Select to view content in your preferred language

Detect Objects Using Deep Learning Error with new RTX 3060

12647
37
04-21-2021 06:34 PM
Hulseyj
Occasional Contributor

Error appears when I use "Detect Objects Using Deep Learning" in ArcGIS Pro with new RTX3060 GPU. The model used is usa_building_footprints.dlpk. I am using ArcGIS Pro 2.7.3. I have no issues running the model with my GTX 1650 Super GPU, same computer, same settings. It appears ArcPro engages the GPU as the application is shown running on the GPU: 

Hulseyj_0-1619055089608.png

 

However, the program shows as "Running" but never progresses past 0%.

Hulseyj_1-1619055089613.png

 

After about 30 minutes of "running", the following error message is returned:

Hulseyj_2-1619055089655.png

 

I used the Input Parameters and Environment below. Again, it runs fine with the GTX 1650 Super GPU. All drivers are up to date on the new RTX3060. 

 

Hulseyj_3-1619055089665.png

 

Thank you for help or troubleshooting ideas.

37 Replies
DanPatterson
MVP Esteemed Contributor

recent blogs on this and related topics especially since your hardware is new

Deep Learning with ArcGIS Pro Tips & Tricks: Part 1 (esri.com)

Deep Learning with ArcGIS Pro Tips & Tricks: Part 2 (esri.com)


... sort of retired...
0 Kudos
Hulseyj
Occasional Contributor

Thanks, Dan. I have worked through the recommended steps in the blogs. The CUDA Toolkit has been installed, and running deviceQuery show both cards are CUDA-capable and detected. 

Hulseyj_0-1619060734543.png

BandwidthTest passed:

Hulseyj_1-1619061492402.png

The Deep Learning Libraries for Arc Pro have been installed. I am using the default arcgispro-py3 environment. 

I am not sure what else to try. The out-of-box model works fine with the GTX 1650 Super, but not with the new RTX 3060.

0 Kudos
Tim_McGinnes
Frequent Contributor

An issue was just posted to Esri's deep learning GitHub site about this (may be caused by a lack of CUDA 11 support in the underlying packages):

https://github.com/Esri/deep-learning-frameworks/issues/17 

Probably worth adding your name to that as well - the more people raising it the more likely it might get fixed.

0 Kudos
Hulseyj
Occasional Contributor

Thanks Tim. I added my name and issue to that post on GitHub.

0 Kudos
AndrewEastop
Regular Contributor

Hi Hulseyj, I have just experienced the same problem as yourself. Did you find a resolution?

0 Kudos
Hulseyj
Occasional Contributor

Thanks Andrew. Glad to know I am not the only one experiencing the issue. No resolution yet.

0 Kudos
AndrewEastop
Regular Contributor
0 Kudos
_Cartographer_
Emerging Contributor

This is 100% caused by lack of CUDA 11 support and lack of updated libraries for PyTorch, TensorFlow, etc.

The latest RTX 30 series GPUs require CUDA 11.

ESRI's current "Deep Learning Frameworks" as linked above use CUDA 10.1

Unfortunately, looking at the pre-release frameworks for ArcGIS Pro 2.8 (available here: https://anaconda.org/Esri/deep-learning-essentials/files) they have still not updated to CUDA 11, or updated PyTorch/TensorFlow. I do not understand why not. I think this comes down to demand.

Everyone who has this issue should be posting and making sure that ESRI knows this is a problem for an increasing number of users -- and will obviously only increase further as more people get the latest GPUs. With how much ESRI is touting ArcGIS deep learning capabilities, this slow response is quite surprising.

NathanPamperin1
Occasional Contributor

Getting the same error (below) trying to train deep learning models in some instances. I am running an Nvidia Geforce RTX 3080 (laptop). I can get the same models trained on a desktop Quadro RTX 5000 that has CUDA toolkit 11.2 installed but also has an instance of CUDA 10.1 installed as well. For what its worth...

File "C:\Users\natep\AppData\Local\ESRI\conda\envs\DeepLearning\Lib\site-packages\torchvision\ops\boxes.py", line 95, in remove_small_boxes
keep = keep.nonzero().squeeze(1)
RuntimeError: copy_if failed to synchronize: cudaErrorAssert: device-side assert triggered
Failed to execute (TrainDeepLearningModel).

 

0 Kudos