feat(notebooks): update the api

#### notebooks:v1 The following keys were added: - schemas.DataprocParameters (Total Keys: 3) - schemas.ExecutionTemplate.properties.dataprocParameters.$ref (Total Keys: 1) - schemas.ExecutionTemplate.properties.jobType.type (Total Keys: 1)
googleapis · Aug 17, 2021 · b975c34 · b975c34
1 parent 8739999
commit b975c34
Show file tree

Hide file tree

Showing 3 changed files with 66 additions and 1 deletion.
diff --git a/docs/dyn/notebooks_v1.projects.locations.executions.html b/docs/dyn/notebooks_v1.projects.locations.executions.html
@@ -117,7 +117,11 @@ <h3>Method Details</h3>
       &quot;type&quot;: &quot;A String&quot;, # Type of this accelerator.
     },
     &quot;containerImageUri&quot;: &quot;A String&quot;, # Container Image URI to a DLVM Example: &#x27;gcr.io/deeplearning-platform-release/base-cu100&#x27; More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
+    &quot;dataprocParameters&quot;: { # Parameters used in Dataproc JobType executions. # Parameters used in Dataproc JobType executions.
+      &quot;cluster&quot;: &quot;A String&quot;, # URI for cluster used to run Dataproc execution. Format: &#x27;projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}
+    },
     &quot;inputNotebookFile&quot;: &quot;A String&quot;, # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{project_id}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
+    &quot;jobType&quot;: &quot;A String&quot;, # The type of Job to be used on this execution.
     &quot;labels&quot;: { # Labels for execution. If execution is scheduled, a field included will be &#x27;nbs-scheduled&#x27;. Otherwise, it is an immediate execution, and an included field will be &#x27;nbs-immediate&#x27;. Use fields to efficiently index between various types of executions.
       &quot;a_key&quot;: &quot;A String&quot;,
     },
@@ -224,7 +228,11 @@ <h3>Method Details</h3>
       &quot;type&quot;: &quot;A String&quot;, # Type of this accelerator.
     },
     &quot;containerImageUri&quot;: &quot;A String&quot;, # Container Image URI to a DLVM Example: &#x27;gcr.io/deeplearning-platform-release/base-cu100&#x27; More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
+    &quot;dataprocParameters&quot;: { # Parameters used in Dataproc JobType executions. # Parameters used in Dataproc JobType executions.
+      &quot;cluster&quot;: &quot;A String&quot;, # URI for cluster used to run Dataproc execution. Format: &#x27;projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}
+    },
     &quot;inputNotebookFile&quot;: &quot;A String&quot;, # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{project_id}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
+    &quot;jobType&quot;: &quot;A String&quot;, # The type of Job to be used on this execution.
     &quot;labels&quot;: { # Labels for execution. If execution is scheduled, a field included will be &#x27;nbs-scheduled&#x27;. Otherwise, it is an immediate execution, and an included field will be &#x27;nbs-immediate&#x27;. Use fields to efficiently index between various types of executions.
       &quot;a_key&quot;: &quot;A String&quot;,
     },
@@ -273,7 +281,11 @@ <h3>Method Details</h3>
           &quot;type&quot;: &quot;A String&quot;, # Type of this accelerator.
         },
         &quot;containerImageUri&quot;: &quot;A String&quot;, # Container Image URI to a DLVM Example: &#x27;gcr.io/deeplearning-platform-release/base-cu100&#x27; More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
+        &quot;dataprocParameters&quot;: { # Parameters used in Dataproc JobType executions. # Parameters used in Dataproc JobType executions.
+          &quot;cluster&quot;: &quot;A String&quot;, # URI for cluster used to run Dataproc execution. Format: &#x27;projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}
+        },
         &quot;inputNotebookFile&quot;: &quot;A String&quot;, # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{project_id}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
+        &quot;jobType&quot;: &quot;A String&quot;, # The type of Job to be used on this execution.
         &quot;labels&quot;: { # Labels for execution. If execution is scheduled, a field included will be &#x27;nbs-scheduled&#x27;. Otherwise, it is an immediate execution, and an included field will be &#x27;nbs-immediate&#x27;. Use fields to efficiently index between various types of executions.
           &quot;a_key&quot;: &quot;A String&quot;,
         },

