Upload model - xlq6w7qj

README.md

@@ -1,7 +1,7 @@
 ---
 language: en
-license: mit
 library_name: pytorch
+license: mit
 ---
 
 
@@ -36,7 +36,7 @@ The data was processed using the protocol in [2]
 ## Results
 
 The training logs for the current model can be found here:
-- https://wandb.ai/openclimatefix/sat_pred/runs/ob9v9128
+- https://wandb.ai/openclimatefix/sat_pred/runs/xlq6w7qj
 
 
 ### Software

data_config.yaml

@@ -0,0 +1,96 @@
+_target_: sat_pred.dataset.SatelliteDataModule
+batch_size: 2
+channels:
+  IR_016:
+    clip_max: 100
+    clip_min: 0
+    mean: 7.4
+    missing_value: -10.5
+    std: 10.5
+  IR_039:
+    clip_max: 330
+    clip_min: 200
+    mean: 267.5
+    missing_value: 185.1
+    std: 14.9
+  IR_087:
+    clip_max: 310
+    clip_min: 190
+    mean: 261.3
+    missing_value: 174.2
+    std: 15.8
+  IR_097:
+    clip_max: 280
+    clip_min: 190
+    mean: 239.4
+    missing_value: 181.2
+    std: 8.8
+  IR_108:
+    clip_max: 320
+    clip_min: 190
+    mean: 263.5
+    missing_value: 174.0
+    std: 16.0
+  IR_120:
+    clip_max: 310
+    clip_min: 190
+    mean: 262.4
+    missing_value: 173.9
+    std: 16.1
+  IR_134:
+    clip_max: 280
+    clip_min: 190
+    mean: 246.1
+    missing_value: 179.8
+    std: 10.2
+  VIS006:
+    clip_max: 100
+    clip_min: 0
+    mean: 8.0
+    missing_value: -11.4
+    std: 11.4
+  VIS008:
+    clip_max: 100
+    clip_min: 0
+    mean: 9.3
+    missing_value: -13.0
+    std: 13.0
+  WV_062:
+    clip_max: 270
+    clip_min: 190
+    mean: 229.5
+    missing_value: 185.2
+    std: 4.8
+  WV_073:
+    clip_max: 270
+    clip_min: 190
+    mean: 243.9
+    missing_value: 181.9
+    std: 8.1
+dataset_pickle_dir: /tmp/sat_pred_dataset_cache
+forecast_mins: 180
+history_mins: 165
+num_workers: 8
+persistent_workers: false
+pin_memory: true
+prefetch_factor: 2
+sample_freq_mins: 15
+seed: ${seed}
+train_periods:
+- - 2008-01-01 00:00
+  - 2017-12-31 23:55
+val_periods:
+- - 2018-01-01 00:00
+  - 2018-12-31 23:55
+zarr_path:
+- /mnt/storage_u2_30tb_a/ml_training_zarrs/sat/uk_sat_crops/v6_symlink_minimal/rss-2008-3000m-uk-only.zarr
+- /mnt/storage_u2_30tb_a/ml_training_zarrs/sat/uk_sat_crops/v6_symlink_minimal/rss-2009-3000m-uk-only.zarr
+- /mnt/storage_u2_30tb_a/ml_training_zarrs/sat/uk_sat_crops/v6_symlink_minimal/rss-2010-3000m-uk-only.zarr
+- /mnt/storage_u2_30tb_a/ml_training_zarrs/sat/uk_sat_crops/v6_symlink_minimal/rss-2011-3000m-uk-only.zarr
+- /mnt/storage_u2_30tb_a/ml_training_zarrs/sat/uk_sat_crops/v6_symlink_minimal/rss-2012-3000m-uk-only.zarr
+- /mnt/storage_u2_30tb_a/ml_training_zarrs/sat/uk_sat_crops/v6_symlink_minimal/rss-2013-3000m-uk-only.zarr
+- /mnt/storage_u2_30tb_a/ml_training_zarrs/sat/uk_sat_crops/v6_symlink_minimal/rss-2014-3000m-uk-only.zarr
+- /mnt/storage_u2_30tb_a/ml_training_zarrs/sat/uk_sat_crops/v6_symlink_minimal/rss-2015-3000m-uk-only.zarr
+- /mnt/storage_u2_30tb_a/ml_training_zarrs/sat/uk_sat_crops/v6_symlink_minimal/rss-2016-3000m-uk-only.zarr
+- /mnt/storage_u2_30tb_a/ml_training_zarrs/sat/uk_sat_crops/v6_symlink_minimal/rss-2017-3000m-uk-only.zarr
+- /mnt/storage_u2_30tb_a/ml_training_zarrs/sat/uk_sat_crops/v6_symlink_minimal/rss-2018-3000m-uk-only.zarr

