Fine-tune a model in Studio
Fine-tuning trains a pre-trained model on a new dataset without training from scratch. This process, also known as transfer learning, can produce accurate models with smaller datasets and less training time. To fine-tune JumpStart foundation models, navigate to a model detail card in the Studio UI. For more information on how to open JumpStart in Studio, see Open and use JumpStart in Studio. After navigating to the model detail card of your choice, choose Train in the upper right corner. Note that not all models have fine-tuning available.
Important
Some foundation models require explicit acceptance of an end-user license agreement (EULA) before fine-tuning. For more information, see EULA acceptance in Amazon SageMaker Studio.
Model settings
When using a pre-trained JumpStart foundation model in Amazon SageMaker Studio, the Model artifact location (Amazon S3 URI) is populated by default. To edit the default Amazon S3 URI, choose Enter model artifact location. Not all models support changing the model artifact location.
Data settings
In the Data field, provide an Amazon S3 URI point to your training dataset location. The default Amazon S3 URI points to an example training dataset. To edit the default Amazon S3 URI, choose Enter training dataset and change the URI. Be sure to review the model detail card in Amazon SageMaker Studio for information on formatting training data.
Hyperparameters
You can customize the hyperparameters of the training job that are used to fine-tune the model. The hyperparameters available for each fine-tunable model differ depending on the model.
The following hyperparameters are common among models:
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Epochs – One epoch is one cycle through the entire dataset. Multiple intervals complete a batch, and multiple batches eventually complete an epoch. Multiple epochs are run until the accuracy of the model reaches an acceptable level, or when the error rate drops below an acceptable level.
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Learning rate – The amount that values should be changed between epochs. As the model is refined, its internal weights are being nudged and error rates are checked to see if the model improves. A typical learning rate is 0.1 or 0.01, where 0.01 is a much smaller adjustment and could cause the training to take a long time to converge, whereas 0.1 is much larger and can cause the training to overshoot. It is one of the primary hyperparameters that you might adjust for training your model. Note that for text models, a much smaller learning rate (5e-5 for BERT) can result in a more accurate model.
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Batch size – The number of records from the dataset that is to be selected for each interval to send to the GPUs for training.
Review the tool tip prompts and additional information in the model detail card in the Studio UI to learn more about hyperparameters specific to the model of your choice.
For more information on available hyperparameters, see Commonly supported fine-tuning hyperparameters.
Deployment
Specify the training instance type and output artifact location for your training job. You can only choose from instances that are compatible with the model of your choice within the fine-tuning the Studio UI. The default output artifact location is the SageMaker default bucket. To change the output artifact location, choose Enter output artifact location and change the Amazon S3 URI.
Security
Specify the security settings to use for your training job, including the IAM role that SageMaker uses to train your model, whether your training job should connect to a virtual private cloud (VPC), and any encryption keys to secure your data.
Additional information
In the Additional Information field you can edit the training job name. You can also add and remove tags in the form of key-value pairs to help organize and categorize your fine-tuning training jobs.
After providing information for your fine-tuning configuration, choose Submit. If the pre-trained foundation model that you chose to fine-tune requires explicit agreement of an end-user license agreement (EULA) before training, the EULA is provided in a pop-up window. To accept the terms of the EULA, choose Accept. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
