Push model using huggingface_hub.

README.md

@@ -31,18 +31,14 @@ This model class uses satellite data, numericl weather predictions, and recent G
 The model is trained on data from 2017-2020 and validated on data from 2021. See experimental notes in the [the google doc](https://docs.google.com/document/d/1fbkfkBzp16WbnCg7RDuRDvgzInA6XQu3xh4NCjV-WDA/edit?usp=sharing) for more details.
 
 
-## Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
 ### Preprocessing
 
 Data is prepared with the `ocf_datapipes.training.pvnet` datapipe [2].
 
 
-## Results 
+## Results
 
-The training logs for the current model can be found [here on wandb](https://wandb.ai/openclimatefix/pvnet2.1/runs/nmh1369t).
+The training logs for the current model can be found [here on wandb](https://wandb.ai/openclimatefix/pvnet2.1/runs/None).
 
 The training logs for all model runs of PVNet2 can be found [here](https://wandb.ai/openclimatefix/pvnet2.1).
 
@@ -56,5 +52,4 @@ Trained on a single NVIDIA Tesla T4
 ### Software
 
 - [1] https://github.com/openclimatefix/PVNet
-- [2] https://github.com/openclimatefix/ocf_datapipes
-
+- [2] https://github.com/openclimatefix/ocf_datapipes

config.json

@@ -1 +1 @@
-{"_target_": "pvnet.models.multimodal.multimodal.Model", "image_encoder": {"_target_": "pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet", "_partial_": true}, "encoder_out_features": 256, "encoder_kwargs": {"number_of_conv3d_layers": 6, "conv3d_channels": 32}, "include_sat": true, "include_nwp": true, "add_image_embedding_channel": true, "sat_image_size_pixels": 24, "nwp_image_size_pixels": 24, "number_sat_channels": 11, "number_nwp_channels": 2, "output_network": {"_target_": "pvnet.models.multimodal.linear_networks.networks.ResFCNet2", "_partial_": true}, "output_network_kwargs": {"fc_hidden_features": 128, "n_res_blocks": 6, "res_block_layers": 2, "dropout_frac": 0.0}, "embedding_dim": 16, "include_sun": true, "include_gsp_yield_history": false, "forecast_minutes": 480, "history_minutes": 120, "sat_history_minutes": 90, "nwp_history_minutes": 120, "nwp_forecast_minutes": 480, "optimizer": {"_target_": "pvnet.optimizers.AdamWReduceLROnPlateau", "lr": 0.0001, "weight_decay": 0.25, "amsgrad": true, "patience": 5, "factor": 0.1, "threshold": 0.002}, "min_sat_delay_minutes": 30}
+{"_target_": "pvnet.models.multimodal.multimodal.Model", "image_encoder": {"_target_": "pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet", "_partial_": true}, "encoder_out_features": 256, "encoder_kwargs": {"number_of_conv3d_layers": 6, "conv3d_channels": 32}, "include_sat": true, "include_nwp": true, "add_image_embedding_channel": true, "sat_image_size_pixels": 24, "nwp_image_size_pixels": 24, "number_sat_channels": 11, "number_nwp_channels": 2, "output_network": {"_target_": "pvnet.models.multimodal.linear_networks.networks.ResFCNet2", "_partial_": true}, "output_network_kwargs": {"fc_hidden_features": 128, "n_res_blocks": 6, "res_block_layers": 2, "dropout_frac": 0.0}, "embedding_dim": 16, "include_sun": true, "include_gsp_yield_history": false, "forecast_minutes": 480, "history_minutes": 120, "sat_history_minutes": 90, "nwp_history_minutes": 120, "nwp_forecast_minutes": 480, "optimizer": {"_target_": "pvnet.optimizers.AdamWReduceLROnPlateau", "lr": 0.0001, "weight_decay": 0.25, "amsgrad": true, "patience": 5, "factor": 0.1, "threshold": 0.002}, "min_sat_delay_minutes": 60}

pytorch_model.bin

@@ -1,3 +1,3 @@
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-size 60685313
+oid sha256:c59f2092d4038e1101ec64d2dd234cdc36273b72cadb15ac09d371f78b4171db
+size 46529537