chore: Update discovery artifacts (#1582)
## Deleted keys were detected in the following stable discovery artifacts:
artifactregistry v1 https://github.com/googleapis/google-api-python-client/commit/421f4d14a998f3da97fd979647b5e05287027679
osconfig v1 https://github.com/googleapis/google-api-python-client/commit/ff7bf38f27e52634ef2b9c661d84c9118675944c
vmmigration v1 https://github.com/googleapis/google-api-python-client/commit/e29809a6548a53233925e410d2126d6e0b1600fa
## Deleted keys were detected in the following pre-stable discovery artifacts:
analyticsadmin v1alpha https://github.com/googleapis/google-api-python-client/commit/8666e3e7a134d27f832c00ef8fff2e8a5b601774
containeranalysis v1alpha1 https://github.com/googleapis/google-api-python-client/commit/15898963782a0649d6cb3a0a0c7ba1566b86b853
containeranalysis v1beta1 https://github.com/googleapis/google-api-python-client/commit/15898963782a0649d6cb3a0a0c7ba1566b86b853
osconfig v1alpha https://github.com/googleapis/google-api-python-client/commit/ff7bf38f27e52634ef2b9c661d84c9118675944c
## Discovery Artifact Change Summary:
feat(admin): update the api https://github.com/googleapis/google-api-python-client/commit/34eef11ba78a6e8eda0ec4dd8348e240ac637122
feat(analyticsadmin): update the api https://github.com/googleapis/google-api-python-client/commit/8666e3e7a134d27f832c00ef8fff2e8a5b601774
feat(analyticsdata): update the api https://github.com/googleapis/google-api-python-client/commit/a362e49252915c7da2fe88bfaec9eb7f9c217b11
feat(analyticsreporting): update the api https://github.com/googleapis/google-api-python-client/commit/ec6bf30c38ccf0f258c9f0267c6477b233483702
feat(androidpublisher): update the api https://github.com/googleapis/google-api-python-client/commit/1a6d12e5a619d753e17041696fdfa84626e952d3
feat(apigee): update the api https://github.com/googleapis/google-api-python-client/commit/afc34eebbe98c284718489b94df8bc2293ee31f5
feat(artifactregistry): update the api https://github.com/googleapis/google-api-python-client/commit/421f4d14a998f3da97fd979647b5e05287027679
feat(chat): update the api https://github.com/googleapis/google-api-python-client/commit/ba90d3f0889eac4fb061bbbe913c31eea57c94bb
feat(cloudkms): update the api https://github.com/googleapis/google-api-python-client/commit/f06247e899ba2de5d2c1f0a8d6e8cbb0569143aa
feat(containeranalysis): update the api https://github.com/googleapis/google-api-python-client/commit/15898963782a0649d6cb3a0a0c7ba1566b86b853
feat(content): update the api https://github.com/googleapis/google-api-python-client/commit/8f976a93038ee562d5ed0c9937d52e4b5e2cb8d6
feat(datacatalog): update the api https://github.com/googleapis/google-api-python-client/commit/b7876fdb21b0eeab9c07a73bbf0ca43f5f509906
feat(dataproc): update the api https://github.com/googleapis/google-api-python-client/commit/742a2f738031268771d7146b64ff0e743df79596
feat(dialogflow): update the api https://github.com/googleapis/google-api-python-client/commit/117de7bdb601d11ce48c4ad64225d6d207f0597a
feat(displayvideo): update the api https://github.com/googleapis/google-api-python-client/commit/6abb35b4ba36bfa81516994b9f95a426fa5bbaff
feat(eventarc): update the api https://github.com/googleapis/google-api-python-client/commit/59646721f76e0c02a2185111f9adf38d5c134fde
feat(file): update the api https://github.com/googleapis/google-api-python-client/commit/3508025ee9545033bc424396f2776916cbe1a3e3
feat(firestore): update the api https://github.com/googleapis/google-api-python-client/commit/851dba5e0f09a3dad06f3c8476d1c19da1a5cf93
feat(gkehub): update the api https://github.com/googleapis/google-api-python-client/commit/b62aef0cc1bd0f5f10e1828d941616163136b2f7
feat(iam): update the api https://github.com/googleapis/google-api-python-client/commit/50c48dfe6b63c9b7ff9deacc140d510cb0c50b50
feat(monitoring): update the api https://github.com/googleapis/google-api-python-client/commit/eafbb600bf57440c024be19160c275074c6da03a
feat(notebooks): update the api https://github.com/googleapis/google-api-python-client/commit/c6c8169a866814c2f4cbd622ad005d37442204d5
feat(osconfig): update the api https://github.com/googleapis/google-api-python-client/commit/ff7bf38f27e52634ef2b9c661d84c9118675944c
feat(oslogin): update the api https://github.com/googleapis/google-api-python-client/commit/c26d08f8dc0507a366afa20e899cdbe90af9e82c
feat(playcustomapp): update the api https://github.com/googleapis/google-api-python-client/commit/1898032f15649aaa4bb8469fbd05743e39fc2a28
