vendor: update all dependencies
This commit is contained in:
Nick Craig-Wood
parent
3f0789e2db
commit
08021c4636
2474 changed files with 435818 additions and 282709 deletions
492
vendor/github.com/aws/aws-sdk-go/models/apis/sagemaker/2017-07-24/api-2.json
generated
vendored
492
vendor/github.com/aws/aws-sdk-go/models/apis/sagemaker/2017-07-24/api-2.json
generated
vendored
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@ -46,6 +46,19 @@
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{"shape":"ResourceLimitExceeded"}
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]
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},
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"CreateHyperParameterTuningJob":{
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"name":"CreateHyperParameterTuningJob",
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"http":{
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"method":"POST",
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"requestUri":"/"
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},
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"input":{"shape":"CreateHyperParameterTuningJobRequest"},
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"output":{"shape":"CreateHyperParameterTuningJobResponse"},
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"errors":[
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{"shape":"ResourceInUse"},
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{"shape":"ResourceLimitExceeded"}
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]
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},
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"CreateModel":{
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"name":"CreateModel",
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"http":{
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@ -171,6 +184,18 @@
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"input":{"shape":"DescribeEndpointConfigInput"},
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"output":{"shape":"DescribeEndpointConfigOutput"}
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},
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"DescribeHyperParameterTuningJob":{
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"name":"DescribeHyperParameterTuningJob",
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"http":{
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"method":"POST",
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"requestUri":"/"
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},
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"input":{"shape":"DescribeHyperParameterTuningJobRequest"},
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"output":{"shape":"DescribeHyperParameterTuningJobResponse"},
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"errors":[
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{"shape":"ResourceNotFound"}
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]
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},
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"DescribeModel":{
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"name":"DescribeModel",
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"http":{
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@ -228,6 +253,15 @@
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"input":{"shape":"ListEndpointsInput"},
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"output":{"shape":"ListEndpointsOutput"}
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},
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"ListHyperParameterTuningJobs":{
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"name":"ListHyperParameterTuningJobs",
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"http":{
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"method":"POST",
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"requestUri":"/"
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},
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"input":{"shape":"ListHyperParameterTuningJobsRequest"},
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"output":{"shape":"ListHyperParameterTuningJobsResponse"}
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},
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"ListModels":{
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"name":"ListModels",
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"http":{
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@ -273,6 +307,18 @@
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"input":{"shape":"ListTrainingJobsRequest"},
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"output":{"shape":"ListTrainingJobsResponse"}
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},
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"ListTrainingJobsForHyperParameterTuningJob":{
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"name":"ListTrainingJobsForHyperParameterTuningJob",
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"http":{
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"method":"POST",
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"requestUri":"/"
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},
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"input":{"shape":"ListTrainingJobsForHyperParameterTuningJobRequest"},
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"output":{"shape":"ListTrainingJobsForHyperParameterTuningJobResponse"},
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"errors":[
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{"shape":"ResourceNotFound"}
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]
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},
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"StartNotebookInstance":{
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"name":"StartNotebookInstance",
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"http":{
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@ -284,6 +330,17 @@
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{"shape":"ResourceLimitExceeded"}
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]
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},
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"StopHyperParameterTuningJob":{
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"name":"StopHyperParameterTuningJob",
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"http":{
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"method":"POST",
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"requestUri":"/"
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},
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"input":{"shape":"StopHyperParameterTuningJobRequest"},
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"errors":[
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{"shape":"ResourceNotFound"}
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]
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},
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"StopNotebookInstance":{
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"name":"StopNotebookInstance",
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"http":{
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@ -385,6 +442,23 @@
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"TrainingInputMode":{"shape":"TrainingInputMode"}
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}
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},
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"CategoricalParameterRange":{
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"type":"structure",
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"required":[
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"Name",
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"Values"
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],
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"members":{
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"Name":{"shape":"ParameterKey"},
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"Values":{"shape":"ParameterValues"}
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}
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},
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"CategoricalParameterRanges":{
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"type":"list",
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"member":{"shape":"CategoricalParameterRange"},
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"max":20,
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"min":0
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},
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"Channel":{
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"type":"structure",
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"required":[
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@ -431,6 +505,25 @@
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"type":"string",
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"max":256
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},
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"ContinuousParameterRange":{
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"type":"structure",
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"required":[
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"Name",
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"MinValue",
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"MaxValue"
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],
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"members":{
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"Name":{"shape":"ParameterKey"},
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"MinValue":{"shape":"ParameterValue"},
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"MaxValue":{"shape":"ParameterValue"}
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}
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},
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"ContinuousParameterRanges":{
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"type":"list",
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"member":{"shape":"ContinuousParameterRange"},
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"max":20,
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"min":0
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},
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"CreateEndpointConfigInput":{
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"type":"structure",
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"required":[
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@ -470,6 +563,27 @@
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"EndpointArn":{"shape":"EndpointArn"}
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}
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},
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"CreateHyperParameterTuningJobRequest":{
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"type":"structure",
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"required":[
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"HyperParameterTuningJobName",
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"HyperParameterTuningJobConfig",
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"TrainingJobDefinition"
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],
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"members":{
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"HyperParameterTuningJobName":{"shape":"HyperParameterTuningJobName"},
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"HyperParameterTuningJobConfig":{"shape":"HyperParameterTuningJobConfig"},
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"TrainingJobDefinition":{"shape":"HyperParameterTrainingJobDefinition"},
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"Tags":{"shape":"TagList"}
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}
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},
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"CreateHyperParameterTuningJobResponse":{
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"type":"structure",
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"required":["HyperParameterTuningJobArn"],
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"members":{
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"HyperParameterTuningJobArn":{"shape":"HyperParameterTuningJobArn"}
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}
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},
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"CreateModelInput":{
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"type":"structure",
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"required":[
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@ -687,6 +801,40 @@
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"LastModifiedTime":{"shape":"Timestamp"}
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}
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},
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"DescribeHyperParameterTuningJobRequest":{
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"type":"structure",
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"required":["HyperParameterTuningJobName"],
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"members":{
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"HyperParameterTuningJobName":{"shape":"HyperParameterTuningJobName"}
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}
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},
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"DescribeHyperParameterTuningJobResponse":{
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"type":"structure",
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"required":[
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"HyperParameterTuningJobName",
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"HyperParameterTuningJobArn",
