Regen docs (#364)
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+
+<h1><a href="ml_v1.html">Google Cloud Machine Learning Engine</a> . <a href="ml_v1.projects.html">projects</a></h1>
+<h2>Instance Methods</h2>
+<p class="toc_element">
+ <code><a href="ml_v1.projects.jobs.html">jobs()</a></code>
+</p>
+<p class="firstline">Returns the jobs Resource.</p>
+
+<p class="toc_element">
+ <code><a href="ml_v1.projects.models.html">models()</a></code>
+</p>
+<p class="firstline">Returns the models Resource.</p>
+
+<p class="toc_element">
+ <code><a href="ml_v1.projects.operations.html">operations()</a></code>
+</p>
+<p class="firstline">Returns the operations Resource.</p>
+
+<p class="toc_element">
+ <code><a href="#getConfig">getConfig(name=None, x__xgafv=None)</a></code></p>
+<p class="firstline">Get the service account information associated with your project. You need</p>
+<p class="toc_element">
+ <code><a href="#predict">predict(name=None, body, x__xgafv=None)</a></code></p>
+<p class="firstline">Performs prediction on the data in the request.</p>
+<h3>Method Details</h3>
+<div class="method">
+ <code class="details" id="getConfig">getConfig(name=None, x__xgafv=None)</code>
+ <pre>Get the service account information associated with your project. You need
+this information in order to grant the service account persmissions for
+the Google Cloud Storage location where you put your model training code
+for training the model with Google Cloud Machine Learning.
+
+Args:
+ name: string, Required. The project name.
+
+Authorization: requires `Viewer` role on the specified project. (required)
+ x__xgafv: string, V1 error format.
+ Allowed values
+ 1 - v1 error format
+ 2 - v2 error format
+
+Returns:
+ An object of the form:
+
+ { # Returns service account information associated with a project.
+ "serviceAccountProject": "A String", # The project number for `service_account`.
+ "serviceAccount": "A String", # The service account Cloud ML uses to access resources in the project.
+ }</pre>
+</div>
+
+<div class="method">
+ <code class="details" id="predict">predict(name=None, body, x__xgafv=None)</code>
+ <pre>Performs prediction on the data in the request.
+
+**** REMOVE FROM GENERATED DOCUMENTATION
+
+Args:
+ name: string, Required. The resource name of a model or a version.
+
+Authorization: requires `Viewer` role on the parent project. (required)
+ body: object, The request body. (required)
+ The object takes the form of:
+
+{ # Request for predictions to be issued against a trained model.
+ #
+ # The body of the request is a single JSON object with a single top-level
+ # field:
+ #
+ # <dl>
+ # <dt>instances</dt>
+ # <dd>A JSON array containing values representing the instances to use for
+ # prediction.</dd>
+ # </dl>
+ #
+ # The structure of each element of the instances list is determined by your
+ # model's input definition. Instances can include named inputs or can contain
+ # only unlabeled values.
+ #
+ # Not all data includes named inputs. Some instances will be simple
+ # JSON values (boolean, number, or string). However, instances are often lists
+ # of simple values, or complex nested lists. Here are some examples of request
+ # bodies:
+ #
+ # CSV data with each row encoded as a string value:
+ # <pre>
+ # {"instances": ["1.0,true,\\"x\\"", "-2.0,false,\\"y\\""]}
+ # </pre>
+ # Plain text:
+ # <pre>
+ # {"instances": ["the quick brown fox", "la bruja le dio"]}
+ # </pre>
+ # Sentences encoded as lists of words (vectors of strings):
+ # <pre>
+ # {
+ # "instances": [
+ # ["the","quick","brown"],
+ # ["la","bruja","le"],
+ # ...
+ # ]
+ # }
+ # </pre>
+ # Floating point scalar values:
+ # <pre>
+ # {"instances": [0.0, 1.1, 2.2]}
+ # </pre>
+ # Vectors of integers:
+ # <pre>
+ # {
+ # "instances": [
+ # [0, 1, 2],
+ # [3, 4, 5],
+ # ...
+ # ]
+ # }
+ # </pre>
+ # Tensors (in this case, two-dimensional tensors):
+ # <pre>
+ # {
+ # "instances": [
+ # [
+ # [0, 1, 2],
+ # [3, 4, 5]
+ # ],
+ # ...
+ # ]
+ # }
+ # </pre>
+ # Images can be represented different ways. In this encoding scheme the first
+ # two dimensions represent the rows and columns of the image, and the third
+ # contains lists (vectors) of the R, G, and B values for each pixel.
+ # <pre>
+ # {
+ # "instances": [
+ # [
+ # [
+ # [138, 30, 66],
+ # [130, 20, 56],
+ # ...
+ # ],
+ # [
+ # [126, 38, 61],
+ # [122, 24, 57],
+ # ...
+ # ],
+ # ...
+ # ],
+ # ...
