Clean and regen docs (#401)
diff --git a/docs/dyn/ml_v1beta1.projects.models.html b/docs/dyn/ml_v1beta1.projects.models.html
index 0a36f05..ed302d3 100644
--- a/docs/dyn/ml_v1beta1.projects.models.html
+++ b/docs/dyn/ml_v1beta1.projects.models.html
@@ -136,40 +136,65 @@
# prediction requests. A model can have multiple versions. You can get
# information about all of the versions of a given model by calling
# [projects.models.versions.list](/ml-engine/reference/rest/v1beta1/projects.models.versions/list).
- "name": "A String", # Required.The name specified for the version when it was created.
- #
- # The version name must be unique within the model it is created in.
+ "description": "A String", # Optional. The description specified for the version when it was created.
"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this deployment.
# If not set, Google Cloud ML will choose a version.
- "manualScaling": { # Options for manually scaling a model. # Optional. Manually select the number of nodes to use for serving the
- # model. If unset (i.e., by default), the number of nodes used to serve
- # the model automatically scales with traffic. However, care should be
- # taken to ramp up traffic according to the model's ability to scale. If
- # your model needs to handle bursts of traffic beyond it's ability to
- # scale, it is recommended you set this field appropriately.
+ "manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the
+ # model. You should generally use `automatic_scaling` with an appropriate
+ # `min_nodes` instead, but this option is available if you want predictable
+ # billing. Beware that latency and error rates will increase if the
+ # traffic exceeds that capability of the system to serve it based on
+ # the selected number of nodes.
"nodes": 42, # The number of nodes to allocate for this model. These nodes are always up,
# starting from the time the model is deployed, so the cost of operating
- # this model will be proportional to nodes * number of hours since
- # deployment.
+ # this model will be proportional to `nodes` * number of hours since
+ # last billing cycle.
},
- "lastUseTime": "A String", # Output only. The time the version was last used for prediction.
"deploymentUri": "A String", # Required. The Google Cloud Storage location of the trained model used to
# create the version. See the
- # [overview of model deployment](/ml-engine/docs/concepts/deployment-overview) for
- # more informaiton.
+ # [overview of model
+ # deployment](/ml-engine/docs/concepts/deployment-overview) for more
+ # informaiton.
#
# When passing Version to
# [projects.models.versions.create](/ml-engine/reference/rest/v1beta1/projects.models.versions/create)
# the model service uses the specified location as the source of the model.
# Once deployed, the model version is hosted by the prediction service, so
# this location is useful only as a historical record.
+ # The total number of model files can't exceed 1000.
+ "lastUseTime": "A String", # Output only. The time the version was last used for prediction.
+ "automaticScaling": { # Options for automatically scaling a model. # Automatically scale the number of nodes used to serve the model in
+ # response to increases and decreases in traffic. Care should be
+ # taken to ramp up traffic according to the model's ability to scale
+ # or you will start seeing increases in latency and 429 response codes.
+ "minNodes": 42, # Optional. The minimum number of nodes to allocate for this model. These
+ # nodes are always up, starting from the time the model is deployed, so the
+ # cost of operating this model will be at least
+ # `rate` * `min_nodes` * number of hours since last billing cycle,
+ # where `rate` is the cost per node-hour as documented in
+ # [pricing](https://cloud.google.com/ml-engine/pricing#prediction_pricing),
+ # even if no predictions are performed. There is additional cost for each
+ # prediction performed.
+ #
+ # Unlike manual scaling, if the load gets too heavy for the nodes
+ # that are up, the service will automatically add nodes to handle the
+ # increased load as well as scale back as traffic drops, always maintaining
+ # at least `min_nodes`. You will be charged for the time in which additional
+ # nodes are used.
+ #
+ # If not specified, `min_nodes` defaults to 0, in which case, when traffic
+ # to a model stops (and after a cool-down period), nodes will be shut down
+ # and no charges will be incurred until traffic to the model resumes.
+ },
"createTime": "A String", # Output only. The time the version was created.
