h2o4gpu
H2Oai GPU Edition
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- Jenkinsfile.template
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- FindNVML.cmake
- Utils.cmake
- build.sh
- conda_build_config.yaml
- meta.yaml
- elastic_net.cpp
- elastic_net_ptr_driver.cpp
- examples.h
- kmeans_driver.cpp
- reader.h
- run_all.cpp
- run_many.sh
- simple.txt
- test_utilities.h
- testall.sh
- timer.h
- 05.09-digits-pca-components.png
- 05.09-digits-pixel-components.png
- H2O4GPU_Daal_LinearRegression.ipynb
- H2O4GPU_GBM.ipynb
- H2O4GPU_GLM.ipynb
- H2O4GPU_KMeans_Homesite.ipynb
- H2O4GPU_KMeans_Images.ipynb
- H2O4GPU_KMeans_Quantization.ipynb
- H2O4GPU_Lasso.ipynb
- H2O4GPU_LinearRegression.ipynb
- H2O4GPU_PCA.ipynb
- H2O4GPU_Ridge.ipynb
- H2O4GPU_TruncatedSVD.ipynb
- Multi-GPU-H2O-GLM-simple.ipynb
- Multi-GPU-H2O-GLM.ipynb
- sourced.png
- netflix.py
- parse_netflix.py
- GLM.ipynb
- mapd_to_pygdf_to_h2oaiglm.ipynb
- mapd_to_pygdf_to_h2oaiglm_animonly.ipynb
- cls_angular.png
- cls_euclidean.png
- h2o-logo.jpg
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- logo.png
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- sourced.png
- H2OGPUML_VS_H2O-3_full.ipynb
- H2OGPUML_VS_H2O-3_new.ipynb
- ipums_analysis.ipynb
- sklearn_enet_ipums.ipynb
- H2OGPUGLM.ipynb
- xgboost_simple_demo.ipynb
- yi_jing_01_chien.jpg
- __init__.py
- test.py
- __init__.py
- conf.py
- demo.rst
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- benchmarks.pdf
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- H2O4GPU_Datasheet.pdf
- install_daal.sh
- apply_sklearn.sh
- apply_sklearn_initmerge.sh
- apply_sklearn_link.sh
- apply_sklearn_pipinstall.sh
- checkcommithistory.sh
- convert_ipynb2py.sh
- data_prep.R
- gitrename.sh
- gotags.sh
- install_r.sh
- install_r_deps.sh
- make-docker-devel.sh
- make-docker-runtests-multi-gpu.sh
- make-docker-runtests-single-gpu.sh
- make-docker-runtime.sh
- make_jenkinsfiles.sh
- prepare_sklearn.sh
- remove-all-dockers.sh
- run.sh
- test_r_pkg.sh
- elastic_net_ptr.cpp
- elastic_net_ptr.h
- logger.cpp
- logger.h
- utils.cpp
- utils.h
- cblas.h
- gsl_blas.h
- gsl_linalg.h
- gsl_matrix.h
- gsl_rand.h
- gsl_spblas.h
- gsl_spmat.h
- gsl_vector.h
- cgls.h
- equil_helper.h
- projector_helper.h
- matrix_dense.cpp
- matrix_sparse.cpp
- metrics.cpp
- projector_cgls.cpp
- projector_direct_dense.cpp
- h2o4gpuglm.cpp
- h2o4gpukmeans.cpp
- h2o4gpukmeans_kmeanscpu.h
- arima.cu
- arima.h
- matrix.cu
- matrix.cuh
- device_context.cuh
- als.h
- device_utilities.cu
- device_utilities.h
- factorization.cu
- utils.h
- cblas.h
- cml_blas.cuh
- cml_defs.cuh
- cml_linalg.cuh
- cml_matrix.cuh
- cml_rand.cuh
- cml_spblas.cuh
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- cml_utils.cuh
- cml_vector.cuh
- cgls.cuh
- cub
- cuda_utils.h
- cuda_utils2.h
- equil_helper.cuh
- projector_helper.cuh
- test_utilities.h
- utils.cuh
- kmeans_centroids.h
- kmeans_general.h
- kmeans_h2o4gpu.cu
- kmeans_h2o4gpu.h
- kmeans_impl.h
- kmeans_labels.cu
- kmeans_labels.h
- matrix_dense.cu
- matrix_sparse.cu
- utilities.cu
- utlities.cuh
- pca.cu
- projector_cgls.cu
- projector_direct_dense.cu
- tsvd.cu
- bwcheck.cu
- h2o4gpuglm.cu
- p2pbwcheck.cu
- utils.cu
- warmstart.cu
- matrix.h
- matrix_dense.h
- matrix_sparse.h
- metrics.h
- projector.h
- projector_cgls.h
- projector_direct.h
- arima.h
- factorization.h
- glm.h
- kmeans.h
- pca.h
- tsvd.h
- exception.h
- helper_cuda.h
- helper_cuda_drvapi.h
