diff --git a/notebooks/official/automl/README.md b/notebooks/official/automl/README.md index abb958053..549f9ba3e 100644 --- a/notebooks/official/automl/README.md +++ b/notebooks/official/automl/README.md @@ -43,7 +43,7 @@ The steps performed include: [AutoML training text entity extraction model for online prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_text_entity_extraction_online.ipynb) -In this tutorial, you how to create an AutoML text entity extraction model and deploy for online prediction from a Python script using the Vertex SDK. +Learn how to create an AutoML text entity extraction model and deploy for online prediction from a Python script using the Vertex SDK. The steps performed include: diff --git a/notebooks/official/explainable_ai/README.md b/notebooks/official/explainable_ai/README.md index 57ef8f8e3..1cff73274 100644 --- a/notebooks/official/explainable_ai/README.md +++ b/notebooks/official/explainable_ai/README.md @@ -1,7 +1,7 @@ [Custom training tabular regression model for batch prediction with explainabilty](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_tabular_regression_batch_explain.ipynb) -In this tutorial, you how learn to use `Vertex AI Training and Explainable AI` to create a custom image classification model with explanations, and then you learn to use `Vertex AI Batch Prediction` to make a batch prediction request with explanations. +Learn how to use `Vertex AI Training and Explainable AI` to create a custom image classification model with explanations, and then you learn to use `Vertex AI Batch Prediction` to make a batch prediction request with explanations. The steps performed include: @@ -29,7 +29,7 @@ The steps performed include: [AutoML training tabular classification model for online explanation](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_automl_tabular_classification_online_explain.ipynb) -In this tutorial, you how to learn to use `AutoML` to create a tabular binary classification model from a Python script, and then learn to use `Vertex AI Online Prediction` to make online predictions with explanations. +Learn how to use `AutoML` to create a tabular binary classification model from a Python script, and then learn to use `Vertex AI Online Prediction` to make online predictions with explanations. The steps performed include: diff --git a/notebooks/official/feature_store/README.md b/notebooks/official/feature_store/README.md index b19418063..fc654f95b 100644 --- a/notebooks/official/feature_store/README.md +++ b/notebooks/official/feature_store/README.md @@ -1,5 +1,5 @@ -[Using Vertex AI Feature Store with pandas DataFrame](official/feature_store/sdk-feature-store-pandas.ipynb) +[Using Vertex AI Feature Store with pandas DataFrame](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/sdk-feature-store-pandas.ipynb) Learn how to use `Vertex AI Feature Store` with pandas DataFrame. @@ -13,7 +13,7 @@ The steps performed include: - Online serving with updated feature values. - Point-in-time correctness to fetch feature values for training. -[Online and Batch predictions using Vertex AI Feature Store](official/feature_store/sdk-feature-store.ipynb) +[Online and Batch predictions using Vertex AI Feature Store](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/sdk-feature-store.ipynb) Learn how to use `Vertex AI Feature Store` to import feature data, and to access the feature data for both online serving and offline tasks, such as training. diff --git a/notebooks/official/matching_engine/README.md b/notebooks/official/matching_engine/README.md index 3098439f1..7d1b5eb54 100644 --- a/notebooks/official/matching_engine/README.md +++ b/notebooks/official/matching_engine/README.md @@ -1,5 +1,5 @@ -[Create Vertex AI Matching Engine index](official/matching_engine/sdk_matching_engine_for_indexing.ipynb) +[Create Vertex AI Matching Engine index](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/matching_engine/sdk_matching_engine_for_indexing.ipynb) Learn how to create Approximate Nearest Neighbor (ANN) Index, query against indexes, and validate the performance of the index. @@ -12,7 +12,7 @@ The steps performed include: * Compute recall -[Introduction to builtin Swivel embedding algorithm](official/matching_engine/intro-swivel.ipynb) +[Introduction to builtin Swivel embedding algorithm](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/matching_engine/intro-swivel.ipynb) Learn how to train custom embeddings using Vertex AI Pipelines and deploy the model for serving. @@ -25,7 +25,7 @@ The steps performed include: 5. **Predict**: Calling the deployed endpoint using online prediction. 