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"HyperParameterTuningJobConfig",
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"TrainingJobDefinition",
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"HyperParameterTuningJobStatus",
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"CreationTime",
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"TrainingJobStatusCounters",
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"ObjectiveStatusCounters"
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],
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"members":{
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"HyperParameterTuningJobName":{"shape":"HyperParameterTuningJobName"},
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"HyperParameterTuningJobArn":{"shape":"HyperParameterTuningJobArn"},
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"HyperParameterTuningJobConfig":{"shape":"HyperParameterTuningJobConfig"},
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"TrainingJobDefinition":{"shape":"HyperParameterTrainingJobDefinition"},
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"HyperParameterTuningJobStatus":{"shape":"HyperParameterTuningJobStatus"},
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"CreationTime":{"shape":"Timestamp"},
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"HyperParameterTuningEndTime":{"shape":"Timestamp"},
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"LastModifiedTime":{"shape":"Timestamp"},
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"TrainingJobStatusCounters":{"shape":"TrainingJobStatusCounters"},
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"ObjectiveStatusCounters":{"shape":"ObjectiveStatusCounters"},
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"BestTrainingJob":{"shape":"HyperParameterTrainingJobSummary"},
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"FailureReason":{"shape":"FailureReason"}
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}
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},
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"DescribeModelInput":{
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"type":"structure",
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"required":["ModelName"],
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@ -781,6 +929,7 @@
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"members":{
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"TrainingJobName":{"shape":"TrainingJobName"},
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"TrainingJobArn":{"shape":"TrainingJobArn"},
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"TuningJobArn":{"shape":"HyperParameterTuningJobArn"},
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"ModelArtifacts":{"shape":"ModelArtifacts"},
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"TrainingJobStatus":{"shape":"TrainingJobStatus"},
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"SecondaryStatus":{"shape":"SecondaryStatus"},
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@ -932,6 +1081,171 @@
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"type":"string",
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"max":1024
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},
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"FinalHyperParameterTuningJobObjectiveMetric":{
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"type":"structure",
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"required":[
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"MetricName",
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"Value"
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],
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"members":{
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"Type":{"shape":"HyperParameterTuningJobObjectiveType"},
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"MetricName":{"shape":"MetricName"},
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"Value":{"shape":"MetricValue"}
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}
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},
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"HyperParameterAlgorithmSpecification":{
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"type":"structure",
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"required":[
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"TrainingImage",
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"TrainingInputMode"
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],
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"members":{
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"TrainingImage":{"shape":"AlgorithmImage"},
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"TrainingInputMode":{"shape":"TrainingInputMode"},
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"MetricDefinitions":{"shape":"MetricDefinitionList"}
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}
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},
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"HyperParameterTrainingJobDefinition":{
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"type":"structure",
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"required":[
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"AlgorithmSpecification",
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"RoleArn",
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"InputDataConfig",
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"OutputDataConfig",
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"ResourceConfig",
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"StoppingCondition"
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],
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"members":{
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"StaticHyperParameters":{"shape":"HyperParameters"},
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"AlgorithmSpecification":{"shape":"HyperParameterAlgorithmSpecification"},
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"RoleArn":{"shape":"RoleArn"},
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"InputDataConfig":{"shape":"InputDataConfig"},
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"VpcConfig":{"shape":"VpcConfig"},
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"OutputDataConfig":{"shape":"OutputDataConfig"},
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"ResourceConfig":{"shape":"ResourceConfig"},
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"StoppingCondition":{"shape":"StoppingCondition"}
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}
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},
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"HyperParameterTrainingJobSummaries":{
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"type":"list",
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"member":{"shape":"HyperParameterTrainingJobSummary"}
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},
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"HyperParameterTrainingJobSummary":{
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"type":"structure",
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"required":[
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"TrainingJobName",
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"TrainingJobArn",
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"CreationTime",
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"TrainingJobStatus",
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"TunedHyperParameters"
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],
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"members":{
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"TrainingJobName":{"shape":"TrainingJobName"},
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"TrainingJobArn":{"shape":"TrainingJobArn"},
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"CreationTime":{"shape":"Timestamp"},
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"TrainingStartTime":{"shape":"Timestamp"},
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"TrainingEndTime":{"shape":"Timestamp"},
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"TrainingJobStatus":{"shape":"TrainingJobStatus"},
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"TunedHyperParameters":{"shape":"HyperParameters"},
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"FailureReason":{"shape":"FailureReason"},
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"FinalHyperParameterTuningJobObjectiveMetric":{"shape":"FinalHyperParameterTuningJobObjectiveMetric"},
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"ObjectiveStatus":{"shape":"ObjectiveStatus"}
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}
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},
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"HyperParameterTuningJobArn":{
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"type":"string",
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"max":256,
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"pattern":"arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:hyper-parameter-tuning-job/.*"
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},
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"HyperParameterTuningJobConfig":{
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"type":"structure",
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"required":[
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"Strategy",
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"HyperParameterTuningJobObjective",
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"ResourceLimits",
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"ParameterRanges"
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],
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"members":{
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"Strategy":{"shape":"HyperParameterTuningJobStrategyType"},
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"HyperParameterTuningJobObjective":{"shape":"HyperParameterTuningJobObjective"},
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"ResourceLimits":{"shape":"ResourceLimits"},
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"ParameterRanges":{"shape":"ParameterRanges"}
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}
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},
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"HyperParameterTuningJobName":{
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"type":"string",
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"max":32,
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"min":1,
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"pattern":"^[a-zA-Z0-9](-*[a-zA-Z0-9])*"
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},
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"HyperParameterTuningJobObjective":{
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"type":"structure",
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"required":[
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"Type",
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"MetricName"
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],
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"members":{
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"Type":{"shape":"HyperParameterTuningJobObjectiveType"},
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"MetricName":{"shape":"MetricName"}
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}
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},
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"HyperParameterTuningJobObjectiveType":{
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"type":"string",
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"enum":[
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"Maximize",
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"Minimize"
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]
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},
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"HyperParameterTuningJobSortByOptions":{
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"type":"string",
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"enum":[
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"Name",
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"Status",
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"CreationTime"
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]
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},
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"HyperParameterTuningJobStatus":{
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"type":"string",
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"enum":[
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"Completed",
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"InProgress",
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"Failed",
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"Stopped",
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"Stopping"
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]
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},
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"HyperParameterTuningJobStrategyType":{
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"type":"string",
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"enum":["Bayesian"]
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},
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"HyperParameterTuningJobSummaries":{
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"type":"list",
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"member":{"shape":"HyperParameterTuningJobSummary"}
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},
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"HyperParameterTuningJobSummary":{
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"type":"structure",
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"required":[
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"HyperParameterTuningJobName",
