Acrolinx

Author: v-regandowner

articles/machine-learning/v1/how-to-debug-pipelines.md

@@ -128,7 +128,7 @@ file_path = os.path.join(script_dir, "<file_name>")
 - `run_invocation_timeout`: The `run()` method invocation timeout in seconds. (optional; default value is `60`)
 - `run_max_try`: Maximum try count of `run()` for 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 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. 
+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 you resubmit a run later. 
 
 ### Parameters for creating the ParallelRunStep
 
@@ -355,4 +355,4 @@ model_download_step = PythonScriptStep(
 
 * See the SDK reference for help with the [azureml-pipelines-core](/python/api/azureml-pipeline-core/) package and the [azureml-pipelines-steps](/python/api/azureml-pipeline-steps/) package.
 
-* See the list of [designer exceptions and error codes](../algorithm-module-reference/designer-error-codes.md).
+* See the list of [designer exceptions and error codes](../algorithm-module-reference/designer-error-codes.md).