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66ee87e 6012dcc 66ee87e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | services:
decisionlab:
build:
context: .
args:
# CPU by default. For NVIDIA GPUs use https://download.pytorch.org/whl/cu128
TORCH_INDEX_URL: https://download.pytorch.org/whl/cpu
image: decisionlab:latest
container_name: DecisionLab
ports:
# localhost only by default (DL-SA-009). To reach the lab from other machines set DLAB_BIND=0.0.0.0 in .env.
- "${DLAB_BIND:-127.0.0.1}:9910:9910"
environment:
TRUSTED_MODELING_SHA256: ${TRUSTED_MODELING_SHA256:-}
DEVICE: auto # auto | cuda | cpu
# The three Hub models (shown in this order, then any models in ./models, marked "(local)"):
LIGHTDEC_ARTHUR_REPO: Falconsai/LightDec_Arthur
LIGHTDEC_V2_REPO: Falconsai/LightDec_V2
REFLUX_LAYA_REPO: yasserrmd/enterprise-reflux-laya-v21
LAYA_REPO: convaiinnovations/laya
MODELS_DIR: /models # LightDec and Arthur folders in ./models (mounted below) are added too
LIGHTDEC_VARIANT: fp16 # fp16 | int8 (compact-int8 folder), for every LightDec model
# HF_TOKEN: hf_xxx # only needed for gated or private repos
volumes:
- decisionlab-hf:/data/hf # model cache survives rebuilds
- ./models:/models:ro # unzip each FalconDec model into its own folder here
restart: unless-stopped
# Uncomment for an NVIDIA GPU (needs the NVIDIA Container Toolkit and the cu128 build arg above):
# deploy:
# resources:
# reservations:
# devices:
# - driver: nvidia
# count: 1
# capabilities: [gpu]
volumes:
decisionlab-hf:
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