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"HyperParameterTuningJobArn",
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"HyperParameterTuningJobStatus",
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"Strategy",
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"CreationTime",
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"TrainingJobStatusCounters",
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"ObjectiveStatusCounters"
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],
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"members":{
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"HyperParameterTuningJobName":{"shape":"HyperParameterTuningJobName"},
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"HyperParameterTuningJobArn":{"shape":"HyperParameterTuningJobArn"},
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"HyperParameterTuningJobStatus":{"shape":"HyperParameterTuningJobStatus"},
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"Strategy":{"shape":"HyperParameterTuningJobStrategyType"},
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"CreationTime":{"shape":"Timestamp"},
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"HyperParameterTuningEndTime":{"shape":"Timestamp"},
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"LastModifiedTime":{"shape":"Timestamp"},
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"TrainingJobStatusCounters":{"shape":"TrainingJobStatusCounters"},
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"ObjectiveStatusCounters":{"shape":"ObjectiveStatusCounters"},
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"ResourceLimits":{"shape":"ResourceLimits"}
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}
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},
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"HyperParameters":{
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"type":"map",
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"key":{"shape":"ParameterKey"},
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@ -970,6 +1284,25 @@
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"ml.p3.16xlarge"
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]
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},
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"IntegerParameterRange":{
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"type":"structure",
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"required":[
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"Name",
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"MinValue",
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"MaxValue"
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],
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"members":{
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"Name":{"shape":"ParameterKey"},
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"MinValue":{"shape":"ParameterValue"},
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"MaxValue":{"shape":"ParameterValue"}
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}
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},
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"IntegerParameterRanges":{
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"type":"list",
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"member":{"shape":"IntegerParameterRange"},
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"max":20,
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"min":0
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},
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"KmsKeyId":{
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"type":"string",
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"max":2048
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@ -1018,6 +1351,32 @@
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"NextToken":{"shape":"PaginationToken"}
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}
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},
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"ListHyperParameterTuningJobsRequest":{
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"type":"structure",
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"members":{
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"NextToken":{"shape":"NextToken"},
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"MaxResults":{
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"shape":"MaxResults",
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"box":true
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},
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"SortBy":{"shape":"HyperParameterTuningJobSortByOptions"},
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"SortOrder":{"shape":"SortOrder"},
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"NameContains":{"shape":"NameContains"},
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"CreationTimeAfter":{"shape":"Timestamp"},
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"CreationTimeBefore":{"shape":"Timestamp"},
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"LastModifiedTimeAfter":{"shape":"Timestamp"},
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"LastModifiedTimeBefore":{"shape":"Timestamp"},
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"StatusEquals":{"shape":"HyperParameterTuningJobStatus"}
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}
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},
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"ListHyperParameterTuningJobsResponse":{
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"type":"structure",
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"required":["HyperParameterTuningJobSummaries"],
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"members":{
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"HyperParameterTuningJobSummaries":{"shape":"HyperParameterTuningJobSummaries"},
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"NextToken":{"shape":"NextToken"}
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}
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},
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"ListModelsInput":{
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"type":"structure",
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"members":{
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@ -1102,6 +1461,26 @@
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"NextToken":{"shape":"NextToken"}
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}
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},
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"ListTrainingJobsForHyperParameterTuningJobRequest":{
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"type":"structure",
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"required":["HyperParameterTuningJobName"],
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"members":{
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"HyperParameterTuningJobName":{"shape":"HyperParameterTuningJobName"},
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"NextToken":{"shape":"NextToken"},
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"MaxResults":{"shape":"MaxResults"},
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"StatusEquals":{"shape":"TrainingJobStatus"},
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"SortBy":{"shape":"TrainingJobSortByOptions"},
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"SortOrder":{"shape":"SortOrder"}
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}
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},
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"ListTrainingJobsForHyperParameterTuningJobResponse":{
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"type":"structure",
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"required":["TrainingJobSummaries"],
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"members":{
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"TrainingJobSummaries":{"shape":"HyperParameterTrainingJobSummaries"},
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"NextToken":{"shape":"NextToken"}
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}
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},
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"ListTrainingJobsRequest":{
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"type":"structure",
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"members":{
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@ -1128,6 +1507,14 @@
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"NextToken":{"shape":"NextToken"}
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}
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},
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"MaxNumberOfTrainingJobs":{
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"type":"integer",
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"min":1
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},
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"MaxParallelTrainingJobs":{
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"type":"integer",
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"min":1
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},
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"MaxResults":{
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"type":"integer",
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"max":100,
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|
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@ -1137,6 +1524,34 @@
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"type":"integer",
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||||
"min":1
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},
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"MetricDefinition":{
|
||||
"type":"structure",
|
||||
"required":[
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||||
"Name",
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||||
"Regex"
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||||
],
|
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"members":{
|
||||
"Name":{"shape":"MetricName"},
|
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"Regex":{"shape":"MetricRegex"}
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}
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},
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"MetricDefinitionList":{
|
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"type":"list",
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"member":{"shape":"MetricDefinition"},
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"max":20,
|
||||
"min":0
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||||
},
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"MetricName":{
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"type":"string",
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"max":255,
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||||
"min":1
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},
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"MetricRegex":{
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"type":"string",
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"max":500,
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"min":1
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},
|
||||
"MetricValue":{"type":"float"},
|
||||
"ModelArn":{
|
||||
"type":"string",
|
||||
"max":2048,
|
||||
|
|
@ -1314,6 +1729,26 @@
|
|||
"member":{"shape":"NotebookInstanceSummary"}
|
||||
},
|
||||
"NotebookInstanceUrl":{"type":"string"},
|
||||
"ObjectiveStatus":{
|
||||
"type":"string",
|
||||
"enum":[
|
||||
"Succeeded",
|
||||
"Pending",
|
||||
"Failed"
|
||||
]
|
||||
},
|
||||
"ObjectiveStatusCounter":{
|
||||
"type":"integer",
|
||||
"min":0
|
||||
},
|
||||
"ObjectiveStatusCounters":{
|
||||
"type":"structure",
|
||||
"members":{
|
||||
"Succeeded":{"shape":"ObjectiveStatusCounter"},
|
||||
"Pending":{"shape":"ObjectiveStatusCounter"},
|
||||
"Failed":{"shape":"ObjectiveStatusCounter"}
|
||||
}
|
||||
},
|
||||
"OrderKey":{
|
||||
"type":"string",
|
||||
"enum":[
|
||||
|
|
@ -1337,10 +1772,24 @@
|
|||
"type":"string",
|
||||
"max":256
|
||||
},
|
||||
"ParameterRanges":{
|
||||
"type":"structure",
|
||||
"members":{
|
||||
"IntegerParameterRanges":{"shape":"IntegerParameterRanges"},
|
||||
"ContinuousParameterRanges":{"shape":"ContinuousParameterRanges"},
|
||||
"CategoricalParameterRanges":{"shape":"CategoricalParameterRanges"}
|
||||
}
|
||||
},
|
||||
"ParameterValue":{
|
||||
"type":"string",
|
||||
"max":256
|
||||
},
|
||||
"ParameterValues":{
|
||||
"type":"list",
|
||||
"member":{"shape":"ParameterValue"},
|
||||
"max":20,
|
||||
"min":1
|
||||
},
|
||||
"ProductionVariant":{
|
||||
"type":"structure",
|
||||
"required":[
|
||||
|
|
@ -1454,6 +1903,17 @@
|
|||
},
|
||||
"exception":true
|
||||
},
|
||||
"ResourceLimits":{
|
||||
"type":"structure",
|
||||
"required":[
|
||||
"MaxNumberOfTrainingJobs",
|
||||
"MaxParallelTrainingJobs"
|
||||
],
|
||||
"members":{
|
||||
"MaxNumberOfTrainingJobs":{"shape":"MaxNumberOfTrainingJobs"},
|
||||
"MaxParallelTrainingJobs":{"shape":"MaxParallelTrainingJobs"}
|
||||
}
|
||||
},
|
||||
"ResourceNotFound":{
|
||||
"type":"structure",
|
||||
"members":{
|
||||
|
|
@ -1548,6 +2008,13 @@
|
|||
"NotebookInstanceName":{"shape":"NotebookInstanceName"}
|
||||
}
|
||||
},
|
||||
"StopHyperParameterTuningJobRequest":{
|
||||
"type":"structure",
|
||||
"required":["HyperParameterTuningJobName"],
|
||||
"members":{
|
||||
"HyperParameterTuningJobName":{"shape":"HyperParameterTuningJobName"}
|
||||
}
|
||||
},
|
||||
"StopNotebookInstanceInput":{
|
||||
"type":"structure",
|
||||
"required":["NotebookInstanceName"],
|
||||
|
|
@ -1663,7 +2130,7 @@
|
|||
"TrainingJobArn":{
|
||||
"type":"string",
|
||||
"max":256,
|
||||
"pattern":"arn:aws[a-z\\-]*:sagemaker:[\\p{Alnum}\\-]*:[0-9]{12}:training-job/.*"
|
||||
"pattern":"arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:training-job/.*"
|
||||
},
|
||||
"TrainingJobName":{
|
||||
"type":"string",
