model_garden/notebooks/official/tensorboard

tensorboard

Vertex AI TensorBoard custom training with prebuilt container

Learn how to create a custom training job using prebuilt containers, and monitor your training process on Vertex AI TensorBoard in near real time.

The steps performed include:

* Setup service account and Google Cloud Storage buckets.
* Write your customized training code.
* Package and upload your training code to Google Cloud Storage.
* Create & launch your custom training job with Tensorboard enabled for near real time monitorning.

Vertex AI TensorBoard integration with Vertex AI Pipelines

Learn how to create a training pipeline using the KFP SDK, execute the pipeline in Vertex AI Pipelines, and monitor your training process on Vertex AI TensorBoard in near real time.

The steps performed include:

* Setup a service account and Google Cloud Storage buckets.
* Construct a KFP pipeline with your custom training code.
* Compile and execute the KFP pipeline in Vertex AI Pipelines with Tensorboard enabled for near real time monitorning.

Vertex AI TensorBoard Custom Training with Custom Container

Learn how to create a custom training job using custom containers, and monitor your training process on Vertex AI TensorBoard in near real time.

The steps performed include:

* Create docker repository & config.
* Create a custom container image with your customized training code.
* Setup service account and Google Cloud Storage buckets.
* Create & launch your custom training job with your custom container.

Profile model training performance using Profiler

Learn how to enable Vertex AI TensorBoard Profiler for custom training jobs.

The steps performed include:

- Setup a service account and a Cloud Storage bucket
- Create a TensorBoard instance
- Create and run a custom training job that enables TensorBoard Profiler
- View the TensorBoard Profiler dashboard to debug your model training performance