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walline / prosub

Official repository for ECCV2024 paper "ProSub: Probabilistic Open-Set Semi-Supervised Learning with Subspace-Based Out-of-Distribution Detection"
Apache License 2.0
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can't find gpus when I run the code #1

Open Emilia-Re opened 2 days ago

Emilia-Re commented 2 days ago

Thank you for your work. I want to run your code on my mahcine but encountered many issues with tensorflow. Could you provice more information about your working environment ,like python version, cuda version, driver version,etc. The main problem is that tensorflow can't find gpus on my machine. my working environment is as follows

Python 3.11.4 Ubuntu 22.04.4 LTS

Tue Nov 12 12:05:22 2024       
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.183.01             Driver Version: 535.183.01   CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  NVIDIA GeForce RTX 3090        Off | 00000000:1B:00.0 Off |                  N/A |
| 30%   26C    P8              25W / 350W |      1MiB / 24576MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+
|   1  NVIDIA GeForce RTX 3090        Off | 00000000:3F:00.0 Off |                  N/A |
| 60%   65C    P2             333W / 350W |  18277MiB / 24576MiB |     90%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+
|   2  NVIDIA GeForce RTX 3090        Off | 00000000:40:00.0 Off |                  N/A |
| 30%   45C    P2             134W / 350W |  17629MiB / 24576MiB |    100%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+
|   3  NVIDIA GeForce RTX 3090        Off | 00000000:41:00.0 Off |                  N/A |
| 30%   47C    P2             135W / 350W |  17023MiB / 24576MiB |    100%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+

+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
+---------------------------------------------------------------------------------------+

warnings when I run the code

2024-11-12 12:08:41.507733: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-11-12 12:08:41.510174: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
2024-11-12 12:08:41.547633: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-11-12 12:08:41.547672: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-11-12 12:08:41.547696: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-11-12 12:08:41.554863: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
2024-11-12 12:08:41.555096: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-11-12 12:08:42.503745: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
/root/miniconda3/lib/python3.11/site-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: 

TensorFlow Addons (TFA) has ended development and introduction of new features.
TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.
Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). 

For more information see: https://github.com/tensorflow/addons/issues/2807 

  warnings.warn(
2024-11-12 12:08:43.763024: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2211] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
WARNING:tensorflow:Mixed precision compatibility check (mixed_float16): WARNING
The dtype policy mixed_float16 may run slowly because this machine does not have a GPU. Only Nvidia GPUs with compute capability of at least 7.0 run quickly with mixed_float16.
If you will use compatible GPU(s) not attached to this host, e.g. by running a multi-worker model, you can ignore this warning. This message will only be logged once

It would be very appreciated if you coud provide some suggestions.

walline commented 2 days ago

Thanks for checking out our work!

CUDA and version compatibility can indeed be tricky. This page specifies compatible versions across TensorFlow cuDNN CUDA etc.

I have personally been running the code in a container (as specified in the Dockerfile), which is probably the easiest way to avoid version conflicts.

Maybe you have installed a cpu-only build of TensorFlow? If you are installing with pip, try something like pip install tensorflow-gpu==2.14.0 or pip install tensorflow[and-cuda]==2.14.0.

Let me know if this helps or if you run into any more issues!