# Google Cloud Machine Learning . projects

## Instance Methods

Returns the jobs Resource.

Returns the models Resource.

Returns the operations Resource.

Get the service account information associated with your project. You need

Performs prediction on the data in the request.

### Method Details

`getConfig(name=None, x__xgafv=None)`

    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.
      }

`predict(name=None, body, x__xgafv=None)`

    Performs prediction on the data in the request.

    Responses are very similar to requests. There are two top-level fields,
    each of which are JSON lists:

      predictions
      The list of predictions, one per instance in the request.
      error
      An error message returned instead of a prediction list if any
          instance produced an error.

    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.

    Even though there is one prediction per instance, the format of a
    prediction is not directly related to the format of an instance.
    Predictions take whatever format is specified in the outputs collection
    defined in the model. The collection of predictions is returned in a JSON
    list. Each member of the list can be a simple value, a list, or a JSON
    object of any complexity. If your model has more than one output tensor,
    each prediction will be a JSON object containing a name/value pair for each
    output. The names identify the output aliases in the graph.

    The following examples show some possible responses:

    A simple set of predictions for three input instances, where each
    prediction is an integer value:

    {"predictions": [5, 4, 3]}

    A more complex set of predictions, each containing two named values that
    correspond to output tensors, named **label** and **scores** respectively.
    The value of **label** is the predicted category ("car" or "beach") and
    **scores** contains a list of probabilities for that instance across the
    possible categories.

    {"predictions": [{"label": "beach", "scores": [0.1, 0.9]},
                     {"label": "car", "scores": [0.75, 0.25]}]}

    A response when there is an error processing an input instance:

    {"error": "Divide by zero"}

    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:
          #
          #
          #   instances
          #   A JSON array containing values representing the instances to use for
          #       prediction.
          #
          #
          # 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.
          #
          # Most data does not include 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:
          #
          # {"instances": ["1.0,true,\\"x\\"", "-2.0,false,\\"y\\""]}
          #
          # Plain text:
          #
          # {"instances": ["the quick brown fox", "la bruja le dio"]}
          #
          # Sentences encoded as lists of words (vectors of strings):
          #
          # {"instances": [["the","quick","brown"], ["la","bruja","le"]]}
          #
          # Floating point scalar values:
          #
          # {"instances": [0.0, 1.1, 2.2]}
          #
          # Vectors of integers:
          #
          # {"instances": [[0, 1, 2], [3, 4, 5],...]}
          #
          # Tensors (in this case, two-dimensional tensors):
          #
          # {"instances": [[[0, 1, 2], [3, 4, 5]], ...]}
          #
          # Images represented as a three-dimensional list. In this encoding scheme the
          # first two dimensions represent the rows and columns of the image, and the
          # third contains the R, G, and B values for each pixel.
          #
          # {"instances": [[[[138, 30, 66], [130, 20, 56], ...]]]]}
          #
          # Data must be encoded as UTF-8. If your data uses another character encoding,
          # you must base64 encode the data and mark it as binary. To mark a JSON string
          # as binary, replace it with an object with a single attribute named `b`:
          # {"b": "..."}
          # For example:
          #
          # Two Serialized tf.Examples (fake data, for illustrative purposes only):
          #
          # {"instances": [{"b64": "X5ad6u"}, {"b64": "IA9j4nx"}]}
          #
          # Two JPEG image byte strings (fake data, for illustrative purposes only):
          #
          # {"instances": [{"b64": "ASa8asdf"}, {"b64": "JLK7ljk3"}]}
          #
          # 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:
          #
          # {"instances": [{"a": 1.0,  "b": true,  "c": "x"},
          #                {"a": -2.0, "b": false, "c": "y"}]}
          #
          # 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):
          #
          # {"instances": [{"tag": "beach", "image": {"b64": "ASa8asdf"}},
          #                {"tag": "car", "image": {"b64": "JLK7ljk3"}}]}
          #
          # For a graph with input tensor aliases "tag" (string) and "image"
          # (3-dimensional array of 8-bit ints):
          #
          # {"instances": [{"tag": "beach", "image": [[[263, 1, 10], [262, 2, 11], ...]]},
          #                {"tag": "car", "image": [[[10, 11, 24], [23, 10, 15], ...]]}]}
          #
          # 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.
      }