add evaluation records

doc/howto.md

@@ -1,21 +1,21 @@
-# How Tos
+# How-Tos
 
 ## Concepts
 
 - `baseline`, one of the best KPI records in the history, the baseline should keep being updated.
-- `Factor`, alias for KPI to make code more readable.
+- `Factor`, an alias for KPI to make the code more readable.
 
 ## Philosophy
 
-`MacroCE` is a highly customizable framework which 
+`MacroCE` is a highly customizable framework which
 
 - triggers the execution of every evaluation and compare the KPIs with baseline,
 - helps to maintain the latest baseline,
 - displays the evaluation status or human-readable reports with a web page, and give an alert by sending emails if the evaluation of some version fails.
-  
+
 It doesn't
 
-- care about the details about the evalution, in other words, one can embed any logics into a evaluation.
+- care about the details about the evaluation, in other words, one can embed any logics into an evaluation.
 
 
 ## Adding new KPIs
@@ -28,17 +28,17 @@ It doesn't
 
 The baseline repo is at [baseline repo](https://github.com/Superjomn/paddle-modelci-baseline).
 
-Let's take the existing evaluation [resnet30](https://github.com/Superjomn/paddle-modelci-baseline/tree/master/resnet30) 
+Let's take the existing evaluation [resnet30](https://github.com/Superjomn/paddle-modelci-baseline/tree/master/resnet30)
 for example, the files required by `MacroCE` are
 
 - `train.xsh`, it defines how to run this evaluation and generate the KPI records,
 - `continuous_evaluation.py`, it tells `MacroCE` the KPI Factors this evaluation uses.
 - `history/`, this is a directory and includes the baseline records.
 
-The `train.xsh` is just a script and nothing to do with MacroCE, so one can embed any logics in it, 
+The `train.xsh` is just a script and nothing to do with MacroCE so that one can embed any logics in it,
 for example, any logics including data preparation, or even request a remote service.
 
-The `continuous_evaluation.py` of `resnet30` looks like this
+The `continuous_evaluation.py` of `resnet30` looks like this.
 
 ```python
 import os
@@ -56,30 +56,30 @@ tracking_factors = [
 ```
 
 it creates two instances of KPI Factors `train_cost_factor` and `train_duration_factor`,
-both are defined in `MacroCE/core.py`. 
+both are defined in `MacroCE/core.py`.
 The list `tracking_factors` is required by `MacroCE` which tells the KPIs this evaluation want to evaluate.
 
 Into the details of `train.xsh`, the `resnet30/model.py` defines some details of running the model (MacroCE
 do not care those logics, but the `train.xsh` need to add records to the KPI Factors in some way).
 
-Some code snippets related to adding KPI records are as follows
+Some code snippets related to adding KPI records are as follows.
 
 ```python
 # import KPI Factor instances
 from continuous_evaluation import (train_cost_factor,
                                    train_duration_factor,
                                    tracking_factors)
 # ...
-                                   
+
 for batch_id, data in enumerate(train_reader()):
     # ... train the model
 
     # add factor record
     train_cost_factor.add_record(np.array(avg_cost_, dtype='float32'))
     train_duration_factor.add_record(batch_end - batch_start)
-
 
-# when the execution ends, persist all the factors to to files.
+
+# when the execution ends, persist all the factors to files.
 for factor in tracking_factors:
     factor.persist()
 ```