|
||||
|
|
@ -1671,6 +2138,15 @@
|
|||
"min":1,
|
||||
"pattern":"^[a-zA-Z0-9](-*[a-zA-Z0-9])*"
|
||||
},
|
||||
"TrainingJobSortByOptions":{
|
||||
"type":"string",
|
||||
"enum":[
|
||||
"Name",
|
||||
"CreationTime",
|
||||
"Status",
|
||||
"FinalObjectiveMetricValue"
|
||||
]
|
||||
},
|
||||
"TrainingJobStatus":{
|
||||
"type":"string",
|
||||
"enum":[
|
||||
|
|
@ -1681,6 +2157,20 @@
|
|||
"Stopped"
|
||||
]
|
||||
},
|
||||
"TrainingJobStatusCounter":{
|
||||
"type":"integer",
|
||||
"min":0
|
||||
},
|
||||
"TrainingJobStatusCounters":{
|
||||
"type":"structure",
|
||||
"members":{
|
||||
"Completed":{"shape":"TrainingJobStatusCounter"},
|
||||
"InProgress":{"shape":"TrainingJobStatusCounter"},
|
||||
"RetryableError":{"shape":"TrainingJobStatusCounter"},
|
||||
"NonRetryableError":{"shape":"TrainingJobStatusCounter"},
|
||||
"Stopped":{"shape":"TrainingJobStatusCounter"}
|
||||
}
|
||||
},
|
||||
"TrainingJobSummaries":{
|
||||
"type":"list",
|
||||
"member":{"shape":"TrainingJobSummary"}
|
||||
|
|
|
|||
397
vendor/github.com/aws/aws-sdk-go/models/apis/sagemaker/2017-07-24/docs-2.json
generated
vendored
397
vendor/github.com/aws/aws-sdk-go/models/apis/sagemaker/2017-07-24/docs-2.json
generated
vendored
|
|
@ -5,31 +5,36 @@
|
|||
"AddTags": "<p>Adds or overwrites one or more tags for the specified Amazon SageMaker resource. You can add tags to notebook instances, training jobs, models, endpoint configurations, and endpoints. </p> <p>Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see <a href=\"http://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what\">Using Cost Allocation Tags</a> in the <i>AWS Billing and Cost Management User Guide</i>. </p>",
|
||||
"CreateEndpoint": "<p>Creates an endpoint using the endpoint configuration specified in the request. Amazon SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateEndpointConfig.html\">CreateEndpointConfig</a> API. </p> <note> <p> Use this API only for hosting models using Amazon SageMaker hosting services. </p> </note> <p>The endpoint name must be unique within an AWS Region in your AWS account. </p> <p>When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them. </p> <p>When Amazon SageMaker receives the request, it sets the endpoint status to <code>Creating</code>. After it creates the endpoint, it sets the status to <code>InService</code>. Amazon SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/API_DescribeEndpoint.html\">DescribeEndpoint</a> API.</p> <p>For an example, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/ex1.html\">Exercise 1: Using the K-Means Algorithm Provided by Amazon SageMaker</a>. </p>",
|
||||
"CreateEndpointConfig": "<p>Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the <code>CreateModel</code> API, to deploy and the resources that you want Amazon SageMaker to provision. Then you call the <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateEndpoint.html\">CreateEndpoint</a> API.</p> <note> <p> Use this API only if you want to use Amazon SageMaker hosting services to deploy models into production. </p> </note> <p>In the request, you define one or more <code>ProductionVariant</code>s, each of which identifies a model. Each <code>ProductionVariant</code> parameter also describes the resources that you want Amazon SageMaker to provision. This includes the number and type of ML compute instances to deploy. </p> <p>If you are hosting multiple models, you also assign a <code>VariantWeight</code> to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. Amazon SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B. </p>",
|
||||
"CreateHyperParameterTuningJob": "<p>Starts a hyperparameter tuning job.</p>",
|
||||
"CreateModel": "<p>Creates a model in Amazon SageMaker. In the request, you name the model and describe one or more containers. For each container, you specify the docker image containing inference code, artifacts (from prior training), and custom environment map that the inference code uses when you deploy the model into production. </p> <p>Use this API to create a model only if you want to use Amazon SageMaker hosting services. To host your model, you create an endpoint configuration with the <code>CreateEndpointConfig</code> API, and then create an endpoint with the <code>CreateEndpoint</code> API. </p> <p>Amazon SageMaker then deploys all of the containers that you defined for the model in the hosting environment. </p> <p>In the <code>CreateModel</code> request, you must define a container with the <code>PrimaryContainer</code> parameter. </p> <p>In the request, you also provide an IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other AWS resources, you grant necessary permissions via this role.</p>",
|
||||
"CreateNotebookInstance": "<p>Creates an Amazon SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook. </p> <p>In a <code>CreateNotebookInstance</code> request, specify the type of ML compute instance that you want to run. Amazon SageMaker launches the instance, installs common libraries that you can use to explore datasets for model training, and attaches an ML storage volume to the notebook instance. </p> <p>Amazon SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use Amazon SageMaker with a specific algorithm or with a machine learning framework. </p> <p>After receiving the request, Amazon SageMaker does the following:</p> <ol> <li> <p>Creates a network interface in the Amazon SageMaker VPC.</p> </li> <li> <p>(Option) If you specified <code>SubnetId</code>, Amazon SageMaker creates a network interface in your own VPC, which is inferred from the subnet ID that you provide in the input. When creating this network interface, Amazon SageMaker attaches the security group that you specified in the request to the network interface that it creates in your VPC.</p> </li> <li> <p>Launches an EC2 instance of the type specified in the request in the Amazon SageMaker VPC. If you specified <code>SubnetId</code> of your VPC, Amazon SageMaker specifies both network interfaces when launching this instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it.</p> </li> </ol> <p>After creating the notebook instance, Amazon SageMaker returns its Amazon Resource Name (ARN).</p> <p>After Amazon SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating Amazon SageMaker endpoints, and validate hosted models. </p> <p>For more information, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works.html\">How It Works</a>. </p>",
|
||||
"CreateNotebookInstanceLifecycleConfig": "<p>Creates a lifecycle configuration that you can associate with a notebook instance. A <i>lifecycle configuration</i> is a collection of shell scripts that run when you create or start a notebook instance.</p> <p>Each lifecycle configuration script has a limit of 16384 characters.</p> <p>The value of the <code>$PATH</code> environment variable that is available to both scripts is <code>/sbin:bin:/usr/sbin:/usr/bin</code>.</p> <p>View CloudWatch Logs for notebook instance lifecycle configurations in log group <code>/aws/sagemaker/NotebookInstances</code> in log stream <code>[notebook-instance-name]/[LifecycleConfigHook]</code>.</p> <p>Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.</p> <p>For information about notebook instance lifestyle configurations, see <a>notebook-lifecycle-config</a>.</p>",
|
||||
"CreatePresignedNotebookInstanceUrl": "<p>Returns a URL that you can use to connect to the Juypter server from a notebook instance. In the Amazon SageMaker console, when you choose <code>Open</code> next to a notebook instance, Amazon SageMaker opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page. </p>",
|
||||
"CreateTrainingJob": "<p> Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify. </p> <p>If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a deep learning service other than Amazon SageMaker, provided that you know how to use them for inferences. </p> <p>In the request body, you provide the following: </p> <ul> <li> <p> <code>AlgorithmSpecification</code> - Identifies the training algorithm to use. </p> </li> <li> <p> <code>HyperParameters</code> - Specify these algorithm-specific parameters to influence the quality of the final model. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/algos.html\">Algorithms</a>. </p> </li> <li> <p> <code>InputDataConfig</code> - Describes the training dataset and the Amazon S3 location where it is stored.</p> </li> <li> <p> <code>OutputDataConfig</code> - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results of model training. </p> <p/> </li> <li> <p> <code>ResourceConfig</code> - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance. </p> </li> <li> <p> <code>RoleARN</code> - The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete model training. </p> </li> <li> <p> <code>StoppingCondition</code> - Sets a duration for training. Use this parameter to cap model training costs. </p> </li> </ul> <p> For more information about Amazon SageMaker, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works.html\">How It Works</a>. </p>",
|
||||
"CreatePresignedNotebookInstanceUrl": "<p>Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the Amazon SageMaker console, when you choose <code>Open</code> next to a notebook instance, Amazon SageMaker opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page. </p>",
|
||||
"CreateTrainingJob": "<p>Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify. </p> <p>If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a deep learning service other than Amazon SageMaker, provided that you know how to use them for inferences. </p> <p>In the request body, you provide the following: </p> <ul> <li> <p> <code>AlgorithmSpecification</code> - Identifies the training algorithm to use. </p> </li> <li> <p> <code>HyperParameters</code> - Specify these algorithm-specific parameters to influence the quality of the final model. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/algos.html\">Algorithms</a>. </p> </li> <li> <p> <code>InputDataConfig</code> - Describes the training dataset and the Amazon S3 location where it is stored.</p> </li> <li> <p> <code>OutputDataConfig</code> - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results of model training. </p> <p/> </li> <li> <p> <code>ResourceConfig</code> - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance. </p> </li> <li> <p> <code>RoleARN</code> - The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete model training. </p> </li> <li> <p> <code>StoppingCondition</code> - Sets a duration for training. Use this parameter to cap model training costs. </p> </li> </ul> <p> For more information about Amazon SageMaker, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works.html\">How It Works</a>. </p>",
|
||||
"DeleteEndpoint": "<p>Deletes an endpoint. Amazon SageMaker frees up all of the resources that were deployed when the endpoint was created. </p>",
|
||||
"DeleteEndpointConfig": "<p>Deletes an endpoint configuration. The <code>DeleteEndpoingConfig</code> API deletes only the specified configuration. It does not delete endpoints created using the configuration. </p>",
|
||||
"DeleteEndpointConfig": "<p>Deletes an endpoint configuration. The <code>DeleteEndpointConfig</code> API deletes only the specified configuration. It does not delete endpoints created using the configuration. </p>",
|
||||
"DeleteModel": "<p>Deletes a model. The <code>DeleteModel</code> API deletes only the model entry that was created in Amazon SageMaker when you called the <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateModel.html\">CreateModel</a> API. It does not delete model artifacts, inference code, or the IAM role that you specified when creating the model. </p>",
|
||||
"DeleteNotebookInstance": "<p> Deletes an Amazon SageMaker notebook instance. Before you can delete a notebook instance, you must call the <code>StopNotebookInstance</code> API. </p> <important> <p>When you delete a notebook instance, you lose all of your data. Amazon SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance. </p> </important>",
|
||||
"DeleteNotebookInstanceLifecycleConfig": "<p>Deletes a notebook instance lifecycle configuration.</p>",
|
||||
"DeleteTags": "<p>Deletes the specified tags from an Amazon SageMaker resource.</p> <p>To list a resource's tags, use the <code>ListTags</code> API. </p>",
|
||||
"DescribeEndpoint": "<p>Returns the description of an endpoint.</p>",
|
||||
"DescribeEndpointConfig": "<p>Returns the description of an endpoint configuration created using the <code>CreateEndpointConfig</code> API.</p>",
|
||||
"DescribeHyperParameterTuningJob": "<p>Gets a description of a hyperparameter tuning job.</p>",
|
||||
"DescribeModel": "<p>Describes a model that you created using the <code>CreateModel</code> API.</p>",
|
||||
"DescribeNotebookInstance": "<p>Returns information about a notebook instance.</p>",
|
||||
"DescribeNotebookInstanceLifecycleConfig": "<p>Returns a description of a notebook instance lifecycle configuration.</p> <p>For information about notebook instance lifestyle configurations, see <a>notebook-lifecycle-config</a>.</p>",
|
||||
"DescribeTrainingJob": "<p>Returns information about a training job.</p>",
|
||||
"ListEndpointConfigs": "<p>Lists endpoint configurations.</p>",
|
||||
"ListEndpoints": "<p>Lists endpoints.</p>",
|
||||
"ListHyperParameterTuningJobs": "<p>Gets a list of objects that describe the hyperparameter tuning jobs launched in your account.</p>",
|
||||
"ListModels": "<p>Lists models created with the <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateModel.html\">CreateModel</a> API.</p>",
|
||||
"ListNotebookInstanceLifecycleConfigs": "<p>Lists notebook instance lifestyle configurations created with the API.</p>",
|
||||
"ListNotebookInstances": "<p>Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region. </p>",
|
||||
"ListTags": "<p>Returns the tags for the specified Amazon SageMaker resource.</p>",
|
||||
"ListTrainingJobs": "<p>Lists training jobs.</p>",
|
||||
"ListTrainingJobsForHyperParameterTuningJob": "<p>Gets a list of objects that describe the training jobs that a hyperparameter tuning job launched.</p>",
|
||||
"StartNotebookInstance": "<p>Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume. After configuring the notebook instance, Amazon SageMaker sets the notebook instance status to <code>InService</code>. A notebook instance's status must be <code>InService</code> before you can connect to your Jupyter notebook. </p>",
