From 4b91d79915f4c006ff4f015992f8acd8ab4c94bc Mon Sep 17 00:00:00 2001 From: Robert Nishihara Date: Sun, 10 Jul 2016 15:06:44 -0700 Subject: [PATCH] fixes to documentation (#242) --- doc/install-on-macosx.md | 15 +++++++++------ doc/install-on-ubuntu.md | 13 +++++++------ doc/install-on-windows.md | 2 ++ examples/lbfgs/README.md | 4 +++- install-dependencies.sh | 2 +- 5 files changed, 22 insertions(+), 14 deletions(-) diff --git a/doc/install-on-macosx.md b/doc/install-on-macosx.md index 7d8209e2f..186f557e1 100644 --- a/doc/install-on-macosx.md +++ b/doc/install-on-macosx.md @@ -10,25 +10,28 @@ git clone https://github.com/amplab/ray.git ### Dependencies -First install the dependencies using brew. +First install the dependencies using brew. We currently do not support Python 3. +If you have trouble installing the Python packages, you may find it easier to +install [Anaconda](https://www.continuum.io/downloads). ``` brew update brew install git cmake automake autoconf libtool boost libjpeg graphviz sudo easy_install pip sudo pip install ipython --user -sudo pip install numpy typing funcsigs subprocess32 protobuf==3.0.0-alpha-2 boto3 botocore Pillow colorama graphviz --ignore-installed six +sudo pip install numpy typing funcsigs subprocess32 protobuf==3.0.0a2 boto3 botocore Pillow colorama graphviz --ignore-installed six ``` ### Build -Then run the setup scripts. +Then run the setup scripts. Note that `./setup.sh` could take about 10 minutes +to run. ``` cd ray -./setup.sh # This builds all necessary third party libraries (e.g., gRPC and Apache Arrow). -./build.sh # This builds Ray. -source setup-env.sh # This adds Ray to your Python path. +./setup.sh # Build all necessary third party libraries (e.g., gRPC and Apache Arrow). +./build.sh # Build Ray. +source setup-env.sh # Add Ray to your Python path. ``` For convenience, you may also want to add the line `source diff --git a/doc/install-on-ubuntu.md b/doc/install-on-ubuntu.md index 1fb93ea7b..ab0d37791 100644 --- a/doc/install-on-ubuntu.md +++ b/doc/install-on-ubuntu.md @@ -10,23 +10,24 @@ git clone https://github.com/amplab/ray.git ### Dependencies -First install the dependencies. +First install the dependencies. We currently do not support Python 3. ``` sudo apt-get update sudo apt-get install -y git cmake build-essential autoconf curl libtool python-dev python-numpy python-pip libboost-all-dev unzip libjpeg8-dev graphviz -sudo pip install ipython typing funcsigs subprocess32 protobuf==3.0.0-alpha-2 boto3 botocore Pillow colorama graphviz +sudo pip install ipython typing funcsigs subprocess32 protobuf==3.0.0a2 boto3 botocore Pillow colorama graphviz ``` ### Build -Then run the setup scripts. +Then run the setup scripts. Note that `./setup.sh` could take about 10 minutes +to run. ``` cd ray -./setup.sh # This builds all necessary third party libraries (e.g., gRPC and Apache Arrow). -./build.sh # This builds Ray. -source setup-env.sh # This adds Ray to your Python path. +./setup.sh # Build all necessary third party libraries (e.g., gRPC and Apache Arrow). +./build.sh # Build Ray. +source setup-env.sh # Add Ray to your Python path. ``` For convenience, you may also want to add the line `source diff --git a/doc/install-on-windows.md b/doc/install-on-windows.md index 58e82452f..a60403378 100644 --- a/doc/install-on-windows.md +++ b/doc/install-on-windows.md @@ -3,6 +3,8 @@ Ray currently does not run on Windows. However, it can be compiled with the following instructions. +We currently do not support Python 3. + **Note:** A batch file is provided that clones any missing third-party libraries and applies patches to them. Do not attempt to open the solution before the batch file applies the patches; otherwise, if the projects have been modified, diff --git a/examples/lbfgs/README.md b/examples/lbfgs/README.md index 0e30f20cb..677b35eea 100644 --- a/examples/lbfgs/README.md +++ b/examples/lbfgs/README.md @@ -17,7 +17,9 @@ Optimization is at the heart of many machine learning algorithms. Much of machine learning involves specifying a loss function and finding the parameters that minimize the loss. If we can compute the gradient of the loss function, then we can apply a variety of gradient-based optimization algorithms. L-BFGS is -one such algorithm. +one such algorithm. It is a quasi-Newton method that uses gradient information +to approximate the inverse Hessian of the loss function in a computationally +efficient manner. ### The serial version diff --git a/install-dependencies.sh b/install-dependencies.sh index 40d8afd96..6da1807f6 100755 --- a/install-dependencies.sh +++ b/install-dependencies.sh @@ -31,7 +31,7 @@ if [[ $platform == "linux" ]]; then # These commands must be kept in sync with the installation instructions. sudo apt-get update sudo apt-get install -y git cmake build-essential autoconf curl libtool python-dev python-numpy python-pip libboost-all-dev unzip libjpeg8-dev graphviz - sudo pip install ipython typing funcsigs subprocess32 protobuf==3.0.0-alpha-2 boto3 botocore Pillow colorama graphviz + sudo pip install ipython typing funcsigs subprocess32 protobuf==3.0.0a2 boto3 botocore Pillow colorama graphviz elif [[ $platform == "macosx" ]]; then # These commands must be kept in sync with the installation instructions. brew install git cmake automake autoconf libtool boost libjpeg graphviz