Specifications for Editing a Model Configuration File¶
You must edit a configuration file config.json when publishing a model. The model configuration file describes the model usage, computing framework, precision, inference code dependency package, and model API.
Configuration File Format¶
The configuration file is in JSON format. Table 1 describes the parameters.
Parameter | Mandatory | Type | Description |
|---|---|---|---|
model_algorithm | Yes | String | Model algorithm, which is set by the model developer to help users understand the usage of the model. The value must start with a letter and contain no more than 36 characters. Special characters |
model_type | Yes | String | Model AI engine, which indicates the computing framework used by a model. Common AI engines and Image are supported.
|
runtime | No | String | Model runtime environment. Python 3.6 is used by default. The value of runtime depends on the value of model_type. If model_type is set to Image, you do not need to set runtime. If model_type is set to another mainstream framework, select the engine and runtime environment. |
swr_location | No | String | SWR image address.
|
metrics | No | Object | Model precision information, including the F1 score, recall, precision, and accuracy. For details about the metrics object structure, see Table 2. The result is displayed in the model precision area on the AI application details page. |
apis | No | api array | Format of the requests received and returned by a model. The value is structure data. It is the RESTful API array provided by a model. For details about the API data structure, see Table 3. For details about the code example, see Code Example of apis Parameters.
|
dependencies | No | dependency array | Package on which the model inference code depends, which is structure data. Model developers need to provide the package name, installation mode, and version constraints. Only the pip installation mode is supported. Table 6 describes the dependency array. If the model package does not contain the customize_service.py file, you do not need to set this parameter. Dependency packages cannot be installed for custom image models. |
health | No | health data structure | Configuration of an image health interface. This parameter is mandatory only when model_type is set to Image. If services cannot be interrupted during a rolling upgrade, a health check API must be provided for ModelArts to call. For details about the health data structure, see Table 8. |
Parameter | Mandatory | Type | Description |
|---|---|---|---|
f1 | No | Number | F1 score. The value is rounded to 17 decimal places. |
recall | No | Number | Recall. The value is rounded to 17 decimal places. |
precision | No | Number | Precision. The value is rounded to 17 decimal places. |
accuracy | No | Number | Accuracy. The value is rounded to 17 decimal places. |
Parameter | Mandatory | Type | Description |
|---|---|---|---|
url | No | String | Request path. The default value is a slash (/). For a custom image model (model_type is Image), set this parameter to the actual request path exposed in the image. For a non-custom image model (model_type is not Image), the URL can only be /. |
method | No | String | Request method. The default value is POST. |
request | No | Object | Request body. For details, see Table 4. |
response | No | Object | Response body. For details, see Table 5. |
Parameter | Mandatory | Type | Description |
|---|---|---|---|
Content-type | No for real-time services Yes for batch services | String | Data is sent in a specified content format. The default value is application/json. The options are as follows:
Note For machine learning models, only application/json is supported. |
data | No for real-time services Yes for batch services | String | The request body is described in JSON schema. |
Parameter | Mandatory | Type | Description |
|---|---|---|---|
Content-type | No for real-time services Yes for batch services | String | Data is sent in a specified content format. The default value is application/json. Note For machine learning models, only application/json is supported. |
data | No for real-time services Yes for batch services | String | The response body is described in JSON schema. |
Parameter | Mandatory | Type | Description |
|---|---|---|---|
installer | Yes | String | Installation method. Only pip is supported. |
packages | Yes | package array | Dependency package collection. For details about the package structure array, see Table 7. |
Parameter | Mandatory | Type | Description |
|---|---|---|---|
package_name | Yes | String | Dependency package name. Special characters |
package_version | No | String | Dependency package version. If the dependency package does not rely on package versions, leave this field blank. Special characters |
restraint | No | String | Version restriction. This parameter is mandatory only when package_version is configured. Possible values are EXACT, ATLEAST, and ATMOST.
|
Parameter | Mandatory | Type | Description |
|---|---|---|---|
url | Yes | String | Request URL of the health check API. |
protocol | No | String | Request protocol of the health check API. Only HTTP is supported. |
initial_delay_seconds | No | String | After an instance is started, a health check starts after seconds configured in initial_delay_seconds. |
timeout_seconds | No | String | Health check timeout duration in the unit of second. This parameter cannot be left blank. |
Code Example of apis Parameters¶
[{
"url": "/",
"method": "post",
"request": {
"Content-type": "multipart/form-data",
"data": {
"type": "object",
"properties": {
"images": {
"type": "file"
}
}
}
},
"response": {
"Content-type": "applicaton/json",
"data": {
"type": "object",
"properties": {
"mnist_result": {
"type": "array",
"item": [
{
"type": "string"
}
]
}
}
}
}
}]
Example of an Object Detection Model Configuration File¶
The following code uses the TensorFlow engine as an example. You can modify the model_type parameter based on the actual engine type.
