I built from source code(v1.0.0), but Conv2 speed is slower than that installed with conda install command.
conv2d cpu time - 709909.681us cuda time - 709897.758us
conv2d cpu time - 3431.992us cuda time - 3430.720us

I know that in general, for intel cpus, conda uses MKL (Math Kernel Library), which is a highly tuned a BLAS implementation for Intel CPUs. That could be one of the reasons explaining the CPU performance difference (you typically find similar performance differences when using NumPy when comparing conda and source/pip installations). However, I think on a GPU, you won’t notice that much of a difference.