llama-factory torch cuda

### LLaMA-Factory Project Setup with PyTorch and CUDA Configuration Tutorial For setting up the LLaMA-Factory project, ensuring that both Python environment creation and GPU support through CUDA are correctly configured is crucial. The following sections provide a detailed guide on how to set this up. #### Creating the Conda Environment To start off, it's important to establish an appropriate development environment using `conda`. This ensures all dependencies required by LLaMA-Factory can be managed effectively: ```bash conda create --name llama_factory python=3.11 ``` After creating the environment, activate it before proceeding further[^1]: ```bash conda activate llama_factory ``` #### Installing Required Packages Including PyTorch with CUDA Support Once inside the newly created virtual environment, install necessary packages including PyTorch specifically built for CUDA compatibility. It’s essential to choose versions of these libraries which work well together as indicated below: ```bash pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117 ``` This command installs PyTorch along with its extensions (`torchvision`, `torchaudio`) compiled against CUDA 11.7, assuming one has compatible hardware drivers installed already. #### Verifying Installation Success Post-installation verification steps help confirm whether everything was successfully put into place without issues related to missing components or misconfigurations: Check if CUDA-capable devices exist within your system via running small snippets like so in Python console after importing relevant modules from PyTorch library: ```python import torch print(torch.cuda.is_available()) print(torch.cuda.device_count()) print(torch.__version__) ``` If everything went smoothly during installation phase then output should indicate availability status being true alongside non-zero count value indicating presence of at least single GPU device available while also showing version string ending with '+cuXX' where XX represents specific major/minor release numbers associated with underlying NVIDIA driver stack used when building binary distributions provided above[^3]. #### Troubleshooting Common Issues Encountered During Deployment Phase In cases where users encounter errors such as "CUDA detection failed", several potential causes could lead to such failures ranging from mismatched software stacks down to improper driver installations. Ensuring correct setup involves checking multiple aspects starting from verifying proper functioning state of graphics card itself followed by confirming successful loading of corresponding kernel module responsible for interfacing between operating systems calls made towards accessing low-level functionalities exposed by said hardware component[^2]. --related questions-- 1. How do I resolve 'CUDA SETUP: CUDA detection failed!' error encountered while fine-tuning models? 2. What steps must be taken post-setup to ensure optimal performance tuning for deep learning tasks utilizing GPUs? 3. Can you explain more about choosing suitable versions among different releases offered under PyTorch distribution channels based upon existing infrastructure constraints? 4. Are there any additional tools recommended beyond those mentioned here for monitoring resource utilization metrics during model training sessions?