Comments (1)
Hi @selintunr. cog predict
starts up a container, runs the model once, and shuts down. To keep it running, you can do cog build
to build the image, docker run
to run the container, and curl
to send predictions to the running container.
From the README:
$ cog build -t my-colorization-model --> Building Docker image... --> Built my-colorization-model:latest $ docker run -d -p 5000:5000 --gpus all my-colorization-model $ curl http://localhost:5000/predictions -X POST \ -H 'Content-Type: application/json' \ -d '{"input": {"image": "https://.../input.jpg"}}'
from cog.
Related Issues (20)
- Disable cache in github workflow (Github action self-hosted runner)
- COPY other files for use in run segment of cog.yaml build
- build fail HOT 1
- Container suddenly stopping without explicit reason
- B008 Do not perform function call `Input` in argument defaults; instead, perform the call within the function, or read the default from a module-level singleton variable
- Cog Push Error: This image doesn't look like it was built with Cog.
- ERROR: failed to solve: circular dependency detected on stage: weights HOT 4
- Infinite loops happening without logs
- How to create a queuing mechanism in cog using "celery" and "redis" for video processing? HOT 1
- Concurrency configuration
- AttributeError: 'URLFile' object has no attribute '__target__'
- Tell me if the model doesn't exist before wasting time building it
- Unable to compute gradient in during inference HOT 2
- How to push paddleOCR to Replicate HOT 1
- Add support for UI For predict in container HOT 1
- ControlNet and Predictions
- Got ImportError in cog while installing packages from surya-ocr ImportError: cannot import name 'computed_field' from 'pydantic' (/usr/local/lib/python3.11/site-packages/pydantic/
- Update environment variable docs HOT 5
- Asking for clarification on the current and future state of async inference using cog
- Support multi-stage Dockerfile
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from cog.