"isDefault": True or False, # Output only. If true, this version will be used to handle prediction
# requests that do not specify a version.
#
# You can change the default version by calling
# [projects.methods.versions.setDefault](/ml-engine/reference/rest/v1beta1/projects.models.versions/setDefault).
- "description": "A String", # Optional. The description specified for the version when it was created.
+ "name": "A String", # Required.The name specified for the version when it was created.
+ #
+ # The version name must be unique within the model it is created in.
},
"name": "A String", # Required. The name specified for the model when it was created.
#
@@ -213,40 +238,65 @@
# prediction requests. A model can have multiple versions. You can get
# information about all of the versions of a given model by calling
# [projects.models.versions.list](/ml-engine/reference/rest/v1beta1/projects.models.versions/list).
- "name": "A String", # Required.The name specified for the version when it was created.
- #
- # The version name must be unique within the model it is created in.
+ "description": "A String", # Optional. The description specified for the version when it was created.
"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this deployment.
# If not set, Google Cloud ML will choose a version.
- "manualScaling": { # Options for manually scaling a model. # Optional. Manually select the number of nodes to use for serving the
- # model. If unset (i.e., by default), the number of nodes used to serve
- # the model automatically scales with traffic. However, care should be
- # taken to ramp up traffic according to the model's ability to scale. If
- # your model needs to handle bursts of traffic beyond it's ability to
- # scale, it is recommended you set this field appropriately.
+ "manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the
+ # model. You should generally use `automatic_scaling` with an appropriate
+ # `min_nodes` instead, but this option is available if you want predictable
+ # billing. Beware that latency and error rates will increase if the
+ # traffic exceeds that capability of the system to serve it based on
+ # the selected number of nodes.
"nodes": 42, # The number of nodes to allocate for this model. These nodes are always up,
# starting from the time the model is deployed, so the cost of operating
- # this model will be proportional to nodes * number of hours since
- # deployment.
+ # this model will be proportional to `nodes` * number of hours since
+ # last billing cycle.
},
- "lastUseTime": "A String", # Output only. The time the version was last used for prediction.
"deploymentUri": "A String", # Required. The Google Cloud Storage location of the trained model used to
# create the version. See the
- # [overview of model deployment](/ml-engine/docs/concepts/deployment-overview) for
- # more informaiton.
+ # [overview of model
+ # deployment](/ml-engine/docs/concepts/deployment-overview) for more
+ # informaiton.
#
# When passing Version to
# [projects.models.versions.create](/ml-engine/reference/rest/v1beta1/projects.models.versions/create)
# the model service uses the specified location as the source of the model.
# Once deployed, the model version is hosted by the prediction service, so
# this location is useful only as a historical record.
+ # The total number of model files can't exceed 1000.
+ "lastUseTime": "A String", # Output only. The time the version was last used for prediction.
+ "automaticScaling": { # Options for automatically scaling a model. # Automatically scale the number of nodes used to serve the model in
+ # response to increases and decreases in traffic. Care should be
+ # taken to ramp up traffic according to the model's ability to scale
+ # or you will start seeing increases in latency and 429 response codes.
+ "minNodes": 42, # Optional. The minimum number of nodes to allocate for this model. These
+ # nodes are always up, starting from the time the model is deployed, so the
+ # cost of operating this model will be at least
+ # `rate` * `min_nodes` * number of hours since last billing cycle,
+ # where `rate` is the cost per node-hour as documented in
+ # [pricing](https://cloud.google.com/ml-engine/pricing#prediction_pricing),
+ # even if no predictions are performed. There is additional cost for each
+ # prediction performed.
+ #
+ # Unlike manual scaling, if the load gets too heavy for the nodes
+ # that are up, the service will automatically add nodes to handle the
+ # increased load as well as scale back as traffic drops, always maintaining
+ # at least `min_nodes`. You will be charged for the time in which additional
+ # nodes are used.
