Open jaylong255 opened 2 days ago
Here's a high-level overview of preparing a laptop with an NVIDIA GeForce GPU running Ubuntu to run LLaMA models:
Install CUDA Toolkit:
Install cuDNN:
Install NVIDIA Drivers:
Set Up Python Environment:
conda
or venv
to create a virtual environment to manage dependencies without cluttering your system.Install Necessary Libraries:
pip install torch torchvision torchaudio
- PyTorch with CUDA support for GPU acceleration.pip install transformers
- Hugging Face's Transformers library, which supports LLaMA and many other models.Download the LLaMA Model:
transformers
CLI or manually download from a source you have access to.Model Loading and Running:
Use the transformers
library to load the model:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "your-llama-model-path" # Replace with actual path or model ID if available publicly
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
model.to("cuda") # Move model to GPU
inputs = tokenizer("Your input text", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Optimize for GPU Usage:
bitsandbytes
or deepspeed
.Testing and Tweaking:
Remember, running large models like LLaMA, especially the bigger variants, might require significant GPU memory. If your GeForce GPU has limited VRAM, you might need to use model parallelism or offloading strategies, or consider models that are optimized for lower memory usage.
Also, ensure your laptop's power settings are configured for performance to avoid thermal throttling during extended model runs.
sudo apt install -y openssh-server
sudo systemctl enable ssh
``
sudo apt install -y build-essential libssl-dev dkms
sudo sh cuda_12.6.1_560.35.03_linux.run
Here's a step-by-step guide on installing the CUDA Toolkit on an Ubuntu system with an NVIDIA GeForce GPU:
lsb_release -a
).nvidia-smi
to check your GPU model. Ensure it's compatible with the CUDA version you plan to install..run
file for your Ubuntu version (e.g., cuda_12.2.1_510.47.03_linux.run
).sudo apt update
sudo apt install -y build-essential libssl-dev dkms
.run
file:
cd /path/to/downloaded/file
chmod +x cuda*.run
sudo ./cuda*.run
.bashrc
or .bash_profile
:
nano ~/.bashrc
export PATH=/usr/local/cuda-<version>/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-<version>/lib64:$LD_LIBRARY_PATH
.bashrc
:
source ~/.bashrc
nvcc --version
This should print the version of the CUDA Toolkit you installed.
sudo apt update
sudo apt install nvidia-driver-<version> # Replace <version> with the appropriate driver version for your GPU
sudo reboot
After reboot, check if the driver is loaded with nvidia-smi
.
Create a simple CUDA program:
cat <<EOF > hello.cu
#include <stdio.h>
__global__ void helloFromGPU(void) {
printf("Hello World from GPU!\n");
}
int main(void) {
helloFromGPU<<<1, 1>>>();
cudaDeviceSynchronize();
return 0;
}
EOF
nvcc hello.cu -o hello
./hello
You should see "Hello World from GPU!" if everything is set up correctly.
This installation will prepare your system to use NVIDIA GPUs for CUDA-accelerated computing tasks, including machine learning with PyTorch, TensorFlow, or other CUDA-enabled libraries. Remember, CUDA versions need to match with your GPU capabilities and other software like cuDNN or TensorFlow, so always check for compatibility.
If you encounter this message during the CUDA Toolkit installation, it's because NVIDIA has detected an existing driver installation through your package manager (like apt
on Ubuntu). Here's how to proceed:
Remove the Existing Driver:
sudo apt remove nvidia-*
sudo reboot
After reboot, resume the CUDA installation. This will let the CUDA installer handle the driver installation, ensuring compatibility with the CUDA version you're installing.
If you prefer to keep your current NVIDIA drivers (perhaps because they're newer or better optimized for your system), you can choose Continue
during the installation.
Note: Ensure your existing driver is compatible with the CUDA version you're installing. Check NVIDIA's compatibility matrix or documentation.
