more fixes

README.md

@@ -29,9 +29,9 @@ A tool that adapts models trained by above algorithms to be inferred by fixed po
 Applications demonstrating usecases of these algorithms.
 
 ### Organization
- - The `edgeml_tf` directory contains the specification of these architectures in TensorFlow,
+ - The `tf` directory contains the `edgeml_tf` package which specifies these architectures in TensorFlow,
    and `examples/tf` contains sample training routines for these algorithms.
- - The `edgeml_pytorch` directory contains the specification of these architectures in PyTorch,
+ - The `pytorch` directory contains the `edgeml_pytorch` package which specifies these architectures in PyTorch,
    and `examples/pytorch` contains sample training routines for these algorithms.
  - The `cpp` directory has training and inference code for Bonsai and ProtoNN algorithms in C++.
  - The `applications` directory has code/demonstrations of applications of the EdgeML algorithms. 

pytorch/README.md

@@ -4,12 +4,12 @@ This directory includes PyTorch implementations of various techniques and
 algorithms developed as part of EdgeML. Currently, the following algorithms are
 available in Tensorflow:
 
-1. [Bonsai](../docs/publications/Bonsai.pdf)
+1. [Bonsai](/docs/publications/Bonsai.pdf)
 2. S-RNN
-3. [FastRNN & FastGRNN](../docs/publications/FastGRNN.pdf)
+3. [FastRNN & FastGRNN](/docs/publications/FastGRNN.pdf)
-4. [ProtoNN](../docs/publications/ProtoNN.pdf)
+4. [ProtoNN](/docs/publications/ProtoNN.pdf)
 
-The PyTorch compute graphs for these algoriths are packaged as `edgeml_pytorch.graph`.
+The PyTorch graphs for these algoriths are packaged as `edgeml_pytorch.graph`.
 Trainers for these algorithms are in `edgeml_pytorch.trainer`. 
 Usage directions and examples for these algorithms are provided in 
 `$EDGEML_ROOT/examples/pytorch` directory. To get started with any 
@@ -20,7 +20,9 @@ of the provided algorithms, please follow the notebooks in the the
 
 
 It is highly recommended that EdgeML be installed in a virtual environment. 
-Please create a new virtual environment using your environment manager ([virtualenv](https://virtualenv.pypa.io/en/stable/userguide/#usage) or [Anaconda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-with-commands)).
+Please create a new virtual environment using your environment manager
+ ([virtualenv](https://virtualenv.pypa.io/en/stable/userguide/#usage) or
+  [Anaconda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-with-commands)).
 Make sure the new environment is active before running the below mentioned commands.
 
 Use pip to install requirements before installing the `edgeml_pytorch` library.
@@ -29,7 +31,7 @@ Details for cpu based installation and gpu based installation provided below.
 ### CPU
 
 ``` 
-pip install -r requirements-cpu-pytorch.txt
+pip install -r requirements-cpu.txt
 pip install -e .
 ```
 
@@ -40,7 +42,7 @@ Tested on Python3.6 with >= PyTorch 1.1.0.
 Install appropriate CUDA and cuDNN [Tested with >= CUDA 8.1 and cuDNN >= 6.1]
 
 ```
-pip install -r requirements-gpu-pytorch.txt
+pip install -r requirements-gpu.txt
 pip install -e .
 ```
 

pytorch/setup.py

@@ -2,11 +2,11 @@ import setuptools #enables develop
 
 setuptools.setup(
     name='edgeml',
-    version='0.2.2',
+    version='0.3.0',
     description='PyTorch code for ML algorithms for edge devices developed at Microsoft Research India.',
     author_email="edgeml@microsoft.com",
-    packages=['edgeml_pytorch']
+    packages=['edgeml_pytorch'],
     license='MIT License',
-    long_description=open('edgeml_pytorch/README.md').read(),
+    long_description=open('README.md').read(),
     url='https://github.com/Microsoft/EdgeML',
 )

tf/README.md

@@ -1,13 +1,13 @@
 ## Edge Machine Learning: Tensorflow Library 
 
-This directory includes, Tensorflow implementations of various techniques and
+This directory includes Tensorflow implementations of various techniques and
 algorithms developed as part of EdgeML. Currently, the following algorithms are
 available in Tensorflow:
 
-1. [Bonsai](../docs/publications/Bonsai.pdf)
+1. [Bonsai](/docs/publications/Bonsai.pdf)
-2. [EMI-RNN](../docs/publications/emi-rnn-nips18.pdf)
+2. [EMI-RNN](/docs/publications/emi-rnn-nips18.pdf)
-3. [FastRNN & FastGRNN](../docs/publications/FastGRNN.pdf)
+3. [FastRNN & FastGRNN](/docs/publications/FastGRNN.pdf)
-4. [ProtoNN](../docs/publications/ProtoNN.pdf)
+4. [ProtoNN](/docs/publications/ProtoNN.pdf)
 
 The TensorFlow compute graphs for these algoriths are packaged as
 `edgeml_tf.graph`. Trainers for these algorithms are in `edgeml_tf.trainer`.
@@ -19,7 +19,9 @@ the notebooks in the `examples/tf` directory.
 ## Installation
 
 It is highly recommended that EdgeML be installed in a virtual environment. 
-Please create a new virtual environment using your environment manager ([virtualenv](https://virtualenv.pypa.io/en/stable/userguide/#usage) or [Anaconda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-with-commands)).
+Please create a new virtual environment using your environment manager
+ ([virtualenv](https://virtualenv.pypa.io/en/stable/userguide/#usage) or 
+ [Anaconda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-with-commands)).
 Make sure the new environment is active before running the below mentioned commands.
 
 Use pip to install the requirements before installing the `edgeml_tf` library. 
@@ -28,7 +30,7 @@ Details for cpu based installation and gpu based installation provided below.
 ### CPU
 
 ``` 
-pip install -r requirements-cpu-tf.txt
+pip install -r requirements-cpu.txt
 pip install -e .
 ```
 
@@ -39,7 +41,7 @@ Tested on Python3.5 and python 2.7 with >= Tensorflow 1.6.0.
 Install appropriate CUDA and cuDNN [Tested with >= CUDA 8.1 and cuDNN >= 6.1]
 
 ```
-pip install -r requirements-gpu-tf.txt
+pip install -r requirements-gpu.txt
 pip install -e .
 ```