+ #
+ # If not specified, `min_nodes` defaults to 0, in which case, when traffic
+ # to a model stops (and after a cool-down period), nodes will be shut down
+ # and no charges will be incurred until traffic to the model resumes.
+ },
"createTime": "A String", # Output only. The time the version was created.
"isDefault": True or False, # Output only. If true, this version will be used to handle prediction
# requests that do not specify a version.
#
# You can change the default version by calling
# [projects.methods.versions.setDefault](/ml-engine/reference/rest/v1beta1/projects.models.versions/setDefault).
- "description": "A String", # Optional. The description specified for the version when it was created.
+ "name": "A String", # Required.The name specified for the version when it was created.
+ #
+ # The version name must be unique within the model it is created in.
},
"name": "A String", # Required. The name specified for the model when it was created.
#
@@ -285,22 +335,6 @@
# long-running operation should document the metadata type, if any.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
- "done": True or False, # If the value is `false`, it means the operation is still in progress.
- # If true, the operation is completed, and either `error` or `response` is
- # available.
- "response": { # The normal response of the operation in case of success. If the original
- # method returns no data on success, such as `Delete`, the response is
- # `google.protobuf.Empty`. If the original method is standard
- # `Get`/`Create`/`Update`, the response should be the resource. For other
- # methods, the response should have the type `XxxResponse`, where `Xxx`
- # is the original method name. For example, if the original method name
- # is `TakeSnapshot()`, the inferred response type is
- # `TakeSnapshotResponse`.
- "a_key": "", # Properties of the object. Contains field @type with type URL.
- },
- "name": "A String", # The server-assigned name, which is only unique within the same service that
- # originally returns it. If you use the default HTTP mapping, the
- # `name` should have the format of `operations/some/unique/name`.
"error": { # The `Status` type defines a logical error model that is suitable for different # The error result of the operation in case of failure or cancellation.
# programming environments, including REST APIs and RPC APIs. It is used by
# [gRPC](https://github.com/grpc). The error model is designed to be:
@@ -318,7 +352,7 @@
# error message is needed, put the localized message in the error details or
# localize it in the client. The optional error details may contain arbitrary
# information about the error. There is a predefined set of error detail types
- # in the package `google.rpc` which can be used for common error conditions.
+ # in the package `google.rpc` that can be used for common error conditions.
#
# # Language mapping
#
@@ -341,7 +375,7 @@
# errors.
#
# - Workflow errors. A typical workflow has multiple steps. Each step may
- # have a `Status` message for error reporting purpose.
+ # have a `Status` message for error reporting.
#
# - Batch operations. If a client uses batch request and batch response, the
# `Status` message should be used directly inside batch response, one for
@@ -364,6 +398,22 @@
},
],
},
+ "done": True or False, # If the value is `false`, it means the operation is still in progress.
+ # If true, the operation is completed, and either `error` or `response` is
+ # available.
+ "response": { # The normal response of the operation in case of success. If the original
+ # method returns no data on success, such as `Delete`, the response is
+ # `google.protobuf.Empty`. If the original method is standard
+ # `Get`/`Create`/`Update`, the response should be the resource. For other
+ # methods, the response should have the type `XxxResponse`, where `Xxx`
+ # is the original method name. For example, if the original method name
+ # is `TakeSnapshot()`, the inferred response type is
+ # `TakeSnapshotResponse`.
+ "a_key": "", # Properties of the object. Contains field @type with type URL.
+ },
+ "name": "A String", # The server-assigned name, which is only unique within the same service that
+ # originally returns it. If you use the default HTTP mapping, the
+ # `name` should have the format of `operations/some/unique/name`.
}</pre>
</div>
@@ -411,40 +461,65 @@
# prediction requests. A model can have multiple versions. You can get
# information about all of the versions of a given model by calling
# [projects.models.versions.list](/ml-engine/reference/rest/v1beta1/projects.models.versions/list).
- "name": "A String", # Required.The name specified for the version when it was created.
- #
- # The version name must be unique within the model it is created in.