|
||||
"StopHyperParameterTuningJob": "<p>Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.</p> <p>All model artifacts output from the training jobs are stored in Amazon Simple Storage Service (Amazon S3). All data that the training jobs write toAmazon CloudWatch Logs are still available in CloudWatch. After the tuning job moves to the <code>Stopped</code> state, it releases all reserved resources for the tuning job.</p>",
|
||||
"StopNotebookInstance": "<p>Terminates the ML compute instance. Before terminating the instance, Amazon SageMaker disconnects the ML storage volume from it. Amazon SageMaker preserves the ML storage volume. </p> <p>To access data on the ML storage volume for a notebook instance that has been terminated, call the <code>StartNotebookInstance</code> API. <code>StartNotebookInstance</code> launches another ML compute instance, configures it, and attaches the preserved ML storage volume so you can continue your work. </p>",
|
||||
"StopTrainingJob": "<p>Stops a training job. To stop a job, Amazon SageMaker sends the algorithm the <code>SIGTERM</code> signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of the training is not lost. </p> <p>Training algorithms provided by Amazon SageMaker save the intermediate results of a model training job. This intermediate data is a valid model artifact. You can use the model artifacts that are saved when Amazon SageMaker stops a training job to create a model. </p> <p>When it receives a <code>StopTrainingJob</code> request, Amazon SageMaker changes the status of the job to <code>Stopping</code>. After Amazon SageMaker stops the job, it sets the status to <code>Stopped</code>.</p>",
|
||||
"UpdateEndpoint": "<p> Deploys the new <code>EndpointConfig</code> specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previous <code>EndpointConfig</code> (there is no availability loss). </p> <p>When Amazon SageMaker receives the request, it sets the endpoint status to <code>Updating</code>. After updating the endpoint, it sets the status to <code>InService</code>. To check the status of an endpoint, use the <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/API_DescribeEndpoint.html\">DescribeEndpoint</a> API. </p>",
|
||||
|
|
@ -51,16 +56,29 @@
|
|||
"AlgorithmImage": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"AlgorithmSpecification$TrainingImage": "<p>The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for built-in algorithms, see <a>sagemaker-algo-docker-registry-paths</a>.</p>"
|
||||
"AlgorithmSpecification$TrainingImage": "<p>The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for built-in algorithms, see <a>sagemaker-algo-docker-registry-paths</a>.</p>",
|
||||
"HyperParameterAlgorithmSpecification$TrainingImage": "<p> The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see <a>sagemaker-algo-docker-registry-paths</a>.</p>"
|
||||
}
|
||||
},
|
||||
"AlgorithmSpecification": {
|
||||
"base": "<p>Specifies the training algorithm to use in a <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateTrainingJob.html\">CreateTrainingJob</a> request. </p> <p>For more information about algorithms provided by Amazon SageMaker, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/algos.html\">Algorithms</a>. For information about using your own algorithms, see <a>your-algorithms</a>. </p>",
|
||||
"base": "<p>Specifies the training algorithm to use in a <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateTrainingJob.html\">CreateTrainingJob</a> request.</p> <p>For more information about algorithms provided by Amazon SageMaker, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/algos.html\">Algorithms</a>. For information about using your own algorithms, see <a>your-algorithms</a>. </p>",
|
||||
"refs": {
|
||||
"CreateTrainingJobRequest$AlgorithmSpecification": "<p>The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by Amazon SageMaker, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/algos.html\">Algorithms</a>. For information about providing your own algorithms, see <a>your-algorithms</a>. </p>",
|
||||
"DescribeTrainingJobResponse$AlgorithmSpecification": "<p>Information about the algorithm used for training, and algorithm metadata. </p>"
|
||||
}
|
||||
},
|
||||
"CategoricalParameterRange": {
|
||||
"base": "<p>A list of categorical hyperparameters to tune.</p>",
|
||||
"refs": {
|
||||
"CategoricalParameterRanges$member": null
|
||||
}
|
||||
},
|
||||
"CategoricalParameterRanges": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"ParameterRanges$CategoricalParameterRanges": "<p>The array of <a>CategoricalParameterRange</a> objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.</p>"
|
||||
}
|
||||
},
|
||||
"Channel": {
|
||||
"base": "<p>A channel is a named input source that training algorithms can consume. </p>",
|
||||
"refs": {
|
||||
|
|
@ -76,7 +94,7 @@
|
|||
"CompressionType": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"Channel$CompressionType": "<p>If training data is compressed, the compression type. The default value is <code>None</code>. <code>CompressionType</code> is used only in PIPE input mode. In FILE mode, leave this field unset or set it to None.</p>"
|
||||
"Channel$CompressionType": "<p>If training data is compressed, the compression type. The default value is <code>None</code>. <code>CompressionType</code> is used only in Pipe input mode. In File mode, leave this field unset or set it to None.</p>"
|
||||
}
|
||||
},
|
||||
"ContainerDefinition": {
|
||||
|
|
@ -98,6 +116,18 @@
|
|||
"Channel$ContentType": "<p>The MIME type of the data.</p>"
|
||||
}
|
||||
},
|
||||
"ContinuousParameterRange": {
|
||||
"base": "<p>A list of continuous hyperparameters to tune.</p>",
|
||||
"refs": {
|
||||
"ContinuousParameterRanges$member": null
|
||||
}
|
||||
},
|
||||
"ContinuousParameterRanges": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"ParameterRanges$ContinuousParameterRanges": "<p>The array of <a>ContinuousParameterRange</a> objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.</p>"
|
||||
}
|
||||
},
|
||||
"CreateEndpointConfigInput": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
|
|
@ -118,6 +148,16 @@
|
|||
"refs": {
|
||||
}
|
||||
},
|
||||
"CreateHyperParameterTuningJobRequest": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
}
|
||||
},
|
||||
"CreateHyperParameterTuningJobResponse": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
}
|
||||
},
|
||||
"CreateModelInput": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
|
|
@ -242,6 +282,16 @@
|
|||
"refs": {
|
||||
}
|
||||
},
|
||||
"DescribeHyperParameterTuningJobRequest": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
}
|
||||
},
|
||||
"DescribeHyperParameterTuningJobResponse": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
}
|
||||
},
|
||||
"DescribeModelInput": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
|
|
@ -422,18 +472,127 @@
|
|||
"base": null,
|
||||
"refs": {
|
||||
"DescribeEndpointOutput$FailureReason": "<p>If the status of the endpoint is <code>Failed</code>, the reason why it failed. </p>",
|
||||
"DescribeHyperParameterTuningJobResponse$FailureReason": "<p>If the tuning job failed, the reason it failed.</p>",
|
||||
"DescribeNotebookInstanceOutput$FailureReason": "<p>If status is failed, the reason it failed.</p>",
|
||||
"DescribeTrainingJobResponse$FailureReason": "<p>If the training job failed, the reason it failed. </p>",
|
||||
"HyperParameterTrainingJobSummary$FailureReason": "<p>The reason that the </p>",
|
||||
"ResourceInUse$Message": null,
|
||||
"ResourceLimitExceeded$Message": null,
|
||||
"ResourceNotFound$Message": null
|
||||
}
|
||||
},
|
||||
"FinalHyperParameterTuningJobObjectiveMetric": {
|
||||
"base": "<p>Shows the final value for the objective metric for a training job that was launched by a hyperparameter tuning job. You define the objective metric in the <code>HyperParameterTuningJobObjective</code> parameter of <a>HyperParameterTuningJobConfig</a>.</p>",
|
||||
"refs": {
|
||||
"HyperParameterTrainingJobSummary$FinalHyperParameterTuningJobObjectiveMetric": "<p>The object that specifies the value of the objective metric of the tuning job that launched this training job.</p>"
|
||||
}
|
||||
},
|
||||
"HyperParameterAlgorithmSpecification": {
|
||||
"base": "<p>Specifies which training algorithm to use for training jobs that a hyperparameter tuning job launches and the metrics to monitor.</p>",
|
||||
"refs": {
|
||||
"HyperParameterTrainingJobDefinition$AlgorithmSpecification": "<p>The object that specifies the algorithm to use for the training jobs that the tuning job launches.</p>"
|
||||
}
|
||||
},
|
||||
"HyperParameterTrainingJobDefinition": {
|
||||
"base": "<p>Defines the training jobs launched by a hyperparameter tuning job.</p>",
|
||||
"refs": {
|
||||
"CreateHyperParameterTuningJobRequest$TrainingJobDefinition": "<p>The object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.</p>",
|
||||
"DescribeHyperParameterTuningJobResponse$TrainingJobDefinition": "<p>The object that specifies the definition of the training jobs that this tuning job launches.</p>"
|
||||
}
|
||||
},
|
||||
"HyperParameterTrainingJobSummaries": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"ListTrainingJobsForHyperParameterTuningJobResponse$TrainingJobSummaries": "<p>A list of objects that describe the training jobs that the <code>ListTrainingJobsForHyperParameterTuningJob</code> request returned.</p>"
|
||||
}
|
||||
},
|
||||
"HyperParameterTrainingJobSummary": {
|
||||
"base": "<p>Specifies summary information about a training job.</p>",
|
||||
"refs": {
|
||||
"DescribeHyperParameterTuningJobResponse$BestTrainingJob": "<p>A object that describes the training job that completed with the best current .</p>",
|
||||
"HyperParameterTrainingJobSummaries$member": null
|
||||
}
|
||||
},
|
||||
"HyperParameterTuningJobArn": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"CreateHyperParameterTuningJobResponse$HyperParameterTuningJobArn": "<p>The Amazon Resource Name (ARN) of the tuning job.</p>",
|
||||
"DescribeHyperParameterTuningJobResponse$HyperParameterTuningJobArn": "<p>The Amazon Resource Name (ARN) of the tuning job.</p>",
|
||||
"DescribeTrainingJobResponse$TuningJobArn": "<p>The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.</p>",
|
||||
"HyperParameterTuningJobSummary$HyperParameterTuningJobArn": "<p>The Amazon Resource Name (ARN) of the tuning job.</p>"
|
||||
}
|
||||
},
|
||||
"HyperParameterTuningJobConfig": {
|
||||
"base": "<p>Configures a hyperparameter tuning job.</p>",
|
||||
"refs": {
|
||||
"CreateHyperParameterTuningJobRequest$HyperParameterTuningJobConfig": "<p>The object that describes the tuning job, including the search strategy, metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job.</p>",
|
||||
"DescribeHyperParameterTuningJobResponse$HyperParameterTuningJobConfig": "<p>The object that specifies the configuration of the tuning job.</p>"
|
||||
}
|
||||
},
|
||||
"HyperParameterTuningJobName": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"CreateHyperParameterTuningJobRequest$HyperParameterTuningJobName": "<p>The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same AWS account and AWS Region. Names are not case sensitive, and must be between 1-32 characters.</p>",
|
||||
"DescribeHyperParameterTuningJobRequest$HyperParameterTuningJobName": "<p>The name of the tuning job to describe.</p>",
|
||||
"DescribeHyperParameterTuningJobResponse$HyperParameterTuningJobName": "<p>The name of the tuning job.</p>",
|
||||
"HyperParameterTuningJobSummary$HyperParameterTuningJobName": "<p>The name of the tuning job.</p>",
|
||||
"ListTrainingJobsForHyperParameterTuningJobRequest$HyperParameterTuningJobName": "<p>The name of the tuning job whose training jobs you want to list.</p>",
|
||||
"StopHyperParameterTuningJobRequest$HyperParameterTuningJobName": "<p>The name of the tuning job to stop.</p>"
|
||||
}
|
||||
},
|
||||
"HyperParameterTuningJobObjective": {
|
||||
"base": "<p>Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the <code>Type</code> parameter.</p>",
|
||||
"refs": {
|
||||
"HyperParameterTuningJobConfig$HyperParameterTuningJobObjective": "<p>The object that specifies the objective metric for this tuning job.</p>"
|
||||
}
|
||||
},
|
||||
"HyperParameterTuningJobObjectiveType": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"FinalHyperParameterTuningJobObjectiveMetric$Type": "<p>Whether to minimize or maximize the objective metric. Valid values are Minimize and Maximize.</p>",
|
||||
"HyperParameterTuningJobObjective$Type": "<p>Whether to minimize or maximize the objective metric.</p>"
|
||||
}
|
||||
},
|
||||
"HyperParameterTuningJobSortByOptions": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"ListHyperParameterTuningJobsRequest$SortBy": "<p>The field to sort results by. The default is <code>Name</code>.</p>"
|
||||
}
|
||||
},
|
||||
"HyperParameterTuningJobStatus": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"DescribeHyperParameterTuningJobResponse$HyperParameterTuningJobStatus": "<p>The status of the tuning job: InProgress, Completed, Failed, Stopping, or Stopped.</p>",
|
||||
"HyperParameterTuningJobSummary$HyperParameterTuningJobStatus": "<p>The status of the tuning job.</p>",
|
||||
"ListHyperParameterTuningJobsRequest$StatusEquals": "<p>A filter that returns only tuning jobs with the specified status.</p>"
|
||||
}
|
||||
},
|
||||
"HyperParameterTuningJobStrategyType": {
|
||||
"base": "<p>The strategy hyperparameter tuning uses to find the best combination of hyperparameters for your model. Currently, the only supported value is <code>Bayesian</code>.</p>",
|
||||
"refs": {
|
||||
"HyperParameterTuningJobConfig$Strategy": "<p>Specifies the search strategy for hyperparameters. Currently, the only valid value is <code>Bayesian</code>.</p>",
|
||||
"HyperParameterTuningJobSummary$Strategy": "<p>Specifies the search strategy hyperparameter tuning uses to choose which hyperparameters to use for each iteration. Currently, the only valid value is Bayesian.</p>"
|
||||
}
|
||||
},
|
||||
