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Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	1	<html><body>
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Dan O'Meara	dd49464	2020-05-01 07:42:23 -0700	[diff] [blame]	75	<h1><a href="ml_v1.html">AI Platform Training & Prediction API</a> . <a href="ml_v1.projects.html">projects</a> . <a href="ml_v1.projects.models.html">models</a> . <a href="ml_v1.projects.models.versions.html">versions</a></h1>
Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	76	<h2>Instance Methods</h2>
				77	<p class="toc_element">
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	78	<code><a href="#close">close()</a></code></p>
				79	<p class="firstline">Close httplib2 connections.</p>
				80	<p class="toc_element">
Dan O'Meara	dd49464	2020-05-01 07:42:23 -0700	[diff] [blame]	81	<code><a href="#create">create(parent, body=None, x__xgafv=None)</a></code></p>
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	82	<p class="firstline">Creates a new version of a model from a trained TensorFlow model. If the version created in the cloud by this call is the first deployed version of the specified model, it will be made the default version of the model. When you add a version to a model that already has one or more versions, the default version does not automatically change. If you want a new version to be the default, you must call projects.models.versions.setDefault.</p>
Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	83	<p class="toc_element">
Thomas Coffee	2f24537	2017-03-27 10:39:26 -0700	[diff] [blame]	84	<code><a href="#delete">delete(name, x__xgafv=None)</a></code></p>
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	85	<p class="firstline">Deletes a model version. Each model can have multiple versions deployed and in use at any given time. Use this method to remove a single version. Note: You cannot delete the version that is set as the default version of the model unless it is the only remaining version.</p>
Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	86	<p class="toc_element">
Thomas Coffee	2f24537	2017-03-27 10:39:26 -0700	[diff] [blame]	87	<code><a href="#get">get(name, x__xgafv=None)</a></code></p>
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	88	<p class="firstline">Gets information about a model version. Models can have multiple versions. You can call projects.models.versions.list to get the same information that this method returns for all of the versions of a model.</p>
Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	89	<p class="toc_element">
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	90	<code><a href="#list">list(parent, filter=None, pageSize=None, pageToken=None, x__xgafv=None)</a></code></p>
				91	<p class="firstline">Gets basic information about all the versions of a model. If you expect that a model has many versions, or if you need to handle only a limited number of results at a time, you can request that the list be retrieved in batches (called pages). If there are no versions that match the request parameters, the list request returns an empty response body: {}.</p>
Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	92	<p class="toc_element">
				93	<code><a href="#list_next">list_next(previous_request, previous_response)</a></code></p>
				94	<p class="firstline">Retrieves the next page of results.</p>
				95	<p class="toc_element">
Dan O'Meara	dd49464	2020-05-01 07:42:23 -0700	[diff] [blame]	96	<code><a href="#patch">patch(name, body=None, updateMask=None, x__xgafv=None)</a></code></p>
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	97	<p class="firstline">Updates the specified Version resource. Currently the only update-able fields are `description`, `requestLoggingConfig`, `autoScaling.minNodes`, and `manualScaling.nodes`.</p>
Bu Sun Kim	715bd7f	2019-06-14 16:50:42 -0700	[diff] [blame]	98	<p class="toc_element">
				99	<code><a href="#setDefault">setDefault(name, body=None, x__xgafv=None)</a></code></p>
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	100	<p class="firstline">Designates a version to be the default for the model. The default version is used for prediction requests made against the model that don't specify a version. The first version to be created for a model is automatically set as the default. You must make any subsequent changes to the default version setting manually using this method.</p>
Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	101	<h3>Method Details</h3>
				102	<div class="method">
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	103	<code class="details" id="close">close()</code>
				104	<pre>Close httplib2 connections.</pre>
				105	</div>
Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	106
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	107	<div class="method">
				108	<code class="details" id="create">create(parent, body=None, x__xgafv=None)</code>
				109	<pre>Creates a new version of a model from a trained TensorFlow model. If the version created in the cloud by this call is the first deployed version of the specified model, it will be made the default version of the model. When you add a version to a model that already has one or more versions, the default version does not automatically change. If you want a new version to be the default, you must call projects.models.versions.setDefault.
Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	110
				111	Args:
Bu Sun Kim	715bd7f	2019-06-14 16:50:42 -0700	[diff] [blame]	112	parent: string, Required. The name of the model. (required)
Dan O'Meara	dd49464	2020-05-01 07:42:23 -0700	[diff] [blame]	113	body: object, The request body.
Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	114	The object takes the form of:
				115
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	116	{ # Represents a version of the model. 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.
				117	"deploymentUri": "A String", # Required. The Cloud Storage location of the trained model used to create the version. See the [guide to model deployment](/ml-engine/docs/tensorflow/deploying-models) for more information. When passing Version to 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.
				118	"requestLoggingConfig": { # Configuration for logging request-response pairs to a BigQuery table. Online prediction requests to a model version and the responses to these requests are converted to raw strings and saved to the specified BigQuery table. Logging is constrained by [BigQuery quotas and limits](/bigquery/quotas). If your project exceeds BigQuery quotas or limits, AI Platform Prediction does not log request-response pairs, but it continues to serve predictions. If you are using [continuous evaluation](/ml-engine/docs/continuous-evaluation/), you do not need to specify this configuration manually. Setting up continuous evaluation automatically enables logging of request-response pairs. # Optional. Only specify this field in a projects.models.versions.patch request. Specifying it in a projects.models.versions.create request has no effect. Configures the request-response pair logging on predictions from this Version.
				119	"bigqueryTableName": "A String", # Required. Fully qualified BigQuery table name in the following format: " project_id.dataset_name.table_name" The specified table must already exist, and the "Cloud ML Service Agent" for your project must have permission to write to it. The table must have the following [schema](/bigquery/docs/schemas): Field nameType Mode model STRING REQUIRED model_version STRING REQUIRED time TIMESTAMP REQUIRED raw_data STRING REQUIRED raw_prediction STRING NULLABLE groundtruth STRING NULLABLE
				120	"samplingPercentage": 3.14, # Percentage of requests to be logged, expressed as a fraction from 0 to 1. For example, if you want to log 10% of requests, enter `0.1`. The sampling window is the lifetime of the model version. Defaults to 0.
Bu Sun Kim	4ed7d3f	2020-05-27 12:20:54 -0700	[diff] [blame]	121	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	122	"predictionClass": "A String", # Optional. The fully qualified name (module_name.class_name) of a class that implements the Predictor interface described in this reference field. The module containing this class should be included in a package provided to the [`packageUris` field](#Version.FIELDS.package_uris). Specify this field if and only if you are deploying a [custom prediction routine (beta)](/ml-engine/docs/tensorflow/custom-prediction-routines). If you specify this field, you must set [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater and you must set `machineType` to a [legacy (MLS1) machine type](/ml-engine/docs/machine-types-online-prediction). The following code sample provides the Predictor interface: class Predictor(object): """Interface for constructing custom predictors.""" def predict(self, instances, kwargs): """Performs custom prediction. Instances are the decoded values from the request. They have already been deserialized from JSON. Args: instances: A list of prediction input instances. kwargs: A dictionary of keyword args provided as additional fields on the predict request body. Returns: A list of outputs containing the prediction results. This list must be JSON serializable. """ raise NotImplementedError() @classmethod def from_path(cls, model_dir): """Creates an instance of Predictor using the given path. Loading of the predictor should be done in this method. Args: model_dir: The local directory that contains the exported model file along with any additional files uploaded when creating the version resource. Returns: An instance implementing this Predictor class. """ raise NotImplementedError() Learn more about [the Predictor interface and custom prediction routines](/ml-engine/docs/tensorflow/custom-prediction-routines).
				123	"framework": "A String", # Optional. The machine learning framework AI Platform uses to train this version of the model. Valid values are `TENSORFLOW`, `SCIKIT_LEARN`, `XGBOOST`. If you do not specify a framework, AI Platform will analyze files in the deployment_uri to determine a framework. If you choose `SCIKIT_LEARN` or `XGBOOST`, you must also set the runtime version of the model to 1.4 or greater. Do not specify a framework if you're deploying a [custom prediction routine](/ml-engine/docs/tensorflow/custom-prediction-routines). If you specify a [Compute Engine (N1) machine type](/ml-engine/docs/machine-types-online-prediction) in the `machineType` field, you must specify `TENSORFLOW` for the framework.
				124	"description": "A String", # Optional. The description specified for the version when it was created.
				125	"autoScaling": { # 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. Note that you cannot use AutoScaling if your version uses [GPUs](#Version.FIELDS.accelerator_config). Instead, you must use specify `manual_scaling`.
				126	"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. Therefore, 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 the [pricing guide](/ml-engine/docs/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 `min_nodes` is not specified and AutoScaling is used with a [legacy (MLS1) machine type](/ml-engine/docs/machine-types-online-prediction), `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. If `min_nodes` is not specified and AutoScaling is used with a [Compute Engine (N1) machine type](/ml-engine/docs/machine-types-online-prediction), `min_nodes` defaults to 1. `min_nodes` must be at least 1 for use with a Compute Engine machine type. Note that you cannot use AutoScaling if your version uses [GPUs](#Version.FIELDS.accelerator_config). Instead, you must use ManualScaling. You can set `min_nodes` when creating the model version, and you can also update `min_nodes` for an existing version: update_body.json: { 'autoScaling': { 'minNodes': 5 } } HTTP request: PATCH https://ml.googleapis.com/v1/{name=projects//models//versions/*}?update_mask=autoScaling.minNodes -d @./update_body.json
				127	},
				128	"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.
