KFrank:

Do you know how I might determine which compute capability a
binary uses before installing it?


The compute capability is unfortunately not encoded in the file names, so the best approach would be to install the binary and just see, if your GPU works, or print:
print(torch.__config__.show())
> PyTorch built with:
  - GCC 4.9
  - Intel(R) Math Kernel Library Version 2019.0.4 Product Build 20190411 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v0.18.1 (Git Hash 7de7e5d02bf687f971e7668963649728356e0c20)
  - OpenMP 201307 (a.k.a. OpenMP 4.0)
  - NNPACK is enabled
  - CUDA Runtime 10.0
  - NVCC architecture flags: -gencode;arch=compute_35,code=sm_35;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_50,code=compute_50
  - CuDNN 7.5.1
  - Magma 2.5.0
  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS=  -Wno-deprecated -fvisibility-inlines-hidden -fopenmp -O2 -fPIC -Wno-narrowing -Wall -Wextra -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math, DISABLE_NUMA=1, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, USE_CUDA=True, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=True, USE_NNPACK=True, USE_OPENMP=ON, 




 KFrank:

Am I right that the “cu80” part of the file name suggests that the
binary is built with CUDA SDK 8.0 and that it could therefore
potentially support compute capability 2.0 – 6.2?


Generally yes, but the used compute capability has to be specified using the TORCH_CUDA_ARCH_LIST flag as used here 47.
So while the binaries support e.g. CUDA10, the compute capability might be limited so specific architectures.



 KFrank:

(Also am I right that the .tar.bz2 files are linux binaries, while the
.whl file is the sole windows binary?)


That shouldn’t be the case. These .whl packages 14 are used for linux systems.
I’m not sure, if conda uses tar.bz2, while pip uses .whl?
Anyway, both are just zipped containers, so I’m not sure if the file ending is even important.