videogames.ai About Hardware guide
9 February 2021

RTX 2060 Super for Machine Learning

by Mathieu Poliquin

The RTX 2060 Super is the card I use currently and I think it’s the best bang for the buck for my use case and I think for most use cases provided your models fit inside 8GB and with the new 16bit precision mode offered in RTX cards you can almost double the memory available for you model with no precision related issues in most cases. Which makes it the best choice over the previous generation, for example the GTX 1080 8GB, which doesn’t have proper 16bit support

My specific model is a MaxSun iCraft RTX 2060 Super, the only complaint I have is the high temperatures. It reaches 80C quite quick under 100% load (38C under rest) even thought the airflow in the computer case is very good, but I think it’s related to the effectiveness of the gpu fans. When I set fan speed to 100% the temperatures are ok but quite noisy.

Performance tests results

Test P106-100
PPO2 Atari Pong ~1670 frame/sec
Resnet50 batch=32 181.88 images/sec
Resnet50 batch=32 (16 bit) 292.80 images/sec
Resnet50 batch=64 (16 bit) 324.83 images/sec
Isaac gym/OpenAI - Shadowhand 30952 steps/s
Host to Device 1122 MB/s
Device to Host 1218.3 MB/s
Device to Device 168540.0 MB/s
Quake 2 RTX 55 fps

Software

Hardware

OpenAI baselines + retro

When training ML models on games the CPU is also heavily used for simulation so the GPU is not 100% utilized but used in spikes. That said you can still get a big performance boost using OpenAI’s baselines and Retro frameworks of about 500 fps with same CPU with their default CNN model

details of the setup here: https://www.videogames.ai/2019/01/29/Setup-OpenAI-baselines-retro.html

Isaac gym

I get Isaac 30952 steps/s on the ShadowHand example. I actually did a video of Isaac gym on the RTX 2060, you can see it here as well as a comparaison with the p106-100:

Quake 2 RTX

I get around 55 fps at the beginning of the first level (demo version). You can see the gpu profiling details inside the screenshot quake_rtx

Here are the options I used: quake_rtx_options

Resnet 50 test

For the resnet 50 test I use tensorflow’s benchmarks repo on github: https://github.com/tensorflow/benchmarks/tree/master/scripts/tf_cnn_benchmarks

I used tensorflow version 1.14

python3 tf_cnn_benchmarks.py --num_gpus=1 --batch_size=64 --model=resnet50 --use_fp16

resnet50

CUDA-Z

cuda-z


tags: RTX 2060 Super - gpu - machine learning - resnet50 - unreal - review