Get Training Status

algorithm

string

Algorithm used to create the model. Returned only when the modelType is image-detection. Default is object-detection.

2.0

createdAt

date

Date and time that the model was created.

1.0

datasetId

long

ID of the dataset trained to create the model.

1.0

datasetVersionId

int

N/A

1.0

earlyStopping

boolean

Specifies whether the training process stopped before completing all the epochs. The training process stops before the specified number of epochs when the model has reached the optimal accuracy. The lastEpochDone value specifies the last training iteration.

For detection datasets, the training process completes all the epochs, it doesn't stop early.

2.0

epochs

int

Number of epochs used during training.

1.0

failureMsg

string

Reason the dataset training failed. Returned only if the training status is FAILED.

1.0

language

string

Model language inherited from the dataset language. Default is N/A.

2.0

lastEpochDone

int

Last training iteration performed.

2.0

learningRate

float

Learning rate used during training.

1.0

modelId

string

ID of the model. Contains letters and numbers.

1.0

modelType

string

Type of data from which the model was created. Valid values are:

• image
• image-detection—Available in Einstein Vision API version 2.0 and later.
• image-multi-label—Available in Einstein Vision API version 2.0 and later.

1.0

name

string

Name of the model.

1.0

object

string

Object returned; in this case, training.

1.0

progress

float

How far the training job has progressed. Values are between 0–1.

1.0

queuePosition

int

Where the training job is in the queue. This field appears in the response only if the status is QUEUED.

1.0

status

string

Status of the model training. Valid values are:

• QUEUED—The model training is in the queue.
• RUNNING—The model training is running.
• SUCCEEDED—The model training succeeded, and you can use the model.
• FAILED—The model training failed.

1.0

trainParams

string

Training parameters passed into the request.

1.0

trainStats

object

Statistics about the training.

1.0

updatedAt

string

Date and time that the model was last updated.

1.0

datasetLoadTime

string
in HH:MM:SS:SSS format

Time it took to load the dataset to be trained.

1.0

examples

int

Total number of examples in the dataset from which the model was created.

1.0

globalResourceDownloadTime

string
in HH:MM:SS:SSS format

Time it took to load global resources. Returned only when modelType is image-detection, text-intent, or text-sentiment.

2.0

labels

int

Total number of labels in the dataset from which the model was created.

1.0

lastEpochDone

int

Number of the last training iteration that completed.

1.0

modelSaveTime

string
in HH:MM:SS:SSS format

Time it took to save the model.

1.0

testSplitSize

int

Number of examples (from the dataset total number of examples) used to test the model. testSplitSize + trainSplitSize is equal to examples.

1.0

totalTime

string
in HH:MM:SS:SSS format

Total training time: datasetLoadTime + trainingTime + modelSaveTime + globalResourceDownloadTime

1.0

trainingTime

string
in HH:MM:SS:SSS format

Time it took to train the dataset to create the model.

1.0

trainSplitSize

int

Number of examples (from the dataset total number of examples) used to train the model. trainSplitSize + testSplitSize is equal to examples.

1.0