"HyperParameterTuningJobSummaries": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"ListHyperParameterTuningJobsResponse$HyperParameterTuningJobSummaries": "<p>A list of objects that describe the tuning jobs that the <code>ListHyperParameterTuningJobs</code> request returned.</p>"
|
||||
}
|
||||
},
|
||||
"HyperParameterTuningJobSummary": {
|
||||
"base": "<p>Provides summary information about a hyperparameter tuning job.</p>",
|
||||
"refs": {
|
||||
"HyperParameterTuningJobSummaries$member": null
|
||||
}
|
||||
},
|
||||
"HyperParameters": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"CreateTrainingJobRequest$HyperParameters": "<p>Algorithm-specific parameters. You set hyperparameters before you start the learning process. Hyperparameters influence the quality of the model. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/algos.html\">Algorithms</a>. </p> <p>You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the <code>Length Constraint</code>. </p>",
|
||||
"DescribeTrainingJobResponse$HyperParameters": "<p>Algorithm-specific parameters. </p>"
|
||||
"CreateTrainingJobRequest$HyperParameters": "<p>Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/algos.html\">Algorithms</a>. </p> <p>You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the <code>Length Constraint</code>. </p>",
|
||||
"DescribeTrainingJobResponse$HyperParameters": "<p>Algorithm-specific parameters. </p>",
|
||||
"HyperParameterTrainingJobDefinition$StaticHyperParameters": "<p>Specifies the values of hyperparameters that do not change for the tuning job.</p>",
|
||||
"HyperParameterTrainingJobSummary$TunedHyperParameters": "<p>A list of the hyperparameters for which you specified ranges to search.</p>"
|
||||
}
|
||||
},
|
||||
"Image": {
|
||||
|
|
@ -446,7 +605,8 @@
|
|||
"base": null,
|
||||
"refs": {
|
||||
"CreateTrainingJobRequest$InputDataConfig": "<p>An array of <code>Channel</code> objects. Each channel is a named input source. <code>InputDataConfig</code> describes the input data and its location. </p> <p>Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, <code>training_data</code> and <code>validation_data</code>. The configuration for each channel provides the S3 location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format. </p> <p>Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams. </p>",
|
||||
"DescribeTrainingJobResponse$InputDataConfig": "<p>An array of <code>Channel</code> objects that describes each data input channel. </p>"
|
||||
"DescribeTrainingJobResponse$InputDataConfig": "<p>An array of <code>Channel</code> objects that describes each data input channel. </p>",
|
||||
"HyperParameterTrainingJobDefinition$InputDataConfig": "<p>An array of objects that specify the input for the training jobs that the tuning job launches.</p>"
|
||||
}
|
||||
},
|
||||
"InstanceType": {
|
||||
|
|
@ -458,6 +618,18 @@
|
|||
"UpdateNotebookInstanceInput$InstanceType": "<p>The Amazon ML compute instance type.</p>"
|
||||
}
|
||||
},
|
||||
"IntegerParameterRange": {
|
||||
"base": "<p>For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.</p>",
|
||||
"refs": {
|
||||
"IntegerParameterRanges$member": null
|
||||
}
|
||||
},
|
||||
"IntegerParameterRanges": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"ParameterRanges$IntegerParameterRanges": "<p>The array of <a>IntegerParameterRange</a> objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.</p>"
|
||||
}
|
||||
},
|
||||
"KmsKeyId": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
|
|
@ -465,7 +637,7 @@
|
|||
"CreateNotebookInstanceInput$KmsKeyId": "<p> If you provide a AWS KMS key ID, Amazon SageMaker uses it to encrypt data at rest on the ML storage volume that is attached to your notebook instance. </p>",
|
||||
"DescribeEndpointConfigOutput$KmsKeyId": "<p>AWS KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.</p>",
|
||||
"DescribeNotebookInstanceOutput$KmsKeyId": "<p> AWS KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance. </p>",
|
||||
"OutputDataConfig$KmsKeyId": "<p>The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. </p> <note> <p>If the configuration of the output S3 bucket requires server-side encryption for objects, and you don't provide the KMS key ID, Amazon SageMaker uses the default service key. For more information, see <a href=\"https://docs.aws.amazon.com/AmazonS3/latest/dev/UsingKMSEncryption.html\">KMS-Managed Encryption Keys</a> in Amazon Simple Storage Service developer guide.</p> </note> <note> <p> The KMS key policy must grant permission to the IAM role you specify in your <code>CreateTrainingJob</code> request. <a href=\"http://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html\">Using Key Policies in AWS KMS</a> in the AWS Key Management Service Developer Guide. </p> </note>",
|
||||
"OutputDataConfig$KmsKeyId": "<p>The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. </p> <note> <p>If you don't provide the KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see <a href=\"https://docs.aws.amazon.com/AmazonS3/latest/dev/UsingKMSEncryption.html\">KMS-Managed Encryption Keys</a> in Amazon Simple Storage Service developer guide.</p> </note> <note> <p> The KMS key policy must grant permission to the IAM role you specify in your <code>CreateTrainingJob</code> request. <a href=\"http://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html\">Using Key Policies in AWS KMS</a> in the AWS Key Management Service Developer Guide. </p> </note>",
|
||||
"ResourceConfig$VolumeKmsKeyId": "<p>The Amazon Resource Name (ARN) of a AWS Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.</p>"
|
||||
}
|
||||
},
|
||||
|
|
@ -502,6 +674,16 @@
|
|||
"refs": {
|
||||
}
|
||||
},
|
||||
"ListHyperParameterTuningJobsRequest": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
}
|
||||
},
|
||||
"ListHyperParameterTuningJobsResponse": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
}
|
||||
},
|
||||
"ListModelsInput": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
|
|
@ -548,6 +730,16 @@
|
|||
"refs": {
|
||||
}
|
||||
},
|
||||
"ListTrainingJobsForHyperParameterTuningJobRequest": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
}
|
||||
},
|
||||
"ListTrainingJobsForHyperParameterTuningJobResponse": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
}
|
||||
},
|
||||
"ListTrainingJobsRequest": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
|
|
@ -558,14 +750,28 @@
|
|||
"refs": {
|
||||
}
|
||||
},
|
||||
"MaxNumberOfTrainingJobs": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"ResourceLimits$MaxNumberOfTrainingJobs": "<p>The maximum number of training jobs that a hyperparameter tuning job can launch.</p>"
|
||||
}
|
||||
},
|
||||
"MaxParallelTrainingJobs": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"ResourceLimits$MaxParallelTrainingJobs": "<p>The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.</p>"
|
||||
}
|
||||
},
|
||||
"MaxResults": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"ListEndpointConfigsInput$MaxResults": "<p>The maximum number of training jobs to return in the response.</p>",
|
||||
"ListEndpointsInput$MaxResults": "<p>The maximum number of endpoints to return in the response.</p>",
|
||||
"ListHyperParameterTuningJobsRequest$MaxResults": "<p>The maximum number of tuning jobs to return.</p>",
|
||||
"ListModelsInput$MaxResults": "<p>The maximum number of models to return in the response.</p>",
|
||||
"ListNotebookInstanceLifecycleConfigsInput$MaxResults": "<p>The maximum number of lifecycle configurations to return in the response.</p>",
|
||||
"ListNotebookInstancesInput$MaxResults": "<p>The maximum number of notebook instances to return.</p>",
|
||||
"ListTrainingJobsForHyperParameterTuningJobRequest$MaxResults": "<p>The maximum number of training jobs to return.</p>",
|
||||
"ListTrainingJobsRequest$MaxResults": "<p>The maximum number of training jobs to return in the response.</p>"
|
||||
}
|
||||
},
|
||||
|
|
@ -575,6 +781,38 @@
|
|||
"StoppingCondition$MaxRuntimeInSeconds": "<p>The maximum length of time, in seconds, that the training job can run. If model training does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. Maximum value is 5 days.</p>"
|
||||
}
|
||||
},
|
||||
"MetricDefinition": {
|
||||
"base": "<p>Specifies a metric that the training algorithm writes to <code>stderr</code> or <code>stdout</code>. Amazon SageMakerHyperparamter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.</p>",
|
||||
"refs": {
|
||||
"MetricDefinitionList$member": null
|
||||
}
|
||||
},
|
||||
"MetricDefinitionList": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"HyperParameterAlgorithmSpecification$MetricDefinitions": "<p>An array of objects that specify the metrics that the algorithm emits.</p>"
|
||||
}
|
||||
},
|
||||
"MetricName": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"FinalHyperParameterTuningJobObjectiveMetric$MetricName": "<p>The name of the objective metric.</p>",
|
||||
"HyperParameterTuningJobObjective$MetricName": "<p>The name of the metric to use for the objective metric.</p>",
|
||||
"MetricDefinition$Name": "<p>The name of the metric.</p>"
|
||||
}
|
||||
},
|
||||
"MetricRegex": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"MetricDefinition$Regex": "<p>A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see <a>hpo-define-metrics</a>.</p>"
|
||||
}
|
||||
},
|
||||
"MetricValue": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"FinalHyperParameterTuningJobObjectiveMetric$Value": "<p>The value of the objective metric.</p>"
|
||||
}
|
||||
},
|
||||
"ModelArn": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
|
|
@ -627,7 +865,8 @@
|
|||
"NameContains": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"ListTrainingJobsRequest$NameContains": "<p>A string in the training job name. This filter returns only models whose name contains the specified string.</p>"
|
||||
"ListHyperParameterTuningJobsRequest$NameContains": "<p>A string in the tuning job name. This filter returns only tuning jobs whose name contains the specified string.</p>",
|
||||
"ListTrainingJobsRequest$NameContains": "<p>A string in the training job name. This filter returns only training jobs whose name contains the specified string.</p>"
|
||||
}
|
||||
},
|
||||
"NetworkInterfaceId": {
|
||||
|
|
@ -639,12 +878,16 @@
|
|||
"NextToken": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"ListHyperParameterTuningJobsRequest$NextToken": "<p>If the result of the previous <code>ListHyperParameterTuningJobs</code> request was truncated, the response includes a <code>NextToken</code>. To retrieve the next set of tuning jobs, use the token in the next request.</p>",
|
||||
"ListHyperParameterTuningJobsResponse$NextToken": "<p>If the result of this <code>ListHyperParameterTuningJobs</code> request was truncated, the response includes a <code>NextToken</code>. To retrieve the next set of tuning jobs, use the token in the next request.</p>",
|
||||
"ListNotebookInstanceLifecycleConfigsInput$NextToken": "<p>If the result of a <code>ListNotebookInstanceLifecycleConfigs</code> request was truncated, the response includes a <code>NextToken</code>. To get the next set of lifecycle configurations, use the token in the next request.</p>",
|
||||
"ListNotebookInstanceLifecycleConfigsOutput$NextToken": "<p>If the response is truncated, Amazon SageMaker returns this token. To get the next set of lifecycle configurations, use it in the next request. </p>",
|
||||
"ListNotebookInstancesInput$NextToken": "<p> If the previous call to the <code>ListNotebookInstances</code> is truncated, the response includes a <code>NextToken</code>. You can use this token in your subsequent <code>ListNotebookInstances</code> request to fetch the next set of notebook instances. </p> <note> <p> You might specify a filter or a sort order in your request. When response is truncated, you must use the same values for the filer and sort order in the next request. </p> </note>",
|
||||
"ListNotebookInstancesOutput$NextToken": "<p>If the response to the previous <code>ListNotebookInstances</code> request was truncated, Amazon SageMaker returns this token. To retrieve the next set of notebook instances, use the token in the next request.</p>",
|
||||
"ListTagsInput$NextToken": "<p> If the response to the previous <code>ListTags</code> request is truncated, Amazon SageMaker returns this token. To retrieve the next set of tags, use it in the subsequent request. </p>",
|
||||
"ListTagsOutput$NextToken": "<p> If response is truncated, Amazon SageMaker includes a token in the response. You can use this token in your subsequent request to fetch next set of tokens. </p>",
|
||||
"ListTrainingJobsForHyperParameterTuningJobRequest$NextToken": "<p>If the result of the previous <code>ListTrainingJobsForHyperParameterTuningJob</code> request was truncated, the response includes a <code>NextToken</code>. To retrieve the next set of training jobs, use the token in the next request.</p>",
|
||||
"ListTrainingJobsForHyperParameterTuningJobResponse$NextToken": "<p>If the result of this <code>ListTrainingJobsForHyperParameterTuningJob</code> request was truncated, the response includes a <code>NextToken</code>. To retrieve the next set of training jobs, use the token in the next request.</p>",
|
||||
"ListTrainingJobsRequest$NextToken": "<p>If the result of the previous <code>ListTrainingJobs</code> request was truncated, the response includes a <code>NextToken</code>. To retrieve the next set of training jobs, use the token in the next request. </p>",
|
||||
"ListTrainingJobsResponse$NextToken": "<p>If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of training jobs, use it in the subsequent request.</p>"
|
||||
}
|
||||
|
|
@ -793,6 +1036,27 @@
|
|||
"NotebookInstanceSummary$Url": "<p>The URL that you use to connect to the Jupyter instance running in your notebook instance. </p>"
|
||||
}
|
||||
},
|
||||
"ObjectiveStatus": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"HyperParameterTrainingJobSummary$ObjectiveStatus": "<p>The status of the objective metric for the training job:</p> <ul> <li> <p>Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.</p> </li> </ul> <ul> <li> <p>Pending: The training job is in progress and evaluation of its final objective metric is pending.</p> </li> </ul> <ul> <li> <p>Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.</p> </li> </ul>"
|
||||
}
|
||||
},
|
||||
