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Are there any detailed steps for training our own data? #27
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Hi, thanks for the interest! |
Hi, unfortunately, we don't support training on Windows, only rendering, since I haven't found a way to make screenless rendering on Windows work (as shown by the error). I would suggest an Ubuntu Linux environment or a wsl2 setup. |
Training should work fine in a headless environment. This looks like a driver issue, could you share the environment you're running the training on (python, pytorch, linux and nvidia driver version)? If the driver version is too low, a possible fix is to upgrade your nvidia driver. Another known issue is related to docker (I'm not sure whether it's a similar situation on autodl): NVIDIA/nvidia-docker#1520 |
Here is my environment: |
The driver looks new enough. But I'm curious as to why the 10_nvidia.json file couldn't be created? Is it because of insufficient privilege? |
I'm really sorry to bother you...This is my mistake...I tried creating 10_nvidia.json under both "/etc/glvnd/egl_vendor.d" and "/usr/share/glvnd/egl_vendor.d". But a new EGL error occurred.Complete information is as follows: (easyvolcap) root@autodl-container-45f511a5e8-b65a70b6:~/4K4D# evc -c configs/exps/4k4d/4k4d_actor1_4_r4.yaml,configs/specs/static.yaml,configs/specs/tiny.yaml The above exception was the direct cause of the following exception: ╭─────────────────────────────────────────────────── Traceback (most recent call last) ────────────────────────────────────────────────────╮
(Pdbr) exit |
Hi, it looks like the autodl server doesn't support this particular egl function: This function is used to match the EGL_DEVICE_ID with the CUDA Context id. If the PS: We have a test for such obscure EGL errors in python tests/headless_opengl_tests.py than the training command. |
This is great work. We hope to use 4K4D to reconstruct our own scenes.I'd like to know how long it will take for the training code to be updated?
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