ckpts_from_huggingface_debug.yaml

# Compared to the non-debug version, this file simply runs inference for fewer steps.
experiment_dir: results/spherical-dyffusion
n_forward_steps: 100
forward_steps_in_memory: 50
validation_loader:
  # IMPORTANT: Set the correct data path for the validation dataset
  dataset:
    # =============== Edit this path ===============
    data_path: "/data/climate-model/fv3gfs/2023-09-07-vertically-resolved-1deg-fme-ensemble-dataset-netcdfs/validation/ic_0011"
    # =============================================
    n_repeats: 1   # Use 10 for 100 year rollout (10 x 10 years)
  start_indices:
    first: 0
    n_initial_conditions: 1
  num_data_workers: 8
#  The following specifies which (Spherical) DYffusion checkpoint to use. It can be
#  a) A local path to a checkpoint file
#  b) A huggingface model id (with the prefix "hf:")
checkpoint_path: "hf:salv47/spherical-dyffusion/forecaster-sfno-best-inference_avg_crps.ckpt"
# Override inference parameters and interpolator configuration
overrides:
  diffusion_config:
    hack_for_imprecise_interpolation: True
    # The following two paths are used to load the interpolator model and config
    #   a) If pre-pending "hf:" to the path, the checkpoint will be downloaded from huggingface.
    #   b) Otherwise, it will be loaded from the local filesystem.
    #   c) Set it to null, to try downloading from wandb based on the interpolator_run_id & entity and project below.
    interpolator_local_checkpoint_path: "hf:salv47/spherical-dyffusion/interpolator-sfno-best-val_avg_crps.ckpt"
    interpolator_local_config_path: "hf:salv47/spherical-dyffusion/interpolator_sfno_paper_v0_hydra_config.yaml"
    # As said, alternatively, just mention the entity and project to download the checkpoint from wandb.
    interpolator_wandb_kwargs:
      entity: null
      project: "Spherical-DYffusion"   # potentially replace with the correct project name
# More possible overrides:
#    interpolator_use_ema: True
#    use_cold_sampling_for_last_step: False
#    use_cold_sampling_for_intermediate_steps: False
#    use_cold_sampling_for_init_of_ar_step: True

# Logging configuration
logging:
  project: "Spherical-DYffusion-inference"   # Where to log inference results to
  entity: null  # Replace with <YOUR_ENTITY>
  log_to_screen: true
  log_to_wandb: true
  log_to_file: true
log_video: false
log_zonal_mean_images: false
data_writer:
  # Set below to true to save predictions to xarray files (e.g. for further analysis)
  save_prediction_files: false