+ "description": "A String", # Optional. The description specified for the version when it was created.
"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this deployment.
# If not set, Google Cloud ML will choose a version.
- "manualScaling": { # Options for manually scaling a model. # Optional. Manually select the number of nodes to use for serving the
- # model. If unset (i.e., by default), the number of nodes used to serve
- # the model automatically scales with traffic. However, care should be
- # taken to ramp up traffic according to the model's ability to scale. If
- # your model needs to handle bursts of traffic beyond it's ability to
- # scale, it is recommended you set this field appropriately.
+ "manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the
+ # model. You should generally use `automatic_scaling` with an appropriate
+ # `min_nodes` instead, but this option is available if you want predictable
+ # billing. Beware that latency and error rates will increase if the
+ # traffic exceeds that capability of the system to serve it based on
+ # the selected number of nodes.
"nodes": 42, # The number of nodes to allocate for this model. These nodes are always up,
# starting from the time the model is deployed, so the cost of operating
- # this model will be proportional to nodes * number of hours since
- # deployment.
+ # this model will be proportional to `nodes` * number of hours since
+ # last billing cycle.
},
- "lastUseTime": "A String", # Output only. The time the version was last used for prediction.
"deploymentUri": "A String", # Required. The Google Cloud Storage location of the trained model used to
# create the version. See the
- # [overview of model deployment](/ml-engine/docs/concepts/deployment-overview) for
- # more informaiton.
+ # [overview of model
+ # deployment](/ml-engine/docs/concepts/deployment-overview) for more
+ # informaiton.
#
# When passing Version to
# [projects.models.versions.create](/ml-engine/reference/rest/v1beta1/projects.models.versions/create)
# the model service uses the specified location as the source of the model.
# Once deployed, the model version is hosted by the prediction service, so
# this location is useful only as a historical record.
+ # The total number of model files can't exceed 1000.
+ "lastUseTime": "A String", # Output only. The time the version was last used for prediction.
+ "automaticScaling": { # Options for automatically scaling a model. # Automatically scale the number of nodes used to serve the model in
+ # response to increases and decreases in traffic. Care should be
+ # taken to ramp up traffic according to the model's ability to scale
+ # or you will start seeing increases in latency and 429 response codes.
+ "minNodes": 42, # Optional. The minimum number of nodes to allocate for this model. These
+ # nodes are always up, starting from the time the model is deployed, so the
+ # cost of operating this model will be at least
+ # `rate` * `min_nodes` * number of hours since last billing cycle,
+ # where `rate` is the cost per node-hour as documented in
+ # [pricing](https://cloud.google.com/ml-engine/pricing#prediction_pricing),
+ # even if no predictions are performed. There is additional cost for each
+ # prediction performed.
+ #
+ # Unlike manual scaling, if the load gets too heavy for the nodes
+ # that are up, the service will automatically add nodes to handle the
+ # increased load as well as scale back as traffic drops, always maintaining
+ # at least `min_nodes`. You will be charged for the time in which additional
+ # nodes are used.
+ #
+ # If not specified, `min_nodes` defaults to 0, in which case, when traffic
+ # to a model stops (and after a cool-down period), nodes will be shut down
+ # and no charges will be incurred until traffic to the model resumes.
+ },
"createTime": "A String", # Output only. The time the version was created.
"isDefault": True or False, # Output only. If true, this version will be used to handle prediction
# requests that do not specify a version.
#
# You can change the default version by calling
# [projects.methods.versions.setDefault](/ml-engine/reference/rest/v1beta1/projects.models.versions/setDefault).
- "description": "A String", # Optional. The description specified for the version when it was created.
+ "name": "A String", # Required.The name specified for the version when it was created.
+ #
+ # The version name must be unique within the model it is created in.
},
"name": "A String", # Required. The name specified for the model when it was created.
#
@@ -513,40 +588,65 @@
# prediction requests. A model can have multiple versions. You can get
# information about all of the versions of a given model by calling
# [projects.models.versions.list](/ml-engine/reference/rest/v1beta1/projects.models.versions/list).
- "name": "A String", # Required.The name specified for the version when it was created.