6. **Cleaning up**: Deleting resources created by this tutorial. -[Introduction to builtin Two-towers embedding algorithm](official/matching_engine/two-tower-model-introduction.ipynb) +[Introduction to builtin Two-towers embedding algorithm](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/matching_engine/two-tower-model-introduction.ipynb) Learn how to run the two-tower model. diff --git a/notebooks/official/ml_metadata/README.md b/notebooks/official/ml_metadata/README.md index 212ab6e87..7f15862f2 100644 --- a/notebooks/official/ml_metadata/README.md +++ b/notebooks/official/ml_metadata/README.md @@ -1,5 +1,5 @@ -[Track parameters and metrics for locally trained models](official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb) +[Track parameters and metrics for locally trained models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb) Learn how to use `Vertex ML Metadata` to track training parameters and evaluation metrics. @@ -8,7 +8,7 @@ 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. -[Track parameters and metrics for custom training jobs](official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb) +[Track parameters and metrics for custom training jobs](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb) Learn how to use Vertex AI SDK for Python to: @@ -16,7 +16,7 @@ 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. -[Track artifacts and metrics across Vertex AI Pipelines runs using Vertex ML Metadata](official/ml_metadata/vertex-pipelines-ml-metadata.ipynb) +[Track artifacts and metrics across Vertex AI Pipelines runs using Vertex ML Metadata](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/vertex-pipelines-ml-metadata.ipynb) Learn how to track artifacts and metrics with `Vertex ML Metadata` in `Vertex AI Pipeline` runs. diff --git a/notebooks/official/model-registry/README.md b/notebooks/official/model-registry/README.md index 93bcece7f..25995db54 100644 --- a/notebooks/official/model-registry/README.md +++ b/notebooks/official/model-registry/README.md @@ -1,5 +1,5 @@ -[Deploy BiqQuery ML Model on Vertex AI Model Registry and make predictions](official/model-registry/bqml-vertexai-model-registry.ipynb) +[Deploy BiqQuery ML Model on Vertex AI Model Registry and make predictions](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model-registry/bqml-vertexai-model-registry.ipynb) Learn how to use `Vertex AI Model Registry` with `BigQuery ML` and make batch predictions: diff --git a/notebooks/official/model_evaluation/README.md b/notebooks/official/model_evaluation/README.md index d0ddbb663..0069fbe46 100644 --- a/notebooks/official/model_evaluation/README.md +++ b/notebooks/official/model_evaluation/README.md @@ -1,5 +1,5 @@ -[Evaluating BatchPrediction results from AutoML Tabular Classification model](official/model_evaluation/automl_tabular_classification_model_evaluation.ipynb) +[Evaluating BatchPrediction results from AutoML Tabular Classification model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_tabular_classification_model_evaluation.ipynb) Learn how to train a Vertex AI AutoML Tabular Classification model and learn how to evaluate it through a Vertex AI pipeline job using `google_cloud_pipeline_components`: @@ -12,7 +12,7 @@ The steps performed include: - Evaulate the AutoML model using the `Classification Evaluation Component`. - Import the classification metrics to the AutoML model resource. -[Evaluating BatchPrediction results from AutoML Tabular Classification model](official/model_evaluation/automl_video_classification_model_evaluation.ipynb) +[Evaluating BatchPrediction results from AutoML Tabular Classification model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_video_classification_model_evaluation.ipynb) Learn how to train a Vertex AI AutoML Tabular Classification model and learn how to evaluate it through a Vertex AI pipeline job using `google_cloud_pipeline_components`: @@ -25,7 +25,7 @@ The steps performed include: - Evaulate the AutoML model using the `Classification Evaluation Component`. - Import the classification metrics to the AutoML model resource. -[Evaluating BatchPrediction results from AutoML Tabular regression model](official/model_evaluation/automl_tabular_regression_model_evaluation.ipynb) +[Evaluating BatchPrediction results from AutoML Tabular regression model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_tabular_regression_model_evaluation.ipynb) Learn how to evaluate a Vertex AI model resource through a Vertex AI pipeline job using `google_cloud_pipeline_components`: @@ -39,7 +39,7 @@ The steps performed include: - Evaulate the AutoML model using the `regression evaluation component` - Import the Classification Metrics to the AutoML model resource -[Evaluating Batch Prediction results from Custom Tabular regression model](official/model_evaluation/custom_tabular_regression_model_evaluation.ipynb) +[Evaluating Batch Prediction results from Custom Tabular regression model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/custom_tabular_regression_model_evaluation.ipynb) Learn how to evaluate a Vertex AI model resource through a Vertex AI pipeline job using `google_cloud_pipeline_components`: @@ -55,7 +55,7 @@ The steps performed include: - Evaulate the model using the `regression evaluation component` - Import the