mirror of
https://github.com/GoogleCloudPlatform/vertex-ai-samples.git
synced 2026-09-26 14:42:04 +00:00
4d00356f4b
* Migrate gsutil usage to gcloud storage * changes for 4312 * Apply automated linter fixes * remove model_garden folder changes * remove model_garden folder changes * Update model_garden_pytorch_llama2_peft_hyperparameter_tuning.ipynb --------- Co-authored-by: gurusai-voleti <gvoleti@google.com>
Track parameters and metrics for custom training jobs
Learn how to use Vertex AI SDK for Python to:
The steps performed include:
- Track training parameters and prediction metrics for a custom training job.
- Extract and perform analysis for all parameters and metrics within an Experiment.
Learn more about Vertex ML Metadata.
Learn more about Custom training.
Learn more about Vertex AI Experiments.
Track parameters and metrics for locally trained models
Learn how to use `Vertex ML Metadata` to track training parameters and evaluation metrics.
The steps performed include:
- Track parameters and metrics for a locally trained model.
- Extract and perform analysis for all parameters and metrics within an Experiment.
Learn more about Vertex ML Metadata.
Track artifacts and metrics across Vertex AI Pipelines runs using Vertex ML Metadata
Learn how to track artifacts and metrics with `Vertex ML Metadata` in `Vertex AI Pipeline` runs.
The steps performed include:
* Use the Kubeflow Pipelines SDK to build an ML pipeline that runs on Vertex AI
* The pipeline will create a dataset, train a scikit-learn model, and deploy the model to an endpoint
* Write custom pipeline components that generate artifacts and metadata
* Compare Vertex Pipelines runs, both in the Cloud console and programmatically
* Trace the lineage for pipeline-generated artifacts
* Query your pipeline run metadata
Learn more about Vertex ML Metadata.
Learn more about Vertex AI Pipelines.