description: Train VQ on Bird Dataset

auth:
  # which virtual cluster you belong to (msrlabs, etc.). Everyone has access to "pnrsy".
  vc: msrlabspvc12 # msrlabs
  # physical cluster to use (cam, gcr, rr1) or Azure clusters (eu1, eu2, etc.)
  # cluster: rr2, eu2, eu1 et1 
  cluster: eu2
  # docker environment (vm) in which your job will run. we provide "generic" dockers
  # with the main deep learning toolkit installed (PyTorch, TF, Chainer, etc.)
  docker:
    # image: philly/jobs/custom/generic-docker:py27
    # registry: phillyregistry.azurecr.io
    image: vlnres/vqvae:v1 # chunyl/vqvae:v2
    registry: index.docker.io

storage:
  _default:
    #use_phillyfs: True
    storage_account_name: sslm
    container_name: vqvae
    mount_path: /mnt/_default

code:
  # local directory of the code. this will be uploaded to the server.
  # $CONFIG_DIR is expanded to the directory of this config file
  code_upload: False
  remote_dir: vq-vae-2-pytorch/
  local_dir: $CONFIG_DIR/src

#data:
  # data upload is not required for this example
  #data_upload: False

search:
  job_template:
    name: vq_{experiment_name:s}_{image_size_option:.1f}
    sku: G4 # G4 # G1
    command:
    - python train_vqvae.py --philly --dataset_name bird --size {image_size_option} --batch 512
  max_trials: 20
  type: grid
  params:
    - name: image_size_option
      spec: discrete
      values: [64,128] # [top,bottom]