Classification Metrics to the Vertex AI model resource -[AutoML text classification pipelines using google-cloud-pipeline-components](official/model_evaluation/automl_text_classification_model_evaluation.ipynb) +[AutoML text classification pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_text_classification_model_evaluation.ipynb) Learn how to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` text classification model. diff --git a/notebooks/official/model_monitoring/README.md b/notebooks/official/model_monitoring/README.md index 72d48bdb4..b42349be5 100644 --- a/notebooks/official/model_monitoring/README.md +++ b/notebooks/official/model_monitoring/README.md @@ -1,5 +1,5 @@ -[Vertex AI Model Monitoring with Explainable AI Feature Attributions](official/model_monitoring/model_monitoring.ipynb) +[Vertex AI Model Monitoring with Explainable AI Feature Attributions](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_monitoring/model_monitoring.ipynb) Learn to use the `Vertex AI Model Monitoring` service to detect drift and anomalies in prediction requests from a deployed `Vertex AI Model` resource. diff --git a/notebooks/official/pipelines/README.md b/notebooks/official/pipelines/README.md index 730edb26e..7c2e9a326 100644 --- a/notebooks/official/pipelines/README.md +++ b/notebooks/official/pipelines/README.md @@ -1,5 +1,5 @@ -[AutoML image classification pipelines using google-cloud-pipeline-components](official/pipelines/google_cloud_pipeline_components_automl_images.ipynb) +[AutoML image classification pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_images.ipynb) Learn how to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` image classification model. @@ -15,7 +15,7 @@ The steps performed include: -[Metrics visualization and run comparison using the KFP SDK](official/pipelines/metrics_viz_run_compare_kfp.ipynb) +[Metrics visualization and run comparison using the KFP SDK](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/metrics_viz_run_compare_kfp.ipynb) Learn how to use the KFP SDK to build pipelines that generate evaluation metrics. @@ -28,7 +28,7 @@ The steps performed include: - Execute KFP pipelines - Compare metrics across pipeline runs -[Lightweight Python function-based components, and component I/O](official/pipelines/lightweight_functions_component_io_kfp.ipynb) +[Lightweight Python function-based components, and component I/O](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb) Learn to use the KFP SDK to build lightweight Python function-based components, and then you learn to use `Vertex AI Pipelines` to execute the pipeline. @@ -41,7 +41,7 @@ The steps performed include: - Compile the KFP pipeline. - Execute the KFP pipeline using `Vertex AI Pipelines` -[Custom training with pre-built Google Cloud Pipeline Components](official/pipelines/custom_model_training_and_batch_prediction.ipynb) +[Custom training with pre-built Google Cloud Pipeline Components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/custom_model_training_and_batch_prediction.ipynb) Learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build a custom model. @@ -56,7 +56,7 @@ The steps performed include: -[AutoML Tabular pipelines using google-cloud-pipeline-components](official/pipelines/automl_tabular_classification_beans.ipynb) +[AutoML Tabular pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/automl_tabular_classification_beans.ipynb) Learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` tabular classification model. @@ -72,7 +72,7 @@ The steps performed include: -[Training an acquisition-prediction model using Swivel, BigQuery ML and Vertex AI Pipelines](official/pipelines/google_cloud_pipeline_components_bqml_text.ipynb) +[Training an acquisition-prediction model using Swivel, BigQuery ML and Vertex AI Pipelines](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_bqml_text.ipynb) Learn how to build a simple BigQuery ML pipeline using Vertex AI pipelines in order to calculate text embeddings of content from articles and classify them into the *corporate acquisitions* category. @@ -85,7 +85,7 @@ The steps performed include: - Building and configuring a Kubeflow DSL pipeline with all the created components. - Compiling and running the pipeline in Vertex AI Pipelines. -[Pipelines introduction for KFP](official/pipelines/pipelines_intro_kfp.ipynb) +[Pipelines introduction for KFP](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/pipelines_intro_kfp.ipynb) Learn how to use the KFP SDK to build pipelines that generate evaluation metrics. @@ -94,7 +94,7 @@ The steps performed include: - Define and compile a `Vertex AI` pipeline. - Specify which service account to use for a pipeline run. -[AutoML tabular regression pipelines using google-cloud-pipeline-components](official/pipelines/google_cloud_pipeline_components_automl_tabular.ipynb) +[AutoML tabular regression pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_tabular.ipynb) Learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` tabular