Change:
I am trying to build PyTorch from source on macOS High Sierra Version 10.13.3 with an NVIDIA GeForce GT 750M. I'm following the instructions at prints prints prints prints When I enter , as per the instructions, I receive the following error message:
Carlos - this may be minor but I eventually found problems with ananconda for this an other CUDA-enabled builds. Couple of things that may help: a - ensure that your activated conda environment is env actually the env you want. I was checking the path with and it was showing the incorrect path to python and pip half of the time. I ended up creating new conda environments (switching to miniconda by the way) and passing the python=3.6 flag to ensure a full environment was deployed. Maybe somehow reverts back to an incorrect path b - in your activated environment, c - make sure a recent OS update didn't bump your CommandLineTools to v 9.0 (I found 8.0 was the only that worked, not 8.1) I built this on 10.12, CUDA 9, cudNN 7.0.
Could you see if can build for you? I don't have CUDA 9 on mac, so it'd be great if you can let me know if it works or not. :)
I'm no longer getting an error at installation, but still returns for some reason.

Discussion:
} #if CUDA_VERSION >= 9010 <del> void THCudaBlas_HgemmStridedBatched(THCState *state, char transa, char transb, long m, long n, long k, half alpha, const half *a, long lda, long strideA, const half *b, long ldb, long strideB, half beta, half *c, long ldc, long strideC, long batchCount) </del> <ins> void THCudaBlas_HgemmStridedBatched(THCState *state, char transa, char transb, int64_t m, int64_t n, int64_t k, half alpha, const half *a, int64_t lda, int64_t strideA, const half *b, int64_t ldb, int64_t strideB, half beta, half *c, int64_t ldc, int64_t strideC, int64_t batchCount) </ins> { if( (m >= INT_MAX) || (n >= INT_MAX) || (k >= INT_MAX) || (lda >= INT_MAX)  || (ldb >= INT_MAX) || (ldc >= INT_MAX) || (batchCount >= INT_MAX) )

#endif #if CUDA_VERSION >= 9010 <del> void THCudaBlas_HgemmStridedBatched(THCState *state, char transa, char transb, long m, long n, long k, half alpha, const half *a, long lda, long strideA, const half *b, long ldb, long strideB, half beta, half *c, long ldc, long strideC, long batchCount); </del> <ins> void THCudaBlas_HgemmStridedBatched(THCState *state, char transa, char transb, int64_t m, int64_t n, int64_t k, half alpha, const half *a, int64_t lda, int64_t strideA, const half *b, int64_t ldb, int64_t strideB, half beta, half *c, int64_t ldc, int64_t strideC, int64_t batchCount); </ins> #endif /* Inverse */

} __global__ void createBatchGemmBuffer3(const real** buffer1, const real ** buffer2, const real ** buffer3, real* data1, <del> real * data2, real * data3, long stride1, long stride2, long stride3, long num_batches) { const long idx = blockIdx.x * blockDim.x + threadIdx.x; </del> <ins> real * data2, real * data3, int64_t stride1, int64_t stride2, int64_t stride3, int64_t num_batches) { const int64_t idx = blockIdx.x * blockDim.x + threadIdx.x; </ins> if (idx < num_batches) { buffer1[idx] = data1 + idx * stride1; buffer2[idx] = data2 + idx * stride2;

THCTensor_(data)(state, result_), ldc, result_->stride[0], num_batches); } else { <del> for (long i = 0; i < num_batches; ++i) { </del> <ins> for (int64_t i = 0; i < num_batches; ++i) { </ins> THCudaBlas_Hgemm( state, transpose_batch1,