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Copy file name to clipboardExpand all lines: examples/sklearn/iris-classifier/README.md
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@@ -124,7 +124,7 @@ Create a `cortex.yaml` file and add the configuration below. An `api` provides a
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```bash
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$ cortex deploy
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creating iris-classifier api
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creating iris-classifier
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```
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Track the status of your api using `cortex get`:
@@ -185,7 +185,7 @@ Run `cortex deploy` again to perform a rolling update to your API with the new c
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```bash
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$ cortex deploy
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updating iris-classifier api
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updating iris-classifier
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```
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After making more predictions, your `cortex get` command will show information about your API's past predictions:
@@ -228,7 +228,7 @@ You could also configure GPU compute here if your cluster supports it. Adding co
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```bash
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$ cortex deploy
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updating iris-classifier api
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updating iris-classifier
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```
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Run `cortex get` again:
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```bash
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$ cortex deploy
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iris-classifier api is up to date
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creating another-iris-classifier api
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iris-classifier is up to date
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creating another-iris-classifier
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```
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`cortex deploy` is declarative so the `iris-classifier` API is unchanged while `another-iris-classifier` is created:
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```bash
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$ cortex deploy
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creating batch-iris-classifier api
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creating batch-iris-classifier
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```
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`cortex get` should show all 3 APIs now:
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```bash
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$ cortex delete iris-classifier
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deleting iris-classifier api
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deleting iris-classifier
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$ cortex delete another-iris-classifier
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deleting another-iris-classifier api
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deleting another-iris-classifier
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$ cortex delete batch-iris-classifier
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deleting batch-iris-classifier api
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deleting batch-iris-classifier
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```
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Running `cortex delete` will free up cluster resources and allow Cortex to scale down to the minimum number of instances you specified during cluster installation. It will not spin down your cluster.
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