For a while now I have been wanting to upgrade my video card in my desktop so I could actually use it to do machine\/deep learning tasks (ML). Since I put together a Frankenstein gaming computer for my daughters out of some older parts, I finally justified getting a new card by saying I would then give them my older nVidia card. After a lot of research, I decided the RTX 2060<\/a> was a good balance of how much money I felt like spending versus something that would actually be useful (plus the series comes with dedicated Tensor Cores that work really fast with fp16<\/a> data types).<\/p>\n\n\n\n So after buying the card and installing it, the first thing I wanted to do was to get Tensorflow 2.1<\/a> to work with it. Now, I already had CUDA<\/a> 10.2 and the most up-to-date version of TensorRT<\/a> installed, and knew that I’d have to custom compile Tensorflow’s pip version to work on my system. What I did not know was just how annoying this would turn out to be. My first attempt was to follow the instructions<\/a> from the Tensorflow web site, including applying this<\/a> patch that fixes the nccl bindings to work with CUDA 10.2.<\/p>\n\n\n\n However, all of my attempts failed. I had random compiler errors crop up during the build that I have never had before and could not explain. Tried building it with Clang. Tried different versions of GCC. Considered building with a Catholic priest present to keep the demons at bay. No dice. Never could complete a build successfully on my system. This was a bit to be expected since a lot of people online have trouble getting Tensorflow 2.1 and CUDA 10.2 to play nice together.<\/p>\n\n\n\n I finally broke down and downgraded CUDA on my system to 10.1. I also downgraded TensorRT so it would be compatible with the version of CUDA I now had. Finally I could do a Almost.<\/p>\n\n\n\n I kept getting FINALLY! After several days of trying to custom compile Tensorflow for my system, giving up, downgrading so I could install via pip, running into more errors, etc, I now have a working GPU-accelerated Tensorflow! As an example, running the simple additionrnn.py<\/a> example from Keras, I went from taking around 3.5 seconds per epoch on my Ryzen 7 2700X processor (where I had compiled Tensorflow for CPU only to take advantage of the additional CPU instructions) to taking under 0.5 seconds on the GPU. I’m still experimenting, and modifying some things to use fp16 so I can take advantage of the Tensor Cores in the GPU. <\/p>\n","protected":false},"excerpt":{"rendered":" Or…. how to spend a day hitting your head into your desk. Or…. machine learning is easy, right? \ud83d\ude42 For a while now I have been wanting to upgrade my video card in my desktop so I could actually use … Continue reading pip install tensorflow-gpu<\/code> inside my virtual environment and and it worked and it ran and I could finally run my training on my GPU with fantastic results.<\/p>\n\n\n\nCUDNN_STATUS_INTERNAL_ERROR<\/code> messages every time I tried to run a Keras application on the GPU. Yay. After some Googling, I found this link<\/a> and apparently there’s an issue with Tensorflow and the RTX line. To fix it, you have to add this to your Python code that uses Keras\/Tensorflow:<\/p>\n\n\n\nfrom tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession<\/code><\/p>\n\n\n\n...
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)<\/code><\/p>\n\n\n\n