MSML/papers/Stochastic_Process_Diffusion/tsdiff/forecasting/experiment.yaml at main · morganstanley/MSML · GitHub

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seml:
  executable: experiments/forecasting/experiment.py
  name: tsdiff_forecast
  output_dir: experiments/logs
  project_root_dir: ../../
  conda_environment: place_your_env_here

slurm:
  experiments_per_job: 1
  sbatch_options:
    gres: gpu:1       # num GPUs
    mem: 16G          # memory
    cpus-per-task: 2  # num cores
    time: 0-08:00     # max time, D-HH:MM
    partition: gpu_all


fixed:
  epochs: 10
  learning_rate: 1e-3
  batch_size: 64

grid:
  seed:
    type: range
    min: 1
    max: 4
    step: 1

  dataset:
    type: choice
    options:
      - electricity_nips
      - solar_nips
      - traffic_nips
      - exchange_rate_nips

  diffusion_steps:
    type: choice
    options:
      - 100

  num_cells:
    type: choice
    options:
      - 40

  hidden_dim:
    type: choice
    options:
      - 100

  residual_layers:
    type: choice
    options:
      - 8

old:
  grid:
    network:
      type: choice
      options:
        - timegrad
        - timegrad_old

    noise:
      type: choice
      options:
        - normal

cnn:
  grid:
    network:
      type: choice
      options:
        - timegrad_cnn

    noise:
      type: choice
      options:
        - normal
        - ou
        - gp