create(parent, body, x__xgafv=None)

Creates a model which will later contain one or more versions.

You must add at least one version before you can request predictions from
the model. Add versions by calling
[projects.models.versions.create](/ml-engine/reference/rest/v1/projects.models.versions/create).

Args:
parent: string, Required. The project name.

Authorization: requires `Editor` role on the specified project. (required)
body: object, The request body. (required)
The object takes the form of:

{ # Represents a machine learning solution.
#
# A model can have multiple versions, each of which is a deployed, trained
# model ready to receive prediction requests. The model itself is just a
# container.
"regions": [ # Optional. The list of regions where the model is going to be deployed.
# Currently only one region per model is supported.
# Defaults to 'us-central1' if nothing is set.
# Note:
# * No matter where a model is deployed, it can always be accessed by
# users from anywhere, both for online and batch prediction.
# * The region for a batch prediction job is set by the region field when
# submitting the batch prediction job and does not take its value from
# this field.
"A String",
],
"defaultVersion": { # Represents a version of the model. # Output only. The default version of the model. 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/v1/projects.models.versions/setDefault).
#
# Each version is a trained model deployed in the cloud, ready to handle
# 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/v1/projects.models.versions/list).
"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. # 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
# last billing cycle plus the cost for each prediction performed.
},
"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.
#
# 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.
#
# You can change the default version by calling
# [projects.methods.versions.setDefault](/ml-engine/reference/rest/v1/projects.models.versions/setDefault).
"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.
#
# The model name must be unique within the project it is created in.
"onlinePredictionLogging": True or False, # Optional. If true, enables StackDriver Logging for online prediction.
# Default is false.
"description": "A String", # Optional. The description specified for the model when it was created.
}

x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # Represents a machine learning solution.
#
# A model can have multiple versions, each of which is a deployed, trained
# model ready to receive prediction requests. The model itself is just a
# container.
"regions": [ # Optional. The list of regions where the model is going to be deployed.
# Currently only one region per model is supported.
# Defaults to 'us-central1' if nothing is set.
# Note:
# * No matter where a model is deployed, it can always be accessed by
# users from anywhere, both for online and batch prediction.
# * The region for a batch prediction job is set by the region field when
# submitting the batch prediction job and does not take its value from
# this field.
"A String",
],
"defaultVersion": { # Represents a version of the model. # Output only. The default version of the model. 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/v1/projects.models.versions/setDefault).
#
# Each version is a trained model deployed in the cloud, ready to handle
# 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/v1/projects.models.versions/list).
"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. # 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
# last billing cycle plus the cost for each prediction performed.
},
"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.
#
# 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.
#
# You can change the default version by calling
# [projects.methods.versions.setDefault](/ml-engine/reference/rest/v1/projects.models.versions/setDefault).
"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.
#
# The model name must be unique within the project it is created in.
"onlinePredictionLogging": True or False, # Optional. If true, enables StackDriver Logging for online prediction.
# Default is false.
"description": "A String", # Optional. The description specified for the model when it was created.
}

delete(name, x__xgafv=None)

Deletes a model.

You can only delete a model if there are no versions in it. You can delete
versions by calling
[projects.models.versions.delete](/ml-engine/reference/rest/v1/projects.models.versions/delete).

Args:
name: string, Required. The name of the model.

Authorization: requires `Editor` role on the parent project. (required)
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # This resource represents a long-running operation that is the result of a
# network API call.
"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.
},
"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:
#
# - Simple to use and understand for most users
# - Flexible enough to meet unexpected needs
#
# # Overview
#
# The `Status` message contains three pieces of data: error code, error message,
# and error details. The error code should be an enum value of
# google.rpc.Code, but it may accept additional error codes if needed. The
# error message should be a developer-facing English message that helps
# developers *understand* and *resolve* the error. If a localized user-facing
# 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` that can be used for common error conditions.
#
# # Language mapping
#
# The `Status` message is the logical representation of the error model, but it
# is not necessarily the actual wire format. When the `Status` message is
# exposed in different client libraries and different wire protocols, it can be
# mapped differently. For example, it will likely be mapped to some exceptions
# in Java, but more likely mapped to some error codes in C.
#
# # Other uses
#
# The error model and the `Status` message can be used in a variety of
# environments, either with or without APIs, to provide a
# consistent developer experience across different environments.
#
# Example uses of this error model include:
#
# - Partial errors. If a service needs to return partial errors to the client,
# it may embed the `Status` in the normal response to indicate the partial
# errors.
#
# - Workflow errors. A typical workflow has multiple steps. Each step may
# 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
# each error sub-response.
#
# - Asynchronous operations. If an API call embeds asynchronous operation
# results in its response, the status of those operations should be
# represented directly using the `Status` message.
#
# - Logging. If some API errors are stored in logs, the message `Status` could
# be used directly after any stripping needed for security/privacy reasons.
"message": "A String", # A developer-facing error message, which should be in English. Any
# user-facing error message should be localized and sent in the
# google.rpc.Status.details field, or localized by the client.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There will be a
# common set of message types for APIs to use.
{
"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`.
}

