What should I do if I encounter a CUDA Out of Memory error?

You can upgrade your hardware instantly by adding the '--gpu-type' flag to your command, such as specifying an 'NVIDIA A100 80GB PCIe' to provide more VRAM.

How do I start running code on a cloud GPU?

Install the tool via the official curl command, authenticate using 'gpu auth login' with your RunPod API key, and then prefix your command with 'gpu run' (e.g., 'gpu run python train.py').

Is it possible to run web-based UIs like ComfyUI?

Absolutely. Use the port mapping flag (e.g., '-p 8188:8188') and ensure your application is listening on 0.0.0.0 to access the interface through your local browser.

How does file synchronization work between local and cloud?

GPU CLI automatically syncs your project directory to the pod before running. You can define specific 'outputs' in your gpu.toml file to ensure training weights and logs are synced back to your local machine.

Can I use GPU CLI for interactive debugging?

Yes, use the 'gpu run -i bash' command to open an interactive terminal session directly on the remote GPU pod.

GPU CLI

Name: GPU CLI
Author: gpu-cli

bygpu-cli

Ciencia de Datos y ML

Executes Python and machine learning code on high-performance cloud GPUs directly from your local terminal.

Acerca de

GPU CLI transforms remote GPU execution into a local-like development experience by allowing you to prefix any command with 'gpu' to run it on powerful cloud hardware. It handles the heavy lifting of provisioning instances on providers like RunPod, synchronizing code and results, and caching large models from Hugging Face, making it an essential tool for developers facing local VRAM limitations or CUDA compatibility issues. Whether you are training large language models, running Stable Diffusion workflows, or performing intensive inference, this skill provides a seamless bridge to scalable compute resources with minimal configuration.

Características Principales

Seamless bi-directional synchronization of local source code and remote output files
0 GitHub stars
Integrated model caching for fast loading of Hugging Face, Git LFS, and HTTP assets
Comprehensive job management including background execution, interactive shells, and status monitoring
Port forwarding support for accessing remote Web UIs like ComfyUI or Gradio
Automatic provisioning and management of cloud GPUs including RTX 4090, A100, and H100

Casos de Uso

Running GPU-intensive generative AI tools like ComfyUI, Stable Diffusion, and FLUX with zero local setup
Training and fine-tuning Large Language Models (LLMs) that exceed local hardware capabilities
Resolving CUDA 'Out of Memory' (OOM) errors by instantly scaling to 40GB or 80GB VRAM instances

Acerca de

Características Principales

Seamless bi-directional synchronization of local source code and remote output files
0 GitHub stars
Integrated model caching for fast loading of Hugging Face, Git LFS, and HTTP assets
Comprehensive job management including background execution, interactive shells, and status monitoring
Port forwarding support for accessing remote Web UIs like ComfyUI or Gradio
Automatic provisioning and management of cloud GPUs including RTX 4090, A100, and H100

Casos de Uso

Running GPU-intensive generative AI tools like ComfyUI, Stable Diffusion, and FLUX with zero local setup
Training and fine-tuning Large Language Models (LLMs) that exceed local hardware capabilities
Resolving CUDA 'Out of Memory' (OOM) errors by instantly scaling to 40GB or 80GB VRAM instances