make GPU headers C++03 compatible
parent
db1ca1843e
commit
1114358311
17
INSTALL
17
INSTALL
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@ -177,7 +177,9 @@ requires:
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See the example makefile on how to set the flags.
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The nvcc-specific flags to pass to the compiler:
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The nvcc-specific flags to pass to the compiler, based on your desired
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compute capability. Only compute capability 3.5+ is supported. For
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example:
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-gencode arch=compute_35,code="compute_35"
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-gencode arch=compute_52,code="compute_52"
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@ -237,7 +239,7 @@ to enable and run it.
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Hot to use Faiss in your own projects
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=====================================
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C++
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C++
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---
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The makefile generates a static and a dynamic library
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@ -245,20 +247,19 @@ The makefile generates a static and a dynamic library
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libfaiss.a
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libfaiss.so (or libfaiss.dylib)
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the executable should be linked to one of these. If you use
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the executable should be linked to one of these. If you use
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the static version (.a), add the LDFLAGS found in the Makefile.
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Python
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------
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To import Faiss in your own Python project, you need the files
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To import Faiss in your own Python project, you need the files
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faiss.py
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faiss.py
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swigfaiss.py / swigfaiss_gpu.py
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_swigfaiss.so / _swigfaiss_gpu.so
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_swigfaiss.so / _swigfaiss_gpu.so
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to be visible in the PYTHONPATH or in the current directory.
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to be visible in the PYTHONPATH or in the current directory.
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Then Faiss can be used in python with
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import faiss
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@ -1,4 +1,5 @@
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# Doxyfile 1.8.5
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# This file describes the settings to be used by the documentation system
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@ -131,7 +132,7 @@ INLINE_INHERITED_MEMB = NO
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# shortest path that makes the file name unique will be used
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# The default value is: YES.
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FULL_PATH_NAMES = NO
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FULL_PATH_NAMES = YES
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# The STRIP_FROM_PATH tag can be used to strip a user-defined part of the path.
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# Stripping is only done if one of the specified strings matches the left-hand
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@ -80,7 +80,7 @@ class IndexProxy : public faiss::Index {
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private:
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/// Collection of Index instances, with their managing worker thread
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mutable std::vector<std::pair<faiss::Index*,
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std::unique_ptr<WorkerThread>>> indices_;
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std::unique_ptr<WorkerThread> > > indices_;
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};
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@ -64,7 +64,7 @@ class StandardGpuResources : public GpuResources {
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std::unordered_map<int, cudaStream_t> defaultStreams_;
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/// Other streams we can use, per each device
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std::unordered_map<int, std::vector<cudaStream_t>> alternateStreams_;
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std::unordered_map<int, std::vector<cudaStream_t> > alternateStreams_;
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/// Async copy stream to use for GPU <-> CPU pinned memory copies
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std::unordered_map<int, cudaStream_t> asyncCopyStreams_;
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@ -73,7 +73,7 @@ class StandardGpuResources : public GpuResources {
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std::unordered_map<int, cublasHandle_t> blasHandles_;
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/// Temporary memory provider, per each device
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std::unordered_map<int, std::unique_ptr<StackDeviceMemory>> memory_;
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std::unordered_map<int, std::unique_ptr<StackDeviceMemory> > memory_;
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/// Pinned memory allocation for use with this GPU
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void* pinnedMemAlloc_;
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