From db920810d3a737d7d46275352c0654b61001fd63 Mon Sep 17 00:00:00 2001 From: Puneet Goyal Date: Tue, 21 Mar 2023 22:16:34 +0530 Subject: [PATCH 1/2] Update Neptune-ML-04-Introduction-to-Edge-Classification-Gremlin.ipynb Update broken doc links --- ...4-Introduction-to-Edge-Classification-Gremlin.ipynb | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/src/graph_notebook/notebooks/04-Machine-Learning/Neptune-ML-04-Introduction-to-Edge-Classification-Gremlin.ipynb b/src/graph_notebook/notebooks/04-Machine-Learning/Neptune-ML-04-Introduction-to-Edge-Classification-Gremlin.ipynb index accd383c..f4da8ef0 100644 --- a/src/graph_notebook/notebooks/04-Machine-Learning/Neptune-ML-04-Introduction-to-Edge-Classification-Gremlin.ipynb +++ b/src/graph_notebook/notebooks/04-Machine-Learning/Neptune-ML-04-Introduction-to-Edge-Classification-Gremlin.ipynb @@ -26,7 +26,7 @@ "\n", "
Note: This notebook take approximately 1 hour to complete
\n", "\n", - "[Neptune ML](https://docs.aws.amazon.com/neptune/latest/userguide/machine-learning.html#machine-learning-overview) is a feature of Amazon Neptune that enables users to automate the creation, management, and usage of Graph Neural Network (GNN) machine learning models within Amazon Neptune. Neptune ML is built using [Amazon SageMaker](https://aws.amazon.com/sagemaker/) and [Deep Graph Library](https://www.dgl.ai/) and provides a simple and easy to use mechanism to build/train/maintain these models and then use the predictive capabilities of these models within a Gremlin query to predict elements or property values in the graph. \n", + "[Neptune ML](https://docs.aws.amazon.com/neptune/latest/userguide/machine-learning.html) is a feature of Amazon Neptune that enables users to automate the creation, management, and usage of Graph Neural Network (GNN) machine learning models within Amazon Neptune. Neptune ML is built using [Amazon SageMaker](https://aws.amazon.com/sagemaker/) and [Deep Graph Library](https://www.dgl.ai/) and provides a simple and easy to use mechanism to build/train/maintain these models and then use the predictive capabilities of these models within a Gremlin query to predict elements or property values in the graph. \n", "\n", "For this notebook we are going to show how to perform a common machine learning task known as **edge classification**. Edge classification is a common semi-supervised machine learning task where a model built using labeled edges, ones where the property value exists, can predict the value (or class) of the edges. Edge classification is not unique to GNN based models (look at DeepWalk or node2vec) but the GNN based models in Neptune ML provide additional context to the predictions by combining the connectivity and features of the local neighborhood of a node to create a more predictive model.\n", "\n", @@ -260,7 +260,7 @@ "\n", "# Export the data and model configuration\n", "\n", - "
Note: Before exporting data ensure that Neptune Export has been configured as described here: Neptune Export Service
" + "
Note: Before exporting data ensure that Neptune Export has been configured as described here: Neptune Export Service
" ] }, { @@ -269,7 +269,7 @@ "source": [ "With our product knowledge graph loaded we are ready to export the data and configuration which will be used to train the ML model. \n", "\n", - "The export process is triggered by calling to the [Neptune Export service endpoint](https://docs.aws.amazon.com/neptune/latest/userguide/machine-learning-data-export-service.html). This call contains a configuration object which specifies the type of machine learning model to build, in this example node classification, as well as any feature configurations required. \n", + "The export process is triggered by calling to the [Neptune Export service endpoint](https://docs.aws.amazon.com/neptune/latest/userguide/machine-learning-data-export.html). This call contains a configuration object which specifies the type of machine learning model to build, in this example node classification, as well as any feature configurations required. \n", "\n", "
Note: The configuration used in this notebook specifies only a minimal set of configuration options meaning that our model's predictions are not as accurate as they could be. The parameters included in this configuration are one of a couple of sets of options available to the end user to tune the model and optimize the accuracy of the resulting predictions.
\n", "\n", @@ -312,7 +312,7 @@ "\n", "In our export example below we have specified that the `title` property of our `movie` should be exported and trained as a `text_word2vec` feature and that our `age` field should range from 0-100 and that data should be bucketed into 10 distinct groups. \n", "\n", - "
Important: The example below is an example of a minimal amount of the features of the model configuration parameters and will not create the most accurate model possible. Additional options are available for tuning this configuration to produce an optimal model are described here: Neptune Export Process Parameters
\n", + "
Important: The example below is an example of a minimal amount of the features of the model configuration parameters and will not create the most accurate model possible. Additional options are available for tuning this configuration to produce an optimal model are described here: Neptune Export Process Parameters
\n", "\n", "Running the cell below we set the export configuration and run the export process. Neptune export is capable of automatically creating a clone of the cluster by setting `cloneCluster=True` which takes about 20 minutes to complete and will incur additional costs while the cloned cluster is running. Exporting from the existing cluster takes about 5 minutes but requires that the `neptune_query_timeout` parameter in the [parameter group](https://docs.aws.amazon.com/neptune/latest/userguide/parameters.html) is set to a large enough value (>72000) to prevent timeout errors." ] @@ -670,4 +670,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} From 4dc0726bcf158b6b5d4018d1a328efceb4325a03 Mon Sep 17 00:00:00 2001 From: Michael Chin Date: Tue, 21 Mar 2023 10:00:33 -0700 Subject: [PATCH 2/2] Update ChangeLog.md --- ChangeLog.md | 1 + 1 file changed, 1 insertion(+) diff --git a/ChangeLog.md b/ChangeLog.md index 8fcc25c1..7d333d4a 100644 --- a/ChangeLog.md +++ b/ChangeLog.md @@ -5,6 +5,7 @@ Starting with v1.31.6, this file will contain a record of major features and upd ## Upcoming - Fixed Dockerfile builds breaking with AL2023 ([Link to PR](https://github.com/aws/graph-notebook/pull/466)) - Fixed `--store-to` option for several magics ([Link to PR](https://github.com/aws/graph-notebook/pull/463)) +- Fixed broken documentation links in Neptune-ML-04-Introduction-to-Edge-Classification-Gremlin ([Link to PR](https://github.com/aws/graph-notebook/pull/467)) ## Release 3.7.3 (March 14, 2023) - Fixed detailed mode output for graph summary requests ([Link to PR](https://github.com/aws/graph-notebook/pull/461))