feat(privateca): update the api https://github.com/googleapis/google-api-python-client/commit/8eca373bb25b2dc23dfd6c9fdd09420b3c415521
feat(securitycenter): update the api https://github.com/googleapis/google-api-python-client/commit/7e832748505a52c0b0d2f94163cbedcffe09fcf7
feat(speech): update the api https://github.com/googleapis/google-api-python-client/commit/1a3763caea5a3b4d50d0981ee4f52cc234fc1223
feat(storage): update the api https://github.com/googleapis/google-api-python-client/commit/07237cd66afac512e9962069312cf0bb796b0f39
feat(storagetransfer): update the api https://github.com/googleapis/google-api-python-client/commit/0901d055b0b30eeb9312881cbacde771d647ee56
feat(texttospeech): update the api https://github.com/googleapis/google-api-python-client/commit/6622bd866cc45f42b37a57737872af0f90631e5f
feat(vmmigration): update the api https://github.com/googleapis/google-api-python-client/commit/e29809a6548a53233925e410d2126d6e0b1600fa
diff --git a/docs/dyn/notebooks_v1.projects.locations.executions.html b/docs/dyn/notebooks_v1.projects.locations.executions.html
index d997ba0..c556309 100644
--- a/docs/dyn/notebooks_v1.projects.locations.executions.html
+++ b/docs/dyn/notebooks_v1.projects.locations.executions.html
@@ -118,20 +118,24 @@
},
"containerImageUri": "A String", # 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
"dataprocParameters": { # Parameters used in Dataproc JobType executions. # Parameters used in Dataproc JobType executions.
- "cluster": "A String", # URI for cluster used to run Dataproc execution. Format: 'projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}
+ "cluster": "A String", # URI for cluster used to run Dataproc execution. Format: `projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}`
},
- "inputNotebookFile": "A String", # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{bucket_name}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
+ "inputNotebookFile": "A String", # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: `gs://{bucket_name}/{folder}/{notebook_file_name}` Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb`
"jobType": "A String", # The type of Job to be used on this execution.
+ "kernelSpec": "A String", # Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file.
"labels": { # Labels for execution. If execution is scheduled, a field included will be 'nbs-scheduled'. Otherwise, it is an immediate execution, and an included field will be 'nbs-immediate'. Use fields to efficiently index between various types of executions.
"a_key": "A String",
},
"masterType": "A String", # Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when `scaleTier` is set to `CUSTOM`. You can use certain Compute Engine machine types directly in this field. The following types are supported: - `n1-standard-4` - `n1-standard-8` - `n1-standard-16` - `n1-standard-32` - `n1-standard-64` - `n1-standard-96` - `n1-highmem-2` - `n1-highmem-4` - `n1-highmem-8` - `n1-highmem-16` - `n1-highmem-32` - `n1-highmem-64` - `n1-highmem-96` - `n1-highcpu-16` - `n1-highcpu-32` - `n1-highcpu-64` - `n1-highcpu-96` Alternatively, you can use the following legacy machine types: - `standard` - `large_model` - `complex_model_s` - `complex_model_m` - `complex_model_l` - `standard_gpu` - `complex_model_m_gpu` - `complex_model_l_gpu` - `standard_p100` - `complex_model_m_p100` - `standard_v100` - `large_model_v100` - `complex_model_m_v100` - `complex_model_l_v100` Finally, if you want to use a TPU for training, specify `cloud_tpu` in this field. Learn more about the [special configuration options for training with TPU](https://cloud.google.com/ai-platform/training/docs/using-tpus#configuring_a_custom_tpu_machine).
- "outputNotebookFolder": "A String", # Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: gs://{bucket_name}/{folder} Ex: gs://notebook_user/scheduled_notebooks
+ "outputNotebookFolder": "A String", # Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: `gs://{bucket_name}/{folder}` Ex: `gs://notebook_user/scheduled_notebooks`
"parameters": "A String", # Parameters used within the 'input_notebook_file' notebook.
- "paramsYamlFile": "A String", # Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml
+ "paramsYamlFile": "A String", # Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml`
"scaleTier": "A String", # Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.
