A quick step to solve this error from WordPress and NGINX
crit *2 stat() index.php" failed (13: Permission denied) wordpressMake sure to create permission to the hosted folder by
chmod +x /path/websiteIf the problem still exists, try to add www-data into your user group
gpasswd -a www-data ubuntuIf the page is not found, try to restart your php-fpm services
sudo service php8.1-fpm stop
sudo service php8.1-fpm startHere are several things to run finetuning leveraging multiple GPUs. In my case, I have two RTX 4090 that doing training. First, you can use accelerate modules
For example, I’m using https://github.com/tloen/alpaca-lora/
accelerate config
accelerate launch finetune.pyOr export this variables either in terminal or python/ipynb file. If you have 4 GPUs
CUDA_VISIBLE_DEVICES=0,1,2,3We can also put some small codes
gpu_list = [7]
gpu_list_str = ','.join(map(str, gpu_list))
os.environ.setdefault("CUDA_VISIBLE_DEVICES", gpu_list_str)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")When doing finetuning model, you may encountered with this error message that telling CUDA Out of Memory (OOM) with detail
RuntimeError: CUDA error: out of memory; Compile with TORCH_USE_CUDA_DSA to enable device-side assertionsFor my case, I did upgrade NVIDIA drivers to 5.30 version from 5.25 that cause this problem.
So, the solution is to downgrade my NVIDIA drivers back to 5.25 version and using the latest Transformers and Torch installation like in https://www.yodiw.com/install-transformers-pytorch-tensorflow-ubuntu-2023/
If you load dataset and you want to export it with “to_json”, “to_csv” or “features” and you got this error
Fix AttributeError: 'DatasetDict' object has no attribute 'to' The first step, to check the structure of the dataset by using print.
DatasetDict({
train: Dataset({
features: ['id', 'category', 'instruction', 'context', 'response'],
num_rows: 15015
})
})In this case, the DatasetDict has only train data (usually it has test and etc). Exporting to json will be
ds['train'].to_json('train.json')Yes, you need to access the key from the DatasetDict
The quick way to make the model inferences or fine-tuning running on specific NVIDIA GPU card is by define this variable before execute the script
For instance, I forced its running on GPU 1 by
export CUDA_VISIBLE_DEVICES=1Passage retrieval methods refer to techniques and algorithms used to retrieve relevant passages or segments of text from a larger document or corpus. These methods are commonly employed in information retrieval systems and question-answering systems, where the goal is to locate specific information within a large amount of text.
The quickfix on how to uninstall current Cuda installed in Ubuntu not via software packages is using the uninstaller. For instance, I use cuda 11.8 and I need to downgrade it into 11.6.
So, I need to find the path and trigger this command
sudo /usr/local/cuda-11.8/bin/cuda-uninstallerLast, we can clean-up entire cuda folder
sudo rm -rf /usr/local/cudaIf you have issue with GCC for the installed CUDA and need to downgrade or upgrade it, you can follow this
MAX_GCC_VERSION=11
sudo apt install gcc-$MAX_GCC_VERSION g++-$MAX_GCC_VERSION
sudo ln -s /usr/bin/gcc-$MAX_GCC_VERSION /usr/local/cuda/bin/gcc
sudo ln -s /usr/bin/g++-$MAX_GCC_VERSION /usr/local/cuda/bin/g++When generating question answers from datasets using this project “https://github.com/dmis-lab/LIQUID” I got error
RuntimeError: CUDA error: no kernel image is available for execution on the device
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.I believe this error because my GPU is RTX 4090 and Ada Lovelace is not supported for torch 1.12. To solve this, I upgrade the torch to one next version 1.13
pip install torch==1.13.0The warning showed
NVIDIA GeForce RTX 4090 with CUDA capability sm_89 is not compatible with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_70.
If you want to use the NVIDIA GeForce RTX 4090 GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/And its works!
When doing finetuning model using Lora and HuggingFace transformer, I received this error
RuntimeError: unscale_() has already been called on this optimizer since the last update().This error because using the latest transformer version transformers-4.31.0.dev0. The solution is to revert back to transformers-4.30.2 with
pip install transformers-4.30.2When installing Python module like AutoGPTQ, you may got this errors
/usr/local/cuda/include/crt/host_config.h:132:2: error: #error -- unsupported GNU version! gcc versions later than 11 are not supported! The nvcc flag '-allow-unsupported-compiler' can be used to override this version check; however, using an unsupported host compiler may cause compilation failure or incorrect run time execution. Use at your own risk.
132 | #error -- unsupported GNU version! gcc versions later than 11 are not supported! The nvcc flag '-allow-unsupported-compiler' can be used to override this version check; however, using an unsupported host compiler may cause compilation failure or incorrect run time execution. Use at your own risk.
| ^~~~~
error: command '/usr/local/cuda/bin/nvcc' failed with exit code 1
[end of output]To solve this, we need to install GCC as following the maximum version
MAX_GCC_VERSION=11
sudo apt install gcc-$MAX_GCC_VERSION g++-$MAX_GCC_VERSION
sudo ln -s /usr/bin/gcc-11 /usr/local/cuda/bin/