I build the report using latexmk, that will put all auxillary files in aux/. Using mklatex could work as well.
- Python
https://futhark.readthedocs.io/en/latest/man/futhark-pyopencl.html
https://futhark-lang.org/blog/2018-07-05-python-gotta-go-faster.html
https://futhark-lang.org/blog/2016-04-15-futhark-and-pyopencl.html
- Find out how to call futhark from python (use pyopencl)
- Call futhark from woody
- Export a fitted tree to a format we can use as input in futhark
- Visualise Futhark input data
- Create artificial tree generator
- Convert woody data into futhark data
- GPU-Nodes
- Get woody running on the nodes
- Compile treesolver/math example into "something python"
- Generate 'very big' trees
- Run futhark within Woody
- Futhark
- Flattening -> Figure out what to flatten
- Write basic version
- Write flattened version
- Write pruned version
- Write double-flattened version
First clone and open the Woody submodule:
git submodule init
git submodule update
cd woody
git checkout master
git pull
Create a virtual environment and install Woody's requirements:
mkdir .venv
cd .venv
virtualenv woody
source woody/bin/activate
cd ..
pip install -r requirements.txt
Download Swig from http://www.swig.org/download.html (using wget), install it in your home directory by running in the directory of the extracted tar file:
wget http://prdownloads.sourceforge.net/swig/swig-3.0.12.tar.gz
tar -xzf swig-3.0.12.tar.gz
cd swig-3.0.12
./configure --prefix={install location} --without-pcre
make
make install
export PATH=$PATH:{install location}/bin
Install h2o:
pip install h2o
Install pyopencl:
pip install -U pybind11
pip install -U pyopencl
Return to the Woody directory and run:
python setup.py clean
python setup.py develop
To check Woody is successfully installed run:
cd experiments/small_data
python launch.py
To build a futhark-opencl library:
cd futhark_opencl_example
futhark-pyopencl --library futmath.fut
python math_example.py
models/forest/base.py, line 277: Call predict_all_extern
predict_all_extern is in models/fores/src/tree/base.c
Then cpu_query_forest_all_preds is called.
cpu_query_forest_all_preds is in models/forest/src/tree/cpu/base.c
models/forest/src/tree/include/types.h
We have to make a python function that takes as input a tree and then splits it into 5 arrays.
We want to represent a tree as 5 arrays, containing the data needed to reconstruct each node.
A tree node is:
- left_id, unsigned int
- right_id, unsigned int
- feature, unsigned int
- thres_or_leaf, FLOAT_TYPE
- leaf_criterion, unsigned int
- A tree is a TREE_NODE *root, int n_allocated and int node_counter.
- A forest is an array of trees, and second value is number of trees (n_trees)