Theodore LiandClaude Opus 4.8 97bb727eeb fix(pii): install CUDA torch on amd64 so GLiNER can run on GPU (#5552)
* fix(pii): install CUDA torch on amd64 so GLiNER can run on GPU

The published pii image installed a CPU-only torch build, so GLiNER on the
ECS GPU fleet died at model load with "Attempting to deserialize object on a
CUDA device but torch.cuda.is_available() is False". The Dockerfile already
had a TORCH_INDEX_URL arg, but no CI job ever passed --build-arg, so every
image silently took the cpu default.

Select the wheel index from TARGETARCH instead: amd64 gets cu128, arm64 keeps
the cpu index (cu128 publishes no aarch64 wheel at 2.11.0, and no arm64 target
has a GPU). CUDA torch falls back to CPU when no GPU is present, so one image
still serves both the Fargate CPU tasks and the EC2 GPU tasks off the same tag
— no CI or CDK changes needed.

cu128 keeps sm_75, the compute capability of the fleet's T4s, and its CUDA 12.8
runtime needs driver >=525 via minor-version compatibility, which the ECS GPU
AMI satisfies. cu121 was not an option: that index stops at torch 2.5.1.

Verified in an amd64 build of the changed block:
  2.11.0+cu128  cuda=12.8  arch=sm_75 sm_80 sm_86 sm_90 sm_100 sm_120
arm64 still resolves to 2.11.0+cpu. A build-time assert now fails the image
if amd64 ever silently regresses to a cpu wheel.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QHNEWVrh7k89m8Wtqzhs18

* fix(pii): assert torch CUDA state after every pip install

The check sat directly after the torch install, but requirements-gliner.txt
and requirements-dev.txt are installed afterwards and resolve against PyPI
with no torch pin, so a future gliner bump could swap the wheel that
torch_index selected without tripping the assert.

Neither file changes torch today (verified: torch is 2.11.0+cu128 both before
and after the gliner install), so this guards the invariant rather than fixing
a live regression. Moving it below the last pip install makes it certify the
torch that actually ships.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QHNEWVrh7k89m8Wtqzhs18

---------

Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
2026-07-10 16:21:45 -04:00

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Sim — Integrate, Context, Build, and Monitor AI agents

A workspace to build, deploy and manage AI agents and workflows.

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Open sim.ai

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Open http://localhost:3000

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git clone https://github.com/simstudioai/sim.git && cd sim
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Manual Setup

Requirements: Bun, Node.js v20+, PostgreSQL 12+ with pgvector

  1. Clone and install:
git clone https://github.com/simstudioai/sim.git
cd sim
bun install
bun run prepare  # Set up pre-commit hooks
  1. Set up PostgreSQL with pgvector:
docker run --name simstudio-db -e POSTGRES_PASSWORD=your_password -e POSTGRES_DB=simstudio -p 5432:5432 -d pgvector/pgvector:pg17

Or install manually via the pgvector guide.

  1. Configure environment:
cp apps/sim/.env.example apps/sim/.env
# Create your secrets
perl -i -pe "s/your_encryption_key/$(openssl rand -hex 32)/" apps/sim/.env
perl -i -pe "s/your_internal_api_secret/$(openssl rand -hex 32)/" apps/sim/.env
perl -i -pe "s/your_api_encryption_key/$(openssl rand -hex 32)/" apps/sim/.env
# DB configs for migration
cp packages/db/.env.example packages/db/.env
# Edit both .env files to set DATABASE_URL="postgresql://postgres:your_password@localhost:5432/simstudio"
  1. Run migrations:
cd packages/db && bun run db:migrate
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Or run separately: bun run dev (Next.js) and cd apps/sim && bun run dev:sockets (realtime).

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