FourCastNet_v2 / conf /config.yaml
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project:
name: FourCastNet_v2
seed: 42
output_dir: ./result
checkpoint_dir: ./data/checkpoint
data:
dataset_dir: ./data/era5_fake
train_years: [2014,2015]
val_years: [2016]
test_years: [2018]
official_splits:
train_years: [2014, 2015]
val_years: [2016, 2017]
test_years: [2018]
input_steps: 1
output_steps: 1
time_step_hours: 6
grid_shape: [721, 1440]
normalize: true
variables:
- u10m
- v10m
- u100m
- v100m
- t2m
- sp
- msl
- tcwv
- u50
- u100
- u150
- u200
- u250
- u300
- u400
- u500
- u600
- u700
- u850
- u925
- u1000
- v50
- v100
- v150
- v200
- v250
- v300
- v400
- v500
- v600
- v700
- v850
- v925
- v1000
- z50
- z100
- z150
- z200
- z250
- z300
- z400
- z500
- z600
- z700
- z850
- z925
- z1000
- t50
- t100
- t150
- t200
- t250
- t300
- t400
- t500
- t600
- t700
- t850
- t925
- t1000
- r50
- r100
- r150
- r200
- r250
- r300
- r400
- r500
- r600
- r700
- r850
- r925
- r1000
fake_data:
time_steps_per_year: 20
chunk_time_steps: 1
fill_value: 0.0
materialize_pattern: true
model:
profile: smoke
profiles:
full_resolution:
img_size: [721, 1440]
in_channels: 73
out_channels: 73
spectral_transform: sht
filter_type: non-linear
scale_factor: 6
embed_dim: 256
num_layers: 12
num_blocks: 8
normalization_layer: instance_norm
mlp_mode: distributed
spectral_layers: 3
complex_activation: real
hard_thresholding_fraction: 1.0
big_skip: true
smoke:
img_size: [16, 32]
in_channels: 73
out_channels: 73
spectral_transform: sht
filter_type: linear
scale_factor: 4
embed_dim: 8
num_layers: 2
num_blocks: 1
normalization_layer: instance_norm
mlp_mode: serial
spectral_layers: 1
complex_activation: real
hard_thresholding_fraction: 0.5
big_skip: true
# The smoke profile keeps the same equations but reduces the grid/model.
checkpoint:
initialize_from: scratch
prefix: model_bak
finetune_from: ./data/checkpoint/one_step/model_bak.pt
strict: true
training:
stage: one_step
epochs: 3
batch_size: 1
num_workers: 0
learning_rate: 0.0006
weight_decay: 0.0
optimizer_betas: [0.9, 0.95]
max_grad_norm: 32.0
scheduler: cosine
amp: false
max_train_batches: null
max_val_batches: null
finetune:
autoregressive_steps: 2
epochs: 3
learning_rate: 0.0001
distributed:
backend: nccl
master_addr: 127.0.0.1
master_port: 29500
inference:
checkpoint_path: ./data/checkpoint/finetune/model_bak.pt
rollout_steps: 1
max_samples: 1
save_normalized: false
output_dir: ./result/output
visualization:
variable: t2m
sample_index: 0
output_dir: ./result/figures
cmap: coolwarm
slurm:
job_name: fcnv2_train
nodes: 1
gpus_per_node: 8
cpus_per_task: 8
time: "24:00:00"
partition: null
conda_env: fourcastnetv2_develop