regression model. @@ -110,7 +110,7 @@ The steps performed include: -[Model upload, predict, and evaluate using google-cloud-pipeline-components](official/pipelines/google_cloud_pipeline_components_model_upload_predict_evaluate.ipynb) +[Model upload, predict, and evaluate using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_model_upload_predict_evaluate.ipynb) Learn how to evaluate a custom model using a pipeline with components from `google_cloud_pipeline_components` and a custom pipeline component you build. @@ -122,7 +122,7 @@ The steps performed include: - Compare the evaluation metrics to a threshold. -[Pipeline control structures using the KFP SDK](official/pipelines/control_flow_kfp.ipynb) +[Pipeline control structures using the KFP SDK](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/control_flow_kfp.ipynb) Learn how to use the KFP SDK to build pipelines that use loops and conditionals, including nested examples. @@ -133,7 +133,7 @@ The steps performed include: - Compile the KFP pipeline. - Execute the KFP pipeline using `Vertex AI Pipelines` -[Loan eligibility prediction using `google-cloud-pipeline-components` and Spark ML](official/pipelines/google_cloud_pipeline_components_dataproc_tabular.ipynb) +[Loan eligibility prediction using `google-cloud-pipeline-components` and Spark ML](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_dataproc_tabular.ipynb) Learn how to build a Vertex AI pipeline and train a Random-forest model using Spark ML for loan-eligibility classification problem. @@ -145,7 +145,7 @@ The steps performed include: * Build a Vertex AI pipeline and run the training job. * Use the Spark serving image in order to deploy a Spark model on Vertex AI Endpoint. -[AutoML text classification pipelines using google-cloud-pipeline-components](official/pipelines/google_cloud_pipeline_components_automl_text.ipynb) +[AutoML text classification pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_text.ipynb) Learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` text classification model. diff --git a/notebooks/official/reduction_server/README.md b/notebooks/official/reduction_server/README.md index 6b440e7ef..1bccd1055 100644 --- a/notebooks/official/reduction_server/README.md +++ b/notebooks/official/reduction_server/README.md @@ -1,5 +1,5 @@ -[PyTorch distributed training with Vertex AI Reduction Server](official/reduction_server/pytorch_distributed_training_reduction_server.ipynb) +[PyTorch distributed training with Vertex AI Reduction Server](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/reduction_server/pytorch_distributed_training_reduction_server.ipynb) Learn how to create a Python source distribution with the training code and dependencies to use with a pre-built containers on Vertex AI. diff --git a/notebooks/official/sdk/README.md b/notebooks/official/sdk/README.md index cb675bab2..45d17c66a 100644 --- a/notebooks/official/sdk/README.md +++ b/notebooks/official/sdk/README.md @@ -1,5 +1,5 @@ -[AutoML Video Classification Example](official/sdk/SDK_AutoML_Video_Classification.ipynb) +[AutoML Video Classification Example](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/sdk/SDK_AutoML_Video_Classification.ipynb) The objective of this notebook is to build a AutoML Video Classification Model. @@ -14,7 +14,7 @@ The steps performed include the following: - Perform batch prediction job on the model -[Custom training using Python package, managed text dataset, and TF Serving container](official/sdk/SDK_Custom_Training_Python_Package_Managed_Text_Dataset_Tensorflow_Serving_Container.ipynb) +[Custom training using Python package, managed text dataset, and TF Serving container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/sdk/SDK_Custom_Training_Python_Package_Managed_Text_Dataset_Tensorflow_Serving_Container.ipynb) Learn how to create a Custom Model using Custom Python Package Training and you learn how to serve the model using TensorFlow-Serving Container for online prediction. diff --git a/notebooks/official/structured_data/README.md b/notebooks/official/structured_data/README.md index a418149cc..c3bbf1e6d 100644 --- a/notebooks/official/structured_data/README.md +++ b/notebooks/official/structured_data/README.md @@ -1,5 +1,5 @@ -[BQML and AutoML - Experimenting with Vertex AI](official/structured_data/rapid_prototyping_bqml_automl.ipynb) +[BQML and AutoML - Experimenting with Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/official/structured_data/rapid_prototyping_bqml_automl.ipynb) Learn how to use `Vertex AI Predictions` for rapid prototyping a model. diff --git a/notebooks/official/tabnet/README.md b/notebooks/official/tabnet/README.md index 394b601a3..d2e41c2a3 100644 --- a/notebooks/official/tabnet/README.md +++ b/notebooks/official/tabnet/README.md @@ -1,5 +1,5 @@ -[Vertex AI Explainations with TabNet models](official/tabnet/ai-explanations-tabnet-algorithm.ipynb) +[Vertex