Minor edits

README.rst

@@ -1,31 +1,34 @@
 mlpipeline
 ==========
-This is a simple frawork to organize you machine learning workflow. It automates most of the basic functionalities such as logging, a framework for testing models and gluing together different steps at different stages. This project came about as a result of me abstracting the boilerplate code and automating different parts of the process.
+This is a simple framework to organize you machine learning workflow. It automates most of the basic functionalities such as logging, a framework for testing models and gluing together different steps at different stages. This project came about as a result of me abstracting the boilerplate code and automating different parts of the process.
 
-The aim of this simple framework is to consolidate the different sub-problems (such as loading data, model configurations, training process, evalutaion process, exporting trained models, etc.) when working/researching with machine learning models. This allows the user to define how the different sub-problems are to be solved using their choice of tools and mlpipeline would handle piecing them together.
+The aim of this simple framework is to consolidate the different sub-problems (such as loading data, model configurations, training process, evaluation process, exporting trained models, etc.) when working/researching with machine learning models. This allows the user to define how the different sub-problems are to be solved using their choice of tools and mlpipeline would handle piecing them together.
 
 Core operations
 ---------------
 This framework chains the different operations (sub-problems) depending on the mode it is executed in. mlpipeline currently has 3 modes:
-- TEST mode: When in TEST mode, it doesn't perform any logging or tracking. It creates a temporory empty directory for the experiment to store the artifacts of an experiment in. When developing and testing the different operations, this mode can be used.
-- RUN mode: In this mode, logging and tracking is performed. In addition, for each experiment run (refered to as a experiment version in mlpipeline) a directory is created for artifacts to be stored.
-- EXPORT mode: In this mode, the exporting related operations will be executed instead of the training/evaluation related operations.
 
-In addition to providing different modes, the pipeline also supports logging and recording various details. Currently mlpipeline records all logs, metrics and artifacts using a bacis log files as well using `mlflow <https://github.com/databricks/mlflow>`_.
+* TEST mode: When in TEST mode, it doesn't perform any logging or tracking. It creates a temporary empty directory for the experiment to store the artifacts of an experiment in. When developing and testing the different operations, this mode can be used.
+* RUN mode: In this mode, logging and tracking is performed. In addition, for each experiment run (referred to as a experiment version in mlpipeline) a directory is created for artifacts to be stored.
+* EXPORT mode: In this mode, the exporting related operations will be executed instead of the training/evaluation related operations.
+
+In addition to providing different modes, the pipeline also supports logging and recording various details. Currently mlpipeline records all logs, metrics and artifacts using a basic log files as well using `mlflow <https://github.com/databricks/mlflow>`_.
 
 The following information is recorded:
-- The scripts that were executed/impoerted in relation to an experiment.
-- The any output results
-- The metrics and parameters
+
+* The scripts that were executed/imported in relation to an experiment.
+* The any output results
+* The metrics and parameters
 
 Documentation
 -------------
 The documentation is hosted at `ReadTheDocs <https://mlpipeline.readthedocs.io/>`_.
 
 Installing
 ----------
-Can be installed directly using the Python Package Index using pip:
-    pip install mlpipeline
+Can be installed directly using the Python Package Index using pip::
+  
+  pip install mlpipeline
 
 Usage
 -----

examples/sample-project/experiments/sample_load_experiment.py

@@ -0,0 +1,9 @@
+from mlpipeline import get_experiment
+
+
+def main():
+    print(get_experiment('sample_experiment.py', '', 'version5'))
+
+
+if __name__ == '__main__':
+    main()

examples/sample-project/sample_pipeline_execution.py

@@ -40,14 +40,9 @@ def train_pipeline_with_blacklist():
 
 
 def load_experiment():
-    exp, exp_dir, tracking_uri, run_id = get_experiment("experiments/sample_experiment.py",
-                                                        "experiments",
-                                                        "version5")
-    import os
-    import mlflow
-    mlflow.start_run(run_id=run_id)
-    print(os.listdir(exp_dir), run_id, mlflow.get_artifact_uri(), mlflow.get_tracking_uri())
-    print(mlflow.get_artifact_uri('sample_experiment.py'))
+    print(get_experiment("experiments/sample_experiment.py",
+                         "experiments",
+                         "version5"))
 
 
 if __name__ == "__main__":