model_single.yml

# --------------------------------------------------------------------------------------------------------------------- #
# This yaml file implements 6 hourly FuXi on NSF NCAR HPCs (casper.ucar.edu and derecho.hpc.ucar.edu) 
# the FuXi architecture has been modified to reduce the overall model size
# The model is trained on hourly model-level ERA5 data with top solar irradiance, geopotential, and land-sea mask inputs
# Output variables: model level [U, V, T, Q], single level [SP, t2m], and 500 hPa [U, V, T, Z, Q]
#
# Yingkai Sha
# ksha@ucar.edu
# --------------------------------------------------------------------------------------------------------------------- #
save_loc: '/glade/work/ksha/CREDIT_runs/fuxi_6h/'
seed: 1000

data:
    # upper-air variables
    variables: ['U','V','T','Q']
    save_loc: '/glade/derecho/scratch/wchapman/SixHourly_y_TOTAL*'
    
    # surface variables
    surface_variables: ['SP','t2m','V500','U500','T500','Z500','Q500']
    save_loc_surface: '/glade/derecho/scratch/wchapman/SixHourly_y_TOTAL*'
    
    # dynamic forcing variables
    dynamic_forcing_variables: ['tsi']
    save_loc_dynamic_forcing: '/glade/derecho/scratch/dgagne/credit_solar_6h_0.25deg/*.nc'
    
    # diagnostic variables
    # diagnostic_variables: ['V500','U500','T500','Z500','Q500'] 
    # save_loc_diagnostic: '/glade/derecho/scratch/wchapman/SixHourly_y_TOTAL*'
    
    # static variables
    static_variables: ['Z_GDS4_SFC','LSM'] 
    save_loc_static: '/glade/derecho/scratch/ksha/CREDIT_data/static_norm_old.nc'
    
    # mean / std path
    mean_path: '/glade/derecho/scratch/ksha/CREDIT_data/mean_6h_1979_2018_16lev_0.25deg.nc'
    std_path: '/glade/derecho/scratch/ksha/CREDIT_data/std_residual_6h_1979_2018_16lev_0.25deg.nc'
    
    # train / validation split
    train_years: [1979, 2018]
    valid_years: [2018, 2019]
    
    # data workflow
    scaler_type: 'std_new'  
    
    # number of input states
    # FuXi has 2 input states
    history_len: 2
    valid_history_len: 2
    
    # number of forecast steps to compute loss
    # 0 for single step training / validation
    # larger than 0 for multi-step training / validation
    forecast_len: 0
    valid_forecast_len: 0
    
    # one_shot: True --> compute loss on the last forecast step only
    # one_shot: False --> compute loss on all forecast steps
    one_shot: True
    
    # 1 for hourly model
    lead_time_periods: 6
    
    # do not use skip_period
    skip_periods: null
    
    # compatible with the old 'std'
    static_first: True
    
trainer:
    type: standard # <---------- change to your type
    
    mode: fsdp
    cpu_offload: False
    activation_checkpoint: True
    
    load_weights: True
    load_optimizer: True
    load_scaler: True
    load_sheduler: True

    skip_validation: False
    update_learning_rate: False
    
    save_backup_weights: True
    save_best_weights: True
    
    learning_rate: 1.0e-03 # <-- change to your lr
    weight_decay: 0
    
    train_batch_size: 1
    valid_batch_size: 1
    
    batches_per_epoch: 0
    valid_batches_per_epoch: 0
    stopping_patience: 50
    
    start_epoch: 0
    num_epoch: 2
    reload_epoch: True
    epochs: &epochs 70
    
    use_scheduler: True
    scheduler: {'scheduler_type': 'cosine-annealing', 'T_max': *epochs,  'last_epoch': -1}

