Upload model - gtjmp2r6

README.md

@@ -3,61 +3,72 @@ language: en
 library_name: pytorch
 license: mit
 ---
+<!--
+Do not remove elements like the above surrounded by two curly braces and do not add any more of them. These entries are required by the library and are automaticall infilled when the model is uploaded to huggingface
+-->
 
-
-
-
-
-
+<!-- Title - e.g. PVNet-summation -->
 # PVNet_summation
 
+<!-- Provide a longer summary of what this model is/does. -->
 ## Model Description
 
-<!-- Provide a longer summary of what this model is/does. -->
-This model class sums the output of the PVNet model's GSP level predictions to make a national forecast of UK PV output. More information can be found in the model repo [1], the PVNet model repo [2], and experimental notes in [this google doc](https://docs.google.com/document/d/1fbkfkBzp16WbnCg7RDuRDvgzInA6XQu3xh4NCjV-WDA/edit?usp=sharing).
+<!-- e.g.
+This model uses the output predictions of PVNet to predict the sum from predictions of the parts
+-->
+This model class sums the output of the PVNet model's GSP level predictions to make a national forecast of UK PV output. More information can be found in the model repo [1], the PVNet model repo [2]
 
 - **Developed by:** openclimatefix
-- **Model type:** Fusion model
 - **Language(s) (NLP):** en
 - **License:** mit
 
-
 # Training Details
 
 ## Data
 
-<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+<!-- eg.
+The model is trained on data from 2019-2022 and validated on data from 2022-2023. It uses the
+output predictions from PVNet - see the PVNet model for its inputs
 
-The model is trained on the output predictions of our PVNet model which gives GSP (i.e. UK regional) level predictions
-of solar power across Great Britain. This model is trained to take those predictions and use them to estimate
-the national total with uncertainty estimates.
+-->
 
+The model is trained on the output predictions of our PVNet model which gives GSP (i.e. UK regional) level predictions of solar power across Great Britain. This model is trained to take those predictions and use them to estimate the national total with uncertainty estimates. 
 
-### Preprocessing
+The model is trained on data from 2019-2021 and validated on data from 2022. It uses the
+output predictions from PVNet - see the PVNet model for its inputs
 
-The input data is prepared with the `ocf_data_sampler` [3].
+<!-- The preprocessing section is not strictly nessessary but perhaps nice to have -->
+### Preprocessing
 
+<!-- eg.
+Data is prepared with the `ocf_data_sampler/torch_datasets/datasets/pvnet_uk` Dataset [2].
+-->
 
 ## Results
 
-The training logs for the current model can be found here:
- - [https://wandb.ai/openclimatefix/pvnet_summation/runs/50rkn19f](https://wandb.ai/openclimatefix/pvnet_summation/runs/50rkn19f)
-
-
-The training logs for all model runs of PVNet_summation can be found [here](https://wandb.ai/openclimatefix/pvnet_summation).
-
-Some experimental notes can be found at in [the google doc](https://docs.google.com/document/d/1fbkfkBzp16WbnCg7RDuRDvgzInA6XQu3xh4NCjV-WDA/edit?usp=sharing)
+<!-- Do not remove the lines below -->
+The training logs for this model commit can be found here:
+ - [https://wandb.ai/openclimatefix/pvnet_summation/runs/gtjmp2r6](https://wandb.ai/openclimatefix/pvnet_summation/runs/gtjmp2r6)
 
 
+<!-- The hardware section is also just nice to have -->
 ### Hardware
+<!-- e.g.
+Trained on a single NVIDIA Tesla T4
+-->
 
 Trained on a single NVIDIA Tesla T4
 
+
+<!-- Do not remove the section below -->
 ### Software
 
-- [1] https://github.com/openclimatefix/PVNet_summation
-- [2] https://github.com/openclimatefix/PVNet
-- [3] https://github.com/openclimatefix/ocf-data-sampler
+This model was trained using the following Open Climate Fix packages:
 