+ # ]
+ # }
+ # </pre>
+ # JSON strings must be encoded as UTF-8. To send binary data, you must
+ # base64-encode the data and mark it as binary. To mark a JSON string
+ # as binary, replace it with a JSON object with a single attribute named `b64`:
+ # <pre>{"b64": "..."} </pre>
+ # For example:
+ #
+ # Two Serialized tf.Examples (fake data, for illustrative purposes only):
+ # <pre>
+ # {"instances": [{"b64": "X5ad6u"}, {"b64": "IA9j4nx"}]}
+ # </pre>
+ # Two JPEG image byte strings (fake data, for illustrative purposes only):
+ # <pre>
+ # {"instances": [{"b64": "ASa8asdf"}, {"b64": "JLK7ljk3"}]}
+ # </pre>
+ # If your data includes named references, format each instance as a JSON object
+ # with the named references as the keys:
+ #
+ # JSON input data to be preprocessed:
+ # <pre>
+ # {
+ # "instances": [
+ # {
+ # "a": 1.0,
+ # "b": true,
+ # "c": "x"
+ # },
+ # {
+ # "a": -2.0,
+ # "b": false,
+ # "c": "y"
+ # }
+ # ]
+ # }
+ # </pre>
+ # Some models have an underlying TensorFlow graph that accepts multiple input
+ # tensors. In this case, you should use the names of JSON name/value pairs to
+ # identify the input tensors, as shown in the following exmaples:
+ #
+ # For a graph with input tensor aliases "tag" (string) and "image"
+ # (base64-encoded string):
+ # <pre>
+ # {
+ # "instances": [
+ # {
+ # "tag": "beach",
+ # "image": {"b64": "ASa8asdf"}
+ # },
+ # {
+ # "tag": "car",
+ # "image": {"b64": "JLK7ljk3"}
+ # }
+ # ]
+ # }
+ # </pre>
+ # For a graph with input tensor aliases "tag" (string) and "image"
+ # (3-dimensional array of 8-bit ints):
+ # <pre>
+ # {
+ # "instances": [
+ # {
+ # "tag": "beach",
+ # "image": [
+ # [
+ # [138, 30, 66],
+ # [130, 20, 56],
+ # ...
+ # ],
+ # [
+ # [126, 38, 61],
+ # [122, 24, 57],
+ # ...
+ # ],
+ # ...
+ # ]
+ # },
+ # {
+ # "tag": "car",
+ # "image": [
+ # [
+ # [255, 0, 102],
+ # [255, 0, 97],
+ # ...
+ # ],
+ # [
+ # [254, 1, 101],
+ # [254, 2, 93],
+ # ...
+ # ],
+ # ...
+ # ]
+ # },
+ # ...
+ # ]
+ # }
+ # </pre>
+ # If the call is successful, the response body will contain one prediction
+ # entry per instance in the request body. If prediction fails for any
+ # instance, the response body will contain no predictions and will contian
+ # a single error entry instead.
+ "httpBody": { # Message that represents an arbitrary HTTP body. It should only be used for #
+ # Required. The prediction request body.
+ # payload formats that can't be represented as JSON, such as raw binary or
+ # an HTML page.
+ #
+ #
+ # This message can be used both in streaming and non-streaming API methods in
+ # the request as well as the response.
+ #
+ # It can be used as a top-level request field, which is convenient if one
+ # wants to extract parameters from either the URL or HTTP template into the
+ # request fields and also want access to the raw HTTP body.
+ #
+ # Example:
+ #
+ # message GetResourceRequest {
+ # // A unique request id.
+ # string request_id = 1;
+ #
+ # // The raw HTTP body is bound to this field.
+ # google.api.HttpBody http_body = 2;
+ # }
+ #
+ # service ResourceService {
+ # rpc GetResource(GetResourceRequest) returns (google.api.HttpBody);
+ # rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty);
+ # }
+ #
+ # Example with streaming methods:
+ #
+ # service CaldavService {
+ # rpc GetCalendar(stream google.api.HttpBody)
+ # returns (stream google.api.HttpBody);
+ # rpc UpdateCalendar(stream google.api.HttpBody)
+ # returns (stream google.api.HttpBody);
+ # }
+ #
+ # Use of this type only changes how the request and response bodies are
+ # handled, all other features will continue to work unchanged.
+ "contentType": "A String", # The HTTP Content-Type string representing the content type of the body.
+ "data": "A String", # HTTP body binary data.
+ },
+ }
+
+ x__xgafv: string, V1 error format.
+ Allowed values
+ 1 - v1 error format
+ 2 - v2 error format
+
+Returns:
+ An object of the form:
+
+ { # Message that represents an arbitrary HTTP body. It should only be used for
+ # payload formats that can't be represented as JSON, such as raw binary or
+ # an HTML page.
+ #
+ #
+ # This message can be used both in streaming and non-streaming API methods in
+ # the request as well as the response.
+ #
+ # It can be used as a top-level request field, which is convenient if one
+ # wants to extract parameters from either the URL or HTTP template into the
+ # request fields and also want access to the raw HTTP body.
+ #
+ # Example:
+ #
+ # message GetResourceRequest {
+ # // A unique request id.
+ # string request_id = 1;
+ #
+ # // The raw HTTP body is bound to this field.
+ # google.api.HttpBody http_body = 2;
+ # }
+ #
+ # service ResourceService {
+ # rpc GetResource(GetResourceRequest) returns (google.api.HttpBody);
+ # rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty);
+ # }
+ #
+ # Example with streaming methods:
+ #
+ # service CaldavService {
+ # rpc GetCalendar(stream google.api.HttpBody)
+ # returns (stream google.api.HttpBody);
+ # rpc UpdateCalendar(stream google.api.HttpBody)
+ # returns (stream google.api.HttpBody);
+ # }
+ #
+ # Use of this type only changes how the request and response bodies are
+ # handled, all other features will continue to work unchanged.
+ "contentType": "A String", # The HTTP Content-Type string representing the content type of the body.
+ "data": "A String", # HTTP body binary data.
+ }</pre>
+</div>
+
+</body></html>
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