Clean and regen docs (#401)
diff --git a/docs/dyn/ml_v1.projects.models.html b/docs/dyn/ml_v1.projects.models.html
index 5ace172..830f7aa 100644
--- a/docs/dyn/ml_v1.projects.models.html
+++ b/docs/dyn/ml_v1.projects.models.html
@@ -89,7 +89,7 @@
<code><a href="#get">get(name, x__xgafv=None)</a></code></p>
<p class="firstline">Gets information about a model, including its name, the description (if</p>
<p class="toc_element">
- <code><a href="#list">list(parent, pageSize=None, pageToken=None, x__xgafv=None)</a></code></p>
+ <code><a href="#list">list(parent, pageToken=None, x__xgafv=None, pageSize=None)</a></code></p>
<p class="firstline">Lists the models in a project.</p>
<p class="toc_element">
<code><a href="#list_next">list_next(previous_request, previous_response)</a></code></p>
@@ -139,28 +139,53 @@
"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 more
+ # 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 plus the cost for each prediction performed.
},
- "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/v1/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.
@@ -216,28 +241,53 @@
"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 more
+ # 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 plus the cost for each prediction performed.
},
- "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/v1/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.
@@ -279,28 +329,12 @@
{ # This resource represents a long-running operation that is the result of a
# network API call.
- "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.
- },
"metadata": { # Service-specific metadata associated with the operation. It typically
# contains progress information and common metadata such as create time.
# Some services might not provide such metadata. Any method that returns a
# 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.
- "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>
@@ -414,28 +464,53 @@
"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 more
+ # 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 plus the cost for each prediction performed.
},
- "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/v1/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.
@@ -456,7 +531,7 @@
</div>
<div class="method">
- <code class="details" id="list">list(parent, pageSize=None, pageToken=None, x__xgafv=None)</code>
+ <code class="details" id="list">list(parent, pageToken=None, x__xgafv=None, pageSize=None)</code>
<pre>Lists the models in a project.
Each project can contain multiple models, and each model can have multiple
@@ -466,11 +541,6 @@
parent: string, Required. The name of the project whose models are to be listed.
Authorization: requires `Viewer` role on the specified project. (required)
- pageSize: integer, Optional. The number of models to retrieve per "page" of results. If there
-are more remaining results than this number, the response message will
-contain a valid value in the `next_page_token` field.
-
-The default value is 20, and the maximum page size is 100.
pageToken: string, Optional. A page token to request the next page of results.
You get the token from the `next_page_token` field of the response from
@@ -479,13 +549,16 @@
Allowed values
1 - v1 error format
2 - v2 error format
+ pageSize: integer, Optional. The number of models to retrieve per "page" of results. If there
+are more remaining results than this number, the response message will
+contain a valid value in the `next_page_token` field.
+
+The default value is 20, and the maximum page size is 100.
Returns:
An object of the form:
{ # Response message for the ListModels method.
- "nextPageToken": "A String", # Optional. Pass this token as the `page_token` field of the request for a
- # subsequent call.
"models": [ # The list of models.
{ # Represents a machine learning solution.
#
@@ -516,28 +589,53 @@
"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 more
+ # 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 plus the cost for each prediction performed.
},
- "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/v1/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.
@@ -556,6 +654,8 @@
"description": "A String", # Optional. The description specified for the model when it was created.
},
],
+ "nextPageToken": "A String", # Optional. Pass this token as the `page_token` field of the request for a
+ # subsequent call.
}</pre>
</div>