- #
- # The version name must be unique within the model it is created in.
+ "description": "A String", # Optional. The description specified for the version when it was created.
"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this deployment.
# If not set, Google Cloud ML will choose a version.
- "manualScaling": { # Options for manually scaling a model. # Optional. Manually select the number of nodes to use for serving the
- # model. If unset (i.e., by default), the number of nodes used to serve
- # the model automatically scales with traffic. However, care should be
- # taken to ramp up traffic according to the model's ability to scale. If
- # your model needs to handle bursts of traffic beyond it's ability to
- # scale, it is recommended you set this field appropriately.
+ "manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the
+ # model. You should generally use `automatic_scaling` with an appropriate
+ # `min_nodes` instead, but this option is available if you want predictable
+ # billing. Beware that latency and error rates will increase if the
+ # traffic exceeds that capability of the system to serve it based on
+ # the selected number of nodes.
"nodes": 42, # The number of nodes to allocate for this model. These nodes are always up,
# starting from the time the model is deployed, so the cost of operating
- # this model will be proportional to nodes * number of hours since
- # deployment.
+ # this model will be proportional to `nodes` * number of hours since
+ # last billing cycle.
},
- "lastUseTime": "A String", # Output only. The time the version was last used for prediction.
"deploymentUri": "A String", # Required. The Google Cloud Storage location of the trained model used to
# create the version. See the
- # [overview of model deployment](/ml-engine/docs/concepts/deployment-overview) for
- # more informaiton.
+ # [overview of model
+ # deployment](/ml-engine/docs/concepts/deployment-overview) for more
+ # informaiton.
#
# When passing Version to
# [projects.models.versions.create](/ml-engine/reference/rest/v1beta1/projects.models.versions/create)
# the model service uses the specified location as the source of the model.
# Once deployed, the model version is hosted by the prediction service, so
# this location is useful only as a historical record.
+ # The total number of model files can't exceed 1000.
+ "lastUseTime": "A String", # Output only. The time the version was last used for prediction.
+ "automaticScaling": { # Options for automatically scaling a model. # Automatically scale the number of nodes used to serve the model in
+ # response to increases and decreases in traffic. Care should be
+ # taken to ramp up traffic according to the model's ability to scale
+ # or you will start seeing increases in latency and 429 response codes.
+ "minNodes": 42, # Optional. The minimum number of nodes to allocate for this model. These
+ # nodes are always up, starting from the time the model is deployed, so the
+ # cost of operating this model will be at least
+ # `rate` * `min_nodes` * number of hours since last billing cycle,
+ # where `rate` is the cost per node-hour as documented in
+ # [pricing](https://cloud.google.com/ml-engine/pricing#prediction_pricing),
+ # even if no predictions are performed. There is additional cost for each
+ # prediction performed.
+ #
+ # Unlike manual scaling, if the load gets too heavy for the nodes
+ # that are up, the service will automatically add nodes to handle the
+ # increased load as well as scale back as traffic drops, always maintaining
+ # at least `min_nodes`. You will be charged for the time in which additional
+ # nodes are used.
+ #
+ # If not specified, `min_nodes` defaults to 0, in which case, when traffic
+ # to a model stops (and after a cool-down period), nodes will be shut down
+ # and no charges will be incurred until traffic to the model resumes.
+ },
"createTime": "A String", # Output only. The time the version was created.
"isDefault": True or False, # Output only. If true, this version will be used to handle prediction
# requests that do not specify a version.
#
# You can change the default version by calling
# [projects.methods.versions.setDefault](/ml-engine/reference/rest/v1beta1/projects.models.versions/setDefault).
- "description": "A String", # Optional. The description specified for the version when it was created.
+ "name": "A String", # Required.The name specified for the version when it was created.
+ #
+ # The version name must be unique within the model it is created in.
},
"name": "A String", # Required. The name specified for the model when it was created.
#