Installing MLReef On Premises
The best way to run MLReef on your own on-premises infrastructure is the MLReef Nautilus package. Nautilus is a single docker image containing everything necessary to create machine learning projects and run ML workloads.
Nautilus Contains:
- MLReef Management Service
- Postgres
- Gitlab for hosting Git repositories
- Gitlab Runners for running Machine Learning workloads
- API Gateway
You can always request our support for on-prem or self-hosted installation and maintenance. Minimize transition time to MLReef with our
professional services.
Installation
In order to run MLReef Nautilus locally you just have to execute the following docker run command.
This will start up a local instance of mlreef with persistent docker volumes named mlreef-opt, mlreef-etc,
and mlreefdb-opt containing all user data.
docker run -it --rm --detach --name mlreef \
--volume /var/run/docker.sock:/var/run/docker.sock \
--volume mlreef-opt:/var/opt/gitlab \
--volume mlreef-etc:/etc/gitlab \
--volume mlreefdb-opt:/var/opt/mlreef \
--publish 2022:22 \
--publish 80:80 \
--publish 8081:8081 \
--publish 5050:5050 \
--publish 10080:10080 \
--publish 6000:6000 \
registry.gitlab.com/mlreef/mlreef:latest
The container comes up with a default runner running on same docker network on localhost.
Run mlreef with docker volume binding
In order to run MLReef Nautilus locally with local volume binding, you just have to execute
the following docker run command.
This will start up a local instance of mlreef with persistent data on /root/mlreef-gitlab-opt,
/root/mlreef-gitlab-etc and /root/mlreefdb-opt paths.
docker run -it --rm --detach --name mlreef \
--volume /var/run/docker.sock:/var/run/docker.sock \
--volume /root/mlreef-gitlab-opt:/var/opt/gitlab \
--volume /root/mlreef-gitlab-etc:/etc/gitlab \
--volume /root/mlreefdb-opt:/var/opt/mlreef \
--publish 2022:22 \
--publish 80:80 \
--publish 8081:8081 \
--publish 5050:5050 \
--publish 10080:10080 \
--publish 6000:6000 \
registry.gitlab.com/mlreef/mlreef:latest
The container comes up with a default runner running on same docker network on localhost.
The mlreef app would be available on http://localhost.
Run mlreef in a separate docker network
In order to run MLReef Nautilus locally in a separate docker network, you just have to execute
the following commands.
It includes:
A network mlreef-docker-network creation if not exists and docker run command.
This will start up a local instance of mlreef with persistent docker volumes named mlreef-opt,
mlreef-etc and mlreefdb-opt containing all user data.
You can use any network name instead of mlreef-docker-network.
DOCKER_NETWORK="mlreef-docker-network"
docker network inspect $DOCKER_NETWORK >/dev/null 2>&1 || \
docker network create -d bridge $DOCKER_NETWORK
docker run -it --rm --detach --name mlreef \
--volume /var/run/docker.sock:/var/run/docker.sock \
--net $DOCKER_NETWORK \
--volume mlreef-opt:/var/opt/gitlab \
--volume mlreef-etc:/etc/gitlab \
--volume mlreefdb-opt:/var/opt/mlreef \
--publish 2022:22 \
--publish 80:80 \
--publish 8081:8081 \
--publish 5050:5050 \
--publish 10080:10080 \
--publish 6000:6000 \
registry.gitlab.com/mlreef/mlreef:latest
The container comes up with a default runner running on same docker network on localhost.
The mlreef app would be available on http://localhost.
Adding Machine Learning Runners
The best way to host additional ML runners is to run them as separate docker containers. This can be done on machines separate from the MLReef application server. The runners must have network access to the MLReef application server.
Getting the Registration token
After startup is completed you can query MLReef's Gitlab service for the current
runner registration token by executing this docker exec command
docker exec -it mlreef gitlab-rails runner -e production "puts Gitlab::CurrentSettings.current_application_settings.runners_registration_token" | tr -d '\r'```
Register the Runner with MLReef
Then on the machine hosting the runner you need to execute a normal runner registration command.
The default $GITLAB_PORT is 10080
docker exec -it ml-runner \
gitlab-runner register --non-interactive \
--url="$MLREEF_HOST:$GITLAB_PORT" \
--registration-token "$TOKEN" \
--executor "docker" \
--docker-image alpine:latest \
--docker-privileged="true" \
--docker-volumes /var/run/docker.sock:/var/run/docker.sock \
--description "Packaged Runner" \
--tag-list "docker,local-docker" \
--run-untagged="true" \
--locked="false" \
--access-level="not_protected"
GPU Capabilities
A gitlab-runner can be setup for different executors with GPU capabilities as per following doc: