To troubleshoot a failed fine-tuning job in AWS Bedrock, start by systematically reviewing your input data, configuration settings, and error logs. First, verify that your training dataset is correctly formatted and meets Bedrock’s requirements. For example, if you’re training a text model, ensure your data uses the expected structure (e.g., JSONL format with properly labeled fields) and that there are no missing values or encoding errors. Bedrock may fail silently if the data schema is invalid, so use tools like the AWS CLI or SDK to validate your dataset before submission. Next, check your job configuration parameters, such as hyperparameters (learning rate, batch size) and resource allocation (instance type, training duration). Mismatched settings—like a batch size exceeding memory limits—can cause abrupt failures. Cross-reference Bedrock’s documentation for recommended values based on your model type and dataset size.
Second, analyze logs and error messages to pinpoint the root cause. Bedrock integrates with Amazon CloudWatch, where you can access detailed logs for your training job. Look for keywords like “ResourceExhausted,” “InvalidArgument,” or “InternalError” in log entries. For instance, a “ResourceExhausted” error might indicate insufficient memory for your chosen instance type, requiring an upgrade to a larger instance. If the error is vague, reproduce the issue locally using a smaller dataset or simplified configuration to isolate the problem. Additionally, check AWS service quotas: if your account has reached limits on concurrent jobs or compute resources, the job may be blocked. Use the AWS Service Quotas console to verify and request increases if needed. Permissions are another common culprit—ensure your IAM role has policies allowing Bedrock access to S3 (for data), CloudWatch (logs), and EC2 (compute).
Finally, iterate by testing incremental changes. For example, if your job fails during preprocessing, try running a data validation script separately to catch formatting issues. If training stalls, reduce the dataset size or simplify the model architecture temporarily to test for scalability issues. If all else fails, leverage AWS Support by providing the job ID, logs, and steps you’ve already taken. For recurring issues, consult Bedrock’s community forums or documentation for known bugs—for instance, a specific TensorFlow version might conflict with Bedrock’s runtime. By methodically addressing data, configuration, and infrastructure factors, you’ll resolve most failures efficiently.