Training Log Failure Analysis

For details about common errors reported during third-party model training and solutions, see Table 1.

**Table 1** Common errors reported during third-party model training and solutions
Error	Symptom	Possible Cause	Solution
The dataset list is empty when a training job is created.	When a training job is created, the dataset selection box is empty, and no training dataset is available.	The dataset is not published.	Create a training dataset corresponding to the model and publish the dataset in advance.
Message "root: XXX valid number is 0" is displayed in training logs.	Message "root: XXX valid number is 0" is displayed in logs, indicating that the number of valid samples in the training set or validation set is 0. For example: INFO: root: Train valid number is 0.	The number of valid samples in the dataset is 0. The possible causes are as follows: Data has not been labeled. The labeled data does not meet the specifications.	Check whether the data has been labeled and whether data labels meet algorithm requirements.
Message "ValueError: label_map not match" is displayed in training logs.	The training log contains "ValueError: label_map not match" and the label data, for example: ValueError: label_map not match. {1:'apple', 2:'orange', 3:'banana', 4:'pear'} & {1:'apple', 2:'orange', 3:'banana'}	The number of labels in the training set is different from that in the validation set. For example, there are four labels in the training set, and there are only three in the validation set.	Ensure that the number of labels in the training set is the same as that in the validation set.