				129	"createTime": "A String", # Output only. The time the version was created.
				130	"routes": { # RouteMap is used to override HTTP paths sent to a Custom Container. If specified, the HTTP server implemented in the ContainerSpec must support the route. If unspecified, standard HTTP paths will be used.
				131	"predict": "A String", # HTTP path to send prediction requests.
				132	"health": "A String", # HTTP path to send health check requests.
				133	},
				134	"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
				135	"machineType": "A String", # Optional. The type of machine on which to serve the model. Currently only applies to online prediction service. If this field is not specified, it defaults to `mls1-c1-m2`. Online prediction supports the following machine types: * `mls1-c1-m2` * `mls1-c4-m2` * `n1-standard-2` * `n1-standard-4` * `n1-standard-8` * `n1-standard-16` * `n1-standard-32` * `n1-highmem-2` * `n1-highmem-4` * `n1-highmem-8` * `n1-highmem-16` * `n1-highmem-32` * `n1-highcpu-2` * `n1-highcpu-4` * `n1-highcpu-8` * `n1-highcpu-16` * `n1-highcpu-32` `mls1-c1-m2` is generally available. All other machine types are available in beta. Learn more about the [differences between machine types](/ml-engine/docs/machine-types-online-prediction).
				136	"acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Optional. Accelerator config for using GPUs for online prediction (beta). Only specify this field if you have specified a Compute Engine (N1) machine type in the `machineType` field. Learn more about [using GPUs for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	137	"type": "A String", # The type of accelerator to use.
				138	"count": "A String", # The number of accelerators to attach to each machine running the job.
Bu Sun Kim	6502091	2020-05-20 12:08:20 -0700	[diff] [blame]	139	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	140	"etag": "A String", # `etag` is used for optimistic concurrency control as a way to help prevent simultaneous updates of a model from overwriting each other. It is strongly suggested that systems make use of the `etag` in the read-modify-write cycle to perform model updates in order to avoid race conditions: An `etag` is returned in the response to `GetVersion`, and systems are expected to put that etag in the request to `UpdateVersion` to ensure that their change will be applied to the model as intended.
				141	"explanationConfig": { # Message holding configuration options for explaining model predictions. There are three feature attribution methods supported for TensorFlow models: integrated gradients, sampled Shapley, and XRAI. [Learn more about feature attributions.](/ai-platform/prediction/docs/ai-explanations/overview) # Optional. Configures explainability features on the model's version. Some explanation features require additional metadata to be loaded as part of the model payload.
				142	"xraiAttribution": { # Attributes credit by computing the XRAI taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 Currently only implemented for models with natural image inputs. # Attributes credit by computing the XRAI taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 Currently only implemented for models with natural image inputs.
				143	"numIntegralSteps": 42, # Number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range.
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	144	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	145	"integratedGradientsAttribution": { # Attributes credit by computing the Aumann-Shapley value taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365 # Attributes credit by computing the Aumann-Shapley value taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
				146	"numIntegralSteps": 42, # Number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range.
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	147	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	148	"sampledShapleyAttribution": { # An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. # An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features.
				149	"numPaths": 42, # The number of feature permutations to consider when approximating the Shapley values.
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	150	},
Bu Sun Kim	6502091	2020-05-20 12:08:20 -0700	[diff] [blame]	151	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	152	"labels": { # Optional. One or more labels that you can add, to organize your model versions. Each label is a key-value pair, where both the key and the value are arbitrary strings that you supply. For more information, see the documentation on using labels.
				153	"a_key": "A String",
Bu Sun Kim	6502091	2020-05-20 12:08:20 -0700	[diff] [blame]	154	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	155	"container": { # Specify a custom container to deploy. Our ContainerSpec is a subset of the Kubernetes Container specification. https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.10/#container-v1-core
				156	"ports": [ # Immutable. List of ports to expose from the container. Exposing a port here gives the system additional information about the network connections a container uses, but is primarily informational. Not specifying a port here DOES NOT prevent that port from being exposed. Any port which is listening on the default "0.0.0.0" address inside a container will be accessible from the network.
				157	{ # ContainerPort represents a network port in a single container.
				158	"containerPort": 42, # Number of port to expose on the pod's IP address. This must be a valid port number, 0 < x < 65536.
				159	},
				160	],
				161	"env": [ # Immutable. List of environment variables to set in the container.
				162	{ # EnvVar represents an environment variable present in a Container.
				163	"name": "A String", # Name of the environment variable. Must be a C_IDENTIFIER.
				164	"value": "A String", # Variable references $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. Defaults to "".
				165	},
				166	],
				167	"command": [ # Immutable. Entrypoint array. Not executed within a shell. The docker image's ENTRYPOINT is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. More info: https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#running-a-command-in-a-shell
				168	"A String",
				169	],
				170	"image": "A String", # Docker image name. More info: https://kubernetes.io/docs/concepts/containers/images
				171	"args": [ # Immutable. Arguments to the entrypoint. The docker image's CMD is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. More info: https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#running-a-command-in-a-shell
				172	"A String",
				173	],
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	174	},
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	175	"lastUseTime": "A String", # Output only. The time the version was last used for prediction.
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	176	"serviceAccount": "A String", # Optional. Specifies the service account for resource access control.
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	177	"runtimeVersion": "A String", # Required. The AI Platform runtime version to use for this deployment. For more information, see the [runtime version list](/ml-engine/docs/runtime-version-list) and [how to manage runtime versions](/ml-engine/docs/versioning).
				178	"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.
				179	"manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the model. You should generally use `auto_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.
				180	"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.
				181	},
				182	"state": "A String", # Output only. The state of a version.
				183	"packageUris": [ # Optional. Cloud Storage paths (`gs://…`) of packages for [custom prediction routines](/ml-engine/docs/tensorflow/custom-prediction-routines) or [scikit-learn pipelines with custom code](/ml-engine/docs/scikit/exporting-for-prediction#custom-pipeline-code). For a custom prediction routine, one of these packages must contain your Predictor class (see [`predictionClass`](#Version.FIELDS.prediction_class)). Additionally, include any dependencies used by your Predictor or scikit-learn pipeline uses that are not already included in your selected [runtime version](/ml-engine/docs/tensorflow/runtime-version-list). If you specify this field, you must also set [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater.
				184	"A String",
				185	],
				186	"pythonVersion": "A String", # Required. The version of Python used in prediction. The following Python versions are available: * Python '3.7' is available when `runtime_version` is set to '1.15' or later. * Python '3.5' is available when `runtime_version` is set to a version from '1.4' to '1.14'. * Python '2.7' is available when `runtime_version` is set to '1.15' or earlier. Read more about the Python versions available for [each runtime version](/ml-engine/docs/runtime-version-list).
Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	187	}
				188
				189	x__xgafv: string, V1 error format.
				190	Allowed values
				191	1 - v1 error format
				192	2 - v2 error format
				193
				194	Returns:
				195	An object of the form:
				196
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	197	{ # This resource represents a long-running operation that is the result of a network API call.
				198	"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 be a resource name ending with `operations/{unique_id}`.
				199	"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`.
				200	"a_key": "", # Properties of the object. Contains field @type with type URL.
				201	},
				202	"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.
				203	"error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
				204	"code": 42, # The status code, which should be an enum value of google.rpc.Code.
				205	"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
Bu Sun Kim	6502091	2020-05-20 12:08:20 -0700	[diff] [blame]	206	{
				207	"a_key": "", # Properties of the object. Contains field @type with type URL.
				208	},
				209	],
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	210	"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.
Bu Sun Kim	6502091	2020-05-20 12:08:20 -0700	[diff] [blame]	211	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	212	"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.
Bu Sun Kim	4ed7d3f	2020-05-27 12:20:54 -0700	[diff] [blame]	213	"a_key": "", # Properties of the object. Contains field @type with type URL.
				214	},
Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	215	}</pre>
				216	</div>
				217
				218	<div class="method">
Thomas Coffee	2f24537	2017-03-27 10:39:26 -0700	[diff] [blame]	219	<code class="details" id="delete">delete(name, x__xgafv=None)</code>
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	220	<pre>Deletes a model version. Each model can have multiple versions deployed and in use at any given time. Use this method to remove a single version. Note: You cannot delete the version that is set as the default version of the model unless it is the only remaining version.
Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	221
				222	Args:
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	223	name: string, Required. The name of the version. You can get the names of all the versions of a model by calling projects.models.versions.list. (required)
Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	224	x__xgafv: string, V1 error format.
				225	Allowed values
				226	1 - v1 error format
				227	2 - v2 error format
				228
				229	Returns:
				230	An object of the form:
				231
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	232	{ # This resource represents a long-running operation that is the result of a network API call.
				233	"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 be a resource name ending with `operations/{unique_id}`.
				234	"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`.
