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@gkorland gkorland commented May 2, 2020

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@gkorland gkorland requested a review from itamarhaber May 2, 2020 13:41
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codecov bot commented May 2, 2020

Codecov Report

Merging #349 into master will increase coverage by 0.11%.
The diff coverage is n/a.

Impacted file tree graph

@@            Coverage Diff             @@
##           master     #349      +/-   ##
==========================================
+ Coverage   72.39%   72.50%   +0.11%     
==========================================
  Files          21       21              
  Lines        4336     4288      -48     
==========================================
- Hits         3139     3109      -30     
+ Misses       1197     1179      -18     
Impacted Files Coverage Δ
src/model.c 69.25% <0.00%> (-0.35%) ⬇️
src/redisai.c 78.22% <0.00%> (-0.30%) ⬇️
src/redisai.h 0.00% <0.00%> (ø)
src/libtorch_c/torch_c.cpp 51.71% <0.00%> (ø)
src/dag.c 88.13% <0.00%> (+0.20%) ⬆️
src/model_script_run_session.c 92.00% <0.00%> (+0.33%) ⬆️
src/err.c 90.00% <0.00%> (+11.73%) ⬆️

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* CUDA needs to be installed for GPU support.
* Packages: git, python3, make, wget, g++, & unzip
* CMake 3.0 or higher needs to be installed.
* CUDA needs to be installed for GPU support.
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We currently use Docker for GPU=1 builds (utilizing Nvidia SDK image). I'm not sure we can recommend installing CUDA SDK with a straight face. That is, our build do work if CUDA is installed on the system (like in AWS Deep Learning instances), but I've never tried installing CUDA on a clean, non-Nvidia-recommended machine.

@lantiga lantiga merged commit 484981f into master May 6, 2020
@gkorland gkorland deleted the packages branch October 6, 2020 08:57
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4 participants