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List your business with Quikr and get enquiries filtered specially for you.Be unique within a given project (for models) or model (for versions). There are no rules for names beyond those technical requirements, but here are some best-practices: Model names should be descriptive and distinctiveyou may need to pick them out of lists of many names in logs or reports.
Version names are best kept short and simple.Ps4 games pkg
Model and version limits The Cloud ML Engine quota policy sets a limit of 100 models per project and limits the total number of versions (combined between all models) to 200. Model deployment parameters Cloud ML Engine needs some information to create your model version. Version name A name for the new version that is unique among the names of other versions of the model.
Description You can provide a description for your version. Deployment URI You must provide the URI of the Cloud Storage location where your SavedModel is stored. Cloud ML Engine pulls the model from this location and deploys it. This parameter is called --origin in the gcloud ml-engine versions create command.
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Runtime version Cloud ML Engine uses the latest stable runtime version to deploy your model version unless you specify a different supported one.
The runtime version primarily determines the version of TensorFlow that the prediction service uses to run your model. When you run a batch prediction job you have the option of overriding the assigned runtime version. Online prediction always uses the runtime version set when the model version is deployed. Manual scaling You can specify the number of training nodes to keep running for your model version.
Warning: When you manually set the number of nodes to keep ready for your version, those nodes are considered to be constantly in use, even when not serving predictions.
This means that you are charged the hourly rate for each node from the moment you create the version until you delete it. You can't change this value without deploying your model to a different version. Staging bucket If you are using the gcloud command-line tool to deploy your model, you can use a SavedModel on your local computer. Graph changes for prediction You may have included TensorFlow Ops in your computation graph that were useful primarily in the context of training.
Getting predictions You can send new data to your deployed model versions to get predictions. Online prediction versus batch prediction Cloud ML Engine provides two ways to get predictions from trained models: online prediction (sometimes called HTTP prediction), and batch prediction. The differences are shown in the following table: Online prediction Batch prediction Optimized to minimize the latency of serving predictions.
Optimized to handle a high volume of instances in a job and to run more complex models. Can process one or more instances per request. Predictions returned in the response message. Predictions written to output files in a Cloud Storage location that you specify. Input data passed directly as a JSON string. Input data passed indirectly as one or more URIs of files in Cloud Storage locations.
Returns as soon as possible. Anyone with Viewer access to the project can request. Must be a project Editor to run. Runs on the runtime version and in the region selected when you deploy the model.
Can run in any available region, using any available runtime version. Though you should run with the defaults for deployed model versions.
Runs models deployed to Cloud ML Engine. Runs models deployed to Cloud ML Engine or models stored in accessible Google Cloud Storage locations. The needs of your application dictate the type of prediction you should use. Batch prediction latency If you use a simple model and a small set of input instances, you'll find that there is a considerable difference between how long it takes to finish identical prediction requests using online versus batch prediction. Understanding prediction nodes and resource allocation Cloud ML Engine measures the amount of processing you consume for prediction in node hours.
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