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docker-compose.yml
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docker-compose.yml
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# SPDX-License-Identifier: Apache-2.0
version: "3.8"
services:
claratrain:
container_name: claradevday-pt
hostname: claratrain
##### use vanilla clara train docker
image: nvcr.io/nvidia/clara-train-sdk:v4.1
##### to build image with GPU dashboard inside jupyter lab
# build:
# context: ./dockerWGPUDashboardPlugin/ # Project root
# dockerfile: ./Dockerfile # Relative to context
# image: clara-train-nvdashboard:4.1
depends_on:
- tritonserver
ports:
- "3030:8888" # Jupyter lab port
- "3031:5000" # AIAA port
ipc: host
volumes:
- ${TRAIN_DEV_DAY_ROOT}:/claraDevDay/
- /raid/users/aharouni/data:/data/
command: "jupyter lab /claraDevDay --ip 0.0.0.0 --allow-root --no-browser --config /claraDevDay/scripts/jupyter_notebook_config.py"
# command: tail -f /dev/null
# tty: true
#############################################################
tritonserver:
image: nvcr.io/nvidia/tritonserver:21.02-py3
container_name: aiaa-triton
hostname: tritonserver
restart: unless-stopped
command: >
sh -c "chmod 777 /triton_models &&
/opt/tritonserver/bin/tritonserver \
--model-store /triton_models \
--model-control-mode="poll" \
--repository-poll-secs=5 \
--log-verbose ${TRITON_VERBOSE}"
volumes:
- ${TRAIN_DEV_DAY_ROOT}/AIAA/workspace/triton_models:/triton_models
# shm_size: 1gb
# ulimits:
# memlock: -1
# stack: 67108864
# logging:
# driver: json-file
deploy:
resources:
reservations:
devices:
- driver: nvidia
capabilities: [ gpu ]
# To specify certain GPU uncomment line below
#device_ids: ['0,3']