EGL errors in docker

I followed the instructions to build the local docker file.

docker build . --file Pointnav_DDPPO_baseline.Dockerfile -t pointnav_submission_debug

It built successfully, but local testing via ./test_locally_pointnav_rgbd.sh resulted in the following error:

Neither `ifconfig` (`ifconfig -a`) nor `ip` (`ip address show`) commands are available, listing network interfaces is likely to fail
2020-05-05 07:03:09,730 Overwriting CNN input size of depth: (256, 256)
2020-05-05 07:03:09,731 Overwriting CNN input size of rgb: (256, 256)
2020-05-05 07:03:12,762 Model checkpoint wasn't loaded, evaluating a random model.
2020-05-05 07:03:12,777 Initializing dataset PointNav-v1
2020-05-05 07:03:12,779 initializing sim Sim-v0
WARNING: Logging before InitGoogleLogging() is written to STDERR
F0505 07:03:12.791268    16 WindowlessContext.cpp:114] Check failed: eglDevId < numDevices [EGL] Could not find an EGL device for CUDA device 0
*** Check failure stack trace: ***
submission.sh: line 3:    16 Aborted                 (core dumped) python agent.py --evaluation $AGENT_EVALUATION_TYPE $@

I created an interactive session inside the docker via:

docker run -v /tmp/habitat-challenge-data:/habitat-challenge-data --runtime=nvidia -it pointnav_submission_debug /bin/bash

nvidia-smi worked:

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 418.116.00   Driver Version: 418.116.00   CUDA Version: 10.1     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Quadro GP100        Off  | 00000000:81:00.0 Off |                    0 |
| 26%   37C    P0    31W / 235W |      0MiB / 16278MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   1  Quadro GP100        Off  | 00000000:82:00.0 Off |                    0 |
| 26%   37C    P0    29W / 235W |      0MiB / 16278MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

Running a simple pytorch code on the GPU also worked:

>>> python -c "import torch, torch.nn as nn; device = torch.device('cuda:0'); model = nn.Linear(4, 2); model.to(device);  x = torch.randn(1, 4).to(device); y = model(x); print(y)"

tensor([[0.0405, 0.1198]], device='cuda:0', grad_fn=<AddmmBackward>)

[erikwijmans]

If you system has a non-standard EGL install, i.e. if you need to do something like export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu/nvidia-opengl:${LD_LIBRARY_PATH}, you will likely need to mount /usr/lib/x86_64-linux-gnu/nvidia-opengl (add -v /usr/lib/x86_64-linux-gnu/nvidia-opengl) and set LD_LIBRARY_PATH in the docker container also.

[vincent341]

Hi @rpartsey ,

Thanks very much for your instructions. Let me try. I’m still struggling with it now.