描述你的问题
如图,一直在解析中
fivyi3re1#
它是在演示还是本地部署?取消它并重新做。
ne5o7dgx2#
它在演示中还是本地部署的?取消并重新执行。是的,它是本地部署的。重新执行仍然不起作用。
u59ebvdq3#
@ctjian deploy with GPU; parsing is exponentially faster. Change your docker-compose to this:
include: - path: ./docker-compose-base.yml env_file: ./.env services: ragflow: depends_on: mysql: condition: service_healthy es01: condition: service_healthy image: infiniflow/ragflow:${RAGFLOW_VERSION} container_name: ragflow-server deploy: resources: reservations: devices: - driver: nvidia device_ids: ['0'] capabilities: [gpu] ports: - ${SVR_HTTP_PORT}:9380 - 80:80 - 443:443 volumes: - ./service_conf.yaml:/ragflow/conf/service_conf.yaml - ./ragflow-logs:/ragflow/logs - ./nginx/ragflow.conf:/etc/nginx/conf.d/ragflow.conf - ./nginx/proxy.conf:/etc/nginx/proxy.conf - ./nginx/nginx.conf:/etc/nginx/nginx.conf environment: - TZ=${TIMEZONE} - HF_ENDPOINT=https://huggingface.co networks: - ragflow restart: always
0vvn1miw4#
Thank you. Problem solved.
4条答案
按热度按时间fivyi3re1#
它是在演示还是本地部署?
取消它并重新做。
ne5o7dgx2#
它在演示中还是本地部署的?取消并重新执行。
是的,它是本地部署的。重新执行仍然不起作用。
u59ebvdq3#
@ctjian deploy with GPU; parsing is exponentially faster. Change your docker-compose to this:
0vvn1miw4#
@ctjian deploy with GPU; parsing is exponentially faster. Change your docker-compose to this:
Thank you. Problem solved.