Post-Installation Steps:
Continue
, the CUDA Toolkit will be installed without touching your existing drivers.Compatibility: Always check if the existing driver version matches or is compatible with the CUDA Toolkit version. Mismatches can lead to issues with CUDA applications or system instability.
Driver Updates: If you later decide to update your NVIDIA drivers, ensure you update CUDA as well if necessary, or risk version mismatches.
Performance: NVIDIA's installer often provides a smoother experience with CUDA, which is why removing the existing driver and letting NVIDIA handle it is generally recommended.
Documentation: Refer to NVIDIA's official CUDA installation guide for your specific version, as steps might vary slightly with each release.
Regardless of which option you choose, after installation:
nvcc --version
.nvidia-smi
.By choosing the appropriate option, you can ensure a smooth installation of the CUDA Toolkit tailored to your needs or circumstances.
jay@blast-Breaker:$ sudo ./cuda_12.6.1_560.35.03_linux.run [sudo] password for jay: Installation failed. See log at /var/log/cuda-installer.log for details. jay@blast-Breaker:$ cat /var/log/cuda-installer.log INFO: Driver installation detected by command: apt list --installed | grep -e nvidia-driver-[0-9][0-9][0-9] -e nvidia-[0-9][0-9]0-9: Cleaning up window
INFO: Checking compiler version... INFO: gcc location: /usr/bin/gcc
INFO: gcc version: gcc version 13.2.0 (Ubuntu 13.2.0-23ubuntu4)
INFO: Initializing menu
INFO: Setup complete INFO: Installing: Driver INFO: Installing: 560.35.03 INFO: Executing NVIDIA-Linux-x86_64-560.35.03.run --ui=none --no-questions --accept-license --disable-nouveau --no-cc-version-check --install-libglvnd 2>&1 INFO: Finished with code: 256 [ERROR]: Install of driver component failed. Consult the driver log at /var/log/nvidia-installer.log for more details. [ERROR]: Install of 560.35.03 failed, quitting jay@blast-Breaker:~$
From the log, it appears that the installation of the CUDA Toolkit failed specifically during the driver installation phase. Here's what you can do next:
cat /var/log/nvidia-installer.log
sudo apt remove --purge nvidia-*
sudo apt autoremove
sudo reboot
sudo apt update
sudo apt install -y build-essential dkms libssl-dev
sudo ./cuda_12.6.1_560.35.03_linux.run
Check Kernel Compatibility: Ensure your kernel version is compatible with the CUDA driver. You might need to install a specific kernel version or use a driver version that supports your kernel.
uname -r
Alternative Driver Installation: Sometimes downloading and manually installing the NVIDIA driver from NVIDIA's website before CUDA can help.
.run
file for your GPU from NVIDIA's driver download page.sudo ./NVIDIA-Linux-x86_64-<version>.run
sudo reboot
nvidia-smi
Backup: Before removing drivers or kernels, backup important data.
Blacklist Nouveau: Sometimes, the Nouveau open-source driver interferes:
/etc/modprobe.d/blacklist-nouveau.conf
:
sudo nano /etc/modprobe.d/blacklist-nouveau.conf
Add:
blacklist nouveau
options nouveau modeset=0
Check Logs Again: If you encounter issues after following these steps, re-examine the logs, especially /var/log/syslog
or /var/log/dmesg
for any NVIDIA or kernel-related errors post-reboot.
If these steps don't resolve the issue, consider checking NVIDIA's forums or support resources for any known issues with your specific hardware or kernel version. Sometimes, specific hardware configurations or kernel versions might have unique requirements or bugs.
Driver: Installed Toolkit: Installed in /usr/local/cuda-12.6/
Please make sure that
To uninstall the CUDA Toolkit, run cuda-uninstaller in /usr/local/cuda-12.6/bin To uninstall the NVIDIA Driver, run nvidia-uninstall Logfile is /var/log/cuda-installer.log