Model input
Key: images
Value: image files
Model output
{ "detection_classes": [ "face", "arm" ], "detection_boxes": [ [ 33.6, 42.6, 104.5, 203.4 ], [ 103.1, 92.8, 765.6, 945.7 ] ], "detection_scores": [0.99, 0.73] }
Configuration file
{ "model_type": "TensorFlow", "model_algorithm": "object_detection", "metrics": { "f1": 0.345294, "accuracy": 0.462963, "precision": 0.338977, "recall": 0.351852 }, "apis": [{ "url": "/", "method": "post", "request": { "Content-type": "multipart/form-data", "data": { "type": "object", "properties": { "images": { "type": "file" } } } }, "response": { "Content-type": "application/json", "data": { "type": "object", "properties": { "detection_classes": { "type": "array", "items": [{ "type": "string" }] }, "detection_boxes": { "type": "array", "items": [{ "type": "array", "minItems": 4, "maxItems": 4, "items": [{ "type": "number" }] }] }, "detection_scores": { "type": "array", "items": [{ "type": "number" }] } } } } }], "dependencies": [{ "installer": "pip", "packages": [{ "restraint": "EXACT", "package_version": "1.15.0", "package_name": "numpy" }, { "restraint": "EXACT", "package_version": "5.2.0", "package_name": "Pillow" } ] }] }
Example of an Image Classification Model Configuration File¶
The following code uses the TensorFlow engine as an example. You can modify the model_type parameter based on the actual engine type.
Model input
Key: images
Value: image files
Model output
{ "predicted_label": "flower", "scores": [ ["rose", 0.99], ["begonia", 0.01] ] }
Configuration file
{ "model_type": "TensorFlow", "model_algorithm": "image_classification", "metrics": { "f1": 0.345294, "accuracy": 0.462963, "precision": 0.338977, "recall": 0.351852 }, "apis": [{ "url": "/", "method": "post", "request": { "Content-type": "multipart/form-data", "data": { "type": "object", "properties": { "images": { "type": "file" } } } }, "response": { "Content-type": "application/json", "data": { "type": "object", "properties": { "predicted_label": { "type": "string" }, "scores": { "type": "array", "items": [{ "type": "array", "minItems": 2, "maxItems": 2, "items": [ { "type": "string" }, { "type": "number" } ] }] } } } } }], "dependencies": [{ "installer": "pip", "packages": [{ "restraint": "ATLEAST", "package_version": "1.15.0", "package_name": "numpy" }, { "restraint": "", "package_version": "", "package_name": "Pillow" } ] }] }
Example of a Predictive Analytics Model Configuration File¶
The following code uses the TensorFlow engine as an example. You can modify the model_type parameter based on the actual engine type.
Model input
{ "data": { "req_data": [ { "buying_price": "high", "maint_price": "high", "doors": "2", "persons": "2", "lug_boot": "small", "safety": "low", "acceptability": "acc" }, { "buying_price": "high", "maint_price": "high", "doors": "2", "persons": "2", "lug_boot": "small", "safety": "low", "acceptability": "acc" } ] } }
Model output
{ "data": { "resp_data": [ { "predict_result": "unacc" }, { "predict_result": "unacc" } ] } }
Configuration file
Note
In the code, the data parameter in the request and response structures is described in JSON Schema. The content in data and properties corresponds to the model input and output.
{ "model_type": "TensorFlow", "model_algorithm": "predict_analysis", "metrics": { "f1": 0.345294, "accuracy": 0.462963, "precision": 0.338977, "recall": 0.351852 }, "apis": [ { "url": "/", "method": "post", "request": { "Content-type": "application/json", "data": { "type": "object", "properties": { "data": { "type": "object", "properties": { "req_data": { "items": [ { "type": "object", "properties": {} } ], "type": "array" } } } } } }, "response": { "Content-type": "application/json", "data": { "type": "object", "properties": { "data": { "type": "object", "properties": { "resp_data": { "type": "array", "items": [ { "type": "object", "properties": {} } ] } } } } } } } ], "dependencies": [ { "installer": "pip", "packages": [ { "restraint": "EXACT", "package_version": "1.15.0", "package_name": "numpy" }, { "restraint": "EXACT", "package_version": "5.2.0", "package_name": "Pillow" } ] } ] }
Example of a Custom Image Model Configuration File¶
The model input and output are similar to those in Example of an Object Detection Model Configuration File.
The following is a request example when the input is images.
This is a model prediction request containing the parameter images with the parameter type of file is received. The file upload button is displayed on the inference page. You need to upload files for inference.
{ "Content-type": "multipart/form-data", "data": { "type": "object", "properties": { "images": { "type": "file" } } } }
The following is a request example when the input is JSON data.
This is a JSON request body for model prediction. There is only one prediction request containing the parameter input with the parameter type of string. On the inference page, a text box is displayed for you to enter the prediction request.
{ "Content-type": "application/json", "data": { "type": "object", "properties": { "input": { "type": "string" } } } }
A complete request example is as follows:
{
"model_algorithm": "image_classification",
"model_type": "Image",
"metrics": {
"f1": 0.345294,
"accuracy": 0.462963,
"precision": 0.338977,
"recall": 0.351852
},
"apis": [{
"url": "/",
"method": "post",
"request": {
"Content-type": "multipart/form-data",
"data": {
"type": "object",
"properties": {
"images": {
"type": "file"
}
}
}
},
"response": {
"Content-type": "application/json",
"data": {
"type": "object",
"required": [
"predicted_label",
"scores"
],
"properties": {
"predicted_label": {
"type": "string"
},
"scores": {
"type": "array",
"items": [{
"type": "array",
"minItems": 2,
"maxItems": 2,
"items": [{
"type": "string"
},
{
"type": "number"
}
]
}]
}
}
}
}
}]
}
Example of a Machine Learning Model Configuration File¶
The following uses XGBoost as an example:
Model input
{
"req_data": [
{
"sepal_length": 5,
"sepal_width": 3.3,
"petal_length": 1.4,
"petal_width": 0.2
},
{
"sepal_length": 5,
"sepal_width": 2,
"petal_length": 3.5,
"petal_width": 1
},
{
"sepal_length": 6,
"sepal_width": 2.2,
"petal_length": 5,
"petal_width": 1.5
}
]
}
Model output
{
"resp_data": [
{
"predict_result": "Iris-setosa"
},
{
"predict_result": "Iris-versicolor"
}
]
}
Configuration file
{
"model_type": "XGBoost",
"model_algorithm": "xgboost_iris_test",
"runtime": "python2.7",
"metrics": {
"f1": 0.345294,
"accuracy": 0.462963,
"precision": 0.338977,
"recall": 0.351852
},
"apis": [
{
"url": "/",
"method": "post",
"request": {
"Content-type": "application/json",
"data": {
"type": "object",
"properties": {
"req_data": {
"items": [
{
"type": "object",
"properties": {}
}
],
"type": "array"
}
}
}
},
"response": {
"Content-type": "applicaton/json",
"data": {
"type": "object",
"properties": {
"resp_data": {
"type": "array",
"items": [
{
"type": "object",
"properties": {
"predict_result": {}
}
}
]
}
}
}
}
}
]
}
Example of a Model Configuration File Using a Custom Dependency Package¶
The following example defines the NumPy 1.16.4 dependency environment.
{
"model_algorithm": "image_classification",
"model_type": "TensorFlow",
"runtime": "python3.6",
"apis": [
{
"procotol": "http",
"url": "/",
"method": "post",
"request": {
"Content-type": "multipart/form-data",
"data": {
"type": "object",
"properties": {
"images": {
"type": "file"
}
}
}
},
"response": {
"Content-type": "applicaton/json",
"data": {
"type": "object",
"properties": {
"mnist_result": {
"type": "array",
"item": [
{
"type": "string"
}
]
}
}
}
}
}
],
"metrics": {
"f1": 0.124555,
"recall": 0.171875,
"precision": 0.00234938928519385,
"accuracy": 0.00746268656716417
},
"dependencies": [
{
"installer": "pip",
"packages": [
{
"restraint": "EXACT",
"package_version": "1.16.4",
"package_name": "numpy"
}
]
}
]
}