diff --git a/docs/dyn/notebooks_v1.projects.locations.schedules.html b/docs/dyn/notebooks_v1.projects.locations.schedules.html
@@ -121,7 +121,11 @@ <h3>Method Details</h3>
       &quot;type&quot;: &quot;A String&quot;, # Type of this accelerator.
     },
     &quot;containerImageUri&quot;: &quot;A String&quot;, # Container Image URI to a DLVM Example: &#x27;gcr.io/deeplearning-platform-release/base-cu100&#x27; More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
+    &quot;dataprocParameters&quot;: { # Parameters used in Dataproc JobType executions. # Parameters used in Dataproc JobType executions.
+      &quot;cluster&quot;: &quot;A String&quot;, # URI for cluster used to run Dataproc execution. Format: &#x27;projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}
+    },
     &quot;inputNotebookFile&quot;: &quot;A String&quot;, # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{project_id}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
+    &quot;jobType&quot;: &quot;A String&quot;, # The type of Job to be used on this execution.
     &quot;labels&quot;: { # Labels for execution. If execution is scheduled, a field included will be &#x27;nbs-scheduled&#x27;. Otherwise, it is an immediate execution, and an included field will be &#x27;nbs-immediate&#x27;. Use fields to efficiently index between various types of executions.
       &quot;a_key&quot;: &quot;A String&quot;,
     },
@@ -144,7 +148,11 @@ <h3>Method Details</h3>
           &quot;type&quot;: &quot;A String&quot;, # Type of this accelerator.
         },
         &quot;containerImageUri&quot;: &quot;A String&quot;, # Container Image URI to a DLVM Example: &#x27;gcr.io/deeplearning-platform-release/base-cu100&#x27; More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
+        &quot;dataprocParameters&quot;: { # Parameters used in Dataproc JobType executions. # Parameters used in Dataproc JobType executions.
+          &quot;cluster&quot;: &quot;A String&quot;, # URI for cluster used to run Dataproc execution. Format: &#x27;projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}
+        },
         &quot;inputNotebookFile&quot;: &quot;A String&quot;, # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{project_id}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
+        &quot;jobType&quot;: &quot;A String&quot;, # The type of Job to be used on this execution.
         &quot;labels&quot;: { # Labels for execution. If execution is scheduled, a field included will be &#x27;nbs-scheduled&#x27;. Otherwise, it is an immediate execution, and an included field will be &#x27;nbs-immediate&#x27;. Use fields to efficiently index between various types of executions.
           &quot;a_key&quot;: &quot;A String&quot;,
         },
@@ -257,7 +265,11 @@ <h3>Method Details</h3>
       &quot;type&quot;: &quot;A String&quot;, # Type of this accelerator.
     },
     &quot;containerImageUri&quot;: &quot;A String&quot;, # Container Image URI to a DLVM Example: &#x27;gcr.io/deeplearning-platform-release/base-cu100&#x27; More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
+    &quot;dataprocParameters&quot;: { # Parameters used in Dataproc JobType executions. # Parameters used in Dataproc JobType executions.
+      &quot;cluster&quot;: &quot;A String&quot;, # URI for cluster used to run Dataproc execution. Format: &#x27;projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}
+    },
     &quot;inputNotebookFile&quot;: &quot;A String&quot;, # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{project_id}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
+    &quot;jobType&quot;: &quot;A String&quot;, # The type of Job to be used on this execution.
     &quot;labels&quot;: { # Labels for execution. If execution is scheduled, a field included will be &#x27;nbs-scheduled&#x27;. Otherwise, it is an immediate execution, and an included field will be &#x27;nbs-immediate&#x27;. Use fields to efficiently index between various types of executions.
       &quot;a_key&quot;: &quot;A String&quot;,
     },
@@ -280,7 +292,11 @@ <h3>Method Details</h3>
           &quot;type&quot;: &quot;A String&quot;, # Type of this accelerator.
         },
         &quot;containerImageUri&quot;: &quot;A String&quot;, # Container Image URI to a DLVM Example: &#x27;gcr.io/deeplearning-platform-release/base-cu100&#x27; More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
+        &quot;dataprocParameters&quot;: { # Parameters used in Dataproc JobType executions. # Parameters used in Dataproc JobType executions.
+          &quot;cluster&quot;: &quot;A String&quot;, # URI for cluster used to run Dataproc execution. Format: &#x27;projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}
+        },
         &quot;inputNotebookFile&quot;: &quot;A String&quot;, # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{project_id}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
+        &quot;jobType&quot;: &quot;A String&quot;, # The type of Job to be used on this execution.
         &quot;labels&quot;: { # Labels for execution. If execution is scheduled, a field included will be &#x27;nbs-scheduled&#x27;. Otherwise, it is an immediate execution, and an included field will be &#x27;nbs-immediate&#x27;. Use fields to efficiently index between various types of executions.
           &quot;a_key&quot;: &quot;A String&quot;,
         },
@@ -336,7 +352,11 @@ <h3>Method Details</h3>
           &quot;type&quot;: &quot;A String&quot;, # Type of this accelerator.
         },
         &quot;containerImageUri&quot;: &quot;A String&quot;, # Container Image URI to a DLVM Example: &#x27;gcr.io/deeplearning-platform-release/base-cu100&#x27; More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
+        &quot;dataprocParameters&quot;: { # Parameters used in Dataproc JobType executions. # Parameters used in Dataproc JobType executions.
+          &quot;cluster&quot;: &quot;A String&quot;, # URI for cluster used to run Dataproc execution. Format: &#x27;projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}
+        },
         &quot;inputNotebookFile&quot;: &quot;A String&quot;, # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{project_id}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
+        &quot;jobType&quot;: &quot;A String&quot;, # The type of Job to be used on this execution.
         &quot;labels&quot;: { # Labels for execution. If execution is scheduled, a field included will be &#x27;nbs-scheduled&#x27;. Otherwise, it is an immediate execution, and an included field will be &#x27;nbs-immediate&#x27;. Use fields to efficiently index between various types of executions.
           &quot;a_key&quot;: &quot;A String&quot;,
         },
@@ -359,7 +379,11 @@ <h3>Method Details</h3>
               &quot;type&quot;: &quot;A String&quot;, # Type of this accelerator.
             },
             &quot;containerImageUri&quot;: &quot;A String&quot;, # Container Image URI to a DLVM Example: &#x27;gcr.io/deeplearning-platform-release/base-cu100&#x27; More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
+            &quot;dataprocParameters&quot;: { # Parameters used in Dataproc JobType executions. # Parameters used in Dataproc JobType executions.
+              &quot;cluster&quot;: &quot;A String&quot;, # URI for cluster used to run Dataproc execution. Format: &#x27;projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}
+            },
             &quot;inputNotebookFile&quot;: &quot;A String&quot;, # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{project_id}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
+            &quot;jobType&quot;: &quot;A String&quot;, # The type of Job to be used on this execution.
             &quot;labels&quot;: { # Labels for execution. If execution is scheduled, a field included will be &#x27;nbs-scheduled&#x27;. Otherwise, it is an immediate execution, and an included field will be &#x27;nbs-immediate&#x27;. Use fields to efficiently index between various types of executions.
               &quot;a_key&quot;: &quot;A String&quot;,
             },

