The Lucidworks AI custom model training user interface lets you train and deploy custom models, and provides information about the custom models deployed on your site. For technical information about custom embedding models, see Custom embedding model training. The other embedding models you can use are the pre-trained embedding models that Lucidworks deploys for every organization. For more information, see Lucidworks AI Pre-trained embedding models.

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Custom Models screen

To access the Custom Models screen, navigate to the megamenu and click Models > Custom Models.
Lucidworks AI custom model training user interface
The following table describes the information for each model.
FieldDescription
NameThe name of the model.
Use caseThe model use case used to train the model.
Training statusAlso referred to as state, this is the current status of the model. Options are: , , , , .
Vector sizeThe number of elements and objects in the custom model.
Deployed regionsThe geographic region specified when the custom model is deployed.
Training startedThe date and time the training started.
Training completedThe date and time the training completed.
Created byAlso referred to as Author, this is the user who created the model.
You can also create a new custom model. For more information, see Create a new model.

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Model Details screen

If you hold the pointer over a model on the list and click the entry, the Model Details screen displays. You can view Training details that include Metadata, Summary, and Metrics information about the selected model. You can also:
  • Click Download Model Data to download the JSON file for the model. You can use the parameters from this model in a different model without rekeying the information.
  • Click Delete if the model can be deleted. If the model cannot be deleted because it is associated with deployments, all the deployments must be deleted first. For example, if the model is associated with two deployments, 2 Active Deployments: Deleting Disabled displays instead of the Delete button. This example indicates there are two current deployments for the model, and based on the status of those deployments, the option to delete the model is disabled.

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Training Details

The Training Details screen provides metadata, summary, and metrics information.
Lucidworks AI custom model training details

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Metadata

This metadata provides the data supplied when the model was created.
FieldDescription
idThe unique identifier for the model. The identifier of the model. For custom models, the value is the universally unique identified (UUID) that is the primary key for the model.
AuthorAlso referred to as createdBy, this is the user who created the model.
Use caseThe model use case used to train the model.
ModelTypeThe model type. For custom models, it is the name of the model.
RegionAlso referred to as Deployed region this is the geographic region specified when the model was trained.
StateAlso referred to as Training status, this is the current status of the model.
Vector sizeThe number of elements and objects in the custom model. Default value is 256.
Training startedThe date and time the training started.
Training completedThe date and time the training completed.
Training Data Index CatalogThe location of the catalog of the training data in Google Cloud Storage (GCS).
Training Data Query SignalsThe location of signals in the training data in Google Cloud Storage (GCS).
Error MessageThe errors generated when the training failed. This field only displays for custom models with a TRAINING_FAILED status.
dataset_configThe options for the dataset format used for training are mlp_general (used for the general RNN model type) and mlp_ecommerce (used for an ecommerce RNN model type).
trainer_configThe options for the trainer type used for training are mlp_general and mlp_ecommerce.
Additional dataset_config parametersAny additional fields used to train the model using the Manual Entry method are listed. They are custom parameters.
trainer_config/text_processor_configThis field determines which type of tokenization and embedding is used as the base for the RNN model. This field only displays for custom models with a TRAINING_FAILED status. For more information, see Lucidworks AI Models API text processors. From that topic, select View API specification for detailed API information.
trainer_config.encoder_config.rnn_names_listThis field determines which bi-directional RNN layers are used. Options include gru and lstm. This field only displays for custom models with a TRAINING_FAILED status.
An example of an error message when the training fails is:
Lucidworks AI custom model error message

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Summary

Click the Summary tab to view information about training metrics for the selected model.
Lucidworks AI custom model training details summary
FieldDescription
Best EpochThe number of the epoch where the most relevant results were returned.
Index SizeThe number of bytes in the vector index.
Vector sizeThe number of elements and objects in the custom model.
Training TimeThe number of seconds to successfully train the model.
Num Trn QueriesThe number of unique training queries used in this model.
Num Val QueriesThe number of unique validation queries used in this model.
Num Unique Training PairsThe number of unique training pairs for this model. An example of a training pair is query/document. This value may be larger than the Num Trn Queries because one query might return many documents, and therefore, many pairs.

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Metrics

Click the Metrics tab to view analytics about the trained model. This information provides insights that help you determine if parameters need to be changed or if more data is needed to improve the model for optimal results. The Custom configuration parameter that specifies metrics is dataset_config.metrics_config.monitor_metric. When you select one of the values, the k designates the numbers 1, 3, 5, and 10.
  • hit@k which measures the probability that the prediction is in the first top k model predictions.
  • map@k is the mean average precision metric that evaluates the system to return relevant items in the top k results, and positions more relevant items at the top.
  • mrr@k is the mean reciprocal rank that determines how quickly the system displays the first relevant item in the top k results.
  • ndcg@k is the normalized discounted cumulative gain metric that compares rankings to the optimal order where all relevant items display at the top.
  • recall@k displays the number of relevant items returned in the top k recommendations out of the number of relevant items in the dataset.
The metrics section displays graphs and lets you select the value to view:
Training details metrics value options
The following is an example when the hit@k value is selected. Four graphs display on the screen: hit@1 (pictured), hit@3, hit@5, and hit@10.
Training details metrics with value of hit@1

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Deployment Details

The deployments screen displays information about each time you deployed the selected model.
Lucidworks AI custom model deployments screen
FieldDescription
idThe unique identifier for the model. For custom models, the value is the universally unique identified (UUID) that is the primary key for the model.
RegionThe geographic region specified when the model was deployed.
StateThe current status of the deployed model. Options are: This field specifies the current status of the custom model deployment. Value options include: * DEPLOYING. The model is in the process of being deployed. * DEPLOYED. The model is deployed and available for predictions. * DEPLOY_FAILED. The model failed to deploy. * DELETING. The model deployment is being deleted. The custom_model_deployment record is also deleted if the deployment is successfully deleted. * DELETE_FAILED. The model deployment deletion failed. The model is still deployed and available for predictions.
Deployed AtThe date and time the deployment occurred.
Last UsedThe last date and time this model was used in the /predict endpoint.
Minimum ReplicasThe minimum value of replicas for the model.
Maximum ReplicasThe maximum value of replicas for the model.
parameter_1The value of the first parameter passed in the config object. This parameter is optional. The name of parameter_1 is an example placeholder. The actual name of each custom configuration field is designated by the person creating the query.
parameter_2The value of the second parameter passed in the config object. This parameter is optional. The name of parameter_2 is an example placeholder. The actual name of each custom configuration field is designated by the person creating the query. You can use as many custom configuration fields as needed, it is not limited to the two fields mentioned.
You can also:
  • Click + New Deployment to deploy the model again. For more information, see Create a new deployment.
  • Click the Trash icon to delete a deployment with a status of “Deployed”. You cannot delete a deployment with a status of “Deploying”.