Remove trailing whitespace and newlines in all files. (aws#232)

This is necessary to prevent a massive merge conflict in my next PR.

Author: jarednielsen

NOTICE

@@ -1,2 +1,2 @@
 Tornasole_core
-Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved. 
+Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.

README.md

@@ -1,32 +1,32 @@
 ## Tornasole
 
-Tornasole is an upcoming AWS service designed to be a debugger 
-for machine learning models. It lets you go beyond just looking 
-at scalars like losses and accuracies during training and 
-gives you full visibility into all tensors 'flowing through the graph' 
+Tornasole is an upcoming AWS service designed to be a debugger
+for machine learning models. It lets you go beyond just looking
+at scalars like losses and accuracies during training and
+gives you full visibility into all tensors 'flowing through the graph'
 during training or inference.
 
 Using Tornasole is a two step process:
 
 ### Saving tensors
 
-This needs the `tornasole` package built for the appropriate framework. 
-It allows you to collect the tensors you want at the frequency 
-that you want, and save them for analysis. 
+This needs the `tornasole` package built for the appropriate framework.
+It allows you to collect the tensors you want at the frequency
+that you want, and save them for analysis.
 Please follow the appropriate Readme page to install the correct version.
- 
+
 
 #### [Tornasole TensorFlow](docs/tensorflow/README.md)
 #### [Tornasole MXNet](docs/mxnet/README.md)
 #### [Tornasole PyTorch](docs/pytorch/README.md)
 #### [Tornasole XGBoost](docs/xgboost/README.md)
 
 ### Analysis
-Please refer **[this page](docs/rules/README.md)** for more details about how to analyze. 
-The analysis of these tensors can be done on a separate machine in parallel with the training job. 
+Please refer **[this page](docs/rules/README.md)** for more details about how to analyze.
+The analysis of these tensors can be done on a separate machine in parallel with the training job.
 
 ## ContactUs
 We would like to hear from you. If you have any question or feedback, please reach out to us [email protected]
 
 ## License
-This library is licensed under the Apache 2.0 License. 
+This library is licensed under the Apache 2.0 License.

bin/build_binaries.py

@@ -47,4 +47,4 @@
             # remove other versions
             subprocess.check_call(['aws', 's3', 'rm', '--recursive', '--exclude', 'tornasole-{}*'.format(VERSION),
                                    's3://tornasole-binaries-use1/tornasole_{}/py3/latest/'.format(b)])
-    subprocess.check_call(['rm', '-rf', 'dist', 'build', '*.egg-info', '.eggs'])
+    subprocess.check_call(['rm', '-rf', 'dist', 'build', '*.egg-info', '.eggs'])

bin/sagemaker-containers/mxnet/1.4.1/build.sh

@@ -37,4 +37,4 @@ build() {
 build cpu &
 build gpu &
 
-wait
+wait

bin/sagemaker-containers/permissions.json

@@ -24,4 +24,4 @@
       ]
     }
   ]
-}
+}

bin/sagemaker-containers/pytorch/1.1.0/build.sh

@@ -46,4 +46,4 @@ build() {
 
 build cpu &
 build gpu &
-wait
+wait

bin/sagemaker-containers/tensorflow/tf-train.py

@@ -12,7 +12,7 @@
 parser.add_argument('--tornasole_frequency', type=float, help="How often to save TS data", default=10 )
 parser.add_argument('--run_name', type=str, help="Run Name", default=str(uuid.uuid4()) )
 parser.add_argument('--local_reductions', nargs='+', type=str, default=[] )
-# running in Tf estimator mode, script need to accept --model_dir parameter 
+# running in Tf estimator mode, script need to accept --model_dir parameter
 parser.add_argument('--model_dir', type=str, help="model dir", default=str(uuid.uuid4()) )
 args = parser.parse_args()
 # Network definition
@@ -31,7 +31,7 @@
 graph = tf.get_default_graph()
 list_of_tuples = [op.outputs for op in graph.get_operations()]
 t = str(time.time())
-hook = TornasoleHook("s3://tornasolecodebuildtest/container_testing/ts_outputs/tf"+t, 
+hook = TornasoleHook("s3://tornasolecodebuildtest/container_testing/ts_outputs/tf"+t,
 		     save_config=SaveConfig(save_interval=10))
 sess = tf.train.MonitoredSession(hooks=[hook])
 for i in range(args.steps):

config/buildspec.yml

@@ -20,7 +20,7 @@ phases:
 
   pre_build:
     commands:
-    
+
   build:
     commands:
       - cd $CODEBUILD_SRC_DIR  && python setup.py bdist_wheel --universal && pip install dist/*.whl && cd ..