				235	"a_key": "", # Properties of the object. Contains field @type with type URL.
				236	},
				237	"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.
				238	"error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
				239	"code": 42, # The status code, which should be an enum value of google.rpc.Code.
				240	"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
Bu Sun Kim	6502091	2020-05-20 12:08:20 -0700	[diff] [blame]	241	{
				242	"a_key": "", # Properties of the object. Contains field @type with type URL.
				243	},
				244	],
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	245	"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.
Bu Sun Kim	6502091	2020-05-20 12:08:20 -0700	[diff] [blame]	246	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	247	"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.
Bu Sun Kim	4ed7d3f	2020-05-27 12:20:54 -0700	[diff] [blame]	248	"a_key": "", # Properties of the object. Contains field @type with type URL.
				249	},
Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	250	}</pre>
				251	</div>
				252
				253	<div class="method">
Thomas Coffee	2f24537	2017-03-27 10:39:26 -0700	[diff] [blame]	254	<code class="details" id="get">get(name, x__xgafv=None)</code>
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	255	<pre>Gets information about a model version. Models can have multiple versions. You can call projects.models.versions.list to get the same information that this method returns for all of the versions of a model.
Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	256
				257	Args:
Bu Sun Kim	715bd7f	2019-06-14 16:50:42 -0700	[diff] [blame]	258	name: string, Required. The name of the version. (required)
Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	259	x__xgafv: string, V1 error format.
				260	Allowed values
				261	1 - v1 error format
				262	2 - v2 error format
				263
				264	Returns:
				265	An object of the form:
				266
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	267	{ # Represents a version of the model. 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.
				268	"deploymentUri": "A String", # Required. The Cloud Storage location of the trained model used to create the version. See the [guide to model deployment](/ml-engine/docs/tensorflow/deploying-models) for more information. When passing Version to 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.
				269	"requestLoggingConfig": { # Configuration for logging request-response pairs to a BigQuery table. Online prediction requests to a model version and the responses to these requests are converted to raw strings and saved to the specified BigQuery table. Logging is constrained by [BigQuery quotas and limits](/bigquery/quotas). If your project exceeds BigQuery quotas or limits, AI Platform Prediction does not log request-response pairs, but it continues to serve predictions. If you are using [continuous evaluation](/ml-engine/docs/continuous-evaluation/), you do not need to specify this configuration manually. Setting up continuous evaluation automatically enables logging of request-response pairs. # Optional. Only specify this field in a projects.models.versions.patch request. Specifying it in a projects.models.versions.create request has no effect. Configures the request-response pair logging on predictions from this Version.
				270	"bigqueryTableName": "A String", # Required. Fully qualified BigQuery table name in the following format: " project_id.dataset_name.table_name" The specified table must already exist, and the "Cloud ML Service Agent" for your project must have permission to write to it. The table must have the following [schema](/bigquery/docs/schemas): Field nameType Mode model STRING REQUIRED model_version STRING REQUIRED time TIMESTAMP REQUIRED raw_data STRING REQUIRED raw_prediction STRING NULLABLE groundtruth STRING NULLABLE
				271	"samplingPercentage": 3.14, # Percentage of requests to be logged, expressed as a fraction from 0 to 1. For example, if you want to log 10% of requests, enter `0.1`. The sampling window is the lifetime of the model version. Defaults to 0.
Bu Sun Kim	4ed7d3f	2020-05-27 12:20:54 -0700	[diff] [blame]	272	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	273	"predictionClass": "A String", # Optional. The fully qualified name (module_name.class_name) of a class that implements the Predictor interface described in this reference field. The module containing this class should be included in a package provided to the [`packageUris` field](#Version.FIELDS.package_uris). Specify this field if and only if you are deploying a [custom prediction routine (beta)](/ml-engine/docs/tensorflow/custom-prediction-routines). If you specify this field, you must set [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater and you must set `machineType` to a [legacy (MLS1) machine type](/ml-engine/docs/machine-types-online-prediction). The following code sample provides the Predictor interface: class Predictor(object): """Interface for constructing custom predictors.""" def predict(self, instances, kwargs): """Performs custom prediction. Instances are the decoded values from the request. They have already been deserialized from JSON. Args: instances: A list of prediction input instances. kwargs: A dictionary of keyword args provided as additional fields on the predict request body. Returns: A list of outputs containing the prediction results. This list must be JSON serializable. """ raise NotImplementedError() @classmethod def from_path(cls, model_dir): """Creates an instance of Predictor using the given path. Loading of the predictor should be done in this method. Args: model_dir: The local directory that contains the exported model file along with any additional files uploaded when creating the version resource. Returns: An instance implementing this Predictor class. """ raise NotImplementedError() Learn more about [the Predictor interface and custom prediction routines](/ml-engine/docs/tensorflow/custom-prediction-routines).
				274	"framework": "A String", # Optional. The machine learning framework AI Platform uses to train this version of the model. Valid values are `TENSORFLOW`, `SCIKIT_LEARN`, `XGBOOST`. If you do not specify a framework, AI Platform will analyze files in the deployment_uri to determine a framework. If you choose `SCIKIT_LEARN` or `XGBOOST`, you must also set the runtime version of the model to 1.4 or greater. Do not specify a framework if you're deploying a [custom prediction routine](/ml-engine/docs/tensorflow/custom-prediction-routines). If you specify a [Compute Engine (N1) machine type](/ml-engine/docs/machine-types-online-prediction) in the `machineType` field, you must specify `TENSORFLOW` for the framework.
				275	"description": "A String", # Optional. The description specified for the version when it was created.
				276	"autoScaling": { # 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. Note that you cannot use AutoScaling if your version uses [GPUs](#Version.FIELDS.accelerator_config). Instead, you must use specify `manual_scaling`.
				277	"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. Therefore, 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 the [pricing guide](/ml-engine/docs/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 `min_nodes` is not specified and AutoScaling is used with a [legacy (MLS1) machine type](/ml-engine/docs/machine-types-online-prediction), `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. If `min_nodes` is not specified and AutoScaling is used with a [Compute Engine (N1) machine type](/ml-engine/docs/machine-types-online-prediction), `min_nodes` defaults to 1. `min_nodes` must be at least 1 for use with a Compute Engine machine type. Note that you cannot use AutoScaling if your version uses [GPUs](#Version.FIELDS.accelerator_config). Instead, you must use ManualScaling. You can set `min_nodes` when creating the model version, and you can also update `min_nodes` for an existing version: update_body.json: { 'autoScaling': { 'minNodes': 5 } } HTTP request: PATCH https://ml.googleapis.com/v1/{name=projects//models//versions/*}?update_mask=autoScaling.minNodes -d @./update_body.json
				278	},
				279	"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.
				280	"createTime": "A String", # Output only. The time the version was created.
				281	"routes": { # RouteMap is used to override HTTP paths sent to a Custom Container. If specified, the HTTP server implemented in the ContainerSpec must support the route. If unspecified, standard HTTP paths will be used.
				282	"predict": "A String", # HTTP path to send prediction requests.
				283	"health": "A String", # HTTP path to send health check requests.
				284	},
				285	"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
				286	"machineType": "A String", # Optional. The type of machine on which to serve the model. Currently only applies to online prediction service. If this field is not specified, it defaults to `mls1-c1-m2`. Online prediction supports the following machine types: * `mls1-c1-m2` * `mls1-c4-m2` * `n1-standard-2` * `n1-standard-4` * `n1-standard-8` * `n1-standard-16` * `n1-standard-32` * `n1-highmem-2` * `n1-highmem-4` * `n1-highmem-8` * `n1-highmem-16` * `n1-highmem-32` * `n1-highcpu-2` * `n1-highcpu-4` * `n1-highcpu-8` * `n1-highcpu-16` * `n1-highcpu-32` `mls1-c1-m2` is generally available. All other machine types are available in beta. Learn more about the [differences between machine types](/ml-engine/docs/machine-types-online-prediction).
				287	"acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Optional. Accelerator config for using GPUs for online prediction (beta). Only specify this field if you have specified a Compute Engine (N1) machine type in the `machineType` field. Learn more about [using GPUs for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	288	"type": "A String", # The type of accelerator to use.
				289	"count": "A String", # The number of accelerators to attach to each machine running the job.
Bu Sun Kim	6502091	2020-05-20 12:08:20 -0700	[diff] [blame]	290	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	291	"etag": "A String", # `etag` is used for optimistic concurrency control as a way to help prevent simultaneous updates of a model from overwriting each other. It is strongly suggested that systems make use of the `etag` in the read-modify-write cycle to perform model updates in order to avoid race conditions: An `etag` is returned in the response to `GetVersion`, and systems are expected to put that etag in the request to `UpdateVersion` to ensure that their change will be applied to the model as intended.
				292	"explanationConfig": { # Message holding configuration options for explaining model predictions. There are three feature attribution methods supported for TensorFlow models: integrated gradients, sampled Shapley, and XRAI. [Learn more about feature attributions.](/ai-platform/prediction/docs/ai-explanations/overview) # Optional. Configures explainability features on the model's version. Some explanation features require additional metadata to be loaded as part of the model payload.
