Point omitted

content/en/docs/components/katib/experiment.md

@@ -75,48 +75,18 @@ These are the fields in the experiment configuration spec:
   Refer to the
   [`ObjectiveSpec` type](https://github.com/kubeflow/katib/blob/master/pkg/apis/controller/common/v1beta1/common_types.go#L93).
 
-* **algorithm**: The search algorithm that you want Katib to use to find the
-  best hyperparameters or neural architecture configuration. Examples include
-  random search, grid search, Bayesian optimization, and more.
-  See the [search algorithm details](#search-algorithms) below.
-
-* **trialTemplate**: The template that defines the trial.
-  You must package your ML training code into a Docker image, as described
-  [above](#docker-image). You must configure the model's
-  hyperparameters either as command-line arguments or as environment variables,
-  so that Katib can automatically set the values in each trial.
-
-  You can use one of the following job types to train your model:
-
-  - [Kubernetes Job](https://kubernetes.io/docs/concepts/workloads/controllers/jobs-run-to-completion/)
-    (does not support distributed execution).
-  - [Kubeflow TFJob](/docs/guides/components/tftraining/) (supports
-    distributed execution).
-  - [Kubeflow PyTorchJob](/docs/guides/components/pytorch/) (supports
-    distributed execution).
-
-  See the [`TrialTemplate`
-  type](https://github.com/kubeflow/katib/blob/master/pkg/apis/controller/experiments/v1alpha3/experiment_types.go#L189-L203).
-  The template
-  uses the [Go template format](https://golang.org/pkg/text/template/).
-
-  You can define the job in raw string format or you can use a
-  [ConfigMap](https://kubernetes.io/docs/tasks/configure-pod-container/configure-pod-configmap/).
-  [Here](https://github.com/kubeflow/katib/blob/master/manifests/v1alpha3/katib-controller/trialTemplateConfigmapLabeled.yaml) is an example how to create ConfigMap with trial templates.
-
-* **parallelTrialCount**: The maximum number of hyperparameter sets that Katib
+- **parallelTrialCount**: The maximum number of hyperparameter sets that Katib
   should train in parallel. The default value is 3.
 
 - **maxTrialCount**: The maximum number of trials to run.
   This is equivalent to the number of hyperparameter sets that Katib should
-  generate to test the model. If the `maxTrialCount` value is omitted, your
+  generate to test the model. If the `maxTrialCount` value is **omitted**, your
   experiment is running until the objective goal is reached or the experiment
   reaches a maximum number of failed trials.
 
 - **maxFailedTrialCount**: The maximum number of failed trials before Katib
-  should stop the experiment.
-  This is equivalent to the number of failed hyperparameter sets that Katib
-  should test.
+  should stop the experiment. This is equivalent to the number of failed
+  hyperparameter sets that Katib should test.
   If the number of failed trials exceeds `maxFailedTrialCount`, Katib stops the
   experiment with a status of `Failed`.