AI Explainations with TabNet models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabnet/ai-explanations-tabnet-algorithm.ipynb) Learn how to provide a sample plotting tool to visualize the output of TabNet, which is helpful in explaining the algorithm. @@ -9,7 +9,7 @@ The steps performed are: * Visualize and understand the feature importance based on the masks output. * Clean up the resource created by this tutorial. -[Vertex AI TabNet](official/tabnet/tabnet_vertex_tutorial.ipynb) +[Vertex AI TabNet](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabnet/tabnet_vertex_tutorial.ipynb) Learn how to run TabNet model on Vertex AI. diff --git a/notebooks/official/tabular_workflows/README.md b/notebooks/official/tabular_workflows/README.md index 59e861722..788237f67 100644 --- a/notebooks/official/tabular_workflows/README.md +++ b/notebooks/official/tabular_workflows/README.md @@ -1,5 +1,5 @@ -[TabNet Pipeline](official/tabular_workflows/tabnet_on_vertex_pipelines.ipynb) +[TabNet Pipeline](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabular_workflows/tabnet_on_vertex_pipelines.ipynb) Learn how to create two classification models using Vertex AI TabNet Tabular Workflows. @@ -10,7 +10,7 @@ The steps performed include: -[Wide & Deep Pipeline](official/tabular_workflows/wide_and_deep_on_vertex_pipelines.ipynb) +[Wide & Deep Pipeline](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabular_workflows/wide_and_deep_on_vertex_pipelines.ipynb) Learn how to create two classification models using Vertex AI Wide & Deep Tabular Workflows. diff --git a/notebooks/official/tensorboard/README.md b/notebooks/official/tensorboard/README.md index c82dbf2f3..455dab74c 100644 --- a/notebooks/official/tensorboard/README.md +++ b/notebooks/official/tensorboard/README.md @@ -1,5 +1,5 @@ -[Vertex AI TensorBoard custom training with prebuilt container](official/tensorboard/tensorboard_custom_training_with_prebuilt_container.ipynb) +[Vertex AI TensorBoard custom training with prebuilt container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tensorboard/tensorboard_custom_training_with_prebuilt_container.ipynb) Learn how to create a custom training job using prebuilt containers, and monitor your training process on Vertex AI TensorBoard in near real time. @@ -10,7 +10,7 @@ The steps performed include: * 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 Custom Training with Custom Container](official/tensorboard/tensorboard_custom_training_with_custom_container.ipynb) +[Vertex AI TensorBoard Custom Training with Custom Container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tensorboard/tensorboard_custom_training_with_custom_container.ipynb) Learn how to create a custom training job using custom containers, and monitor your training process on Vertex AI TensorBoard in near real time. diff --git a/notebooks/official/training/README.md b/notebooks/official/training/README.md index f4e30e432..d8d5867d8 100644 --- a/notebooks/official/training/README.md +++ b/notebooks/official/training/README.md @@ -1,5 +1,5 @@ -[PyTorch image classification multi-node distributed data parallel training on cpu using Vertex training with custom container](official/training/multi_node_ddp_gloo_vertex_training_with_custom_container.ipynb) +[PyTorch image classification multi-node distributed data parallel training on cpu using Vertex training with custom container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/training/multi_node_ddp_gloo_vertex_training_with_custom_container.ipynb) Learn how to create a distributed PyTorch training job using Vertex AI SDK for Python and custom containers. @@ -11,7 +11,7 @@ The steps performed include: - Create a Vertex AI tensorboard instance to store your Vertex AI experiment - Run a Vertex AI SDK CustomContainerTrainingJob -[PyTorch Image Classification Multi-Node Distributed Data Parallel Training on GPU using Vertex AI Training with Custom Container](official/training/multi_node_ddp_nccl_vertex_training_with_custom_container.ipynb) +[PyTorch Image Classification Multi-Node Distributed Data Parallel Training on GPU using Vertex AI Training with Custom Container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/training/multi_node_ddp_nccl_vertex_training_with_custom_container.ipynb) Learn how to create a distributed PyTorch training job using Vertex AI SDK for Python and custom containers. diff --git a/notebooks/official/vizier/README.md b/notebooks/official/vizier/README.md index 86c760d7a..794d65032 100644 --- a/notebooks/official/vizier/README.md +++ b/notebooks/official/vizier/README.md @@ -1,5 +1,5 @@ -[Optimizing multiple objectives with Vertex AI Vizier](official/vizier/gapic-vizier-multi-objective-optimization.ipynb) +[Optimizing multiple objectives with Vertex AI Vizier](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/vizier/gapic-vizier-multi-objective-optimization.ipynb) Learn how to use `Vertex AI Vizier` to optimize a multi-objective study.