Update PipelineParameter

Author: tracychms

articles/machine-learning/how-to-use-parallel-run-step.md

@@ -305,7 +305,7 @@ batch_env.docker.base_image = DEFAULT_GPU_IMAGE
 - `run_invocation_timeout`: The `run()` method invocation timeout in seconds. (optional; default value is `60`)
 - `run_max_try`: Max call count for `run()` method against a mini-batch. A `run()` is failed if an exception is thrown, or nothing is returned when `run_invocation_timeout` is reached (optional; default value is `3`). 
 
-You can also use `PipelineParameter` for parameters in `ParallelRunConfig` so that when you resubmit a pipeline run, you can pass in different values. In this example, you use PipelineParameter for `mini_batch_size` and `Process_count_per_node` and you will tune these values when resubmit a run later. 
+You can specify `mini_batch_size`, `node_count`, `process_count_per_node`, `logging_level`, `run_invocation_timeout` and `run_max_try` as `PipelineParameter`, so that when you resubmit a pipeline run, you can fine tune the parameter values. In this example, you use PipelineParameter for `mini_batch_size` and `Process_count_per_node` and you will change these values when resubmit a run later. 
 
 ```python
 from azureml.pipeline.core import PipelineParameter