				293	"xraiAttribution": { # Attributes credit by computing the XRAI taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 Currently only implemented for models with natural image inputs. # Attributes credit by computing the XRAI taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 Currently only implemented for models with natural image inputs.
				294	"numIntegralSteps": 42, # Number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range.
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	295	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	296	"integratedGradientsAttribution": { # Attributes credit by computing the Aumann-Shapley value taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365 # Attributes credit by computing the Aumann-Shapley value taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
				297	"numIntegralSteps": 42, # Number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range.
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	298	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	299	"sampledShapleyAttribution": { # An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. # An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features.
				300	"numPaths": 42, # The number of feature permutations to consider when approximating the Shapley values.
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	301	},
Bu Sun Kim	6502091	2020-05-20 12:08:20 -0700	[diff] [blame]	302	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	303	"labels": { # Optional. One or more labels that you can add, to organize your model versions. Each label is a key-value pair, where both the key and the value are arbitrary strings that you supply. For more information, see the documentation on using labels.
				304	"a_key": "A String",
Bu Sun Kim	6502091	2020-05-20 12:08:20 -0700	[diff] [blame]	305	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	306	"container": { # Specify a custom container to deploy. Our ContainerSpec is a subset of the Kubernetes Container specification. https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.10/#container-v1-core
				307	"ports": [ # Immutable. List of ports to expose from the container. Exposing a port here gives the system additional information about the network connections a container uses, but is primarily informational. Not specifying a port here DOES NOT prevent that port from being exposed. Any port which is listening on the default "0.0.0.0" address inside a container will be accessible from the network.
				308	{ # ContainerPort represents a network port in a single container.
				309	"containerPort": 42, # Number of port to expose on the pod's IP address. This must be a valid port number, 0 < x < 65536.
				310	},
				311	],
				312	"env": [ # Immutable. List of environment variables to set in the container.
				313	{ # EnvVar represents an environment variable present in a Container.
				314	"name": "A String", # Name of the environment variable. Must be a C_IDENTIFIER.
				315	"value": "A String", # Variable references $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. Defaults to "".
				316	},
				317	],
				318	"command": [ # Immutable. Entrypoint array. Not executed within a shell. The docker image's ENTRYPOINT is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. More info: https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#running-a-command-in-a-shell
				319	"A String",
				320	],
				321	"image": "A String", # Docker image name. More info: https://kubernetes.io/docs/concepts/containers/images
				322	"args": [ # Immutable. Arguments to the entrypoint. The docker image's CMD is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. More info: https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#running-a-command-in-a-shell
				323	"A String",
				324	],
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	325	},
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	326	"lastUseTime": "A String", # Output only. The time the version was last used for prediction.
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	327	"serviceAccount": "A String", # Optional. Specifies the service account for resource access control.
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	328	"runtimeVersion": "A String", # Required. The AI Platform runtime version to use for this deployment. For more information, see the [runtime version list](/ml-engine/docs/runtime-version-list) and [how to manage runtime versions](/ml-engine/docs/versioning).
				329	"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.
				330	"manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the model. You should generally use `auto_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.
				331	"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.
				332	},
				333	"state": "A String", # Output only. The state of a version.
				334	"packageUris": [ # Optional. Cloud Storage paths (`gs://…`) of packages for [custom prediction routines](/ml-engine/docs/tensorflow/custom-prediction-routines) or [scikit-learn pipelines with custom code](/ml-engine/docs/scikit/exporting-for-prediction#custom-pipeline-code). For a custom prediction routine, one of these packages must contain your Predictor class (see [`predictionClass`](#Version.FIELDS.prediction_class)). Additionally, include any dependencies used by your Predictor or scikit-learn pipeline uses that are not already included in your selected [runtime version](/ml-engine/docs/tensorflow/runtime-version-list). If you specify this field, you must also set [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater.
				335	"A String",
				336	],
				337	"pythonVersion": "A String", # Required. The version of Python used in prediction. The following Python versions are available: * Python '3.7' is available when `runtime_version` is set to '1.15' or later. * Python '3.5' is available when `runtime_version` is set to a version from '1.4' to '1.14'. * Python '2.7' is available when `runtime_version` is set to '1.15' or earlier. Read more about the Python versions available for [each runtime version](/ml-engine/docs/runtime-version-list).
Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	338	}</pre>
				339	</div>
				340
				341	<div class="method">
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	342	<code class="details" id="list">list(parent, filter=None, pageSize=None, pageToken=None, x__xgafv=None)</code>
				343	<pre>Gets basic information about all the versions of a model. If you expect that a model has many versions, or if you need to handle only a limited number of results at a time, you can request that the list be retrieved in batches (called pages). If there are no versions that match the request parameters, the list request returns an empty response body: {}.
Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	344
				345	Args:
Bu Sun Kim	715bd7f	2019-06-14 16:50:42 -0700	[diff] [blame]	346	parent: string, Required. The name of the model for which to list the version. (required)
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	347	filter: string, Optional. Specifies the subset of versions to retrieve.
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	348	pageSize: integer, Optional. The number of versions 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.
				349	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.
Bu Sun Kim	6502091	2020-05-20 12:08:20 -0700	[diff] [blame]	350	x__xgafv: string, V1 error format.
				351	Allowed values
				352	1 - v1 error format
				353	2 - v2 error format
Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	354
				355	Returns:
				356	An object of the form:
				357
				358	{ # Response message for the ListVersions method.
Bu Sun Kim	6502091	2020-05-20 12:08:20 -0700	[diff] [blame]	359	"versions": [ # The list of versions.
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	360	{ # Represents a version of the model. 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.
				361	"deploymentUri": "A String", # Required. The Cloud Storage location of the trained model used to create the version. See the [guide to model deployment](/ml-engine/docs/tensorflow/deploying-models) for more information. When passing Version to 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.
				362	"requestLoggingConfig": { # Configuration for logging request-response pairs to a BigQuery table. Online prediction requests to a model version and the responses to these requests are converted to raw strings and saved to the specified BigQuery table. Logging is constrained by [BigQuery quotas and limits](/bigquery/quotas). If your project exceeds BigQuery quotas or limits, AI Platform Prediction does not log request-response pairs, but it continues to serve predictions. If you are using [continuous evaluation](/ml-engine/docs/continuous-evaluation/), you do not need to specify this configuration manually. Setting up continuous evaluation automatically enables logging of request-response pairs. # Optional. Only specify this field in a projects.models.versions.patch request. Specifying it in a projects.models.versions.create request has no effect. Configures the request-response pair logging on predictions from this Version.
				363	"bigqueryTableName": "A String", # Required. Fully qualified BigQuery table name in the following format: " project_id.dataset_name.table_name" The specified table must already exist, and the "Cloud ML Service Agent" for your project must have permission to write to it. The table must have the following [schema](/bigquery/docs/schemas): Field nameType Mode model STRING REQUIRED model_version STRING REQUIRED time TIMESTAMP REQUIRED raw_data STRING REQUIRED raw_prediction STRING NULLABLE groundtruth STRING NULLABLE
				364	"samplingPercentage": 3.14, # Percentage of requests to be logged, expressed as a fraction from 0 to 1. For example, if you want to log 10% of requests, enter `0.1`. The sampling window is the lifetime of the model version. Defaults to 0.
Bu Sun Kim	4ed7d3f	2020-05-27 12:20:54 -0700	[diff] [blame]	365	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	366	"predictionClass": "A String", # Optional. The fully qualified name (module_name.class_name) of a class that implements the Predictor interface described in this reference field. The module containing this class should be included in a package provided to the [`packageUris` field](#Version.FIELDS.package_uris). Specify this field if and only if you are deploying a [custom prediction routine (beta)](/ml-engine/docs/tensorflow/custom-prediction-routines). If you specify this field, you must set [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater and you must set `machineType` to a [legacy (MLS1) machine type](/ml-engine/docs/machine-types-online-prediction). The following code sample provides the Predictor interface: class Predictor(object): """Interface for constructing custom predictors.""" def predict(self, instances, kwargs): """Performs custom prediction. Instances are the decoded values from the request. They have already been deserialized from JSON. Args: instances: A list of prediction input instances. kwargs: A dictionary of keyword args provided as additional fields on the predict request body. Returns: A list of outputs containing the prediction results. This list must be JSON serializable. """ raise NotImplementedError() @classmethod def from_path(cls, model_dir): """Creates an instance of Predictor using the given path. Loading of the predictor should be done in this method. Args: model_dir: The local directory that contains the exported model file along with any additional files uploaded when creating the version resource. Returns: An instance implementing this Predictor class. """ raise NotImplementedError() Learn more about [the Predictor interface and custom prediction routines](/ml-engine/docs/tensorflow/custom-prediction-routines).
				367	"framework": "A String", # Optional. The machine learning framework AI Platform uses to train this version of the model. Valid values are `TENSORFLOW`, `SCIKIT_LEARN`, `XGBOOST`. If you do not specify a framework, AI Platform will analyze files in the deployment_uri to determine a framework. If you choose `SCIKIT_LEARN` or `XGBOOST`, you must also set the runtime version of the model to 1.4 or greater. Do not specify a framework if you're deploying a [custom prediction routine](/ml-engine/docs/tensorflow/custom-prediction-routines). If you specify a [Compute Engine (N1) machine type](/ml-engine/docs/machine-types-online-prediction) in the `machineType` field, you must specify `TENSORFLOW` for the framework.
