quick fix for SDK instructions (#358)

* Quick fix in SDK documentation

Takes some excerpts from Christian's website PR. 

I will be revisiting after release to figure out the overlap with User guide.

* SDK Packages Overview (#359)

Co-authored-by: Christian Kadner <ckadner@us.ibm.com>

sdk/README.md

@@ -13,7 +13,7 @@ backed by Tekton.
 
 <!-- START of ToC generated by running ./tools/mdtoc.sh sdk/README.md -->
 
-  - [Extensions to the Kubeflow Pipelines SDK](#extensions-to-the-kubeflow-pipelines-sdk)
+  - [SDK Packages Overview](#sdk-packages-overview)
   - [Project Prerequisites](#project-prerequisites)
   - [Installation](#installation)
   - [Compiling a Kubeflow Pipelines DSL Script](#compiling-a-kubeflow-pipelines-dsl-script)
@@ -26,23 +26,31 @@ backed by Tekton.
 <!-- END of ToC generated by running ./tools/mdtoc.sh sdk/README.md -->
 
 
-## Extensions to the Kubeflow Pipelines SDK
+## SDK Packages Overview
 
-In addition to the functionality provided by the Kubeflow Pipelines
-[SDK](https://www.kubeflow.org/docs/pipelines/sdk/sdk-overview/) the `kfp-tekton`
-SDK provides a `TektonCompiler` and a `TektonClient`:
+The `kfp-tekton` SDK is an extension to the [Kubeflow Pipelines SDK](/docs/pipelines/sdk/sdk-overview/)
+adding the `TektonCompiler` and the `TektonClient`:
 
-`TektonCompiler`:
+* `kfp_tekton.compiler` includes classes and methods for compiling pipeline Python DSL into a Tekton PipelineRun YAML spec. The methods in this package
+  include, but are not limited to, the following:
 
- - `kfp_tekton.compiler.TektonCompiler.compile` compiles Python DSL code into a 
-   YAML file containing a Tekton `PipelineRun` which can be deployed directly to
-   a Tekton enabled Kubernetes cluster or uploaded to the Kubeflow Pipelines
-   dashboard with the Tekton backend.
-
-`TektonClient`:
+  * `kfp_tekton.compiler.TektonCompiler.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_tekton.TektonClient.create_run_from_pipeline_func` compiles DSL pipeline
-   function and runs the pipeline on a Kubernetes cluster with KFP and Tekton
+* `kfp_tekton.TektonClient` contains the Python client libraries for the [Kubeflow Pipelines API](/docs/pipelines/reference/api/kubeflow-pipeline-api-spec/).
+  Methods in this package include, but are not limited to, the following:
+
+  * `kfp_tekton.TektonClient.upload_pipeline` uploads a local file to create a new pipeline in Kubeflow Pipelines.
+  * `kfp_tekton.TektonClient.create_experiment` creates a pipeline
+    [experiment](/docs/pipelines/concepts/experiment/) and returns an
+    experiment object.
+  * `kfp_tekton.TektonClient.run_pipeline` runs a pipeline and returns a run object.
+  * `kfp_tekton.TektonClient.create_run_from_pipeline_func` compiles a pipeline
+    function and submits it for execution on Kubeflow Pipelines.
+  * `kfp_tekton.TektonClient.create_run_from_pipeline_package` runs a local 
+    pipeline package on Kubeflow Pipelines.
 
 
 ## Project Prerequisites
@@ -144,8 +152,16 @@ export DEFAULT_STORAGE_SIZE=2Gi
 ## Running the Compiled Pipeline on a Tekton Cluster
 
 After compiling the `sdk/python/tests/compiler/testdata/parallel_join.py` DSL script
-in the step above, we need to deploy the generated Tekton YAML to our Kubernetes
-cluster with `kubectl`. The Tekton server will automatically start a pipeline run.
+in the step above, we need to deploy the generated Tekton YAML to Kubeflow Pipeline engine.
+
+You can run the pipeline directly using a pre-compiled file and KFP-Tekton SDK. For more details, please look at the [KFP-Tekton user guide SDK documentation](https://github.com/kubeflow/kfp-tekton/tree/master/guides/kfp-user-guide#2-run-pipelines-using-the-kfp_tektontektonclient-in-python)
+
+```python
+experiment = kfp_tekton.TektonClient.create_experiment(name=EXPERIMENT_NAME, namespace=KUBEFLOW_PROFILE_NAME)
+run = client.run_pipeline(experiment.id, 'parallal-join-pipeline', 'pipeline.yaml')
+``` 
+
+You can also deploy directly on Tekton cluster with `kubectl`. The Tekton server will automatically start a pipeline run.
 We can then follow the logs using the `tkn` CLI.
 
     kubectl apply -f pipeline.yaml