"ObjectiveStatusCounter": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"ObjectiveStatusCounters$Succeeded": "<p>The number of training jobs whose final objective metric was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.</p>",
|
||||
"ObjectiveStatusCounters$Pending": "<p>The number of training jobs that are in progress and pending evaluation of their final objective metric.</p>",
|
||||
"ObjectiveStatusCounters$Failed": "<p>The number of training jobs whose final objective metric was not evaluated and used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.</p>"
|
||||
}
|
||||
},
|
||||
"ObjectiveStatusCounters": {
|
||||
"base": "<p>Specifies the number of training jobs that this hyperparameter tuning job launched, categorized by the status of their objective metric. The objective metric status shows whether the final objective metric for the training job has been evaluated by the tuning job and used in the hyperparameter tuning process.</p>",
|
||||
"refs": {
|
||||
"DescribeHyperParameterTuningJobResponse$ObjectiveStatusCounters": "<p>The object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched.</p>",
|
||||
"HyperParameterTuningJobSummary$ObjectiveStatusCounters": "<p>The object that specifies the numbers of training jobs, categorized by objective metric status, that this tuning job launched.</p>"
|
||||
}
|
||||
},
|
||||
"OrderKey": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
|
|
@ -805,7 +1069,8 @@
|
|||
"base": "<p>Provides information about how to store model training results (model artifacts).</p>",
|
||||
"refs": {
|
||||
"CreateTrainingJobRequest$OutputDataConfig": "<p>Specifies the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts. </p>",
|
||||
"DescribeTrainingJobResponse$OutputDataConfig": "<p>The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts. </p>"
|
||||
"DescribeTrainingJobResponse$OutputDataConfig": "<p>The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts. </p>",
|
||||
"HyperParameterTrainingJobDefinition$OutputDataConfig": "<p>Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.</p>"
|
||||
}
|
||||
},
|
||||
"PaginationToken": {
|
||||
|
|
@ -822,13 +1087,33 @@
|
|||
"ParameterKey": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"HyperParameters$key": null
|
||||
"CategoricalParameterRange$Name": "<p>The name of the categorical hyperparameter to tune.</p>",
|
||||
"ContinuousParameterRange$Name": "<p>The name of the continuous hyperparameter to tune.</p>",
|
||||
"HyperParameters$key": null,
|
||||
"IntegerParameterRange$Name": "<p>The name of the hyperparameter to search.</p>"
|
||||
}
|
||||
},
|
||||
"ParameterRanges": {
|
||||
"base": "<p>Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches.</p>",
|
||||
"refs": {
|
||||
"HyperParameterTuningJobConfig$ParameterRanges": "<p>The object that specifies the ranges of hyperparameters that this tuning job searches.</p>"
|
||||
}
|
||||
},
|
||||
"ParameterValue": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"HyperParameters$value": null
|
||||
"ContinuousParameterRange$MinValue": "<p>The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and <code>MaxValue</code>for tuning.</p>",
|
||||
"ContinuousParameterRange$MaxValue": "<p>The maximum value for the hyperparameter. The tuning job uses floating-point values between <code>MinValue</code> value and this value for tuning.</p>",
|
||||
"HyperParameters$value": null,
|
||||
"IntegerParameterRange$MinValue": "<p>The minimum value of the hyperparameter to search.</p>",
|
||||
"IntegerParameterRange$MaxValue": "<p>The maximum value of the hyperparameter to search.</p>",
|
||||
"ParameterValues$member": null
|
||||
}
|
||||
},
|
||||
"ParameterValues": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"CategoricalParameterRange$Values": "<p>A list of the categories for the hyperparameter.</p>"
|
||||
}
|
||||
},
|
||||
"ProductionVariant": {
|
||||
|
|
@ -865,7 +1150,7 @@
|
|||
"RecordWrapper": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"Channel$RecordWrapperType": "<p/> <p>Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format, in which caseAmazon SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see <a href=\"https://mxnet.incubator.apache.org/how_to/recordio.html?highlight=im2rec\">Create a Dataset Using RecordIO</a>. </p> <p>In FILE mode, leave this field unset or set it to None.</p> <p/>"
|
||||
"Channel$RecordWrapperType": "<p/> <p>Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format, in which case, Amazon SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see <a href=\"https://mxnet.incubator.apache.org/how_to/recordio.html?highlight=im2rec\">Create a Dataset Using RecordIO</a>. </p> <p>In FILE mode, leave this field unset or set it to None.</p> <p/>"
|
||||
}
|
||||
},
|
||||
"ResourceArn": {
|
||||
|
|
@ -880,7 +1165,8 @@
|
|||
"base": "<p>Describes the resources, including ML compute instances and ML storage volumes, to use for model training. </p>",
|
||||
"refs": {
|
||||
"CreateTrainingJobRequest$ResourceConfig": "<p>The resources, including the ML compute instances and ML storage volumes, to use for model training. </p> <p>ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want Amazon SageMaker to use the ML storage volume to store the training data, choose <code>File</code> as the <code>TrainingInputMode</code> in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.</p>",
|
||||
"DescribeTrainingJobResponse$ResourceConfig": "<p>Resources, including ML compute instances and ML storage volumes, that are configured for model training. </p>"
|
||||
"DescribeTrainingJobResponse$ResourceConfig": "<p>Resources, including ML compute instances and ML storage volumes, that are configured for model training. </p>",
|
||||
"HyperParameterTrainingJobDefinition$ResourceConfig": "<p>The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.</p> <p>Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want Amazon SageMaker to use the storage volume to store the training data, choose <code>File</code> as the <code>TrainingInputMode</code> in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.</p>"
|
||||
}
|
||||
},
|
||||
"ResourceInUse": {
|
||||
|
|
@ -893,6 +1179,13 @@
|
|||
"refs": {
|
||||
}
|
||||
},
|
||||
"ResourceLimits": {
|
||||
"base": "<p>Specifies the maximum number of training jobs and parallel training jobs that a hyperparameter tuning job can launch.</p>",
|
||||
"refs": {
|
||||
"HyperParameterTuningJobConfig$ResourceLimits": "<p>The object that specifies the maximum number of training jobs and parallel training jobs for this tuning job.</p>",
|
||||
"HyperParameterTuningJobSummary$ResourceLimits": "<p>The object that specifies the maximum number of training jobs and parallel training jobs allowed for this tuning job.</p>"
|
||||
}
|
||||
},
|
||||
"ResourceNotFound": {
|
||||
"base": "<p>Resource being access is not found.</p>",
|
||||
"refs": {
|
||||
|
|
@ -901,13 +1194,14 @@
|
|||
"RoleArn": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"CreateModelInput$ExecutionRoleArn": "<p>The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances. Deploying on ML compute instances is part of model hosting. For more information, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html\">Amazon SageMaker Roles</a>. </p>",
|
||||
"CreateNotebookInstanceInput$RoleArn": "<p> When you send any requests to AWS resources from the notebook instance, Amazon SageMaker assumes this role to perform tasks on your behalf. You must grant this role necessary permissions so Amazon SageMaker can perform these tasks. The policy must allow the Amazon SageMaker service principal (sagemaker.amazonaws.com) permissions to assume this role. For more information, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html\">Amazon SageMaker Roles</a>. </p>",
|
||||
"CreateTrainingJobRequest$RoleArn": "<p>The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf. </p> <p>During model training, Amazon SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html\">Amazon SageMaker Roles</a>. </p>",
|
||||
"CreateModelInput$ExecutionRoleArn": "<p>The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances. Deploying on ML compute instances is part of model hosting. For more information, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html\">Amazon SageMaker Roles</a>. </p> <note> <p>To be able to pass this role to Amazon SageMaker, the caller of this API must have the <code>iam:PassRole</code> permission.</p> </note>",
|
||||
"CreateNotebookInstanceInput$RoleArn": "<p> When you send any requests to AWS resources from the notebook instance, Amazon SageMaker assumes this role to perform tasks on your behalf. You must grant this role necessary permissions so Amazon SageMaker can perform these tasks. The policy must allow the Amazon SageMaker service principal (sagemaker.amazonaws.com) permissions to assume this role. For more information, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html\">Amazon SageMaker Roles</a>. </p> <note> <p>To be able to pass this role to Amazon SageMaker, the caller of this API must have the <code>iam:PassRole</code> permission.</p> </note>",
|
||||
"CreateTrainingJobRequest$RoleArn": "<p>The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf. </p> <p>During model training, Amazon SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html\">Amazon SageMaker Roles</a>. </p> <note> <p>To be able to pass this role to Amazon SageMaker, the caller of this API must have the <code>iam:PassRole</code> permission.</p> </note>",
|
||||
"DescribeModelOutput$ExecutionRoleArn": "<p>The Amazon Resource Name (ARN) of the IAM role that you specified for the model.</p>",
|
||||
"DescribeNotebookInstanceOutput$RoleArn": "<p> Amazon Resource Name (ARN) of the IAM role associated with the instance. </p>",
|
||||
"DescribeTrainingJobResponse$RoleArn": "<p>The AWS Identity and Access Management (IAM) role configured for the training job. </p>",
|
||||
"UpdateNotebookInstanceInput$RoleArn": "<p>Amazon Resource Name (ARN) of the IAM role to associate with the instance.</p>"
|
||||
"HyperParameterTrainingJobDefinition$RoleArn": "<p>The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.</p>",
|
||||
"UpdateNotebookInstanceInput$RoleArn": "<p>The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access the notebook instance. For more information, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html\">Amazon SageMaker Roles</a>. </p> <note> <p>To be able to pass this role to Amazon SageMaker, the caller of this API must have the <code>iam:PassRole</code> permission.</p> </note>"
|
||||
}
|
||||
},
|
||||
"S3DataDistribution": {
|
||||
|
|
@ -971,6 +1265,8 @@
|
|||
"SortOrder": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"ListHyperParameterTuningJobsRequest$SortOrder": "<p>The sort order for results. The default is <code>Ascending</code>.</p>",
|
||||
"ListTrainingJobsForHyperParameterTuningJobRequest$SortOrder": "<p>The sort order for results. The default is <code>Ascending</code>.</p>",
|
||||
"ListTrainingJobsRequest$SortOrder": "<p>The sort order for results. The default is <code>Ascending</code>.</p>"
|
||||
}
|
||||
},
|
||||
|
|
@ -979,6 +1275,11 @@
|
|||
"refs": {
|
||||
}
|
||||
},
|
||||
"StopHyperParameterTuningJobRequest": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
}
|
||||
},
|
||||
"StopNotebookInstanceInput": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
|
|
@ -993,7 +1294,8 @@
|
|||
"base": "<p>Specifies how long model training can run. When model training reaches the limit, Amazon SageMaker ends the training job. Use this API to cap model training cost.</p> <p>To stop a job, Amazon SageMaker sends the algorithm the <code>SIGTERM</code> signal, which delays job termination for120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of training is not lost. </p> <p>Training algorithms provided by Amazon SageMaker automatically saves the intermediate results of a model training job (it is best effort case, as model might not be ready to save as some stages, for example training just started). This intermediate data is a valid model artifact. You can use it to create a model (<code>CreateModel</code>). </p>",
|
||||
"refs": {
|
||||
"CreateTrainingJobRequest$StoppingCondition": "<p>Sets a duration for training. Use this parameter to cap model training costs. To stop a job, Amazon SageMaker sends the algorithm the <code>SIGTERM</code> signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts. </p> <p>When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job. This intermediate data is a valid model artifact. You can use it to create a model using the <code>CreateModel</code> API. </p>",
|
||||
"DescribeTrainingJobResponse$StoppingCondition": "<p>The condition under which to stop the training job. </p>"
|
||||
"DescribeTrainingJobResponse$StoppingCondition": "<p>The condition under which to stop the training job. </p>",
|
||||
"HyperParameterTrainingJobDefinition$StoppingCondition": "<p>Sets a maximum duration for the training jobs that the tuning job launches. Use this parameter to limit model training costs. </p> <p>To stop a job, Amazon SageMaker sends the algorithm the <code>SIGTERM</code> signal. This delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.</p> <p>When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job.</p>"
|
||||
}
|
||||
},
|
||||
"SubnetId": {
|
||||
|
|
@ -1036,6 +1338,7 @@
|
|||
"AddTagsOutput$Tags": "<p>A list of tags associated with the Amazon SageMaker resource.</p>",
|
||||
"CreateEndpointConfigInput$Tags": "<p>An array of key-value pairs. For more information, see <a href=\"http://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what\">Using Cost Allocation Tags</a> in the <i>AWS Billing and Cost Management User Guide</i>. </p>",
|
||||
"CreateEndpointInput$Tags": "<p>An array of key-value pairs. For more information, see <a href=\"http://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what\">Using Cost Allocation Tags</a>in the <i>AWS Billing and Cost Management User Guide</i>. </p>",
|
||||
"CreateHyperParameterTuningJobRequest$Tags": "<p>An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see <a href=\"http://docs.aws.amazon.com//awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what\">Using Cost Allocation Tags</a> in the <i>AWS Billing and Cost Management User Guide</i>.</p>",
|
||||