get(name, x__xgafv=None)

Gets information about a model, including its name, the description (if
set), and the default version (if at least one version of the model has
been deployed).

Args:
name: string, Required. The name of the model.

Authorization: requires `Viewer` role on the parent project. (required)
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # Represents a machine learning solution.
#
# A model can have multiple versions, each of which is a deployed, trained
# model ready to receive prediction requests. The model itself is just a
# container.
"regions": [ # Optional. The list of regions where the model is going to be deployed.
# Currently only one region per model is supported.
# Defaults to 'us-central1' if nothing is set.
# Note:
# * No matter where a model is deployed, it can always be accessed by
# users from anywhere, both for online and batch prediction.
# * The region for a batch prediction job is set by the region field when
# submitting the batch prediction job and does not take its value from
# this field.
"A String",
],
"defaultVersion": { # Represents a version of the model. # Output only. The default version of the model. 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/v1/projects.models.versions/setDefault).
#
# Each version is a trained model deployed in the cloud, ready to handle
# 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/v1/projects.models.versions/list).
"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. # 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
# last billing cycle plus the cost for each prediction performed.
},
"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.
#
# 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.
#
# You can change the default version by calling
# [projects.methods.versions.setDefault](/ml-engine/reference/rest/v1/projects.models.versions/setDefault).
"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.
#
# The model name must be unique within the project it is created in.
"onlinePredictionLogging": True or False, # Optional. If true, enables StackDriver Logging for online prediction.
# Default is false.
"description": "A String", # Optional. The description specified for the model when it was created.
}

list(parent, pageToken=None, x__xgafv=None, pageSize=None)

Lists the models in a project.

Each project can contain multiple models, and each model can have multiple
versions.

Args:
parent: string, Required. The name of the project whose models are to be listed.

Authorization: requires `Viewer` role on the specified project. (required)
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
the previous call.
x__xgafv: string, V1 error format.
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.
"models": [ # The list of models.
{ # Represents a machine learning solution.
#
# A model can have multiple versions, each of which is a deployed, trained
# model ready to receive prediction requests. The model itself is just a
# container.
"regions": [ # Optional. The list of regions where the model is going to be deployed.
# Currently only one region per model is supported.
# Defaults to 'us-central1' if nothing is set.
# Note:
# * No matter where a model is deployed, it can always be accessed by
# users from anywhere, both for online and batch prediction.
# * The region for a batch prediction job is set by the region field when
# submitting the batch prediction job and does not take its value from
# this field.
"A String",
],
"defaultVersion": { # Represents a version of the model. # Output only. The default version of the model. 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/v1/projects.models.versions/setDefault).
#
# Each version is a trained model deployed in the cloud, ready to handle
# 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/v1/projects.models.versions/list).
"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. # 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
# last billing cycle plus the cost for each prediction performed.
},
"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.
#
# 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.
#
# You can change the default version by calling
# [projects.methods.versions.setDefault](/ml-engine/reference/rest/v1/projects.models.versions/setDefault).
"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.
#
# The model name must be unique within the project it is created in.
"onlinePredictionLogging": True or False, # Optional. If true, enables StackDriver Logging for online prediction.
# Default is false.
"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.
}

list_next(previous_request, previous_response)

Retrieves the next page of results.

Args:
  previous_request: The request for the previous page. (required)
  previous_response: The response from the request for the previous page. (required)

Returns:
  A request object that you can call 'execute()' on to request the next
  page. Returns None if there are no more items in the collection.

Google Cloud Machine Learning Engine . projects . models

Instance Methods

Method Details