config/protoc_downloader.sh

@@ -5,4 +5,4 @@ PROTOC_ZIP=protoc-3.7.1-linux-x86_64.zip
 curl -OL https://github.com/google/protobuf/releases/download/v3.7.1/$PROTOC_ZIP
 unzip -o $PROTOC_ZIP -d /usr/local bin/protoc
 unzip -o $PROTOC_ZIP -d /usr/local include/*
-rm -f $PROTOC_ZIP
+rm -f $PROTOC_ZIP

config/upload_on_end.sh

@@ -28,4 +28,3 @@ else
     upload_dirs integration_tests_logs pytest_reports wheels
     echo "INFO BUILD SUCCEEDED !!! , ACCESS BUILD LOGS THROUGH GITHUB OR TROUGH THE LINK PR:$GITHUB_PR_URL . CODEBUILD:$CODEBUILD_BUILD_URL. Test logs are on S3 here:$S3_TEST_REPORT_URL"
 fi
-

docs/mxnet/README.md

@@ -49,7 +49,7 @@ pip install tornasole_mxnet/*
 **Please note** : If, while installing tornasole, you get a version conflict issue between botocore and boto3,
 you might need to run the following
 ```
-pip uninstall -y botocore boto3 aioboto3 aiobotocore && pip install botocore==1.12.91 boto3==1.9.91 aiobotocore==0.10.2 aioboto3==6.4.1   
+pip uninstall -y botocore boto3 aioboto3 aiobotocore && pip install botocore==1.12.91 boto3==1.9.91 aiobotocore==0.10.2 aioboto3==6.4.1
 ```
 
 ## Quickstart
@@ -271,7 +271,7 @@ hook = TornasoleHook(out_dir=out_dir, include_collections=['weights', 'bias','gr
                                                     'default', 'ReluActivation', 'flatten'])
 ```
 
-Refer [API](api.md) for a list of the reductions available as well as examples.  
+Refer [API](api.md) for a list of the reductions available as well as examples.
 
 
 ### How to save tensors

docs/mxnet/api.md

@@ -5,71 +5,71 @@ Tornasole MXNet provides the following constructs:
 TornasoleHook is the entry point for Tornasole into your program.
 
 ```
-    
+
 	class TornasoleHook
     """
-    A class used to represent the hook which gets attached to the 
-    training process. 
-    
-    
-    
+    A class used to represent the hook which gets attached to the
+    training process.
+
+
+
     Attributes
     ----------
     out_dir : str
         represents a path to which the outputs of tornasole will be written to.
         This can be a local path or an S3 prefix of the form s3://bucket_name/prefix.
-        Note that for Sagemaker, you always need to specify the out_dir as `/opt/ml/output/tensors`. 
-        In the future, we will make this the default in Sagemaker environments. 
-        
+        Note that for Sagemaker, you always need to specify the out_dir as `/opt/ml/output/tensors`.
+        In the future, we will make this the default in Sagemaker environments.
+
     dry_run : bool
         when dry_run is set to True, behavior is only described in the log file.
-        The tensors are not actually saved. 
-        
+        The tensors are not actually saved.
+
     worker: str
         name of worker in a multi process training job
         outputs and tensors are organized by this name during retrieval.
-        
+
     save_config: SaveConfig object or a dictionary from mode to SaveConfig objects
-        SaveConfig allows you to customize when tensors are saved. 
-        Hook takes SaveConfig object which is applied as 
+        SaveConfig allows you to customize when tensors are saved.
+        Hook takes SaveConfig object which is applied as
         default for all included tensors.
-        A collection can optionally have its own SaveConfig object 
+        A collection can optionally have its own SaveConfig object
         which overrides this for its tensors.
         If you pass a dictionary from mode->SaveConfig, then that
         SaveConfig is applied to tensors included for that mode.
-        example: {modes.TRAIN: SaveConfig(save_interval=10), 
+        example: {modes.TRAIN: SaveConfig(save_interval=10),
                   modes.EVAL:SaveConfig(save_interval=1)}
         Refer to documentation for SaveConfig.
-    
+
     reduction_config: ReductionConfig object
-        ReductionConfig allows you to save tensors as their reductions 
-        instead of saving full tensors. 
+        ReductionConfig allows you to save tensors as their reductions
+        instead of saving full tensors.
         If ReductionConfig is passed then the chosen reductions are applied
         as default for all tensors included.
         A collection can optionally have its own ReductionConfig object
-        which overrides this for its tensors. 
-       
+        which overrides this for its tensors.
+
     include_regex: list of str
         takes as input the list of string representing regular expressions. Tensors whose names match
         these regular expressions will be saved. These tensors will be available as part of the `default`
         collection.
-            
+
     include_collections: list of str
         takes as input the names of collections which should be saved.
         by default, ['weights','gradients', 'bias', 'default'] are passed to include_collections.
-    
+
     save_all: bool
         a shortcut for saving all tensors in the model.
         tensors are all grouped into the `default` collection
-    
+
     def __init__(self,
         out_dir,
         dry_run=False,
         worker='worker0',
         reduction_config=None,
         save_config=SaveConfig(save_interval=100),
         include_regex=None,
-        include_collections=['weights', 'gradients', 'bias', 'default'], 
+        include_collections=['weights', 'gradients', 'bias', 'default'],
         save_all=False,
         ):
 ```
@@ -96,10 +96,10 @@ The _reduction\_config_ is optional. If not specified, the reductions are not ap
 
 ### Collection
 
-Collection object helps group tensors for easier handling of tensors being saved. 
-A collection has its own list of tensors, include/exclude regex patterns, reduction config and save config. 
-This allows setting of different save and reduction configs for different tensors. 
-These collections are then also available during analysis with `tornasole_rules`. 
+Collection object helps group tensors for easier handling of tensors being saved.
+A collection has its own list of tensors, include/exclude regex patterns, reduction config and save config.
+This allows setting of different save and reduction configs for different tensors.
+These collections are then also available during analysis with `tornasole_rules`.
 
 #### Creating or accessing a collection
 
@@ -131,75 +131,75 @@ The following methods can be called on a collection object.
 | ```coll.set_reduction_config()```  | Sets reduction config for the collection  |
 
 ### SaveConfig
-SaveConfig class allows you to customize the frequency of saving tensors. 
-The hook takes a SaveConfig object which is applied as 
-default to all tensors included. 
-A collection can also have its own SaveConfig object which is applied 
+SaveConfig class allows you to customize the frequency of saving tensors.
+The hook takes a SaveConfig object which is applied as
+default to all tensors included.
+A collection can also have its own SaveConfig object which is applied
 to the tensors belonging to that collection.
 
-SaveConfig also allows you to save tensors when certain tensors become nan. 
+SaveConfig also allows you to save tensors when certain tensors become nan.
 This list of tensors to watch for is taken as a list of strings representing names of tensors.
 