    # Automatic Mixed Precision: False
    amp: False
    
    # rescale loss as loss = loss / grad_accum_every
    grad_accum_every: 1 
    # gradient clipping
    grad_max_norm: 1.0
    
    # number of workers
    thread_workers: 4
    valid_thread_workers: 0
    
model:
    type: "fuxi"
    
    frames: 2               # number of input states
    image_height: 640       # number of latitude grids
    image_width: 1280       # number of longitude grids
    levels: 16              # number of upper-air variable levels
    channels: 4             # upper-air variable channels
    surface_channels: 7     # surface variable channels
    input_only_channels: 3  # dynamic forcing, forcing, static channels
    output_only_channels: 0 # diagnostic variable channels
    
    # patchify layer
    patch_height: 4         # number of latitude grids in each 3D patch
    patch_width: 4          # number of longitude grids in each 3D patch
    frame_patch_size: 2     # number of input states in each 3D patch
    
    # hidden layers
    dim: 1024               # dimension (default: 1536)
    num_groups: 32          # number of groups (default: 32)
    num_heads: 8            # number of heads (default: 8)
    window_size: 7          # window size (default: 7)
    depth: 16               # number of swin transformers (default: 48)
    
    # map boundary padding
    pad_lon: 80             # number of grids to pad on 0 and 360 deg lon
    pad_lat: 80             # number of grids to pad on -90 and 90 deg lat
    
    # use spectral norm
    use_spectral_norm: True
    
loss: 
    # the main training loss
    training_loss: "mse"
    
    # power loss (x), spectral_loss (x)
    use_power_loss: False 
    use_spectral_loss: False
    
    # use latitude weighting
    use_latitude_weights: True
    latitude_weights: "/glade/u/home/wchapman/MLWPS/DataLoader/LSM_static_variables_ERA5_zhght.nc"
    
    # turn-off variable weighting
    use_variable_weights: False
    # variable_weights:
    #     U: [0.132, 0.123, 0.113, 0.104, 0.095, 0.085, 0.076, 0.067, 0.057, 0.048, 0.039, 0.029, 0.02 , 0.011, 0.005]
    #     V: [0.132, 0.123, 0.113, 0.104, 0.095, 0.085, 0.076, 0.067, 0.057, 0.048, 0.039, 0.029, 0.02 , 0.011, 0.005]
    #     T: [0.132, 0.123, 0.113, 0.104, 0.095, 0.085, 0.076, 0.067, 0.057, 0.048, 0.039, 0.029, 0.02 , 0.011, 0.005]
    #     Q: [0.132, 0.123, 0.113, 0.104, 0.095, 0.085, 0.076, 0.067, 0.057, 0.048, 0.039, 0.029, 0.02 , 0.011, 0.005]
    #     SP: 0.1
    #     t2m: 1.0
    #     V500: 0.1
    #     U500: 0.1
    #     T500: 0.1
    #     Z500: 0.1
    #     Q500: 0.1
    
predict:
    forecasts:
        type: "custom"       # keep it as "custom"
        start_year: 2020     # year of the first initialization (where rollout will start)
        start_month: 1       # month of the first initialization
        start_day: 1         # day of the first initialization
        start_hours: [0, 12] # hour-of-day for each initialization, 0 for 00Z, 12 for 12Z
        duration: 30         # number of days to initialize, starting from the (year, mon, day) above
                             # duration should be divisible by the number of GPUs 
                             # (e.g., duration: 384 for 365-day rollout using 32 GPUs)
        days: 2              # forecast lead time as days (1 means 24-hour forecast)
        
    save_forecast: '/glade/derecho/scratch/ksha/CREDIT/fuxi_6h/'
    save_vars: ['SP','t2m','V500','U500','T500','Z500','Q500']
    
    # turn-off low-pass filter
    use_laplace_filter: False
    
    # deprecated
    # save_format: "nc"
    
pbs: #derecho
    conda: "/glade/work/ksha/miniconda3/envs/credit"
    project: "NAML0001"
    job_name: "fuxi_6h"
    walltime: "12:00:00"
    nodes: 8
    ncpus: 64
    ngpus: 4
    mem: '480GB'
    queue: 'main'