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

docs: scope trainer environment variables per job

Browse Source 
- Add a Per-Job Environment section explaining that runtime.environment reaches only the GPUs declared in the same job YAML, with one-YAML-per-GPU-group examples for local, cross-PCIe workaround, and NVSwitch NVLink tuning setups
- Replace the NCCL workaround pair in the runtime schema example with ASTR_LOG_LEVEL and ASTR_BACKEND and document value semantics (str() rendering, null exports empty, no host-shell passthrough)
- Comment out the blanket NCCL exports in the get-started multi-GPU example so they are opt-in per docs/guides/distributed.md
- Add a hard rule against copying NCCL workarounds into every training config
This commit is contained in:
ViperEkura
2026-08-31 14:24:51 +08:00
parent 0546331637
commit a7d4cb25c5
2 changed files with 86 additions and 8 deletions
Download Patch File Download Diff File Expand all files Collapse all files
docs/get-started.md
+3 -2
View File
@@ -190,8 +190,9 @@ python scripts/tools/train.py \
```bash
export CUDA_VISIBLE_DEVICES=0,1,2,3
export NCCL_P2P_DISABLE=1
export NCCL_NET_GDR_LEVEL=0
# Only if this host's NCCL transport is broken; see docs/guides/distributed.md:
# export NCCL_P2P_DISABLE=1
# export NCCL_NET_GDR_LEVEL=0
python scripts/tools/train.py \
--train_type=seq \