 ```
-    
+
     class SaveConfig:
-    
+
     Attributes
     ----------
-    
+
     save_interval: int
-    allows you to save every n steps by passing n to save_interval  
-    
+    allows you to save every n steps by passing n to save_interval
+
     skip_num_steps: int
     allows you to avoid saving for the first n steps of the job.
     it defaults to 0, i.e. don't skip any steps in the beginning.
-    
+
     save_steps: list of int
     save at all the steps given in this list.
     if this is given, it ignores the save_interval.
-    
+
     when_nan: list of str representing name of tensor
     saves the tensors to which this saveConfig is attached
     whenever any of the tensors in this list become nan or infinite.
     This means that if your save_interval is set to 10, and 'loss' is in when_nan
     your tensors will be saved whenever save_interval is multiple of 10 as well as
     whenever loss becomes nan or infinite.
-```  
+```
 
 The default value of _save\_interval_ is 100. The TornasoleHook that uses a default SaveConfig object will store the tensors every 100th step.
 
 
-### ReductionConfig 
+### ReductionConfig
 ReductionConfig allows the saving of certain reductions of tensors instead
 of saving the full tensor. The motivation here is to reduce the amount of data
 saved, and increase the speed in cases where you don't need the full
 tensor.  The reduction operations which are computed in the training process
-and then saved. 
-During analysis, these are available as reductions of the original tensor. 
+and then saved.
+During analysis, these are available as reductions of the original tensor.
 Please note that using reduction config means that you will not have
 the full tensor available during analysis, so this can restrict what you can do with the tensor saved.
-The hook takes a ReductionConfig object which is applied as default to all tensors included. 
-A collection can also have its own ReductionConfig object which is applied 
+The hook takes a ReductionConfig object which is applied as default to all tensors included.
+A collection can also have its own ReductionConfig object which is applied
 to the tensors belonging to that collection.
 
 ```
-    
+
     Attributes
     ----------
-    
+
     reductions: list of str
       takes list of names of reductions to be computed.
       should be one of 'min', 'max', 'median', 'mean', 'std', 'sum', 'prod'
-    
+
     abs_reductions: list of str
       takes list of names of reductions to be computed after converting the tensor
       to abs(tensor) i.e. reductions are applied on the absolute values of tensor.
       should be one of 'min', 'max', 'median', 'mean', 'std', 'sum', 'prod'
-    
+
     norms: list of str
       takes names of norms to be computed of the tensor.
       should be one of 'l1', 'l2'
-    
+
     abs_norms: list of str
         takes names of norms to be computed of the tensor after taking absolute value
         should be one of 'l1', 'l2'

docs/pytorch/README.md

@@ -49,7 +49,7 @@ pip install tornasole_pytorch/*
 **Please note** : If, while installing tornasole, you get a version conflict issue between botocore and boto3,
 you might need to run the following
 ```
-pip uninstall -y botocore boto3 aioboto3 aiobotocore && pip install botocore==1.12.91 boto3==1.9.91 aiobotocore==0.10.2 aioboto3==6.4.1   
+pip uninstall -y botocore boto3 aioboto3 aiobotocore && pip install botocore==1.12.91 boto3==1.9.91 aiobotocore==0.10.2 aioboto3==6.4.1
 ```
 
 ## Quickstart
@@ -268,7 +268,7 @@ These reduction config instances can be passed to the hook as follows
 import tornasole.pytorch as ts
 hook = ts.TornasoleHook(..., reduction_config=ts.ReductionConfig(norms=['l1']), ...)
 ```
-Refer [API](api.md) for a full list of the reductions available.   
+Refer [API](api.md) for a full list of the reductions available.
 
 
 ### How to save tensors