ViperEkura/AstrAI
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

docs: fix stale docs and align with code

Browse Source 
- update cuda_kernels layout, arch flags, and add FP8 section
- fix install docs: kernels auto-build when nvcc + CUDA detected
- mark ignored OpenAI request params and complete KVCache fields
- add docker docs to indexes and astrai.optim to module overview
- refresh document update timestamps
This commit is contained in:
ViperEkura
2026-08-23 14:23:28 +08:00
parent 75304d084d
commit 10fec8dca1
10 changed files with 134 additions and 58 deletions
Download Patch File Download Diff File Expand all files Collapse all files
docs/get-started.md
+13 -4
View File
@@ -26,17 +26,24 @@ This guide walks you through installing AstrAI, downloading a model, running inf
git clone https://github.com/ViperEkura/AstrAI.git
cd AstrAI
# Basic install (pure PyTorch, no custom CUDA kernels)
# Kernels auto-build when nvcc + CUDA are detected; skip with CSRC_KERNELS=false
pip install -e .
# With CUDA kernels (optional, for fused attention and rotary embedding)
# Force the CUDA kernel build (fused attention, rotary embedding, FP8 GEMM)
# CSRC_KERNELS=true pip install -e . --no-build-isolation
# With dev dependencies (pytest, ruff)
# pip install -e ".[dev]"
```
> **CUDA kernels** are opt-in at build time (`CSRC_KERNELS=true`). Once built, `CudaBackend` is the default attention backend on GPU (cuda > flash > torch priority). Override via `ASTR_BACKEND` env var or `attn_backend()` context manager. Fused rotary embedding kernel is auto-dispatched when available. Skip for CPU-only usage.
> **CUDA kernels** build automatically when `nvcc` is on `PATH` and
> `torch.cuda.is_available()` returns `True`; set `CSRC_KERNELS=false` to skip
> them, or `CSRC_KERNELS=true` to force them (required when building in an
> isolated environment with `--no-build-isolation`). Once built, `CudaBackend`
> is the default attention backend on GPU (cuda > flash > torch priority).
> Override via `ASTR_BACKEND` env var or `attn_backend()` context manager.
> Fused rotary embedding kernel is auto-dispatched when available. Skip for
> CPU-only usage.
## 2. Download Model Weights
@@ -258,5 +265,7 @@ docker compose up -d
| Multi-GPU DDP / FSDP | [Distributed Guide](guides/distributed.md) |
| System architecture | [Architecture](developer/architecture.md) |
| Data pipeline internals | [Data Flow](developer/dataflow.md) |
| YAML-driven containerized serving | [Docker Serving](developer/docker-serving.md) |
| YAML-driven containerized training | [Docker Training](developer/docker-training.md) |
> Document Update Time: 2026-07-31
> Document Update Time: 2026-08-22