Update README.md

felker · web-flow · commit 417048cf15af · 2020-08-27T11:41:24.000-05:00
diff --git a/README.md b/README.md
@@ -9,29 +9,28 @@ The Fusion Recurrent Neural Net (FRNN) software is a Python package that impleme
 
 It consists of 4 core modules:
 
-- `models`: Python classes necessary to construct, train and optimize deep RNN models. Including a distributed data-parallel synchronous implementation of mini-batch gradient descent. FRNN makes use of MPI for communication and supports Tensorflow and Theano backends through Keras. FRNN allows running hyperparameter search optimizations
+- `models`: Python classes necessary to construct, train and optimize deep RNN models. Including a distributed data-parallel synchronous implementation of mini-batch gradient descent. FRNN makes use of MPI for communication and supports TensorFlow via the high-level Keras API. FRNN offers the built-in ability to run hyperparameter search optimizations.
 
 - `preprocessors`: signal preprocessing and normalization classes, including the methods necessary to prepare physical data for stateful LSTM training.
 
-- `primitives`: contains abstractions specific to the domain, implemented as Python classes. For instance: Shot - a measurement of plasma current as a function of time. The Shot object contains attributes corresponding to unique identifier of a shot, disruption time in milliseconds, time profile of the shot converted to time-to- disruption values, validity of a shot (whether plasma current reaches a certain value during the shot), etc. Other primitives include `Machines` and `Signals` which carry the relevant information necessary for incorporating physics data into the overall pipeline. Signals know the Machine they live on, their mds+ paths, code for being downloaded, preprocessing approaches, their dimensionality, etc. Machines know which Signals are defined on them, which mds+ server houses the data, etc.
+- `primitives`: contains abstractions specific to the domain, implemented as Python classes. For instance, `Shot`: a measurement of plasma current as a function of time. The Shot object contains attributes corresponding to unique identifier of a shot, disruption time in milliseconds, time profile of the shot converted to time-to-disruption values, validity of a shot (whether plasma current reaches a certain value during the shot), etc. Other primitives include `Machines` and `Signals` which carry the relevant information necessary for incorporating physics data into the overall pipeline. Signals know the Machine they live on, their mds+ paths, code for being downloaded, preprocessing approaches, their dimensionality, etc. Machines know which Signals are defined on them, which mds+ server houses the data, etc.
 
 - `utilities`: a set of auxiliary functions for preprocessing, performance evaluation and learning curves analysis.
 
 In addition to the `utilities` FRNN supports TensorBoard scaler variable summaries, histogramms of layers, activations and gradients and graph visualizations.
 
-This is a pure Python implementation for Python versions 2.7 and 3.6.
+This is a pure Python implementation for Python versions 3.6+.
 
 ## Installation
 
-The package comes with a standard setup script and a list of dependencies which include: mpi4py, TensorFlow, Theano,
-Keras, h5py, Pathos. It also requires a standard set of CUDA drivers to run on GPU.
+The package comes with a standard setup script and a list of dependencies which include: mpi4py, TensorFlow, h5py, Pathos. It also requires a standard set of CUDA drivers to run on GPU.
 
 Then checkout the repo and use the setup script:
 
 ```bash
 git clone https://github.com/PPPLDeepLearning/plasma-python
 cd plasma-python
-python setup.py install
+pip install -e .
 ```
 
 with `sudo` if superuser permissions are needed or `--home=~` to install in a home directory. The latter option requires an appropriate `PYTHONPATH`.