"serviceAccount": "A String", # The email address of a service account to use when running the execution. You must have the `iam.serviceAccounts.actAs` permission for the specified service account.
"vertexAiParameters": { # Parameters used in Vertex AI JobType executions. # Parameters used in Vertex AI JobType executions.
+ "env": { # Environment variables. At most 100 environment variables can be specified and unique. Example: GCP_BUCKET=gs://my-bucket/samples/
+ "a_key": "A String",
+ },
"network": "A String", # The full name of the Compute Engine [network](/compute/docs/networks-and-firewalls#networks) to which the Job should be peered. For example, `projects/12345/global/networks/myVPC`. [Format](https://cloud.google.com/compute/docs/reference/rest/v1/networks/insert) is of the form `projects/{project}/global/networks/{network}`. Where {project} is a project number, as in `12345`, and {network} is a network name. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network.
},
},
@@ -232,20 +236,24 @@
},
"containerImageUri": "A String", # 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
"dataprocParameters": { # Parameters used in Dataproc JobType executions. # Parameters used in Dataproc JobType executions.
- "cluster": "A String", # URI for cluster used to run Dataproc execution. Format: 'projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}
+ "cluster": "A String", # URI for cluster used to run Dataproc execution. Format: `projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}`
},
- "inputNotebookFile": "A String", # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{bucket_name}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
+ "inputNotebookFile": "A String", # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: `gs://{bucket_name}/{folder}/{notebook_file_name}` Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb`
"jobType": "A String", # The type of Job to be used on this execution.
+ "kernelSpec": "A String", # Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file.
"labels": { # Labels for execution. If execution is scheduled, a field included will be 'nbs-scheduled'. Otherwise, it is an immediate execution, and an included field will be 'nbs-immediate'. Use fields to efficiently index between various types of executions.
"a_key": "A String",
},
"masterType": "A String", # Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when `scaleTier` is set to `CUSTOM`. You can use certain Compute Engine machine types directly in this field. The following types are supported: - `n1-standard-4` - `n1-standard-8` - `n1-standard-16` - `n1-standard-32` - `n1-standard-64` - `n1-standard-96` - `n1-highmem-2` - `n1-highmem-4` - `n1-highmem-8` - `n1-highmem-16` - `n1-highmem-32` - `n1-highmem-64` - `n1-highmem-96` - `n1-highcpu-16` - `n1-highcpu-32` - `n1-highcpu-64` - `n1-highcpu-96` Alternatively, you can use the following legacy machine types: - `standard` - `large_model` - `complex_model_s` - `complex_model_m` - `complex_model_l` - `standard_gpu` - `complex_model_m_gpu` - `complex_model_l_gpu` - `standard_p100` - `complex_model_m_p100` - `standard_v100` - `large_model_v100` - `complex_model_m_v100` - `complex_model_l_v100` Finally, if you want to use a TPU for training, specify `cloud_tpu` in this field. Learn more about the [special configuration options for training with TPU](https://cloud.google.com/ai-platform/training/docs/using-tpus#configuring_a_custom_tpu_machine).
- "outputNotebookFolder": "A String", # Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: gs://{bucket_name}/{folder} Ex: gs://notebook_user/scheduled_notebooks
+ "outputNotebookFolder": "A String", # Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: `gs://{bucket_name}/{folder}` Ex: `gs://notebook_user/scheduled_notebooks`
"parameters": "A String", # Parameters used within the 'input_notebook_file' notebook.
- "paramsYamlFile": "A String", # Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml
+ "paramsYamlFile": "A String", # Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml`
"scaleTier": "A String", # Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.
"serviceAccount": "A String", # The email address of a service account to use when running the execution. You must have the `iam.serviceAccounts.actAs` permission for the specified service account.
"vertexAiParameters": { # Parameters used in Vertex AI JobType executions. # Parameters used in Vertex AI JobType executions.
+ "env": { # Environment variables. At most 100 environment variables can be specified and unique. Example: GCP_BUCKET=gs://my-bucket/samples/
+ "a_key": "A String",
+ },
"network": "A String", # The full name of the Compute Engine [network](/compute/docs/networks-and-firewalls#networks) to which the Job should be peered. For example, `projects/12345/global/networks/myVPC`. [Format](https://cloud.google.com/compute/docs/reference/rest/v1/networks/insert) is of the form `projects/{project}/global/networks/{network}`. Where {project} is a project number, as in `12345`, and {network} is a network name. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network.
