Struct PredictRequest

pub struct PredictRequest {
    pub name: String,
    pub http_body: Option<HttpBody>,
}

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.

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:

{"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 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.

{
  "instances": [
    [
      [
        [138, 30, 66],
        [130, 20, 56],
        ...
      ],
      [
        [126, 38, 61],
        [122, 24, 57],
        ...
      ],
      ...
    ],
    ...
  ]
}

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:

{"b64": "..."} 

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": [
        [
          [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],
          ...
        ],
        ...
      ]
    },
    ...
  ]
}

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.

Fields

name: String

Required. The resource name of a model or a version.

Authorization: requires Viewer role on the parent project.

http_body: Option<HttpBody>

Required. The prediction request body.

Trait Implementations

impl Clone for PredictRequest

fn clone(&self) -> PredictRequest

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for PredictRequest

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for PredictRequest

fn default() -> PredictRequest

Returns the “default value” for a type.

impl Message for PredictRequest

fn encoded_len(&self) -> usize

Returns the encoded length of the message without a length delimiter.

fn clear(&mut self)

Clears the message, resetting all fields to their default.

fn encode<B>(&self, buf: &mut B) -> Result<(), EncodeError>

where B: BufMut, Self: Sized,

Encodes the message to a buffer.

fn encode_length_delimited<B>(&self, buf: &mut B) -> Result<(), EncodeError>

where B: BufMut, Self: Sized,

Encodes the message with a length-delimiter to a buffer.

fn decode<B>(buf: B) -> Result<Self, DecodeError>

where B: Buf, Self: Default,

Decodes an instance of the message from a buffer.

fn decode_length_delimited<B>(buf: B) -> Result<Self, DecodeError>

where B: Buf, Self: Default,

Decodes a length-delimited instance of the message from the buffer.

fn merge<B>(&mut self, buf: B) -> Result<(), DecodeError>

where B: Buf, Self: Sized,

Decodes an instance of the message from a buffer, and merges it into self.

fn merge_length_delimited<B>(&mut self, buf: B) -> Result<(), DecodeError>

where B: Buf, Self: Sized,

Decodes a length-delimited instance of the message from buffer, and merges it into self.

impl PartialEq for PredictRequest

fn eq(&self, other: &PredictRequest) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for PredictRequest