				368	"description": "A String", # Optional. The description specified for the version when it was created.
				369	"autoScaling": { # 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. Note that you cannot use AutoScaling if your version uses [GPUs](#Version.FIELDS.accelerator_config). Instead, you must use specify `manual_scaling`.
				370	"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. Therefore, 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 the [pricing guide](/ml-engine/docs/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 `min_nodes` is not specified and AutoScaling is used with a [legacy (MLS1) machine type](/ml-engine/docs/machine-types-online-prediction), `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. If `min_nodes` is not specified and AutoScaling is used with a [Compute Engine (N1) machine type](/ml-engine/docs/machine-types-online-prediction), `min_nodes` defaults to 1. `min_nodes` must be at least 1 for use with a Compute Engine machine type. Note that you cannot use AutoScaling if your version uses [GPUs](#Version.FIELDS.accelerator_config). Instead, you must use ManualScaling. You can set `min_nodes` when creating the model version, and you can also update `min_nodes` for an existing version: update_body.json: { 'autoScaling': { 'minNodes': 5 } } HTTP request: PATCH https://ml.googleapis.com/v1/{name=projects//models//versions/*}?update_mask=autoScaling.minNodes -d @./update_body.json
				371	},
				372	"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.
				373	"createTime": "A String", # Output only. The time the version was created.
				374	"routes": { # RouteMap is used to override HTTP paths sent to a Custom Container. If specified, the HTTP server implemented in the ContainerSpec must support the route. If unspecified, standard HTTP paths will be used.
				375	"predict": "A String", # HTTP path to send prediction requests.
				376	"health": "A String", # HTTP path to send health check requests.
				377	},
				378	"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
				379	"machineType": "A String", # Optional. The type of machine on which to serve the model. Currently only applies to online prediction service. If this field is not specified, it defaults to `mls1-c1-m2`. Online prediction supports the following machine types: * `mls1-c1-m2` * `mls1-c4-m2` * `n1-standard-2` * `n1-standard-4` * `n1-standard-8` * `n1-standard-16` * `n1-standard-32` * `n1-highmem-2` * `n1-highmem-4` * `n1-highmem-8` * `n1-highmem-16` * `n1-highmem-32` * `n1-highcpu-2` * `n1-highcpu-4` * `n1-highcpu-8` * `n1-highcpu-16` * `n1-highcpu-32` `mls1-c1-m2` is generally available. All other machine types are available in beta. Learn more about the [differences between machine types](/ml-engine/docs/machine-types-online-prediction).
				380	"acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Optional. Accelerator config for using GPUs for online prediction (beta). Only specify this field if you have specified a Compute Engine (N1) machine type in the `machineType` field. Learn more about [using GPUs for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	381	"type": "A String", # The type of accelerator to use.
				382	"count": "A String", # The number of accelerators to attach to each machine running the job.
Bu Sun Kim	6502091	2020-05-20 12:08:20 -0700	[diff] [blame]	383	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	384	"etag": "A String", # `etag` is used for optimistic concurrency control as a way to help prevent simultaneous updates of a model from overwriting each other. It is strongly suggested that systems make use of the `etag` in the read-modify-write cycle to perform model updates in order to avoid race conditions: An `etag` is returned in the response to `GetVersion`, and systems are expected to put that etag in the request to `UpdateVersion` to ensure that their change will be applied to the model as intended.
				385	"explanationConfig": { # Message holding configuration options for explaining model predictions. There are three feature attribution methods supported for TensorFlow models: integrated gradients, sampled Shapley, and XRAI. [Learn more about feature attributions.](/ai-platform/prediction/docs/ai-explanations/overview) # Optional. Configures explainability features on the model's version. Some explanation features require additional metadata to be loaded as part of the model payload.
				386	"xraiAttribution": { # Attributes credit by computing the XRAI taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 Currently only implemented for models with natural image inputs. # Attributes credit by computing the XRAI taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 Currently only implemented for models with natural image inputs.
				387	"numIntegralSteps": 42, # Number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range.
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	388	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	389	"integratedGradientsAttribution": { # Attributes credit by computing the Aumann-Shapley value taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365 # Attributes credit by computing the Aumann-Shapley value taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
				390	"numIntegralSteps": 42, # Number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range.
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	391	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	392	"sampledShapleyAttribution": { # An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. # An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features.
				393	"numPaths": 42, # The number of feature permutations to consider when approximating the Shapley values.
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	394	},
Bu Sun Kim	6502091	2020-05-20 12:08:20 -0700	[diff] [blame]	395	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	396	"labels": { # Optional. One or more labels that you can add, to organize your model versions. Each label is a key-value pair, where both the key and the value are arbitrary strings that you supply. For more information, see the documentation on using labels.
				397	"a_key": "A String",
Bu Sun Kim	6502091	2020-05-20 12:08:20 -0700	[diff] [blame]	398	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	399	"container": { # Specify a custom container to deploy. Our ContainerSpec is a subset of the Kubernetes Container specification. https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.10/#container-v1-core
				400	"ports": [ # Immutable. List of ports to expose from the container. Exposing a port here gives the system additional information about the network connections a container uses, but is primarily informational. Not specifying a port here DOES NOT prevent that port from being exposed. Any port which is listening on the default "0.0.0.0" address inside a container will be accessible from the network.
				401	{ # ContainerPort represents a network port in a single container.
				402	"containerPort": 42, # Number of port to expose on the pod's IP address. This must be a valid port number, 0 < x < 65536.
				403	},
				404	],
				405	"env": [ # Immutable. List of environment variables to set in the container.
				406	{ # EnvVar represents an environment variable present in a Container.
				407	"name": "A String", # Name of the environment variable. Must be a C_IDENTIFIER.
				408	"value": "A String", # Variable references $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. Defaults to "".
				409	},
				410	],
				411	"command": [ # Immutable. Entrypoint array. Not executed within a shell. The docker image's ENTRYPOINT is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. More info: https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#running-a-command-in-a-shell
				412	"A String",
				413	],
				414	"image": "A String", # Docker image name. More info: https://kubernetes.io/docs/concepts/containers/images
				415	"args": [ # Immutable. Arguments to the entrypoint. The docker image's CMD is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. More info: https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#running-a-command-in-a-shell
				416	"A String",
				417	],
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	418	},
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	419	"lastUseTime": "A String", # Output only. The time the version was last used for prediction.
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	420	"serviceAccount": "A String", # Optional. Specifies the service account for resource access control.
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	421	"runtimeVersion": "A String", # Required. The AI Platform runtime version to use for this deployment. For more information, see the [runtime version list](/ml-engine/docs/runtime-version-list) and [how to manage runtime versions](/ml-engine/docs/versioning).
				422	"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.
				423	"manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the model. You should generally use `auto_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.
				424	"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.
				425	},
				426	"state": "A String", # Output only. The state of a version.
				427	"packageUris": [ # Optional. Cloud Storage paths (`gs://…`) of packages for [custom prediction routines](/ml-engine/docs/tensorflow/custom-prediction-routines) or [scikit-learn pipelines with custom code](/ml-engine/docs/scikit/exporting-for-prediction#custom-pipeline-code). For a custom prediction routine, one of these packages must contain your Predictor class (see [`predictionClass`](#Version.FIELDS.prediction_class)). Additionally, include any dependencies used by your Predictor or scikit-learn pipeline uses that are not already included in your selected [runtime version](/ml-engine/docs/tensorflow/runtime-version-list). If you specify this field, you must also set [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater.
				428	"A String",
				429	],
				430	"pythonVersion": "A String", # Required. The version of Python used in prediction. The following Python versions are available: * Python '3.7' is available when `runtime_version` is set to '1.15' or later. * Python '3.5' is available when `runtime_version` is set to a version from '1.4' to '1.14'. * Python '2.7' is available when `runtime_version` is set to '1.15' or earlier. Read more about the Python versions available for [each runtime version](/ml-engine/docs/runtime-version-list).
Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	431	},
				432	],
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	433	"nextPageToken": "A String", # Optional. Pass this token as the `page_token` field of the request for a subsequent call.
Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	434	}</pre>
				435	</div>
				436
				437	<div class="method">
				438	<code class="details" id="list_next">list_next(previous_request, previous_response)</code>
				439	<pre>Retrieves the next page of results.
				440
				441	Args:
				442	previous_request: The request for the previous page. (required)
				443	previous_response: The response from the request for the previous page. (required)
				444
				445	Returns:
Bu Sun Kim	6502091	2020-05-20 12:08:20 -0700	[diff] [blame]	446	A request object that you can call 'execute()' on to request the next
Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	447	page. Returns None if there are no more items in the collection.
				448	</pre>
				449	</div>
				450
				451	<div class="method">
Dan O'Meara	dd49464	2020-05-01 07:42:23 -0700	[diff] [blame]	452	<code class="details" id="patch">patch(name, body=None, updateMask=None, x__xgafv=None)</code>
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	453	<pre>Updates the specified Version resource. Currently the only update-able fields are `description`, `requestLoggingConfig`, `autoScaling.minNodes`, and `manualScaling.nodes`.
