You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
430 lines
13 KiB
430 lines
13 KiB
<html><body>
|
|
<style>
|
|
|
|
body, h1, h2, h3, div, span, p, pre, a {
|
|
margin: 0;
|
|
padding: 0;
|
|
border: 0;
|
|
font-weight: inherit;
|
|
font-style: inherit;
|
|
font-size: 100%;
|
|
font-family: inherit;
|
|
vertical-align: baseline;
|
|
}
|
|
|
|
body {
|
|
font-size: 13px;
|
|
padding: 1em;
|
|
}
|
|
|
|
h1 {
|
|
font-size: 26px;
|
|
margin-bottom: 1em;
|
|
}
|
|
|
|
h2 {
|
|
font-size: 24px;
|
|
margin-bottom: 1em;
|
|
}
|
|
|
|
h3 {
|
|
font-size: 20px;
|
|
margin-bottom: 1em;
|
|
margin-top: 1em;
|
|
}
|
|
|
|
pre, code {
|
|
line-height: 1.5;
|
|
font-family: Monaco, 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', 'Lucida Console', monospace;
|
|
}
|
|
|
|
pre {
|
|
margin-top: 0.5em;
|
|
}
|
|
|
|
h1, h2, h3, p {
|
|
font-family: Arial, sans serif;
|
|
}
|
|
|
|
h1, h2, h3 {
|
|
border-bottom: solid #CCC 1px;
|
|
}
|
|
|
|
.toc_element {
|
|
margin-top: 0.5em;
|
|
}
|
|
|
|
.firstline {
|
|
margin-left: 2 em;
|
|
}
|
|
|
|
.method {
|
|
margin-top: 1em;
|
|
border: solid 1px #CCC;
|
|
padding: 1em;
|
|
background: #EEE;
|
|
}
|
|
|
|
.details {
|
|
font-weight: bold;
|
|
font-size: 14px;
|
|
}
|
|
|
|
</style>
|
|
|
|
<h1><a href="ml_v1beta1.html">Google Cloud Machine Learning Engine</a> . <a href="ml_v1beta1.projects.html">projects</a></h1>
|
|
<h2>Instance Methods</h2>
|
|
<p class="toc_element">
|
|
<code><a href="ml_v1beta1.projects.jobs.html">jobs()</a></code>
|
|
</p>
|
|
<p class="firstline">Returns the jobs Resource.</p>
|
|
|
|
<p class="toc_element">
|
|
<code><a href="ml_v1beta1.projects.models.html">models()</a></code>
|
|
</p>
|
|
<p class="firstline">Returns the models Resource.</p>
|
|
|
|
<p class="toc_element">
|
|
<code><a href="ml_v1beta1.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, 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, 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, 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, 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.
|
|
"extensions": [ # Application specific response metadata. Must be set in the first response
|
|
# for streaming APIs.
|
|
{
|
|
"a_key": "", # Properties of the object. Contains field @type with type URL.
|
|
},
|
|
],
|
|
},
|
|
}
|
|
|
|
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.
|
|
"extensions": [ # Application specific response metadata. Must be set in the first response
|
|
# for streaming APIs.
|
|
{
|
|
"a_key": "", # Properties of the object. Contains field @type with type URL.
|
|
},
|
|
],
|
|
}</pre>
|
|
</div>
|
|
|
|
</body></html> |