Submitted by amitraderinthemaking t3_xwltow in MachineLearning
amitraderinthemaking OP t1_ir8s27e wrote
Reply to comment by LiquidDinosaurs69 in [D] Fast inferencing in C++ for neural networks by amitraderinthemaking
This is something else I was considering. Thank you, I'll take a look and compare times.
LiquidDinosaurs69 t1_ir8t1kh wrote
I did this for a simple feedforward neural net and it worked pretty good.
amitraderinthemaking OP t1_ir8tp7v wrote
Out of curiosity did you measure the time? My network is 6 layers deep, 100 units in the hidden layer so it's rather simple.
LiquidDinosaurs69 t1_ir8v1nq wrote
No I didn’t measure the time. But I had a network that had 2 hidden layers with 35 units per layer and I was using it as a component of a single threaded simulation that was running inference over 1000 times a second on an older CPU. Can I ask why you don’t want to use the gpu? Cuda would speed things up a lot if you need more speed.
LiquidDinosaurs69 t1_ir8vdvj wrote
Actually, here’s the code where I implemented inference for my neural net if you’re interested. It’s very simple. https://github.com/jyurkanin/auvsl_dynamics/blob/float_model/src/TireNetwork.cpp
And here’s a handy script I made to help generate the c code for loading the weights into libeigen vectors. (Just use the print_c_network function) https://github.com/jyurkanin/auvsl_dynamics/blob/float_model/scripts/pretrain.py
Also look at my cmakelists.txt to make sure you had the compiler flags that will make your code run as fast as possible
amitraderinthemaking OP t1_ir8zuij wrote
Ah thank you SO much for sharing I will definitely take a look!
So unfortunately we don't have GPU available on our production systems yet -- we are not an ML oriented team at all (this would be the first project tbh).
But we'd eventually make a case for GPU for certain. Thing is, this method (with ML) should be faster than the current way of doing things before we can move further, you know.
Thanks again for sharing.
LiquidDinosaurs69 t1_ira12g8 wrote
Sounds cool. I’m just glad that the code I wrote for my grad school research might be useful for someone.
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