Bu Sun Kim	715bd7f	2019-06-14 16:50:42 -0700	[diff] [blame]	454
				455	Args:
				456	name: string, Required. The name of the model. (required)
Dan O'Meara	dd49464	2020-05-01 07:42:23 -0700	[diff] [blame]	457	body: object, The request body.
Bu Sun Kim	715bd7f	2019-06-14 16:50:42 -0700	[diff] [blame]	458	The object takes the form of:
				459
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	460	{ # Represents a version of the model. 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.
				461	"deploymentUri": "A String", # Required. The Cloud Storage location of the trained model used to create the version. See the [guide to model deployment](/ml-engine/docs/tensorflow/deploying-models) for more information. When passing Version to 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.
				462	"requestLoggingConfig": { # Configuration for logging request-response pairs to a BigQuery table. Online prediction requests to a model version and the responses to these requests are converted to raw strings and saved to the specified BigQuery table. Logging is constrained by [BigQuery quotas and limits](/bigquery/quotas). If your project exceeds BigQuery quotas or limits, AI Platform Prediction does not log request-response pairs, but it continues to serve predictions. If you are using [continuous evaluation](/ml-engine/docs/continuous-evaluation/), you do not need to specify this configuration manually. Setting up continuous evaluation automatically enables logging of request-response pairs. # Optional. Only specify this field in a projects.models.versions.patch request. Specifying it in a projects.models.versions.create request has no effect. Configures the request-response pair logging on predictions from this Version.
				463	"bigqueryTableName": "A String", # Required. Fully qualified BigQuery table name in the following format: " project_id.dataset_name.table_name" The specified table must already exist, and the "Cloud ML Service Agent" for your project must have permission to write to it. The table must have the following [schema](/bigquery/docs/schemas): Field nameType Mode model STRING REQUIRED model_version STRING REQUIRED time TIMESTAMP REQUIRED raw_data STRING REQUIRED raw_prediction STRING NULLABLE groundtruth STRING NULLABLE
				464	"samplingPercentage": 3.14, # Percentage of requests to be logged, expressed as a fraction from 0 to 1. For example, if you want to log 10% of requests, enter `0.1`. The sampling window is the lifetime of the model version. Defaults to 0.
Bu Sun Kim	4ed7d3f	2020-05-27 12:20:54 -0700	[diff] [blame]	465	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	466	"predictionClass": "A String", # Optional. The fully qualified name (module_name.class_name) of a class that implements the Predictor interface described in this reference field. The module containing this class should be included in a package provided to the [`packageUris` field](#Version.FIELDS.package_uris). Specify this field if and only if you are deploying a [custom prediction routine (beta)](/ml-engine/docs/tensorflow/custom-prediction-routines). If you specify this field, you must set [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater and you must set `machineType` to a [legacy (MLS1) machine type](/ml-engine/docs/machine-types-online-prediction). The following code sample provides the Predictor interface: class Predictor(object): """Interface for constructing custom predictors.""" def predict(self, instances, kwargs): """Performs custom prediction. Instances are the decoded values from the request. They have already been deserialized from JSON. Args: instances: A list of prediction input instances. kwargs: A dictionary of keyword args provided as additional fields on the predict request body. Returns: A list of outputs containing the prediction results. This list must be JSON serializable. """ raise NotImplementedError() @classmethod def from_path(cls, model_dir): """Creates an instance of Predictor using the given path. Loading of the predictor should be done in this method. Args: model_dir: The local directory that contains the exported model file along with any additional files uploaded when creating the version resource. Returns: An instance implementing this Predictor class. """ raise NotImplementedError() Learn more about [the Predictor interface and custom prediction routines](/ml-engine/docs/tensorflow/custom-prediction-routines).
				467	"framework": "A String", # Optional. The machine learning framework AI Platform uses to train this version of the model. Valid values are `TENSORFLOW`, `SCIKIT_LEARN`, `XGBOOST`. If you do not specify a framework, AI Platform will analyze files in the deployment_uri to determine a framework. If you choose `SCIKIT_LEARN` or `XGBOOST`, you must also set the runtime version of the model to 1.4 or greater. Do not specify a framework if you're deploying a [custom prediction routine](/ml-engine/docs/tensorflow/custom-prediction-routines). If you specify a [Compute Engine (N1) machine type](/ml-engine/docs/machine-types-online-prediction) in the `machineType` field, you must specify `TENSORFLOW` for the framework.
				468	"description": "A String", # Optional. The description specified for the version when it was created.
				469	"autoScaling": { # 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. Note that you cannot use AutoScaling if your version uses [GPUs](#Version.FIELDS.accelerator_config). Instead, you must use specify `manual_scaling`.
				470	"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. Therefore, 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 the [pricing guide](/ml-engine/docs/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 `min_nodes` is not specified and AutoScaling is used with a [legacy (MLS1) machine type](/ml-engine/docs/machine-types-online-prediction), `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. If `min_nodes` is not specified and AutoScaling is used with a [Compute Engine (N1) machine type](/ml-engine/docs/machine-types-online-prediction), `min_nodes` defaults to 1. `min_nodes` must be at least 1 for use with a Compute Engine machine type. Note that you cannot use AutoScaling if your version uses [GPUs](#Version.FIELDS.accelerator_config). Instead, you must use ManualScaling. You can set `min_nodes` when creating the model version, and you can also update `min_nodes` for an existing version: update_body.json: { 'autoScaling': { 'minNodes': 5 } } HTTP request: PATCH https://ml.googleapis.com/v1/{name=projects//models//versions/*}?update_mask=autoScaling.minNodes -d @./update_body.json
				471	},
				472	"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.
				473	"createTime": "A String", # Output only. The time the version was created.
				474	"routes": { # RouteMap is used to override HTTP paths sent to a Custom Container. If specified, the HTTP server implemented in the ContainerSpec must support the route. If unspecified, standard HTTP paths will be used.
				475	"predict": "A String", # HTTP path to send prediction requests.
				476	"health": "A String", # HTTP path to send health check requests.
				477	},
				478	"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
				479	"machineType": "A String", # Optional. The type of machine on which to serve the model. Currently only applies to online prediction service. If this field is not specified, it defaults to `mls1-c1-m2`. Online prediction supports the following machine types: * `mls1-c1-m2` * `mls1-c4-m2` * `n1-standard-2` * `n1-standard-4` * `n1-standard-8` * `n1-standard-16` * `n1-standard-32` * `n1-highmem-2` * `n1-highmem-4` * `n1-highmem-8` * `n1-highmem-16` * `n1-highmem-32` * `n1-highcpu-2` * `n1-highcpu-4` * `n1-highcpu-8` * `n1-highcpu-16` * `n1-highcpu-32` `mls1-c1-m2` is generally available. All other machine types are available in beta. Learn more about the [differences between machine types](/ml-engine/docs/machine-types-online-prediction).
				480	"acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Optional. Accelerator config for using GPUs for online prediction (beta). Only specify this field if you have specified a Compute Engine (N1) machine type in the `machineType` field. Learn more about [using GPUs for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	481	"type": "A String", # The type of accelerator to use.
				482	"count": "A String", # The number of accelerators to attach to each machine running the job.
Bu Sun Kim	6502091	2020-05-20 12:08:20 -0700	[diff] [blame]	483	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	484	"etag": "A String", # `etag` is used for optimistic concurrency control as a way to help prevent simultaneous updates of a model from overwriting each other. It is strongly suggested that systems make use of the `etag` in the read-modify-write cycle to perform model updates in order to avoid race conditions: An `etag` is returned in the response to `GetVersion`, and systems are expected to put that etag in the request to `UpdateVersion` to ensure that their change will be applied to the model as intended.
				485	"explanationConfig": { # Message holding configuration options for explaining model predictions. There are three feature attribution methods supported for TensorFlow models: integrated gradients, sampled Shapley, and XRAI. [Learn more about feature attributions.](/ai-platform/prediction/docs/ai-explanations/overview) # Optional. Configures explainability features on the model's version. Some explanation features require additional metadata to be loaded as part of the model payload.
				486	"xraiAttribution": { # Attributes credit by computing the XRAI taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 Currently only implemented for models with natural image inputs. # Attributes credit by computing the XRAI taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 Currently only implemented for models with natural image inputs.
				487	"numIntegralSteps": 42, # Number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range.
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	488	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	489	"integratedGradientsAttribution": { # Attributes credit by computing the Aumann-Shapley value taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365 # Attributes credit by computing the Aumann-Shapley value taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
				490	"numIntegralSteps": 42, # Number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range.
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	491	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	492	"sampledShapleyAttribution": { # An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. # An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features.
				493	"numPaths": 42, # The number of feature permutations to consider when approximating the Shapley values.
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	494	},
Bu Sun Kim	6502091	2020-05-20 12:08:20 -0700	[diff] [blame]	495	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	496	"labels": { # Optional. One or more labels that you can add, to organize your model versions. Each label is a key-value pair, where both the key and the value are arbitrary strings that you supply. For more information, see the documentation on using labels.
