launcher/docker-images/deepseek-v4-flash-dspark-src

deepseek-v4-flash-dspark-src — build DeepSeek-V4-Flash DSpark from the b12x fork

EXPERIMENTAL. Builds a DeepSeek-V4-Flash DSpark serving image from source, so we are not locked to the prebuilt aidendle94/sparkrun-vllm-ds4-gb10 image.

Stack (pinned in versions.env):

Layer	Pin
vLLM fork	local-inference-lab/vllm @ feat/gg-ds4-0731-launcher = e63a190ee
torch	2.12.0+cu130
FlashInfer	ce40d25a (0.6.15), built from source
b12x	voipmonitor/b12x @ 284a2ea
NCCL	5067397c
CUDA base	nvidia/cuda:13.2.0-devel-ubuntu24.04 @ pinned digest
arch	aarch64 / sm_121a

The python locks were regenerated from the fork's own requirements/ at the pinned commit (torch 2.12.0, flashinfer 0.6.15, cutlass-dsl 4.6.0, tokenspeed/humming-kernels), not from the GLM-5.2 harness locks (which pin torch 2.11 / flashinfer 0.6.12 and are therefore invalid for this stack).

Workflow

1. Reset/refresh the locks (only needed when the fork pins change):

   bash gen-locks.sh          # writes locks/python-build.txt & python-runtime.txt
   

2. Build:

   TAG=vllm-ds4-dspark-src:probe bash build.sh
   

   via the launcher on a build node:

   sm121 build --recipe deepseek-v4-flash-dspark-src --build-node dgx3.lan
   

   This uploads this directory, runs build.sh (torch wheels + from-source FlashInfer/vLLM + b12x editable) in a tmux build session, and stamps the sm121.checksum label.

Notes / caveats

• Multi-hour build. Expect 1–3h+ on a DGX Spark; runs in tmux on the node.
• The runtime lock pins flashinfer 0.6.14 (from the fork's requirements/cuda.txt) but we then install the 0.6.15 from-source wheel over it — the built wheel wins. If pip objects to install-order, pin the runtime lock differently.
• FlashInfer 0.6.15 is built from source and installed with --no-deps, so its own install_requires are never auto-resolved into the lock. gen-locks.sh therefore re-adds its hard transitive deps explicitly (cuda-tile==1.5.0) and nccl4py==0.3.1) so pip check passes. deep-gemm (sdist-only) and vllm-flash-attn (x86-only) have no aarch64 wheel and are not required by the fork's vLLM, and ray is not needed on the mp distributed backend — so all three stay out of the aarch64 runtime lock. Note the import names differ from the PyPI names: cuda-tile → cuda.tile, nccl4py → nccl.
• overlay/ and patches/ are intentionally empty (GLM-specific pieces dropped); add DS4 patches here if the fork needs a local fix.
• Config/flags for serving mirror the fork's serve-ds4-flash-spark.sh.