How severe does this issue affect your experience of using Ray?
- High: It blocks me to complete my task.
I have a local cluster that I set up through the cluster launcher with docker enabled. However, the volumes that I specify to be mounted into the container dont get mounted.
This is my cluster config yaml:
# A unique identifier for the head node and workers of this cluster.
cluster_name: default
# Running Ray in Docker images is optional (this docker section can be commented out).
# This executes all commands on all nodes in the docker container,
# and opens all the necessary ports to support the Ray cluster.
# Empty string means disabled. Assumes Docker is installed.
docker:
image: "rayproject/ray-ml:latest-gpu" # You can change this to latest-cpu if you don't need GPU support and want a faster startup
# image: rayproject/ray:latest-gpu # use this one if you don't need ML dependencies, it's faster to pull
container_name: "ray_container"
# If true, pulls latest version of image. Otherwise, `docker run` will only pull the image
# if no cached version is present.
pull_before_run: True
run_options: # Extra options to pass into "docker run"
- --ulimit nofile=65536:65536
head_run_options: # Extra options to pass into "docker run" for the head node
# mout ~/data to ~/data in the container
- "-v /home/myuser/data:/home/ray/data:z" # doesn't work without :z either
# etc....
auth:
ssh_user: myuser
# etc...
Everything else works fine (conda envs and so on).
Does someone know what is wrong with my docker config? Or where can I see logs/errors with regards to the docker command.
(I ssh into the head node with ray attach to check if the folder is there or not. ATM I only have a head node)