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Description
Prerequisites
Please answer the following questions for yourself before submitting an issue.
- I am running the latest code. Development is very rapid so there are no tagged versions as of now.
- I carefully followed the README.md.
- I searched using keywords relevant to my issue to make sure that I am creating a new issue that is not already open (or closed).
- I reviewed the Discussions, and have a new bug or useful enhancement to share.
Expected Behavior
There's no tagged cuda image on ghcr so after buildting the Dockerfile.cuda image,
docker run --gpus=all --rm -it -p 8000:8000 -v /home/***/models:/models -e MODEL=/models/GPT4-X-Alpasta-30b_q4_0.bin llama_cpp_server_cudaCurrent Behavior
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== CUDA ==
==========
CUDA Version 12.1.1
Container image Copyright (c) 2016-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
This container image and its contents are governed by the NVIDIA Deep Learning Container License.
By pulling and using the container, you accept the terms and conditions of this license:
https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license
A copy of this license is made available in this container at /NGC-DL-CONTAINER-LICENSE for your convenience.
llama.cpp: loading model from /models/GPT4-X-Alpasta-30b_q4_0.bin
llama_model_load_internal: format = ggjt v2 (latest)
llama_model_load_internal: n_vocab = 32016
llama_model_load_internal: n_ctx = 2048
llama_model_load_internal: n_embd = 6656
llama_model_load_internal: n_mult = 256
llama_model_load_internal: n_head = 52
llama_model_load_internal: n_layer = 60
llama_model_load_internal: n_rot = 128
llama_model_load_internal: ftype = 2 (mostly Q4_0)
llama_model_load_internal: n_ff = 17920
llama_model_load_internal: n_parts = 1
llama_model_load_internal: model size = 30B
llama_model_load_internal: ggml ctx size = 135.75 KB
llama_model_load_internal: mem required = 21695.61 MB (+ 3124.00 MB per state)
WARNING: failed to allocate 0.13 MB of pinned memory: forward compatibility was attempted on non supported HW
CUDA error 804 at ggml-cuda.cu:405: forward compatibility was attempted on non supported HW
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abetlen commentedon May 18, 2023
@Pipboyguy a couple things to test, is what version of CUDA do you have installed on your base os? Also, this may sound stupid but most bugs with CUDA often are, have you tried rebooting since the most recent CUDA installation?
Pipboyguy commentedon May 18, 2023
llama_cpp.serverworks perfectly outside of docker with virtualenv so I believe this is isolated to the nvidia-docker setup.Here's some info about my host system:
Pipboyguy commentedon May 18, 2023
Please see PR #235. Eliminates issue
Pipboyguy commentedon May 18, 2023
This only eliminates it for me so would be nice to get more testers
gjmulder commentedon May 18, 2023
Exactly what NVidia GPU are you having issues with? 12.1 seems to support my ancient GTX 1080Ti (Pascal architecture). I have a 980Ti (Maxwell) somewhere, but I'd have to plug it in and hope it still works.
CUDA Compatibility Matrix
Pipboyguy commentedon May 18, 2023
Running a RTX 4080. Outdated drivers perhaps?
gjmulder commentedon May 18, 2023
FROM nvidia/cuda:12.1.1-devel-ubuntu20.04is working with my 3090Ti.
My Ubuntu Docker host:
Pipboyguy commentedon May 18, 2023
My Pop!_OS host:
gjmulder commentedon May 23, 2023
Should be addressed in #258.
Pipboyguy commentedon May 23, 2023
shall we close this issue?
d0rc commentedon Jun 7, 2023
Just got it on 4090 with last master:
Commit:
2d7bf110edd8c49209401a16132052cba706ffd0Built with:
make LLAMA_CUBLAS=1gjmulder commentedon Jun 7, 2023
Are you running in a virtualized environment and trying to access your NVidia GPU?
If so, try and update your nvidia driver in your VM or Docker instance.
Pipboyguy commentedon Jun 7, 2023
What worked for me was upgrading my nvidia-driver on the host, then Cuda version 12.1 should work. Also try CUDA 11.7 if upgrading nvidia driver is pain. Very likely the issue in your case as well @d0rc
gjmulder commentedon Jun 7, 2023
That makes more sense. I can see that an older driver in the VM works fine with a newer driver on the host, but not vice versa.