},
},
@@ -263,7 +271,7 @@
Args:
parent: string, Required. Format: `parent=projects/{project_id}/locations/{location}` (required)
- filter: string, Filter applied to resulting executions. Currently only supports filtering executions by a specified schedule_id. Format: "schedule_id="
+ filter: string, Filter applied to resulting executions. Currently only supports filtering executions by a specified schedule_id. Format: `schedule_id=`
orderBy: string, Sort by field.
pageSize: integer, Maximum return size of the list call.
pageToken: string, A previous returned page token that can be used to continue listing from the last result.
@@ -288,20 +296,24 @@
},
"containerImageUri": "A String", # 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
"dataprocParameters": { # Parameters used in Dataproc JobType executions. # Parameters used in Dataproc JobType executions.
- "cluster": "A String", # URI for cluster used to run Dataproc execution. Format: 'projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}
+ "cluster": "A String", # URI for cluster used to run Dataproc execution. Format: `projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}`
},
- "inputNotebookFile": "A String", # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{bucket_name}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
+ "inputNotebookFile": "A String", # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: `gs://{bucket_name}/{folder}/{notebook_file_name}` Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb`
"jobType": "A String", # The type of Job to be used on this execution.
+ "kernelSpec": "A String", # Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file.
"labels": { # Labels for execution. If execution is scheduled, a field included will be 'nbs-scheduled'. Otherwise, it is an immediate execution, and an included field will be 'nbs-immediate'. Use fields to efficiently index between various types of executions.
"a_key": "A String",
},
"masterType": "A String", # Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when `scaleTier` is set to `CUSTOM`. You can use certain Compute Engine machine types directly in this field. The following types are supported: - `n1-standard-4` - `n1-standard-8` - `n1-standard-16` - `n1-standard-32` - `n1-standard-64` - `n1-standard-96` - `n1-highmem-2` - `n1-highmem-4` - `n1-highmem-8` - `n1-highmem-16` - `n1-highmem-32` - `n1-highmem-64` - `n1-highmem-96` - `n1-highcpu-16` - `n1-highcpu-32` - `n1-highcpu-64` - `n1-highcpu-96` Alternatively, you can use the following legacy machine types: - `standard` - `large_model` - `complex_model_s` - `complex_model_m` - `complex_model_l` - `standard_gpu` - `complex_model_m_gpu` - `complex_model_l_gpu` - `standard_p100` - `complex_model_m_p100` - `standard_v100` - `large_model_v100` - `complex_model_m_v100` - `complex_model_l_v100` Finally, if you want to use a TPU for training, specify `cloud_tpu` in this field. Learn more about the [special configuration options for training with TPU](https://cloud.google.com/ai-platform/training/docs/using-tpus#configuring_a_custom_tpu_machine).
- "outputNotebookFolder": "A String", # Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: gs://{bucket_name}/{folder} Ex: gs://notebook_user/scheduled_notebooks
+ "outputNotebookFolder": "A String", # Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: `gs://{bucket_name}/{folder}` Ex: `gs://notebook_user/scheduled_notebooks`
"parameters": "A String", # Parameters used within the 'input_notebook_file' notebook.
- "paramsYamlFile": "A String", # Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml
+ "paramsYamlFile": "A String", # Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml`
"scaleTier": "A String", # Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.
"serviceAccount": "A String", # The email address of a service account to use when running the execution. You must have the `iam.serviceAccounts.actAs` permission for the specified service account.
"vertexAiParameters": { # Parameters used in Vertex AI JobType executions. # Parameters used in Vertex AI JobType executions.
+ "env": { # Environment variables. At most 100 environment variables can be specified and unique. Example: GCP_BUCKET=gs://my-bucket/samples/
+ "a_key": "A String",
+ },
"network": "A String", # The full name of the Compute Engine [network](/compute/docs/networks-and-firewalls#networks) to which the Job should be peered. For example, `projects/12345/global/networks/myVPC`. [Format](https://cloud.google.com/compute/docs/reference/rest/v1/networks/insert) is of the form `projects/{project}/global/networks/{network}`. Where {project} is a project number, as in `12345`, and {network} is a network name. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network.
},
},
@@ -313,7 +325,7 @@
},
],
"nextPageToken": "A String", # Page token that can be used to continue listing from the last result in the next list call.
- "unreachable": [ # Executions IDs that could not be reached. For example, ['projects/{project_id}/location/{location}/executions/imagenet_test1', 'projects/{project_id}/location/{location}/executions/classifier_train1'].
+ "unreachable": [ # Executions IDs that could not be reached. For example: ['projects/{project_id}/location/{location}/executions/imagenet_test1', 'projects/{project_id}/location/{location}/executions/classifier_train1']
"A String",
],
}</pre>