full_experiment_config.yaml

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+model_name: simvp_v3_wsd
+seed: 27267831
+trainer:
+  _target_: lightning.pytorch.trainer.trainer.Trainer
+  accelerator: gpu
+  devices:
+  - 1
+  precision: 16-mixed
+  gradient_clip_val: 0.5
+  detect_anomaly: false
+  overfit_batches: 0.0
+  min_epochs: null
+  max_epochs: 80
+  reload_dataloaders_every_n_epochs: 0
+  num_sanity_val_steps: 8
+  fast_dev_run: false
+  accumulate_grad_batches: 1
+  limit_train_batches: 12000
+  limit_val_batches: 400
+  log_every_n_steps: 50
+model:
+  _target_: sat_pred.training_module.TrainingModule
+  model:
+    _target_: sat_pred.models.simvp_model.SimVP
+    num_channels: 11
+    history_len: 12
+    forecast_len: 12
+    hid_S: 64
+    hid_T: 512
+    N_S: 4
+    N_T: 4
+  restore_optimizer_state: true
+  optimizer:
+    _target_: sat_pred.optimizers.AdamWWSD
+    lr: 0.0005
+    warmup_epochs: 0
+    decay_epochs: 30
+    total_epochs: 80
+    final_factor: 0.02
+    weight_decay: 0.01
+  target_loss: MAE
+  video_plot_t0_times:
+  - '2018-06-08T12:00:00'
+  - '2018-12-26T12:00:00'
+  - '2018-06-14T12:00:00'
+  - '2018-05-08T12:00:00'
+  - '2018-05-12T12:00:00'
+  - '2018-01-29T12:00:00'
+  video_crop_plots: null
+datamodule:
+  _target_: sat_pred.dataset.SatelliteDataModule
+  zarr_path:
+  - /mnt/storage_u2_30tb_a/ml_training_zarrs/sat/uk_sat_crops/v6_symlink_minimal/rss-2008-3000m-uk-only.zarr
+  - /mnt/storage_u2_30tb_a/ml_training_zarrs/sat/uk_sat_crops/v6_symlink_minimal/rss-2009-3000m-uk-only.zarr
+  - /mnt/storage_u2_30tb_a/ml_training_zarrs/sat/uk_sat_crops/v6_symlink_minimal/rss-2010-3000m-uk-only.zarr
+  - /mnt/storage_u2_30tb_a/ml_training_zarrs/sat/uk_sat_crops/v6_symlink_minimal/rss-2011-3000m-uk-only.zarr
+  - /mnt/storage_u2_30tb_a/ml_training_zarrs/sat/uk_sat_crops/v6_symlink_minimal/rss-2012-3000m-uk-only.zarr
+  - /mnt/storage_u2_30tb_a/ml_training_zarrs/sat/uk_sat_crops/v6_symlink_minimal/rss-2013-3000m-uk-only.zarr
+  - /mnt/storage_u2_30tb_a/ml_training_zarrs/sat/uk_sat_crops/v6_symlink_minimal/rss-2014-3000m-uk-only.zarr
+  - /mnt/storage_u2_30tb_a/ml_training_zarrs/sat/uk_sat_crops/v6_symlink_minimal/rss-2015-3000m-uk-only.zarr
+  - /mnt/storage_u2_30tb_a/ml_training_zarrs/sat/uk_sat_crops/v6_symlink_minimal/rss-2016-3000m-uk-only.zarr
+  - /mnt/storage_u2_30tb_a/ml_training_zarrs/sat/uk_sat_crops/v6_symlink_minimal/rss-2017-3000m-uk-only.zarr
+  - /mnt/storage_u2_30tb_a/ml_training_zarrs/sat/uk_sat_crops/v6_symlink_minimal/rss-2018-3000m-uk-only.zarr
+  history_mins: 165
+  forecast_mins: 180
+  sample_freq_mins: 15
+  train_periods:
+  - - 2008-01-01 00:00
+    - 2017-12-31 23:55
+  val_periods:
+  - - 2018-01-01 00:00
+    - 2018-12-31 23:55
+  num_workers: 8
+  prefetch_factor: 2
+  batch_size: 2
+  pin_memory: true
+  persistent_workers: false
+  dataset_pickle_dir: /tmp/sat_pred_dataset_cache
+  seed: 27267831
+  channels:
+    VIS006:
+      mean: 8.0
+      std: 11.4
+      clip_min: 0
+      clip_max: 100
+      missing_value: -11.4
+    VIS008:
+      mean: 9.3
+      std: 13.0
+      clip_min: 0
+      clip_max: 100
+      missing_value: -13.0
+    IR_016:
+      mean: 7.4
+      std: 10.5
+      clip_min: 0
+      clip_max: 100
+      missing_value: -10.5
+    IR_039:
+      mean: 267.5
+      std: 14.9
+      clip_min: 200
+      clip_max: 330
+      missing_value: 185.1
+    WV_062:
+      mean: 229.5
+      std: 4.8
+      clip_min: 190
+      clip_max: 270
+      missing_value: 185.2
+    WV_073:
+      mean: 243.9
+      std: 8.1
+      clip_min: 190
+      clip_max: 270
+      missing_value: 181.9
+    IR_087:
+      mean: 261.3
+      std: 15.8
+      clip_min: 190
+      clip_max: 310
+      missing_value: 174.2
+    IR_097:
+      mean: 239.4
+      std: 8.8
+      clip_min: 190
+      clip_max: 280
+      missing_value: 181.2
+    IR_108:
+      mean: 263.5
+      std: 16.0
+      clip_min: 190
+      clip_max: 320
+      missing_value: 174.0
+    IR_120:
+      mean: 262.4
+      std: 16.1
+      clip_min: 190
+      clip_max: 310
+      missing_value: 173.9
+    IR_134:
+      mean: 246.1
+      std: 10.2
+      clip_min: 190
+      clip_max: 280
+      missing_value: 179.8
+callbacks:
+  early_stopping:
+    _target_: lightning.pytorch.callbacks.EarlyStopping
+    monitor: MAE/val
+    mode: min
+    patience: 200
+    min_delta: 0
+  learning_rate_monitor:
+    _target_: lightning.pytorch.callbacks.LearningRateMonitor
+    logging_interval: epoch
+  gradient_accumulation:
+    _target_: lightning.pytorch.callbacks.GradientAccumulationScheduler
+    _convert_: all
+    scheduling:
+      0: 16
+      2: 16
+      5: 16
+      10: 16
+  model_summary:
+    _target_: lightning.pytorch.callbacks.ModelSummary
+    max_depth: 4
+  progress_bar:
+    _target_: lightning.pytorch.callbacks.TQDMProgressBar
+    refresh_rate: 50
+  model_checkpoint:
+    _target_: lightning.pytorch.callbacks.ModelCheckpoint
+    monitor: MAE/val
+    mode: min
+    save_top_k: 1
+    save_last: true
+    every_n_epochs: 1
+    verbose: false
+    filename: epoch={epoch}-step={step}
+    dirpath: checkpoints/simvp_v3_wsd
+    auto_insert_metric_name: false
+    save_on_train_epoch_end: false
+logger:
+  wandb:
+    _target_: lightning.pytorch.loggers.wandb.WandbLogger
+    project: sat_pred
+    name: simvp_v3_wsd
+    save_dir: ./
+    offline: false
+    id: null
+    log_model: false
+    prefix: ''
+    job_type: train
+    group: ''
+    tags: []

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:cc35b136a9b0383cc989af09f3b370693a5a147835132282d9086af9e1579e8e
-size 55940132
+oid sha256:25ad335bd226416bb00ed4fb2f75248e65d0df29b23fa6b50c55116145c26041
+size 121117892

model_config.yaml

@@ -1,4 +1,8 @@
+N_S: 4
+N_T: 4
 _target_: sat_pred.models.simvp_model.SimVP
 forecast_len: 12
+hid_S: 64
+hid_T: 512
 history_len: 12
 num_channels: 11