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release v0.2.2
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‎CHANGELOG.md‎

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Here list all notable changes in GraphVite library.
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v0.2.2 - 2020-03-11
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-------------------
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- New model QuatE and its benchmarks on 5 knowledge graph datasets.
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- Add an option to skip `faiss` in compilation.
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- Fix instructions for conda installation.
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v0.2.1 - 2019-11-12
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-------------------
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- New dataset `Wikidata5m` and its benchmarks,

‎CMakeLists.txt‎

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@@ -89,21 +89,23 @@ endif()
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# faiss
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if (NOT FAISS_PATH)
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ExternalProject_Add(faiss
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GIT_REPOSITORY https://github.com/facebookresearch/faiss.git
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UPDATE_DISCONNECTED ${SKIP_UPDATE}
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PREFIX faiss
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SOURCE_DIR ${EXTERNAL_DIR}/faiss
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BINARY_DIR ${EXTERNAL_DIR}/faiss
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INSTALL_DIR ${PROJECT_BINARY_DIR}/faiss
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CONFIGURE_COMMAND test -e makefile.inc ||
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${EXTERNAL_DIR}/faiss/configure --with-cuda=${CUDA_ROOT} --with-cuda-arch=${CUDA_ARCH}
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BUILD_COMMAND test -e libfaiss.so || make -j
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INSTALL_COMMAND test -e ${PROJECT_BINARY_DIR}/faiss/include/faiss/gpu/GpuIndexFlat.h ||
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make install prefix=${PROJECT_BINARY_DIR}/faiss)
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include_directories(${PROJECT_BINARY_DIR}/faiss/include)
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link_directories(${PROJECT_BINARY_DIR}/faiss/lib)
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set(FAISS_LIBRARY ${PROJECT_BINARY_DIR}/faiss/lib/libfaiss.so)
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if (NOT NO_FAISS)
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ExternalProject_Add(faiss
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GIT_REPOSITORY https://github.com/facebookresearch/faiss.git
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UPDATE_DISCONNECTED ${SKIP_UPDATE}
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PREFIX faiss
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SOURCE_DIR ${EXTERNAL_DIR}/faiss
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BINARY_DIR ${EXTERNAL_DIR}/faiss
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INSTALL_DIR ${PROJECT_BINARY_DIR}/faiss
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CONFIGURE_COMMAND test -e makefile.inc ||
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${EXTERNAL_DIR}/faiss/configure --with-cuda=${CUDA_ROOT} --with-cuda-arch=${CUDA_ARCH}
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BUILD_COMMAND test -e libfaiss.so || make -j
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INSTALL_COMMAND test -e ${PROJECT_BINARY_DIR}/faiss/include/faiss/gpu/GpuIndexFlat.h ||
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make install prefix=${PROJECT_BINARY_DIR}/faiss)
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include_directories(${PROJECT_BINARY_DIR}/faiss/include)
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link_directories(${PROJECT_BINARY_DIR}/faiss/lib)
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set(FAISS_LIBRARY ${PROJECT_BINARY_DIR}/faiss/lib/libfaiss.so)
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endif()
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else()
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get_filename_component(FAISS_PARENT ${FAISS_PATH} DIRECTORY)
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include_directories(${FAISS_PARENT})
@@ -125,10 +127,12 @@ set(CMAKE_CUDA_FLAGS_DEBUG "-g -G")
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set(CMAKE_CUDA_FLAGS_RELEASE "-O3")
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set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} ${CUDA_ARCH}")
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if (FAST_COMPILE)
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DFAST_COMPILE")
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set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -DFAST_COMPILE")
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endif()
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foreach (MACRO FAST_COMPILE NO_FAISS)
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if (${${MACRO}})
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -D${MACRO}")
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set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -D${MACRO}")
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endif()
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endforeach()
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# output directory
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set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${PROJECT_BINARY_DIR}/lib)

‎README.md‎

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[![Install with conda](https://anaconda.org/milagraph/graphvite/badges/installer/conda.svg)][conda]
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[![License](https://anaconda.org/milagraph/graphvite/badges/license.svg)][license]
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[![Downloads](https://anaconda.org/milagraph/graphvite/badges/downloads.svg)][conda]
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[conda]: https://anaconda.org/milagraph/graphvite
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[license]: LICENSE
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[Docs] | [Tutorials] | [Benchmarks] | [Pre-trained models]
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[Docs] | [Tutorials] | [Benchmarks] | [Pre-trained Models]
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[Docs]: https://graphvite.io/docs/latest/api/application
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[Tutorials]: https://graphvite.io/tutorials
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### From Conda ###
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```bash
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conda install -c milagraph graphvite cudatoolkit=$(nvcc -V | grep -Po "(?<=V)\d+.\d+")
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conda install -c milagraph -c conda-forge graphvite cudatoolkit=$(nvcc -V | grep -Po "(?<=V)\d+.\d+")
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```
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If you only need embedding training without evaluation, you can use the following
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alternative with minimal dependencies.
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```bash
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conda install -c milagraph graphvite-mini cudatoolkit=$(nvcc -V | grep -Po "(?<=V)\d+.\d+")
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conda install -c milagraph -c conda-forge graphvite-mini cudatoolkit=$(nvcc -V | grep -Po "(?<=V)\d+.\d+")
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```
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### From Source ###

