FV SuperAgentandAryan Kumar
866a3cdb91
Co-authored-by: Aryan Kumar <156166681+aryan5v@users.noreply.github.com>
100 lines
3.5 KiB
Markdown
100 lines
3.5 KiB
Markdown
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# 🔧 Installation
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Start with [Hardware and runtimes](hardware.md) to distinguish NVIDIA RTX and
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data center GPUs, DGX Spark, Apple Silicon MLX, and AMD ROCm.
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The installation paths below cover:
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- [NVIDIA CUDA](installation/gpu.md)
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- **NVIDIA DGX Spark / GB10 (ARM64 + CUDA 13)** — [install](installation/spark.md),
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[performance](installation/spark_performance.md),
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[pair two Sparks](installation/spark_pair.md)
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- [Apple silicon (MLX)](installation/mlx.md)
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- [AMD / ROCm support and current limits](installation/amd.md)
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## Quick Installation
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### Using uv (recommended)
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Use uv as the default environment manager for faster and more stable installs.
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The commands below target NVIDIA CUDA 12; use `UV_TORCH_BACKEND=cu130` on
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CUDA 13. Apple silicon users should follow the [MLX install guide](installation/mlx.md).
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```bash
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# Create and activate a new uv environment
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uv venv --python 3.12 --seed
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source .venv/bin/activate
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UV_TORCH_BACKEND=cu126 uv pip install fastvideo
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```
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### Using Conda (alternative)
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```bash
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# Create and activate a new conda environment
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conda create -n fastvideo python=3.12 -y
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conda activate fastvideo
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UV_TORCH_BACKEND=cu126 uv pip install fastvideo
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```
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### From source
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FastVideo pins PyTorch 2.12.0. Select its CUDA build explicitly with
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`UV_TORCH_BACKEND=cu126` for CUDA 12 or `UV_TORCH_BACKEND=cu130` for CUDA 13.
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```bash
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git clone https://github.com/hao-ai-lab/FastVideo.git
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cd FastVideo
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UV_TORCH_BACKEND=cu126 uv pip install -e .
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# optional: install flash-attn
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uv pip install flash-attn --no-build-isolation -v
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```
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Alternative with Conda environment (still drives installs through `uv`):
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```bash
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UV_TORCH_BACKEND=cu126 uv pip install -e .
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uv pip install flash-attn --no-build-isolation -v
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```
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## Install with an AI coding agent
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FastVideo is a monorepo with rich agent guidance (see
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[`AGENTS.md`](https://github.com/hao-ai-lab/FastVideo/blob/main/AGENTS.md)).
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If you use Claude Code, Cursor, or another coding agent, paste the prompt
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below; it detects your platform and follows the matching guide:
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```text
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Install FastVideo (https://github.com/hao-ai-lab/FastVideo) into a fresh uv virtual environment.
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1. Detect the platform: run `uname -m`, `nvidia-smi`, and `nvcc --version`.
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2. Read and follow the matching install guide exactly (in this repo, or at
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https://hao-ai-lab.github.io/FastVideo/getting_started/installation/):
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- NVIDIA GPU, x86_64 -> docs/getting_started/installation/gpu.md
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- NVIDIA DGX Spark / GB10, aarch64, CUDA 13 -> docs/getting_started/installation/spark.md
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- Apple Silicon, macOS -> docs/getting_started/installation/mlx.md
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3. Use uv for every step. If a command fails, debug it and tell me what you changed.
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4. Verify the result:
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python -c "import fastvideo, torch; print('cuda', torch.cuda.is_available())"
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fastvideo --help
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5. Report which platform you detected and any deviations you had to make.
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```
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## Requirements
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- **Python**: 3.10-3.12 is the tested and recommended range (the commands
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above pin 3.12)
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- **NVIDIA GPUs**: CUDA 12.6+ with compute capability 7.0+
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- **Apple Silicon**: macOS 14.0+ with M1/M2/M3/M4 chips
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- **CPU**: x86_64 architecture (for CPU-only inference)
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## Next Steps
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- [Quick Start](quick_start.md) - Generate your first video
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- [Inference Cookbook](../cookbook/index.md) - Choose a maintained recipe
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- [Configuration](../inference/configuration.md) - Learn about configuration options
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- [Examples](../inference/examples/examples_inference_index.md) - Explore scripts and notebooks
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