----
-**Migration Note**: This model was migrated on 2025-08-08 to pvnet-summation version 1.0.0.post0+git.cd7464d2.dirty
+- [1] https://github.com/openclimatefix/pvnet-summation
+- [2] https://github.com/openclimatefix/ocf-data-sampler
+
+<!-- Especially do not change the two lines below -->
+The versions of these packages can be found below:
+ - pvnet_summation==1.0.0.post1+git.36f3523d.dirty
+ - ocf-data-sampler==0.4.0

datamodule_config.yaml

@@ -1,3 +1,3 @@
 pvnet_model:
   model_id: openclimatefix/pvnet_uk_region
-  revision: 9d27d1a3b985c3f2c39417c53f6bdc6576b75a9b
+  revision: 4703a866aaa8f8be89e0d034cb2bf605f20f0017

full_experiment_config.yaml

@@ -0,0 +1,103 @@
+trainer:
+  _target_: lightning.pytorch.trainer.trainer.Trainer
+  accelerator: gpu
+  devices: auto
+  min_epochs: null
+  max_epochs: 100
+  reload_dataloaders_every_n_epochs: 0
+  num_sanity_val_steps: 8
+  fast_dev_run: false
+  log_every_n_steps: 50
+model:
+  _target_: pvnet_summation.training.lightning_module.PVNetSummationLightningModule
+  model:
+    _target_: pvnet_summation.models.dense_model.DenseModel
+    output_quantiles:
+    - 0.02
+    - 0.1
+    - 0.25
+    - 0.5
+    - 0.75
+    - 0.9
+    - 0.98
+    output_network:
+      _target_: pvnet.models.late_fusion.linear_networks.networks.ResFCNet
+      _partial_: true
+      fc_hidden_features: 128
+      n_res_blocks: 2
+      res_block_layers: 2
+      dropout_frac: 0.2
+    predict_difference_from_sum: true
+    history_minutes: 120
+    forecast_minutes: 480
+    interval_minutes: 30
+    num_input_locations: 331
+    input_quantiles:
+    - 0.02
+    - 0.1
+    - 0.25
+    - 0.5
+    - 0.75
+    - 0.9
+    - 0.98
+  optimizer:
+    _target_: pvnet_summation.optimizers.AdamWReduceLROnPlateau
+    lr: 3.0e-05
+    weight_decay: 0.25
+    amsgrad: true
+    patience: 10
+    factor: 0.1
+    threshold: 0.0
+datamodule:
+  configuration: /home/jamesfulton/repos/PVNet/configs/datamodule/configuration/sat_pred.yaml
+  batch_size: 32
+  num_workers: 28
+  prefetch_factor: 4
+  persistent_workers: true
+  max_num_train_samples: null
+  max_num_val_samples: null
+  pvnet_model:
+    model_id: /home/jamesfulton/tmp/2NWP+cloud
+    revision: null
+  train_period:
+  - null
+  - '2021-12-31'
+  val_period:
+  - '2022-01-01'
+  - '2022-12-31'
+callbacks:
+  learning_rate_monitor:
+    _target_: lightning.pytorch.callbacks.LearningRateMonitor
+    logging_interval: epoch
+  model_summary:
+    _target_: lightning.pytorch.callbacks.ModelSummary
+    max_depth: 3
+  model_checkpoint:
+    _target_: lightning.pytorch.callbacks.ModelCheckpoint
+    monitor: ${resolve_monitor_loss:${model.model.output_quantiles}}
+    mode: min
+    save_top_k: 1
+    save_last: true
+    every_n_epochs: 1
+    verbose: false
+    filename: epoch={epoch}-step={step}
+    dirpath: checkpoints/${model_name}
+    auto_insert_metric_name: false
+    save_on_train_epoch_end: false
+logger:
+  wandb:
+    _target_: lightning.pytorch.loggers.wandb.WandbLogger
+    project: pvnet_summation
+    name: ${model_name}
+    save_dir: ./
+    offline: false
+    id: null
+    log_model: false
+    prefix: ''
+    job_type: train
+    group: ''
+    tags: []
+sample_save_dir: /home/jamesfulton/repos/PVNet_summation/presaved_saved_samples
+work_dir: ${hydra:runtime.cwd}
+model_name: 2NWP+cloud
+seed: 2727831

model_config.yaml

@@ -13,12 +13,12 @@ output_network:
   fc_hidden_features: 128
   n_res_blocks: 2
   res_block_layers: 2
-  dropout_frac: 0.1
+  dropout_frac: 0.2
 predict_difference_from_sum: true
-num_input_locations: 331
 history_minutes: 120
 forecast_minutes: 480
 interval_minutes: 30
+num_input_locations: 331
 input_quantiles:
 - 0.02
 - 0.1

model_weights.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:9b0faf8ce2fe7667bda8c6ba120601f6c289e05f3524406e62be17c54e011cfd
 size 19365512