- helper_cuda_gl.h
- helper_cusolver.h
- helper_functions.h
- helper_gl.h
- helper_image.h
- helper_math.h
- helper_string.h
- helper_timer.h
- interface_defs.h
- prox_lib.h
- timer.h
- util.h
- h2o4gpu_c.cpp
- h2o4gpu_c.h
- h2o4gpu_c_api.h
- style.css
- conf.py
- h2o4gpu.rst
- h2o4gpu.solvers.rst
- h2o4gpu.util.rst
- index.rst
- intro.rst
- make.bat
- Makefile
- __init__.py
- lib_utils.py
- __init__.py
- IInput.py
- __init__.py
- __init__.py
- helper_module.py
- __init__.py
- regression.py
- svd.py
- __init__.py
- arima.py
- elastic_net.py
- factorization.py
- kmeans.py
- lasso.py
- linear_regression.py
- logistic.py
- pca.py
- pogs.py
- pogs.README.md
- ridge.py
- truncated_svd.py
- utils.py
- xgboost.py
- __init__.py
- compatibility.py
- typechecks.py
- __init__.py
- gpu.py
- import_data.py
- lightgbm_dynamic.py
- metrics.py
- testing_utils.py
- xgboost_migration.py
- __init__.base.py
- h2o4gpu_exceptions.py
- types.py
- run-pylint.sh
- .gitignore
- __about__.py
- Makefile
- requirements_buildonly.txt
- requirements_runtime.txt
- requirements_runtime_demos_multi_gpu.txt
- requirements_runtime_demos_single_gpu.txt
- requirements_test.txt
- setup.cfg
- setup.py
- getting_started.html
- index.html
- index.html
- fit.h2o4gpu_model.html
- generics.html
- h2o4gpu.elastic_net_classifier.html
- h2o4gpu.elastic_net_regressor.html
- h2o4gpu.gradient_boosting_classifier.html
- h2o4gpu.gradient_boosting_regressor.html
- h2o4gpu.html
- h2o4gpu.kmeans.html
- h2o4gpu.pca.html
- h2o4gpu.random_forest_classifier.html
- h2o4gpu.random_forest_regressor.html
- h2o4gpu.truncated_svd.html
- index.html
- predict.h2o4gpu_model.html
- reexports.html
- transform.h2o4gpu_model.html
- authors.html
- docsearch.css
- extra.css
- extra.js
- index.html
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- link.svg
- pkgdown.css
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- pkgdown.yml
- fit.h2o4gpu_model.Rd
- generics.Rd
- h2o4gpu.elastic_net_classifier.Rd
- h2o4gpu.elastic_net_regressor.Rd
- h2o4gpu.gradient_boosting_classifier.Rd
- h2o4gpu.gradient_boosting_regressor.Rd
- h2o4gpu.kmeans.Rd
- h2o4gpu.pca.Rd
- h2o4gpu.random_forest_classifier.Rd
- h2o4gpu.random_forest_regressor.Rd
- h2o4gpu.Rd
- h2o4gpu.truncated_svd.Rd
- predict.h2o4gpu_model.Rd
- reexports.Rd
- transform.h2o4gpu_model.Rd
- _pkgdown.yml
- extra.css
- extra.js
- auto_generated_wrappers.R
- generics.R
- imports.R
- model.R
- package.R
- reexports.R
- type_resolvers.R
- gen_wrapper_utils.R
- gen_wrappers.R
- helper-utils.R
- test_auto_generated_wrappers.R
- test_model.R
- test_xgboost.R
- testthat.R
- getting_started.Rmd
- .Rbuildignore
- cran-comments.md
- DESCRIPTION
- interface_r.Rproj
- NAMESPACE
- NEWS.md
- README.md
- matrix_dense.i
- arima.i
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- factorization.i
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- tsvd.i
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- metrics.i
- numpy.i
- config.mk
- test_arima.cu
- test_least_squares_solver.cu
- test_als.cu
- test_kmeans_centroids.cu
- test_kmeans_h2o4gpu.cu
- test_kmeans_labels.cu
- test_main.cu
- getresultsbig.sh
- test_glm_hyatt.py
- test_glm_ipums.py
- model_saved.pkl
- test_gpu_prediction_pickledmodel.py
- test_lightgbm.py
- test_xgboost.py
- test_xgboost_dtinput.py
- test_import.py
- test_metrics.py
- getresults.sh
- showresults.sh
- test-LinearModels.ipynb
- test_glm_hyatt.py
- test_glm_ipums.py
