Using emrys


sudo emrys login


For convenience, users should store their settings in a configuation file. Emrys will look for .emrys.yaml in the prioritized order: current directory, $HOME/.config/emrys, and $HOME/. Toml and json formats are also accepted.

Example .emrys.yaml for executing emrys run:

  ## required
  # name of project
  project: "numpy-test"

  # path to pip requirements.txt
  requirements: "test/numpy/requirements.txt"

  # path to main python script for execution
  main: "test/numpy/main.py"

  # path to output folder
  output: "test/numpy/output"

  ## optional
  # path to data directory
  data: "test/numpy/data"

  # Minimum acceptable gpu, ranked here: https://github.com/wminshew/emrys/blob/master/pkg/job/ValidGPU.go
  # default: k80
  gpu: gtx 1080 ti

  # Minimum acceptable amount of supplier RAM
  # default: 8gb
  ram: 4gb

  # Minimum acceptable amount of supplier disk space
  # default: 25gb
  disk: 10gb

  # Minimum acceptable supplier gpu pci-e lanes
  # default: 8x
  pcie: 16x

Running jobs

Once a configuration file is in place, the user can dispatch jobs with the following command:

sudo emrys run

When convenient, flags will override configuration settings.

# use a different python script for execution
sudo emrys run --main test/numpy/other-main.py

How does it work?

Emrys run does ~4 things:

  1. uploads a python script and requirements to the server with which a docker image is built
  2. syncs the data set, if it exists locally, to the server
  3. auctions the job’s execution to supplier’s meeting the user’s hardware requirements
  4. streams output logs back to the user & downloads anything the python script saved in ./output/


Please note there is currently a maximum data set size of 10gb per project. Please email support to request a larger limit.