‎conda/conda_build_config.yaml‎

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cudatoolkit:
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- 9.2
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- 10.0
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- 10.1
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pin_run_as_build:
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cudatoolkit:

‎conda/graphvite-mini/meta.yaml‎

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package:
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name: graphvite-mini
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version: 0.2.1
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version: 0.2.2
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source:
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path: ../..

‎conda/graphvite/meta.yaml‎

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package:
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name: graphvite
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version: 0.2.1
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version: 0.2.2
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source:
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path: ../..

‎config/knowledge_graph/quate_fb15k-237.yaml‎

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application:
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knowledge graph
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resource:
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gpus: []
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cpu_per_gpu: auto
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dim: 2048
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graph:
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file_name: <fb15k237.train>
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build:
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optimizer:
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type: Adam
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lr: 1.0e-5
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weight_decay: 0
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num_partition: auto
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num_negative: 64
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batch_size: 100000
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episode_size: 1
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train:
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model: QuatE
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num_epoch: 1000
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l3_regularization: 5.0e-2
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sample_batch_size: 2000
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adversarial_temperature: 2
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log_frequency: 100
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evaluate:
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task: link prediction
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file_name: <fb15k237.test>
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filter_files:
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- <fb15k237.train>
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- <fb15k237.valid>
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- <fb15k237.test>
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# fast_mode: 3000
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save:
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file_name: quate_fb15k-237.pkl

‎config/knowledge_graph/quate_fb15k.yaml‎

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application:
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knowledge graph
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resource:
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gpus: []
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cpu_per_gpu: auto
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dim: 2048
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graph:
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file_name: <fb15k.train>
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build:
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optimizer:
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type: Adam
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lr: 5.0e-5
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weight_decay: 0
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num_partition: auto
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num_negative: 64
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batch_size: 100000
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episode_size: 1
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train:
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model: QuatE
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num_epoch: 1000
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l3_regularization: 1.0e-2
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sample_batch_size: 2000
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adversarial_temperature: 2
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log_frequency: 100
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evaluate:
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task: link prediction
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file_name: <fb15k.test>
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filter_files:
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- <fb15k.train>
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- <fb15k.valid>
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- <fb15k.test>
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# fast_mode: 3000
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save:
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file_name: quate_fb15k.pkl

‎config/knowledge_graph/quate_wikidata5m.yaml‎

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application:
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knowledge graph
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resource:
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gpus: []
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cpu_per_gpu: auto
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dim: 512
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graph:
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file_name: <wikidata5m.train>
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normalization: true
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build:
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optimizer:
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type: SGD
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lr: 1.0
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weight_decay: 0
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num_partition: auto
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num_negative: 64
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batch_size: 100000
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episode_size: 200
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train:
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model: QuatE
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num_epoch: 2000
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l3_regularization: 5.0e-3
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sample_batch_size: 2000
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adversarial_temperature: 10
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relation_lr_multiplier: 1.0e-4
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log_frequency: 500
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evaluate:
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task: link prediction
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file_name: <wikidata5m.test>
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filter_files:
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- <wikidata5m.train>
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- <wikidata5m.valid>
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- <wikidata5m.test>
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# fast_mode: 1000
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save:
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file_name: quate_wikidata5m.pkl

‎config/knowledge_graph/quate_wn18.yaml‎

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application:
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knowledge graph
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resource:
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gpus: []
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cpu_per_gpu: auto
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dim: 1024
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graph:
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file_name: <wn18.train>
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build:
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optimizer:
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type: Adam
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lr: 1.0e-5
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weight_decay: 0
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num_partition: auto
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num_negative: 64
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batch_size: 100000
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episode_size: 1
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train:
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model: QuatE
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num_epoch: 4000
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l3_regularization: 1.0e-2
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sample_batch_size: 2000
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adversarial_temperature: 2
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log_frequency: 100
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evaluate:
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task: link prediction
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file_name: <wn18.test>
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filter_files:
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- <wn18.train>
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- <wn18.valid>
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- <wn18.test>
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# fast_mode: 3000
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save:
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file_name: quate_wn18.pkl

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