- test_glm_paribas.py
- conversion.py
- football.py
- loaders.py
- metrics.py
- notebook_memory_management.py
- planet_kaggle.py
- timer.py
- utils.py
- 01_airline_GPU.py
- 03_football_GPU.py
- 04_PlanetKaggle_GPU.py
- 05_FraudDetection_GPU.py
- 06_HIGGS_GPU.py
- extractjson.py
- extracttestxgboost.sh
- runtestxgboost.sh
- googletest
- license-grant.png
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- LICENSE.NVIDIA
- LICENSE.SCIKIT-LEARN
- LICENSE.XGBOOST
- pylintrc
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- CODE_OF_CONDUCT.md
- CONTRIBUTING.md
- cub
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- EXAMPLE_SOLVER.md
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- LICENSE
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# Installation Guide
git clone https://github.com/h2oai/h2o4gpu
Downloads the entire project code from GitHub to your computer.
cd h2o4gpu
Moves into the project folder you just downloaded.
2. Official Install Script
Easy Recommended- APT (Debian/Ubuntu ๊ณ์ด) Built into Debian/Ubuntu-based Linux distributions.
- Python 3 Python is required to use pip.
sudo apt-get install libopenblas-dev pbzip2
Installs directly from the APT package repository (Debian/Ubuntu-based).
pip install h2o4gpu-0.3.0-cp36-cp36m-linux_x86_64.whl
Installs the package published on PyPI directly โ no need to clone the source.
Pulled directly from this repo's README.
3. CMake
Mediummkdir build && cd build
Creates a folder to hold the build output and moves into it.
cmake ..
Analyzes the source code and generates build configuration files (must be run inside the build folder).
make
Compiles the code based on the generated build configuration to produce an executable.
4. Node.js
Easycd h2o4gpu-docs-theme
This project's files live in a subfolder, so move into it first.
npm install
Downloads and installs the libraries listed in package.json.
npm start
Starts the development/run server.
5. Python
Easypip install h2o4gpu-0.3.0-cp36-cp36m-linux_x86_64.whl
Installs the package published on PyPI directly โ no need to clone the source.
For more examples using Python API, please check out our [Jupyter notebook demos](https://github.com/h2oai/h2o4gpu/tree/master/examples/py/demos). To run the demos using a local wheel run, at least download `src/interface_py/requirements_runtime_demos.txt` from the Github repo and do:
Runs the Python script (or module).
and then run the jupyter notebook demos.
Type this command into your terminal and run it.
You can run Jupyter Notebooks with H2O4GPU in the below two ways
Type this command into your terminal and run it.
Next follow Conda installation instructions mentioned above. Once you have activated the environment, you will need to downgrade tornado to version 4.5.3 [refer issue #680](https://github.com/h2oai/h2o4gpu/issues/680). Start Jupyter notebook, and navigate to the URL shown in the log output in your browser.
Type this command into your terminal and run it.
Pulled directly from this repo's README.
6. Ruby
Easycd h2o4gpu-docs-theme
This project's files live in a subfolder, so move into it first.
bundle install
Installs the Ruby libraries listed in the Gemfile.
7. Make
Medium- Git Needed to download the project code from GitHub.
- Make Usually pre-installed on Linux/macOS. On Windows, install separately (e.g. via MSYS2 or WSL).
make
Compiles the code based on the generated build configuration to produce an executable.