				497	"a_key": "A String",
Bu Sun Kim	6502091	2020-05-20 12:08:20 -0700	[diff] [blame]	498	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	499	"container": { # Specify a custom container to deploy. Our ContainerSpec is a subset of the Kubernetes Container specification. https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.10/#container-v1-core
				500	"ports": [ # Immutable. List of ports to expose from the container. Exposing a port here gives the system additional information about the network connections a container uses, but is primarily informational. Not specifying a port here DOES NOT prevent that port from being exposed. Any port which is listening on the default "0.0.0.0" address inside a container will be accessible from the network.
				501	{ # ContainerPort represents a network port in a single container.
				502	"containerPort": 42, # Number of port to expose on the pod's IP address. This must be a valid port number, 0 < x < 65536.
				503	},
				504	],
				505	"env": [ # Immutable. List of environment variables to set in the container.
				506	{ # EnvVar represents an environment variable present in a Container.
				507	"name": "A String", # Name of the environment variable. Must be a C_IDENTIFIER.
				508	"value": "A String", # Variable references $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. Defaults to "".
				509	},
				510	],
				511	"command": [ # Immutable. Entrypoint array. Not executed within a shell. The docker image's ENTRYPOINT is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. More info: https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#running-a-command-in-a-shell
				512	"A String",
				513	],
				514	"image": "A String", # Docker image name. More info: https://kubernetes.io/docs/concepts/containers/images
				515	"args": [ # Immutable. Arguments to the entrypoint. The docker image's CMD is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. More info: https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#running-a-command-in-a-shell
				516	"A String",
				517	],
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	518	},
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	519	"lastUseTime": "A String", # Output only. The time the version was last used for prediction.
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	520	"serviceAccount": "A String", # Optional. Specifies the service account for resource access control.
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	521	"runtimeVersion": "A String", # Required. The AI Platform runtime version to use for this deployment. For more information, see the [runtime version list](/ml-engine/docs/runtime-version-list) and [how to manage runtime versions](/ml-engine/docs/versioning).
				522	"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.
				523	"manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the model. You should generally use `auto_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.
				524	"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.
				525	},
				526	"state": "A String", # Output only. The state of a version.
				527	"packageUris": [ # Optional. Cloud Storage paths (`gs://…`) of packages for [custom prediction routines](/ml-engine/docs/tensorflow/custom-prediction-routines) or [scikit-learn pipelines with custom code](/ml-engine/docs/scikit/exporting-for-prediction#custom-pipeline-code). For a custom prediction routine, one of these packages must contain your Predictor class (see [`predictionClass`](#Version.FIELDS.prediction_class)). Additionally, include any dependencies used by your Predictor or scikit-learn pipeline uses that are not already included in your selected [runtime version](/ml-engine/docs/tensorflow/runtime-version-list). If you specify this field, you must also set [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater.
				528	"A String",
				529	],
				530	"pythonVersion": "A String", # Required. The version of Python used in prediction. The following Python versions are available: * Python '3.7' is available when `runtime_version` is set to '1.15' or later. * Python '3.5' is available when `runtime_version` is set to a version from '1.4' to '1.14'. * Python '2.7' is available when `runtime_version` is set to '1.15' or earlier. Read more about the Python versions available for [each runtime version](/ml-engine/docs/runtime-version-list).
Bu Sun Kim	715bd7f	2019-06-14 16:50:42 -0700	[diff] [blame]	531	}
				532
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	533	updateMask: string, Required. Specifies the path, relative to `Version`, of the field to update. Must be present and non-empty. For example, to change the description of a version to "foo", the `update_mask` parameter would be specified as `description`, and the `PATCH` request body would specify the new value, as follows: ``` { "description": "foo" } ``` Currently the only supported update mask fields are `description`, `requestLoggingConfig`, `autoScaling.minNodes`, and `manualScaling.nodes`. However, you can only update `manualScaling.nodes` if the version uses a [Compute Engine (N1) machine type](/ml-engine/docs/machine-types-online-prediction).
Bu Sun Kim	715bd7f	2019-06-14 16:50:42 -0700	[diff] [blame]	534	x__xgafv: string, V1 error format.
				535	Allowed values
				536	1 - v1 error format
				537	2 - v2 error format
				538
				539	Returns:
				540	An object of the form:
				541
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	542	{ # This resource represents a long-running operation that is the result of a network API call.
				543	"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 be a resource name ending with `operations/{unique_id}`.
				544	"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`.
				545	"a_key": "", # Properties of the object. Contains field @type with type URL.
				546	},
				547	"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.
				548	"error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
				549	"code": 42, # The status code, which should be an enum value of google.rpc.Code.
				550	"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
Bu Sun Kim	6502091	2020-05-20 12:08:20 -0700	[diff] [blame]	551	{
				552	"a_key": "", # Properties of the object. Contains field @type with type URL.
				553	},
				554	],
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	555	"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.
Bu Sun Kim	6502091	2020-05-20 12:08:20 -0700	[diff] [blame]	556	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	557	"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.
Bu Sun Kim	4ed7d3f	2020-05-27 12:20:54 -0700	[diff] [blame]	558	"a_key": "", # Properties of the object. Contains field @type with type URL.
				559	},
Bu Sun Kim	715bd7f	2019-06-14 16:50:42 -0700	[diff] [blame]	560	}</pre>
				561	</div>
				562
				563	<div class="method">
				564	<code class="details" id="setDefault">setDefault(name, body=None, x__xgafv=None)</code>
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	565	<pre>Designates a version to be the default for the model. The default version is used for prediction requests made against the model that don't specify a version. The first version to be created for a model is automatically set as the default. You must make any subsequent changes to the default version setting manually using this method.
Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	566
				567	Args:
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	568	name: string, Required. The name of the version to make the default for the model. You can get the names of all the versions of a model by calling projects.models.versions.list. (required)
Bu Sun Kim	715bd7f	2019-06-14 16:50:42 -0700	[diff] [blame]	569	body: object, The request body.
Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	570	The object takes the form of:
				571
				572	{ # Request message for the SetDefaultVersion request.
				573	}
				574
				575	x__xgafv: string, V1 error format.
				576	Allowed values
				577	1 - v1 error format
				578	2 - v2 error format
				579
				580	Returns:
				581	An object of the form:
				582
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	583	{ # Represents a version of the model. 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.
				584	"deploymentUri": "A String", # Required. The Cloud Storage location of the trained model used to create the version. See the [guide to model deployment](/ml-engine/docs/tensorflow/deploying-models) for more information. When passing Version to 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.
				585	"requestLoggingConfig": { # Configuration for logging request-response pairs to a BigQuery table. Online prediction requests to a model version and the responses to these requests are converted to raw strings and saved to the specified BigQuery table. Logging is constrained by [BigQuery quotas and limits](/bigquery/quotas). If your project exceeds BigQuery quotas or limits, AI Platform Prediction does not log request-response pairs, but it continues to serve predictions. If you are using [continuous evaluation](/ml-engine/docs/continuous-evaluation/), you do not need to specify this configuration manually. Setting up continuous evaluation automatically enables logging of request-response pairs. # Optional. Only specify this field in a projects.models.versions.patch request. Specifying it in a projects.models.versions.create request has no effect. Configures the request-response pair logging on predictions from this Version.
				586	"bigqueryTableName": "A String", # Required. Fully qualified BigQuery table name in the following format: " project_id.dataset_name.table_name" The specified table must already exist, and the "Cloud ML Service Agent" for your project must have permission to write to it. The table must have the following [schema](/bigquery/docs/schemas): Field nameType Mode model STRING REQUIRED model_version STRING REQUIRED time TIMESTAMP REQUIRED raw_data STRING REQUIRED raw_prediction STRING NULLABLE groundtruth STRING NULLABLE
				587	"samplingPercentage": 3.14, # Percentage of requests to be logged, expressed as a fraction from 0 to 1. For example, if you want to log 10% of requests, enter `0.1`. The sampling window is the lifetime of the model version. Defaults to 0.
Bu Sun Kim	4ed7d3f	2020-05-27 12:20:54 -0700	[diff] [blame]	588	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	589	"predictionClass": "A String", # Optional. The fully qualified name (module_name.class_name) of a class that implements the Predictor interface described in this reference field. The module containing this class should be included in a package provided to the [`packageUris` field](#Version.FIELDS.package_uris). Specify this field if and only if you are deploying a [custom prediction routine (beta)](/ml-engine/docs/tensorflow/custom-prediction-routines). If you specify this field, you must set [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater and you must set `machineType` to a [legacy (MLS1) machine type](/ml-engine/docs/machine-types-online-prediction). The following code sample provides the Predictor interface: class Predictor(object): """Interface for constructing custom predictors.""" def predict(self, instances, kwargs): """Performs custom prediction. Instances are the decoded values from the request. They have already been deserialized from JSON. Args: instances: A list of prediction input instances. kwargs: A dictionary of keyword args provided as additional fields on the predict request body. Returns: A list of outputs containing the prediction results. This list must be JSON serializable. """ raise NotImplementedError() @classmethod def from_path(cls, model_dir): """Creates an instance of Predictor using the given path. Loading of the predictor should be done in this method. Args: model_dir: The local directory that contains the exported model file along with any additional files uploaded when creating the version resource. Returns: An instance implementing this Predictor class. """ raise NotImplementedError() Learn more about [the Predictor interface and custom prediction routines](/ml-engine/docs/tensorflow/custom-prediction-routines).
