Creates an Autopilot job
Creates an Autopilot job.
Find the best performing model after you run an Autopilot job by calling . Deploy that model by following the steps described in Step 6.1: Deploy the Model to Amazon SageMaker Hosting Services.
For information about how to use Autopilot, see Automate Model Development with Amazon SageMaker Autopilot.
sagemaker_create_auto_ml_job(AutoMLJobName, InputDataConfig, OutputDataConfig, ProblemType, AutoMLJobObjective, AutoMLJobConfig, RoleArn, GenerateCandidateDefinitionsOnly, Tags)
AutoMLJobName |
[required] Identifies an Autopilot job. Must be unique to your account and is case-insensitive. |
InputDataConfig |
[required] Similar to InputDataConfig supported by Tuning. Format(s) supported: CSV. Minimum of 500 rows. |
OutputDataConfig |
[required] Similar to OutputDataConfig supported by Tuning. Format(s) supported: CSV. |
ProblemType |
Defines the kind of preprocessing and algorithms intended for the candidates. Options include: BinaryClassification, MulticlassClassification, and Regression. |
AutoMLJobObjective |
Defines the objective of a an AutoML job. You provide a AutoMLJobObjective$MetricName and Autopilot infers whether to minimize or maximize it. If a metric is not specified, the most commonly used ObjectiveMetric for problem type is automaically selected. |
AutoMLJobConfig |
Contains CompletionCriteria and SecurityConfig. |
RoleArn |
[required] The ARN of the role that is used to access the data. |
GenerateCandidateDefinitionsOnly |
Generates possible candidates without training a model. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings. |
Tags |
Each tag consists of a key and an optional value. Tag keys must be unique per resource. |
A list with the following syntax:
list( AutoMLJobArn = "string" )
svc$create_auto_ml_job(
AutoMLJobName = "string",
InputDataConfig = list(
list(
DataSource = list(
S3DataSource = list(
S3DataType = "ManifestFile"|"S3Prefix",
S3Uri = "string"
)
),
CompressionType = "None"|"Gzip",
TargetAttributeName = "string"
)
),
OutputDataConfig = list(
KmsKeyId = "string",
S3OutputPath = "string"
),
ProblemType = "BinaryClassification"|"MulticlassClassification"|"Regression",
AutoMLJobObjective = list(
MetricName = "Accuracy"|"MSE"|"F1"|"F1macro"|"AUC"
),
AutoMLJobConfig = list(
CompletionCriteria = list(
MaxCandidates = 123,
MaxRuntimePerTrainingJobInSeconds = 123,
MaxAutoMLJobRuntimeInSeconds = 123
),
SecurityConfig = list(
VolumeKmsKeyId = "string",
EnableInterContainerTrafficEncryption = TRUE|FALSE,
VpcConfig = list(
SecurityGroupIds = list(
"string"
),
Subnets = list(
"string"
)
)
)
),
RoleArn = "string",
GenerateCandidateDefinitionsOnly = TRUE|FALSE,
Tags = list(
list(
Key = "string",
Value = "string"
)
)
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