Maybe I could use a pre-built docker image with a mounted volume instead?
Docker cmd is basically docker image name but you can add parameters as well.
For example "Nvidia/cuda" or "Nvidia/cuda -v /mnt/data:/mnt/data"
Am I right that docker_cmd should be like "docker run --mount <...> image" ?
Hi MelancholyBeetle72
You mean the venv creation takes the bulk of the time, or it something else ?
Also, can the image not be pulled from dockerhub but used from the local build instead?
If you have your docker configured to pull from local artifactory, then the agent will do the same 🙂 (it is calling the docker command just like you do)
agent.default_docker.arguments: "--mount type=bind,source=$DATA_DIR,target=/data"
Notice that you are use default docker arguments in the example
If you want the mount to always be there use extra_docker_arguments :
https://github.com/allegroai/trains-agent/blob/9a3f950ac689c50ba3415c42749a4bd8059e89a7/docs/trains.conf#L121
Thanks AgitatedDove14 !
Is there a way to programmatically set the base docker image and extra docker arguments for enqueued tasks? I'm afraid I have no access to trains.conf , and manually editing enqueued experiments in the web UI is not an option.
If I follow the pre-built docker image option, what are the correct configurations?
Also, can the image not be pulled from dockerhub but used from the local build instead?
ah, Task.set_base_docker(docker_cmd) , I reckon
Hmm the agent's venv caching is the next thing on the to do list for the agent (post clearml release).
Currently the easiest thing is to build a new docker image with the entire "Installed packages" section and use that as the base docker image.
(The installed packages format is "requirement" compatible, so you can just use it as is when building the dockerfile)
The second option is to wait for the next clearml-agent release (probably in a couple of weeks)