Starting your Amazon Lookout for Vision model
Before you can use an Amazon Lookout for Vision model to detect anomalies, you must first start the
model. You start a model by calling the StartModel API and passing the following:
ProjectName – The name of the project that contains the model that you want to start.
ModelVersion – The version of the model that you want to start.
MinInferenceUnits – The minimum number of inference
units. For more information, see Inference units.
(Optional) MaxInferenceUnits – The maximum number of inference
units that Amazon Lookout for Vision can use to automatically scale the model. For more information, see Auto-scale inference
units.
The Amazon Lookout for Vision console provides example code that you can use to start and stop a model.
Starting your model (console)
The Amazon Lookout for Vision console provides a AWS CLI command that you can use to start a model. After the model
starts, you can start detecting anomalies in images. For more information, see
Detecting anomalies in an image.
To start a model (console)
-
If you haven't already done so, install and configure the AWS CLI and the AWS SDKs. For more information, see
Step 4: Set up the AWS CLI and AWS SDKs.
Open the Amazon Lookout for Vision console at https://console.aws.amazon.com/lookoutvision/.
Choose Get started.
In the left navigation pane, choose Projects.
On the Projects resources page, choose the project that contains the trained model that you want
to start.
In the Models section, choose the model that you want to start.
On the model's details page, choose Use model
and then choose Integrate API to the cloud.
Under AWS CLI commands, copy the AWS CLI command that calls start-model.
At the command prompt, enter the start-model command that you copied in the previous step. If you are using the
lookoutvision profile to get credentials, add the
--profile lookoutvision-access parameter.
In the console, choose Models in the left navigation page.
Check the Status column for the current status of the model, When the status is
Hosted, you can use the model to detect anomalies in images. For more information,
see Detecting anomalies in an image.
Starting your Amazon Lookout for Vision model (SDK)
You start a model by calling the StartModel
operation.
A model might take a while to start. You can check the current status by calling
DescribeModel.
For more information, see Viewing your models.
To start your model (SDK)
-
If you haven't already done so, install and configure the AWS CLI and the AWS SDKs. For more information, see
Step 4: Set up the AWS CLI and AWS SDKs.
Use the following example code to start a model.
- CLI
-
Change the following values:
project-name to the name of the project that contains the model that
you want to start.
model-version to the version of the model that you want to start.
--min-inference-units to the number of inference units that you want to
use.
(Optional) --max-inference-units to the maximum number of inference units that Amazon Lookout for Vision
can use to automatically scale the model.
aws lookoutvision start-model --project-name "project name"\
--model-version model version\
--min-inference-units minimum number of units\
--max-inference-units max number of units \
--profile lookoutvision-access
- Python
-
This code is taken from the AWS Documentation SDK examples GitHub repository. See the full example
here.
@staticmethod
def start_model(
lookoutvision_client, project_name, model_version, min_inference_units, max_inference_units = None):
"""
Starts the hosting of a Lookout for Vision model.
:param lookoutvision_client: A Boto3 Lookout for Vision client.
:param project_name: The name of the project that contains the version of the
model that you want to start hosting.
:param model_version: The version of the model that you want to start hosting.
:param min_inference_units: The number of inference units to use for hosting.
:param max_inference_units: (Optional) The maximum number of inference units that
Lookout for Vision can use to automatically scale the model.
"""
try:
logger.info(
"Starting model version %s for project %s", model_version, project_name)
if max_inference_units is None:
lookoutvision_client.start_model(
ProjectName = project_name,
ModelVersion = model_version,
MinInferenceUnits = min_inference_units)
else:
lookoutvision_client.start_model(
ProjectName = project_name,
ModelVersion = model_version,
MinInferenceUnits = min_inference_units,
MaxInferenceUnits = max_inference_units)
print("Starting hosting...")
status = ""
finished = False
# Wait until hosted or failed.
while finished is False:
model_description = lookoutvision_client.describe_model(
ProjectName=project_name, ModelVersion=model_version)
status = model_description["ModelDescription"]["Status"]
if status == "STARTING_HOSTING":
logger.info("Host starting in progress...")
time.sleep(10)
continue
if status == "HOSTED":
logger.info("Model is hosted and ready for use.")
finished = True
continue
logger.info("Model hosting failed and the model can't be used.")
finished = True
if status != "HOSTED":
logger.error("Error hosting model: %s", status)
raise Exception(f"Error hosting model: {status}")
except ClientError:
logger.exception("Couldn't host model.")
raise
- Java V2
-
This code is taken from the AWS Documentation SDK examples GitHub repository. See the full example
here.
/**
* Starts hosting an Amazon Lookout for Vision model. Returns when the model has
* started or if hosting fails. You are charged for the amount of time that a
* model is hosted. To stop hosting a model, use the StopModel operation.
*
* @param lfvClient An Amazon Lookout for Vision client.
* @param projectName The name of the project that contains the model that you
* want to host.
* @modelVersion The version of the model that you want to host.
* @minInferenceUnits The number of inference units to use for hosting.
* @maxInferenceUnits The maximum number of inference units that Lookout for
* Vision can use for automatically scaling the model. If the
* value is null, automatic scaling doesn't happen.
* @return ModelDescription The description of the model, which includes the
* model hosting status.
*/
public static ModelDescription startModel(LookoutVisionClient lfvClient, String projectName, String modelVersion,
Integer minInferenceUnits, Integer maxInferenceUnits) throws LookoutVisionException, InterruptedException {
logger.log(Level.INFO, "Starting Model version {0} for project {1}.",
new Object[] { modelVersion, projectName });
StartModelRequest startModelRequest = null;
if (maxInferenceUnits == null) {
startModelRequest = StartModelRequest.builder().projectName(projectName).modelVersion(modelVersion)
.minInferenceUnits(minInferenceUnits).build();
} else {
startModelRequest = StartModelRequest.builder().projectName(projectName).modelVersion(modelVersion)
.minInferenceUnits(minInferenceUnits).maxInferenceUnits(maxInferenceUnits).build();
}
// Start hosting the model.
lfvClient.startModel(startModelRequest);
DescribeModelRequest describeModelRequest = DescribeModelRequest.builder().projectName(projectName)
.modelVersion(modelVersion).build();
ModelDescription modelDescription = null;
boolean finished = false;
// Wait until model is hosted or failure occurs.
do {
modelDescription = lfvClient.describeModel(describeModelRequest).modelDescription();
switch (modelDescription.status()) {
case HOSTED:
logger.log(Level.INFO, "Model version {0} for project {1} is running.",
new Object[] { modelVersion, projectName });
finished = true;
break;
case STARTING_HOSTING:
logger.log(Level.INFO, "Model version {0} for project {1} is starting.",
new Object[] { modelVersion, projectName });
TimeUnit.SECONDS.sleep(60);
break;
case HOSTING_FAILED:
logger.log(Level.SEVERE, "Hosting failed for model version {0} for project {1}.",
new Object[] { modelVersion, projectName });
finished = true;
break;
default:
logger.log(Level.SEVERE, "Unexpected error when hosting model version {0} for project {1}: {2}.",
new Object[] { projectName, modelVersion, modelDescription.status() });
finished = true;
break;
}
} while (!finished);
logger.log(Level.INFO, "Finished starting model version {0} for project {1} status: {2}",
new Object[] { modelVersion, projectName, modelDescription.statusMessage() });
return modelDescription;
}
If the output of the code is Model is hosted and ready for use,
you can use the model to detect anomalies in images. For more information,
see Detecting anomalies in an image.