				590	"framework": "A String", # Optional. The machine learning framework AI Platform uses to train this version of the model. Valid values are `TENSORFLOW`, `SCIKIT_LEARN`, `XGBOOST`. If you do not specify a framework, AI Platform will analyze files in the deployment_uri to determine a framework. If you choose `SCIKIT_LEARN` or `XGBOOST`, you must also set the runtime version of the model to 1.4 or greater. Do not specify a framework if you're deploying a [custom prediction routine](/ml-engine/docs/tensorflow/custom-prediction-routines). If you specify a [Compute Engine (N1) machine type](/ml-engine/docs/machine-types-online-prediction) in the `machineType` field, you must specify `TENSORFLOW` for the framework.
				591	"description": "A String", # Optional. The description specified for the version when it was created.
				592	"autoScaling": { # 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. Note that you cannot use AutoScaling if your version uses [GPUs](#Version.FIELDS.accelerator_config). Instead, you must use specify `manual_scaling`.
				593	"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. Therefore, 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 the [pricing guide](/ml-engine/docs/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 `min_nodes` is not specified and AutoScaling is used with a [legacy (MLS1) machine type](/ml-engine/docs/machine-types-online-prediction), `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. If `min_nodes` is not specified and AutoScaling is used with a [Compute Engine (N1) machine type](/ml-engine/docs/machine-types-online-prediction), `min_nodes` defaults to 1. `min_nodes` must be at least 1 for use with a Compute Engine machine type. Note that you cannot use AutoScaling if your version uses [GPUs](#Version.FIELDS.accelerator_config). Instead, you must use ManualScaling. You can set `min_nodes` when creating the model version, and you can also update `min_nodes` for an existing version: update_body.json: { 'autoScaling': { 'minNodes': 5 } } HTTP request: PATCH https://ml.googleapis.com/v1/{name=projects//models//versions/*}?update_mask=autoScaling.minNodes -d @./update_body.json
				594	},
				595	"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.
				596	"createTime": "A String", # Output only. The time the version was created.
				597	"routes": { # RouteMap is used to override HTTP paths sent to a Custom Container. If specified, the HTTP server implemented in the ContainerSpec must support the route. If unspecified, standard HTTP paths will be used.
				598	"predict": "A String", # HTTP path to send prediction requests.
				599	"health": "A String", # HTTP path to send health check requests.
				600	},
				601	"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
				602	"machineType": "A String", # Optional. The type of machine on which to serve the model. Currently only applies to online prediction service. If this field is not specified, it defaults to `mls1-c1-m2`. Online prediction supports the following machine types: * `mls1-c1-m2` * `mls1-c4-m2` * `n1-standard-2` * `n1-standard-4` * `n1-standard-8` * `n1-standard-16` * `n1-standard-32` * `n1-highmem-2` * `n1-highmem-4` * `n1-highmem-8` * `n1-highmem-16` * `n1-highmem-32` * `n1-highcpu-2` * `n1-highcpu-4` * `n1-highcpu-8` * `n1-highcpu-16` * `n1-highcpu-32` `mls1-c1-m2` is generally available. All other machine types are available in beta. Learn more about the [differences between machine types](/ml-engine/docs/machine-types-online-prediction).
				603	"acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Optional. Accelerator config for using GPUs for online prediction (beta). Only specify this field if you have specified a Compute Engine (N1) machine type in the `machineType` field. Learn more about [using GPUs for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	604	"type": "A String", # The type of accelerator to use.
				605	"count": "A String", # The number of accelerators to attach to each machine running the job.
Bu Sun Kim	6502091	2020-05-20 12:08:20 -0700	[diff] [blame]	606	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	607	"etag": "A String", # `etag` is used for optimistic concurrency control as a way to help prevent simultaneous updates of a model from overwriting each other. It is strongly suggested that systems make use of the `etag` in the read-modify-write cycle to perform model updates in order to avoid race conditions: An `etag` is returned in the response to `GetVersion`, and systems are expected to put that etag in the request to `UpdateVersion` to ensure that their change will be applied to the model as intended.
				608	"explanationConfig": { # Message holding configuration options for explaining model predictions. There are three feature attribution methods supported for TensorFlow models: integrated gradients, sampled Shapley, and XRAI. [Learn more about feature attributions.](/ai-platform/prediction/docs/ai-explanations/overview) # Optional. Configures explainability features on the model's version. Some explanation features require additional metadata to be loaded as part of the model payload.
				609	"xraiAttribution": { # Attributes credit by computing the XRAI taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 Currently only implemented for models with natural image inputs. # Attributes credit by computing the XRAI taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 Currently only implemented for models with natural image inputs.
				610	"numIntegralSteps": 42, # Number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range.
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	611	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	612	"integratedGradientsAttribution": { # Attributes credit by computing the Aumann-Shapley value taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365 # Attributes credit by computing the Aumann-Shapley value taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
				613	"numIntegralSteps": 42, # Number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range.
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	614	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	615	"sampledShapleyAttribution": { # An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. # An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features.
				616	"numPaths": 42, # The number of feature permutations to consider when approximating the Shapley values.
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	617	},
Bu Sun Kim	6502091	2020-05-20 12:08:20 -0700	[diff] [blame]	618	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	619	"labels": { # Optional. One or more labels that you can add, to organize your model versions. Each label is a key-value pair, where both the key and the value are arbitrary strings that you supply. For more information, see the documentation on using labels.
				620	"a_key": "A String",
Bu Sun Kim	6502091	2020-05-20 12:08:20 -0700	[diff] [blame]	621	},
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	622	"container": { # Specify a custom container to deploy. Our ContainerSpec is a subset of the Kubernetes Container specification. https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.10/#container-v1-core
				623	"ports": [ # Immutable. List of ports to expose from the container. Exposing a port here gives the system additional information about the network connections a container uses, but is primarily informational. Not specifying a port here DOES NOT prevent that port from being exposed. Any port which is listening on the default "0.0.0.0" address inside a container will be accessible from the network.
				624	{ # ContainerPort represents a network port in a single container.
				625	"containerPort": 42, # Number of port to expose on the pod's IP address. This must be a valid port number, 0 < x < 65536.
				626	},
				627	],
				628	"env": [ # Immutable. List of environment variables to set in the container.
				629	{ # EnvVar represents an environment variable present in a Container.
				630	"name": "A String", # Name of the environment variable. Must be a C_IDENTIFIER.
				631	"value": "A String", # Variable references $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. Defaults to "".
				632	},
				633	],
				634	"command": [ # Immutable. Entrypoint array. Not executed within a shell. The docker image's ENTRYPOINT is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. More info: https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#running-a-command-in-a-shell
				635	"A String",
				636	],
				637	"image": "A String", # Docker image name. More info: https://kubernetes.io/docs/concepts/containers/images
				638	"args": [ # Immutable. Arguments to the entrypoint. The docker image's CMD is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. More info: https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#running-a-command-in-a-shell
				639	"A String",
				640	],
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	641	},
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	642	"lastUseTime": "A String", # Output only. The time the version was last used for prediction.
Bu Sun Kim	d059ad8	2020-07-22 17:02:09 -0700	[diff] [blame]	643	"serviceAccount": "A String", # Optional. Specifies the service account for resource access control.
Dmitry Frenkel	3e17f89	2020-10-06 16:46:05 -0700	[diff] [blame^]	644	"runtimeVersion": "A String", # Required. The AI Platform runtime version to use for this deployment. For more information, see the [runtime version list](/ml-engine/docs/runtime-version-list) and [how to manage runtime versions](/ml-engine/docs/versioning).
				645	"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.
				646	"manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the model. You should generally use `auto_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.
				647	"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.
				648	},
				649	"state": "A String", # Output only. The state of a version.
				650	"packageUris": [ # Optional. Cloud Storage paths (`gs://…`) of packages for [custom prediction routines](/ml-engine/docs/tensorflow/custom-prediction-routines) or [scikit-learn pipelines with custom code](/ml-engine/docs/scikit/exporting-for-prediction#custom-pipeline-code). For a custom prediction routine, one of these packages must contain your Predictor class (see [`predictionClass`](#Version.FIELDS.prediction_class)). Additionally, include any dependencies used by your Predictor or scikit-learn pipeline uses that are not already included in your selected [runtime version](/ml-engine/docs/tensorflow/runtime-version-list). If you specify this field, you must also set [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater.
				651	"A String",
				652	],
				653	"pythonVersion": "A String", # Required. The version of Python used in prediction. The following Python versions are available: * Python '3.7' is available when `runtime_version` is set to '1.15' or later. * Python '3.5' is available when `runtime_version` is set to a version from '1.4' to '1.14'. * Python '2.7' is available when `runtime_version` is set to '1.15' or earlier. Read more about the Python versions available for [each runtime version](/ml-engine/docs/runtime-version-list).
Sai Cheemalapati	c30d2b5	2017-03-13 12:12:03 -0400	[diff] [blame]	654	}</pre>
				655	</div>
				656
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