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For formatting of the request and response body, see the details of the prediction request. In brief: Make each instance an item in a list, and name the list member instances. You provide input data for batch prediction in one or more text files containing rows of JSON instance data as described above. An input file contains no column headers or other formatting beyond the simple JSON syntax. This means that your data is distributed among an arbitrary cluster of virtual machines, and is processed in an unpredictable order.
To be able to match the returned predictions with your input instances, you must have instance keys defined. An instance key is a value that every instance has that is unique among the instances in a set of data. The simplest key is an index number. You should pass the keys through your graph unaltered in your training application.
If your data doesn't already have instance keys, you can add them as part of your data preprocessing. As new versions of Cloud ML Engine are released, it is possible that models developed against older versions will become obsolete.
This is particularly pertinent if you arrive at an effective model version that remains unchanged for a long period. You should review the Cloud ML Engine versioning policy and make sure that you understand the Cloud ML Engine runtime version that you use to train your model versions.
You can specify a supported Cloud ML Engine runtime version when you create a model version. Doing so establishes the model version's default setting. If you don't specify one explicitly, Cloud ML Engine creates your version using the current default runtime version (typically the most recent stable version). You can specify a runtime version to use when you start a batch prediction job. This is to accommodate getting predictions using a model that is not deployed on Cloud ML Engine.
You should never use a different runtime version than the default for a deployed model. Doing so is likely to cause unexpected errors. You cannot request online predictions from models outside of Cloud ML Engine, so there is no option to override the default runtime version in your request.
The default runtime version set for a model version cannot be changed. To specify a different runtime version for a model version, deploy a new version using the same training artifacts that you used initially. Google Cloud Platform uses zones and regions to define the geographic locations of physical computing resources.
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Cloud ML Engine uses regions to designate its processing. When you deploy a model for prediction, you specify the default region that you want prediction to run in. When you start a batch prediction job, you can specify a region to run the job in, overriding the default region.
Online predictions are always served from the region set when the model was created.
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Batch prediction generates job logs that you can view on Stackdriver Logging. You can also get logs for online prediction requests if you configure your model to generate them when you create it. You can set online prediction logging for a model by setting onlinePredictionLogging to true (True in Python) in the Model resource you use when creating your model with projects.
If you use the gcloud command-line tool to create your model, include the --enable-logging flag when you run gcloud ml-engine models create. You can request batch prediction using a model that you haven't deployed to the Cloud ML Engine service. Instead of specifying a model or version name, you can use the URI of a Google Cloud Storage location where the model you want to run is stored.
Because an undeployed model doesn't have an established default runtime version, you should explicitly set it in dffoo schedule 2020 job request.The businesses that do best on Yelp are the ones that provide a great customer experience to everyone who walks in the door without any expectation or encouragement that they write a review.
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