the work process will be terminated. Use 0 to terminate the backend worker process immediately. Use -1 to wait infinitely. The default value is -1. Note: not implemented yet.

Use the Scale Worker API to dynamically adjust the number of workers to better serve different inference request loads.

There are two different flavour of this API, synchronous vs asynchronous.

The asynchronous call will return immediately with HTTP code 202:

curl -v -X PUT "http://localhost:8081/models/noop?min_worker=3"

< HTTP/1.1 202 Accepted
< content-type: application/json
< x-request-id: 74b65aab-dea8-470c-bb7a-5a186c7ddee6
< content-length: 33
< connection: keep-alive
< 
{
  "status": "Worker updated"
}

The synchronous call will return after all workers has be adjusted with HTTP code 200.

curl -v -X PUT "http://localhost:8081/models/noop?min_worker=3&synchronous=true"

< HTTP/1.1 200 OK
< content-type: application/json
< x-request-id: c4b2804e-42b1-4d6f-9e8f-1e8901fc2c6c
< content-length: 32
< connection: keep-alive
< 
{
  "status": "Worker scaled"
}

Describe model

GET /models/{model_name}

Use the Describe Model API to get detail runtime status of a model:

curl http://localhost:8081/models/noop

{
  "modelName": "noop",
  "modelVersion": "snapshot",
  "modelUrl": "noop.mar",
  "engine": "MXNet",
  "runtime": "python",
  "minWorkers": 1,
  "maxWorkers": 1,
  "batchSize": 1,
  "maxBatchDelay": 100,
  "workers": [
    {
      "id": "9000",
      "startTime": "2018-10-02T13:44:53.034Z",
      "status": "READY",
      "gpu": false,
      "memoryUsage": 89247744
    }
  ]
}

Unregister a model

DELETE /models/{model_name}

Use the Unregister Model API to free up system resources:

curl -X DELETE http://localhost:8081/models/noop

{
  "status": "Model \"noop\" unregistered"
}

List models

GET /models

  • limit - (optional) the maximum number of items to return. It is passed as a query parameter. The default value is 100.
  • next_page_token - (optional) queries for next page. It is passed as a query parameter. This value is return by a previous API call.

Use the Models API to query current registered models:

curl "http://localhost:8081/models"

This API supports pagination:

curl "http://localhost:8081/models?limit=2&next_page_token=2"

{
  "nextPageToken": "4",
  "models": [
    {
      "modelName": "noop",
      "modelUrl": "noop-v1.0"
    },
    {
      "modelName": "noop_v0.1",
      "modelUrl": "noop-v0.1"
    }
  ]
}

API Description

OPTIONS /

To view a full list of inference and management APIs, you can use following command:

# To view all inference APIs:
curl -X OPTIONS http://localhost:8080

# To view all management APIs:
curl -X OPTIONS http://localhost:8081

The out is OpenAPI 3.0.1 json format. You use it to generate client code, see swagger codegen for detail.

Example outputs of the Inference and Management APIs: