Specifications for Compiling the Model Configuration File¶
A model developer needs to compile a configuration file 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 | Data Type | Description |
|---|---|---|---|
model_algorithm | Yes | String | Model algorithm, which is set by the model developer to help model 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. The options are TensorFlow, MindSpore, and Image. Image is not a common AI framework. When model_type is set to Image, a model is imported from a custom image. In this case, swr_location is mandatory. For details about how to make Image images, see Custom Image Specifications. |
runtime | No | String | Model runtime environment. 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 frequently-used framework, select the engine and development environment. |
swr_location | No | String | SWR image address.
|
metrics | No | Object | Model precision information, including the average value, recall rate, precision, and accuracy. For details about the metrics object structure, see Table 2. This parameter is used only to display model information and is optional. |
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.
|
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. For details about the health data structure, see Table 8. |
Parameter | Mandatory | Data Type | Description |
|---|---|---|---|
f1 | No | Number | F1 score. The value is rounded to 17 decimal places. |
recall | No | Number | Recall rate. 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 | Data Type | Description |
|---|---|---|---|
protocol | No | String | Request protocol. The default value is http. Set the parameter value to http or https based on your custom image. For details about other parameter, see Example of the Object Detection Model Configuration File. |
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 about the request structure, see Table 4. |
response | No | Object | Response body. For details about the response structure, see Table 5. |
Parameter | Mandatory | Data Type | Description |
|---|---|---|---|
Content-type | Yes for real-time services No 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 | Yes for real-time services No for batch services | String | The request body is described in JSON schema. |
Parameter | Mandatory | Data Type | Description |
|---|---|---|---|
Content-type | Yes for real-time services No 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 | Yes for real-time services No for batch services | String | The response body is described in JSON schema. |
Parameter | Mandatory | Data 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 the version number, 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 interface |
protocol | No | String | Request protocol of the health check interface. 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 |
Example of the 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": [{ "protocol": "http", "url": "/", "method": "post", "request": { "Content-type": "multipart/form-data", "data": { "type": "object", "properties": { "images": { "type": "file" } } } }, "response": { "Content-type": "multipart/form-data", "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 the 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": [{ "protocol": "http", "url": "/", "method": "post", "request": { "Content-type": "multipart/form-data", "data": { "type": "object", "properties": { "images": { "type": "file" } } } }, "response": { "Content-type": "multipart/form-data", "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 the 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
{ "model_type": "TensorFlow", "model_algorithm": "predict_analysis", "metrics": { "f1": 0.345294, "accuracy": 0.462963, "precision": 0.338977, "recall": 0.351852 }, "apis": [ { "protocol": "http", "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": "multipart/form-data", "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 the Custom Image Model Configuration File¶
The model input and output are similar to those in Example of the Object Detection Model Configuration File.
{
"model_algorithm": "image_classification",
"model_type": "Image",
"metrics": {
"f1": 0.345294,
"accuracy": 0.462963,
"precision": 0.338977,
"recall": 0.351852
},
"apis": [{
"protocol": "http",
"url": "/",
"method": "post",
"request": {
"Content-type": "multipart/form-data",
"data": {
"type": "object",
"properties": {
"images": {
"type": "file"
}
}
}
},
"response": {
"Content-type": "multipart/form-data",
"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 the Machine Learning Model Configuration File¶
The following uses XGBoost as an example:
Model input
{
"data": {
"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
{
"data": {
"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": [
{
"protocol": "http",
"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": "applicaton/json",
"data": {
"type": "object",
"properties": {
"resp_data": {
"type": "array",
"items": [
{
"type": "object",
"properties": {
"predict_result": {
"type": "number"
}
}
}
]
}
}
}
}
}
]
}
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": 0.0023493892851938493,
"accuracy": 0.00746268656716417
},
"dependencies": [{
"installer": "pip",
"packages": [{
"restraint": "EXACT",
"package_version": "1.16.4",
"package_name": "numpy"
}
]
}]
}