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1. #Benchmark cpu gpu neural network training mod
2. #Benchmark cpu gpu neural network training drivers
3. #Benchmark cpu gpu neural network training software
4. #Benchmark cpu gpu neural network training code

Tinygrad targets AMD GPUs as one of their backends. That being said, it’s still quite capable and is able to run full-scale complex networks like Stable Diffusion natively. It’s a minimalist ML framework built from the ground up on a very tiny foundation of basic operations. While poking around online, I discovered the tinygrad library. Benchmarking 7900 XTX Raw FP32 FLOPS ⌗Īfter that bad result, I wanted to see if I could actually reach the 61 TFLOPS of FP32 performance advertised for the card. One thing is clear though: My TensorFlow performance is not anywhere near where it should be for this hardware. Maybe it’s my janky TensorFlow setup, maybe it’s poor ROCm/driver support for the 7900 XTX, or maybe it’s some some obscure boot param I added to my system 3 years ago.

#Benchmark cpu gpu neural network training software

This leads me to believe that there’s a software issue at some point. A Reddit thread from 4 years ago that ran the same benchmark on a Radeon VII - a >4-year-old card with 13.4 TFLOPS FP32 performance - resulted in a score of 147 back then. # enable GPU memory growth so that my computer doesn't entirely

#Benchmark cpu gpu neural network training code

# I edited the `tf_cnn_benchmarks.py` file add the bit of code to > cd benchmarks/scripts/tf_cnn_benchmarks

#Benchmark cpu gpu neural network training drivers

I feel like it’s quite possible that there will be some changes to the ROCm TensorFlow fork in the future or ROCm drivers themselves that fix this performance to be more in line with the card’s actual power. One possibility is that it’s something to do with the hacky way I compiled TensorFlow to work with ROCm 5.5 and the 7900 XTX. I’m not sure why the performance is so bad. The 6900 XT has a theoretical max of 23 TFLOPS of FP32 performance - less than 40% of the 7900 XTX which has 61 TFLOPS of FP32 performance.

My card is performing barely better than the 6900 XT tested by the author. The author of the Reddit post included some timings for a variety of other cards that they tested: 6900XT/System 1 On my 7900 XTX GPU, I achieved 24 seconds per epoch. randint( 0,nclasses - 1,nsamples)ĭset = tf. compile(loss = 'sparse_categorical_crossentropy', optimizer = 'adam')

#Benchmark cpu gpu neural network training mod

Nclasses = 10 nsamples = 3000000 bsize = nsamples // 20 inp_units = 100 mod = tf. Although it’s simple, it has over 7 million parameters: One of the cases they benchmarked is training a very simple multi-layer neural network using random data. I found a Reddit post by cherryteastain that uses TensorFlow to run a few different ML benchmarks on a 6900XT card. I started out by looking for some existing benchmarks that I could pull in to compare. I wanted to get a feel for the actual performance that I can get with the 7900 XTX for a realistic ML training scenario. Simple TensorFlow Neural Network Training Benchmark ⌗ It’s very possible that these results will change dramatically over time, even in the short term.

I ran some benchmarks to get a feel for its real-world performance right now. The recent AI hype wave is also incentivizing AMD to beef ML support on their cards, and they seem to be making real investments in that space. It has 24GB of VRAM, a theoretical 60 TFLOPS of f32, and 120 TFLOPS of f16. That being said, the 7900 XTX is a very powerful card. OpenCL has not been up to the same level in either support or performance. Most ML frameworks have NVIDIA support via CUDA as their primary (or only) option for acceleration. It’s well known that NVIDIA is the clear leader in AI hardware currently. Besides being great for gaming, I wanted to try it out for some machine learning.