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License: MIT License
Building pytorch on blue waters.
License: MIT License
parameterize the version of magma-cuda to install so that it matches the version of the cudatoolkit we are using.
Can we either do without CuDNN, or can we get an alternative that is less encumbered?
Install scipy.
Sleef doesn't get linked right to the rt library, so we get
undefined reference to symbol 'clock_gettime@@GLIBC_2.2.5'
This would make the install cleaner.
Hammer out the exact license that needs to be used for the Dockerfiles, which are adapted from those in the pytorch repo.
MPI may be faster for float32.
It's kind of strange to not use MPI on this machine built and optimized specifically for MPI.
https://mlbench.github.io/2020/09/08/communication-backend-comparison/
Investigate.
When we use shifter with the mpich module for MPI support, somehow the torch C library now wants to link to libgfortran.so.3 , however, the mpich module from shifter doesn't provide that, so we need to do it.
:(
We might have to build our own mpich instead of using the one from the ubuntu package.
https://wiki.mpich.org/mpich/index.php/Summit
https://www.olcf.ornl.gov/tutorials/gpudirect-mpich-enabled-cuda/
https://bluewaters.ncsa.illinois.edu/cuda
https://developer.nvidia.com/blog/introduction-cuda-aware-mpi/
http://www.cs.ucr.edu/~nael/217-f15/lectures/217-lec21.pdf
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