"CreateModelInput$Tags": "<p>An array of key-value pairs. For more information, see <a href=\"http://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what\">Using Cost Allocation Tags</a> in the <i>AWS Billing and Cost Management User Guide</i>. </p>",
|
||||
"CreateNotebookInstanceInput$Tags": "<p>A list of tags to associate with the notebook instance. You can add tags later by using the <code>CreateTags</code> API.</p>",
|
||||
"CreateTrainingJobRequest$Tags": "<p>An array of key-value pairs. For more information, see <a href=\"http://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what\">Using Cost Allocation Tags</a> in the <i>AWS Billing and Cost Management User Guide</i>. </p>",
|
||||
|
|
@ -1063,20 +1366,33 @@
|
|||
"DescribeEndpointConfigOutput$CreationTime": "<p>A timestamp that shows when the endpoint configuration was created.</p>",
|
||||
"DescribeEndpointOutput$CreationTime": "<p>A timestamp that shows when the endpoint was created.</p>",
|
||||
"DescribeEndpointOutput$LastModifiedTime": "<p>A timestamp that shows when the endpoint was last modified.</p>",
|
||||
"DescribeHyperParameterTuningJobResponse$CreationTime": "<p>The date and time that the tuning job started.</p>",
|
||||
"DescribeHyperParameterTuningJobResponse$HyperParameterTuningEndTime": "<p>The date and time that the tuning job ended.</p>",
|
||||
"DescribeHyperParameterTuningJobResponse$LastModifiedTime": "<p>The date and time that the status of the tuning job was modified. </p>",
|
||||
"DescribeModelOutput$CreationTime": "<p>A timestamp that shows when the model was created.</p>",
|
||||
"DescribeTrainingJobResponse$CreationTime": "<p>A timestamp that indicates when the training job was created.</p>",
|
||||
"DescribeTrainingJobResponse$TrainingStartTime": "<p>A timestamp that indicates when training started.</p>",
|
||||
"DescribeTrainingJobResponse$TrainingEndTime": "<p>A timestamp that indicates when model training ended.</p>",
|
||||
"DescribeTrainingJobResponse$TrainingStartTime": "<p>Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of <code>TrainingEndTime</code>. The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.</p>",
|
||||
"DescribeTrainingJobResponse$TrainingEndTime": "<p>Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of <code>TrainingStartTime</code> and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.</p>",
|
||||
"DescribeTrainingJobResponse$LastModifiedTime": "<p>A timestamp that indicates when the status of the training job was last modified.</p>",
|
||||
"EndpointConfigSummary$CreationTime": "<p>A timestamp that shows when the endpoint configuration was created.</p>",
|
||||
"EndpointSummary$CreationTime": "<p>A timestamp that shows when the endpoint was created.</p>",
|
||||
"EndpointSummary$LastModifiedTime": "<p>A timestamp that shows when the endpoint was last modified.</p>",
|
||||
"HyperParameterTrainingJobSummary$CreationTime": "<p>The date and time that the training job was created.</p>",
|
||||
"HyperParameterTrainingJobSummary$TrainingStartTime": "<p>The date and time that the training job started.</p>",
|
||||
"HyperParameterTrainingJobSummary$TrainingEndTime": "<p>The date and time that the training job ended.</p>",
|
||||
"HyperParameterTuningJobSummary$CreationTime": "<p>The date and time that the tuning job was created.</p>",
|
||||
"HyperParameterTuningJobSummary$HyperParameterTuningEndTime": "<p>The date and time that the tuning job ended.</p>",
|
||||
"HyperParameterTuningJobSummary$LastModifiedTime": "<p>The date and time that the tuning job was modified.</p>",
|
||||
"ListEndpointConfigsInput$CreationTimeBefore": "<p>A filter that returns only endpoint configurations created before the specified time (timestamp).</p>",
|
||||
"ListEndpointConfigsInput$CreationTimeAfter": "<p>A filter that returns only endpoint configurations created after the specified time (timestamp).</p>",
|
||||
"ListEndpointsInput$CreationTimeBefore": "<p>A filter that returns only endpoints that were created before the specified time (timestamp).</p>",
|
||||
"ListEndpointsInput$CreationTimeAfter": "<p>A filter that returns only endpoints that were created after the specified time (timestamp).</p>",
|
||||
"ListEndpointsInput$LastModifiedTimeBefore": "<p> A filter that returns only endpoints that were modified before the specified timestamp. </p>",
|
||||
"ListEndpointsInput$LastModifiedTimeAfter": "<p> A filter that returns only endpoints that were modified after the specified timestamp. </p>",
|
||||
"ListHyperParameterTuningJobsRequest$CreationTimeAfter": "<p>A filter that returns only tuning jobs that were created after the specified time.</p>",
|
||||
"ListHyperParameterTuningJobsRequest$CreationTimeBefore": "<p>A filter that returns only tuning jobs that were created before the specified time.</p>",
|
||||
"ListHyperParameterTuningJobsRequest$LastModifiedTimeAfter": "<p>A filter that returns only tuning jobs that were modified after the specified time.</p>",
|
||||
"ListHyperParameterTuningJobsRequest$LastModifiedTimeBefore": "<p>A filter that returns only tuning jobs that were modified before the specified time.</p>",
|
||||
"ListModelsInput$CreationTimeBefore": "<p>A filter that returns only models created before the specified time (timestamp).</p>",
|
||||
"ListModelsInput$CreationTimeAfter": "<p>A filter that returns only models created after the specified time (timestamp).</p>",
|
||||
"ListTrainingJobsRequest$CreationTimeAfter": "<p>A filter that only training jobs created after the specified time (timestamp).</p>",
|
||||
|
|
@ -1092,7 +1408,8 @@
|
|||
"TrainingInputMode": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"AlgorithmSpecification$TrainingInputMode": "<p>The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms support, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/algos.html\">Algorithms</a>. If an algorithm supports the <code>File</code> input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the <code>Pipe</code> input mode, Amazon SageMaker streams data directly from S3 to the container. </p> <p> In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any. </p> <p> For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training. </p>"
|
||||
"AlgorithmSpecification$TrainingInputMode": "<p>The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms support, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/algos.html\">Algorithms</a>. If an algorithm supports the <code>File</code> input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the <code>Pipe</code> input mode, Amazon SageMaker streams data directly from S3 to the container. </p> <p> In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any. </p> <p> For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training. </p>",
|
||||
"HyperParameterAlgorithmSpecification$TrainingInputMode": "<p>The input mode that the algorithm supports: File or Pipe. In File input mode, Amazon SageMaker downloads the training data from Amazon S3 to the storage volume that is attached to the training instance and mounts the directory to the Docker volume for the training container. In Pipe input mode, Amazon SageMaker streams data directly from Amazon S3 to the container. </p> <p>If you specify File mode, make sure that you provision the storage volume that is attached to the training instance with enough capacity to accommodate the training data downloaded from Amazon S3, the model artifacts, and intermediate information.</p> <p/> <p>For more information about input modes, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/algos.html\">Algorithms</a>. </p>"
|
||||
}
|
||||
},
|
||||
"TrainingInstanceCount": {
|
||||
|
|
@ -1112,6 +1429,7 @@
|
|||
"refs": {
|
||||
"CreateTrainingJobResponse$TrainingJobArn": "<p>The Amazon Resource Name (ARN) of the training job.</p>",
|
||||
"DescribeTrainingJobResponse$TrainingJobArn": "<p>The Amazon Resource Name (ARN) of the training job.</p>",
|
||||
"HyperParameterTrainingJobSummary$TrainingJobArn": "<p>The Amazon Resource Name (ARN) of the training job.</p>",
|
||||
"TrainingJobSummary$TrainingJobArn": "<p>The Amazon Resource Name (ARN) of the training job.</p>"
|
||||
}
|
||||
},
|
||||
|
|
@ -1121,18 +1439,44 @@
|
|||
"CreateTrainingJobRequest$TrainingJobName": "<p>The name of the training job. The name must be unique within an AWS Region in an AWS account. It appears in the Amazon SageMaker console. </p>",
|
||||
"DescribeTrainingJobRequest$TrainingJobName": "<p>The name of the training job.</p>",
|
||||
"DescribeTrainingJobResponse$TrainingJobName": "<p> Name of the model training job. </p>",
|
||||
"HyperParameterTrainingJobSummary$TrainingJobName": "<p>The name of the training job.</p>",
|
||||
"StopTrainingJobRequest$TrainingJobName": "<p>The name of the training job to stop.</p>",
|
||||
"TrainingJobSummary$TrainingJobName": "<p>The name of the training job that you want a summary for.</p>"
|
||||
}
|
||||
},
|
||||
"TrainingJobSortByOptions": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"ListTrainingJobsForHyperParameterTuningJobRequest$SortBy": "<p>The field to sort results by. The default is <code>Name</code>.</p>"
|
||||
}
|
||||
},
|
||||
"TrainingJobStatus": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"DescribeTrainingJobResponse$TrainingJobStatus": "<p>The status of the training job. </p> <p>For the <code>InProgress</code> status, Amazon SageMaker can return these secondary statuses:</p> <ul> <li> <p>Starting - Preparing for training.</p> </li> <li> <p>Downloading - Optional stage for algorithms that support File training input mode. It indicates data is being downloaded to ML storage volumes.</p> </li> <li> <p>Training - Training is in progress.</p> </li> <li> <p>Uploading - Training is complete and model upload is in progress.</p> </li> </ul> <p>For the <code>Stopped</code> training status, Amazon SageMaker can return these secondary statuses:</p> <ul> <li> <p>MaxRuntimeExceeded - Job stopped as a result of maximum allowed runtime exceeded.</p> </li> </ul>",
|
||||
"HyperParameterTrainingJobSummary$TrainingJobStatus": "<p>The status of the training job.</p>",
|
||||
"ListTrainingJobsForHyperParameterTuningJobRequest$StatusEquals": "<p>A filter that returns only training jobs with the specified status.</p>",
|
||||
"ListTrainingJobsRequest$StatusEquals": "<p>A filter that retrieves only training jobs with a specific status.</p>",
|
||||
"TrainingJobSummary$TrainingJobStatus": "<p>The status of the training job.</p>"
|
||||
}
|
||||
},
|
||||
"TrainingJobStatusCounter": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
"TrainingJobStatusCounters$Completed": "<p>The number of completed training jobs launched by a hyperparameter tuning job.</p>",
|
||||
"TrainingJobStatusCounters$InProgress": "<p>The number of in-progress training jobs launched by a hyperparameter tuning job.</p>",
|
||||
"TrainingJobStatusCounters$RetryableError": "<p>The number of training jobs that failed, but can be retried. A failed training job can be retried only if it failed because an internal service error occurred.</p>",
|
||||
"TrainingJobStatusCounters$NonRetryableError": "<p>The number of training jobs that failed and can't be retried. A failed training job can't be retried if it failed because a client error occurred.</p>",
|
||||
"TrainingJobStatusCounters$Stopped": "<p>The number of training jobs launched by a hyperparameter tuning job that were manually stopped.</p>"
|
||||
}
|
||||
},
|
||||
"TrainingJobStatusCounters": {
|
||||
"base": "<p>The numbers of training jobs launched by a hyperparameter tuning job, categorized by status.</p>",
|
||||
"refs": {
|
||||
"DescribeHyperParameterTuningJobResponse$TrainingJobStatusCounters": "<p>The object that specifies the number of training jobs, categorized by status, that this tuning job launched.</p>",
|
||||
"HyperParameterTuningJobSummary$TrainingJobStatusCounters": "<p>The object that specifies the numbers of training jobs, categorized by status, that this tuning job launched.</p>"
|
||||
}
|
||||
},
|
||||
"TrainingJobSummaries": {
|
||||
"base": null,
|
||||
"refs": {
|
||||
|
|
@ -1217,10 +1561,11 @@
|
|||
"VpcConfig": {
|
||||
"base": "<p>Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see <a>host-vpc</a> and <a>train-vpc</a>.</p>",
|
||||
"refs": {
|
||||
"CreateModelInput$VpcConfig": "<p>A object that specifies the VPC that you want your model to connect to. Control access to and from your training container by configuring the VPC. For more information, see <a>host-vpc</a>.</p>",
|
||||
"CreateModelInput$VpcConfig": "<p>A object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. For more information, see <a>host-vpc</a>.</p>",
|
||||
"CreateTrainingJobRequest$VpcConfig": "<p>A object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see <a>train-vpc</a> </p>",
|
||||
"DescribeModelOutput$VpcConfig": "<p>A object that specifies the VPC that this model has access to. For more information, see <a>host-vpc</a> </p>",
|
||||
"DescribeTrainingJobResponse$VpcConfig": "<p>A object that specifies the VPC that this training job has access to. For more information, see <a>train-vpc</a>.</p>"
|
||||
"DescribeTrainingJobResponse$VpcConfig": "<p>A object that specifies the VPC that this training job has access to. For more information, see <a>train-vpc</a>.</p>",
|
||||
"HyperParameterTrainingJobDefinition$VpcConfig": "<p>The object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see <a>train-vpc</a>.</p>"
|
||||
}
|
||||
},
|
||||
"VpcSecurityGroupIds": {
|
||||
|
|
|
|||
10
vendor/github.com/aws/aws-sdk-go/models/apis/sagemaker/2017-07-24/paginators-1.json
generated
vendored
10
vendor/github.com/aws/aws-sdk-go/models/apis/sagemaker/2017-07-24/paginators-1.json
generated
vendored
|
|
@ -10,6 +10,11 @@
|
|||
"output_token": "NextToken",
|
||||
"limit_key": "MaxResults"
|
||||
},
|
||||
"ListHyperParameterTuningJobs": {
|
||||
"input_token": "NextToken",
|
||||
"output_token": "NextToken",
|
||||
"limit_key": "MaxResults"
|
||||
},
|
||||
"ListModels": {
|
||||
"input_token": "NextToken",
|
||||
"output_token": "NextToken",
|
||||
|
|
@ -34,6 +39,11 @@
|
|||
"input_token": "NextToken",
|
||||
"output_token": "NextToken",
|
||||
"limit_key": "MaxResults"
|
||||
},
|
||||
"ListTrainingJobsForHyperParameterTuningJob": {
|
||||
"input_token": "NextToken",
|
||||
"output_token": "NextToken",
|
||||
"limit_key": "MaxResults"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue