Follow the step-by-step guide by executing the notebooks in the following folders:
- 1_prepare_data/prepare_data.ipynb - TODO for training --- not ready
- 2_train_model/train_model.ipynb - TODO for training --- not ready
- 3_predict/deploy_endpoint.ipynb
- 2_train_model/model-exporter.ipynb - to export existing model
SageMaker debugger allows you to capture TensorBoard data into a chosen S3 location and monitor the training progress in real-time with TensorBoard.
See 2_train_model/train_model.ipynb for command details.
You can start the TensorBoard server from your notebook with the following command:
job_artifacts_path = estimator.latest_job_tensorboard_artifacts_path()
tensorboard_s3_output_path = f'{job_artifacts_path}/train'
!F_CPP_MIN_LOG_LEVEL=3 AWS_REGION=<ADD YOUR REGION HERE> tensorboard --logdir=$tensorboard_s3_output_path
TensorBoard server will run on your local notebook instance and you can open it by visiting the following url (the default port is typically 6006:
https://your-notebook-instance-name.notebook.your-region.sagemaker.aws/proxy/6006/