Update Pipeline SDK overview page

content/en/docs/pipelines/sdk/sdk-overview.md

@@ -4,10 +4,6 @@ description = "Overview of using the SDK to build components and pipelines"
 weight = 10
 
 +++
-{{% alert title="Out of date" color="warning" %}}
-This guide contains outdated information pertaining to Kubeflow 1.0. This guide
-needs to be updated for Kubeflow 1.1.
-{{% /alert %}}
 
 {{% beta-status 
   feedbacklink="https://github.com/kubeflow/pipelines/issues" %}}
@@ -24,26 +20,15 @@ workflow and how the components interact with each other.
 The Kubeflow Pipelines SDK includes the following packages:
 
 * [`kfp.compiler`](https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.compiler.html)
-  includes classes and methods for building Docker container images for your
-  pipeline components. Methods in this package include, but are not limited
+  includes classes and methods for compiling pipeline Python DSL into a workflow yaml spec
+    Methods in this package include, but are not limited
   to, the following:
 
   * `kfp.compiler.Compiler.compile` compiles your Python DSL code into a single 
     static configuration (in YAML format) that the Kubeflow Pipelines service
     can process. The Kubeflow Pipelines service converts the static 
     configuration into a set of Kubernetes resources for execution.
 
-  * `kfp.compiler.build_docker_image` builds a container image based on a 
-    Dockerfile and pushes the image to a URI. In the parameters, you provide the 
-    path to a Dockerfile containing the image specification, and the URI for the 
-    target image (for example, a container registry).
-
-  * `kfp.compiler.build_python_component` builds a container image for a
-    pipeline component based on a Python function, and pushes the image to a 
-    URI. In the parameters, you provide the Python function that does the work 
-    of the pipeline component, a Docker image to use as a base image, 
-    and the URI for the target image (for example, a container registry).
-
 * [`kfp.components`](https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html)
   includes classes and methods for interacting with pipeline components. 
   Methods in this package include, but are not limited to, the following:
@@ -75,8 +60,6 @@ The Kubeflow Pipelines SDK includes the following packages:
   Methods, classes, and modules in this package include, but are not limited to, 
   the following:
 
-  * `kfp.dsl.ContainerOp` represents a pipeline task (op) implemented by a 
-    container image.
   * `kfp.dsl.PipelineParam` represents a pipeline parameter that you can pass
     from one pipeline component to another. See the guide to 
     [pipeline parameters](/docs/pipelines/sdk/parameters/).
@@ -107,6 +90,18 @@ The Kubeflow Pipelines SDK includes the following packages:
     represents a volume used to pass data between pipeline steps. `ContainerOp`s 
     can mount a `PipelineVolume` either via the constructor's argument 
     `pvolumes` or `add_pvolumes()` method.
+  * [`kfp.dsl.ParallelFor`](https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.dsl.html#kfp.dsl.ParallelFor)
+    represents a parallel for loop over a static or dynamic set of items in a pipeline.
+    Each iteration of the for loop is executed in parallel.
+
+  * [`kfp.dsl.ExitHandler`](https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.dsl.html#kfp.dsl.ExitHandler)
+    represents an exit handler that is invoked upon exiting a pipeline. A typical
+    usage of `ExitHandler` is garbage collection.
+
+  * [`kfp.dsl.Condition`](https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.dsl.html#kfp.dsl.Condition)
+    represents a group of ops, that will only be executed when a certain condition is met.
+    The condition specified need to be determined at runtime, by incorporating at least one task output, 
+    or PipelineParam in the boolean expression.
 
 * [`kfp.Client`](https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.client.html)
   contains the Python client libraries for the [Kubeflow Pipelines 
@@ -117,23 +112,25 @@ The Kubeflow Pipelines SDK includes the following packages:
     [experiment](/docs/pipelines/concepts/experiment/) and returns an
     experiment object.
   * `kfp.Client.run_pipeline` runs a pipeline and returns a run object.
-  * `kfp.Client.pipeline_uploads.upload_pipeline_version` uploads a local file to create a pipeline version. [Follow an example to learn more about creating a pipeline version](/docs/pipelines/tutorials/sdk-examples)
-
-* [`kfp.notebook`](https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.notebook.html)
+  * `kfp.Client.create_run_from_pipeline_func` compiles a pipeline function and submits it
+    for execution on Kubeflow Pipelines.
+  * `kfp.Client.create_run_from_pipeline_package` runs a local pipeline package on Kubeflow Pipelines.
+  * `kfp.Client.upload_pipeline` uploads a local file to create a new pipeline in Kubeflow Pipelines.
+  * `kfp.Client.upload_pipeline_version` uploads a local file to create a pipeline version. [Follow an example to learn more about creating a pipeline version](/docs/pipelines/tutorials/sdk-examples)
 
-* [KFP extension modules](https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.extensions.html)
+* [Kubeflow Pipelines extension modules](https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.extensions.html)
   include classes and functions for specific platforms on which you can use
   Kubeflow Pipelines. Examples include utility functions for on premises,
   Google Cloud Platform (GCP), Amazon Web Services (AWS), and Microsoft Azure.
 
-* [KFP diagnose_me modules](https://github.com/kubeflow/pipelines/tree/master/sdk/python/kfp/cli/diagnose_me)include classes and functions that help with environment diagnostic tasks. 
+* [Kubeflow Pipelines diagnose_me modules](https://github.com/kubeflow/pipelines/tree/master/sdk/python/kfp/cli/diagnose_me)include classes and functions that help with environment diagnostic tasks. 
 
   * `kfp.cli.diagnose_me.dev_env` reports on diagnostic metadata from your development environment, such as your python library version.
   * `kfp.cli.diagnose_me.kubernetes_cluster` reports on diagnostic data from your Kubernetes cluster, such as Kubernetes secrets.
   * `kfp.cli.diagnose_me.gcp` reports on diagnostic data related to your GCP environment.
 
-## KFP CLI tool 
-The KFP CLI tool enables you to use a subset of the Kubeflow Pipelines SDK directly from the command line. The KFP CLI tool provides the following commands:
+## Kubeflow Pipelines CLI tool 
+The Kubeflow Pipelines CLI tool enables you to use a subset of the Kubeflow Pipelines SDK directly from the command line. The Kubeflow Pipelines CLI tool provides the following commands:
 
 * `kfp diagnose_me` runs environment diagnostic with specified parameters.
   * `--json` - Indicates that this command must return its results as JSON. Otherwise, results are returned in human readable format.
@@ -150,7 +147,7 @@ The KFP CLI tool enables you to use a subset of the Kubeflow Pipelines SDK direc
   * `list` - Lists recent pipeline runs.
   * `submit` - Submits a pipeline run.
 
-* `kfp --endpoint <ENDPOINT>` - Specifies the endpoint that the KFP CLI should connect to.
+* `kfp --endpoint <ENDPOINT>` - Specifies the endpoint that the Kubeflow Pipelines CLI should connect to.
 
 ## Installing the SDK