README.md in thundersvm-0.1.0 vs README.md in thundersvm-0.1.1

@@ -1,16 +1,18 @@
 # ThunderSVM
 
-[ThunderSVM](https://github.com/Xtra-Computing/thundersvm) - high-performance parallel SVMs - for Ruby
+[ThunderSVM](https://github.com/Xtra-Computing/thundersvm) - high performance parallel SVMs - for Ruby
 
 :fire: Uses GPUs and multi-core CPUs for blazing performance
 
 For a great intro on support vector machines, check out [this video](https://www.youtube.com/watch?v=efR1C6CvhmE).
 
+[![Build Status](https://travis-ci.org/ankane/thundersvm.svg?branch=master)](https://travis-ci.org/ankane/thundersvm)
+
 ## Installation
 
-First, [install ThunderSVM](https://github.com/Xtra-Computing/thundersvm/blob/master/docs/how-to.md#install-thundersvm). Add this line to your application’s Gemfile:
+Add this line to your application’s Gemfile:
 
 ```ruby
 gem 'thundersvm'
 ```
 
@@ -74,23 +76,23 @@
 
 Defaults shown below
 
 ```ruby
 ThunderSVM::Model.new(
-  svm_type: :c_svc,    # set type of SVM (c_svc, nu_svc, one_class, epsilon_svr, nu_svr)
-  kernel: :rbf,        # set type of kernel function (linear, polynomial, rbf, sigmoid)
-  degree: 3,           # set degree in kernel function
-  gamma: nil,          # set gamma in kernel function
-  coef0: 0,            # set coef0 in kernel function
-  c: 1,                # set the parameter C of C-SVC, epsilon-SVR, and nu-SVR
-  nu: 0.5,             # set the parameter nu of nu-SVC, one-class SVM, and nu-SVR
-  epsilon: 0.1,        # set the epsilon in loss function of epsilon-SVR
+  svm_type: :c_svc,    # type of SVM (c_svc, nu_svc, one_class, epsilon_svr, nu_svr)
+  kernel: :rbf,        # type of kernel function (linear, polynomial, rbf, sigmoid)
+  degree: 3,           # degree in kernel function
+  gamma: nil,          # gamma in kernel function
+  coef0: 0,            # coef0 in kernel function
+  c: 1,                # parameter C of C-SVC, epsilon-SVR, and nu-SVR
+  nu: 0.5,             # parameter nu of nu-SVC, one-class SVM, and nu-SVR
+  epsilon: 0.1,        # epsilon in loss function of epsilon-SVR
   max_memory: 8192,    # constrain the maximum memory size (MB) that thundersvm uses
-  tolerance: 0.001,    # set tolerance of termination criterion
+  tolerance: 0.001,    # tolerance of termination criterion
   probability: false,  # whether to train a SVC or SVR model for probability estimates
   gpu: 0,              # specify which gpu to use
-  cores: nil,          # set the number of cpu cores to use (defaults to all)
+  cores: nil,          # number of cpu cores to use (defaults to all)
   verbose: false       # verbose mode
 )
 ```
 
 ## Data
@@ -110,9 +112,38 @@
 Or the path a file in `libsvm` format (better for sparse data)
 
 ```ruby
 model.fit("train.txt")
 model.predict("test.txt")
+```
+
+## GPUs
+
+To run ThunderSVM on GPUs, you’ll need to build the library from source.
+
+### Linux
+
+```sh
+git clone --recursive --branch v0.3.4 https://github.com/Xtra-Computing/thundersvm
+cd thundersvm
+mkdir build
+cd build
+cmake ..
+make
+```
+
+Specify the path to the shared library with:
+
+```ruby
+ThunderSVM.ffi_lib = "path/to/build/lib/libthundersvm.so"
+```
+
+### Windows
+
+Follow the [official instructions](https://thundersvm.readthedocs.io/en/latest/get-started.html#installation-for-windows). Specify the path to the shared library with:
+
+```ruby
+ThunderSVM.ffi_lib = "path/to/build/lib/libthundersvm.dll"
 ```
 
 ## Resources
 
 - [ThunderSVM: A Fast SVM Library on GPUs and CPUs](https://github.com/Xtra-Computing/thundersvm/blob/master/thundersvm-full.pdf)