diff --git a/googleapiclient/discovery_cache/documents/notebooks.v1.json b/googleapiclient/discovery_cache/documents/notebooks.v1.json
@@ -1609,7 +1609,7 @@
       }
     }
   },
-  "revision": "20210729",
+  "revision": "20210808",
   "rootUrl": "https://notebooks.googleapis.com/",
   "schemas": {
     "AcceleratorConfig": {
@@ -1699,6 +1699,17 @@
       },
       "type": "object"
     },
+    "DataprocParameters": {
+      "description": "Parameters used in Dataproc JobType executions.",
+      "id": "DataprocParameters",
+      "properties": {
+        "cluster": {
+          "description": "URI for cluster used to run Dataproc execution. Format: 'projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}",
+          "type": "string"
+        }
+      },
+      "type": "object"
+    },
     "Disk": {
       "description": "An instance-attached disk resource.",
       "id": "Disk",
@@ -1928,10 +1939,28 @@
           "description": "Container Image URI to a DLVM Example: 'gcr.io/deeplearning-platform-release/base-cu100' More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container",
           "type": "string"
         },
+        "dataprocParameters": {
+          "$ref": "DataprocParameters",
+          "description": "Parameters used in Dataproc JobType executions."
+        },
         "inputNotebookFile": {
           "description": "Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{project_id}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb",
           "type": "string"
         },
+        "jobType": {
+          "description": "The type of Job to be used on this execution.",
+          "enum": [
+            "JOB_TYPE_UNSPECIFIED",
+            "VERTEX_AI",
+            "DATAPROC"
+          ],
+          "enumDescriptions": [
+            "No type specified.",
+            "Custom Job in `aiplatform.googleapis.com`. Default value for an execution.",
+            "Run execution on a cluster with Dataproc as a job. https://cloud.google.com/dataproc/docs/reference/rest/v1/projects.regions.jobs"
+          ],
+          "type": "string"
+        },
         "labels": {
           "additionalProperties": {
             "type": "string"