README.md

Logger Backends

WandB

Run wandb login from your terminal to signup or authenticate your machine (we store your api key in ~/.netrc). You can also set the WANDB_API_KEY environment variable with a key from your settings.

MLFlow

Start mlflow tracker:

• With Docker
• docker-compose up --force-recreate -d mlflow
• Modify .env file to change the default directories.

Trains

Install trains-server (backend) and trains (client) library:

Installing Backend

git clone https://github.com/allegroai/trains-server Update docker daemon and systemctl settings (see installation)

sudo sysctl -w vm.max_map_count=262144

cd backend/trains/ #use the docker-compose.yaml and .env here update .env with TRAINS data directory path (default is /opt/trains) create sub-directories (TODO: add instructions) docker-compose up

Access trains UI at localhost:8080

Install Trains Client

pip install -U trains

trains-init # create ~/trains.conf, ensure server points to localhost:8008, get keys from trains UI.

mv ~/trains.conf . #trains.conf should be able to access this file during the run

Finally, in lxconfig.py for your project:

trconf = TrainsConfig(config_file='./trains.conf')
L = LoggerConfig(trains=trconf)

Monitoring Backends

• Docker Monitoring
  • Terminal: dry
  • UI: portainer , swarmpit

Dispatchers

Docker, Docker-Compose, Docker-Swarm

• Install docker

• Install docker-compose link

MicroK8s / Kubernetes

• Setup your k8s cluster
• Refer to this file for setting up mlflow.
  • tldr: kubectl apply -f mlflow/