Made configs ediatable without risk of versioning + update readme wit…

…h detailed getting started instructions

.gitignore

@@ -1,7 +1,9 @@
 # Custom
 config_tree.txt
+configs/
 lightning_logs/
 logs/
+output/
 checkpoints*
 csv/
 notebooks/

README.md

@@ -4,40 +4,336 @@
 
 This project is used for training PVNet and running PVnet on live data.
 
-PVNet2 largely inherits the same architecture from [PVNet1.0](https://github.com/openclimatefix/predict_pv_yield).
-The NWP and satellite data are sent through some neural network which encodes them down to 1D intermediate representations.
-These are concatenated together with the GSP output history, the calculated solar coordinates (azimuth and elevation) and the GSP ID which has been put through an embedding layer.
-This 1D concatenated feature vector is put through an output network which outputs predictions of the future GSP yield.
-National forecasts are made by adding all the GSP forecasts together.
+PVNet2 largely inherits the same architecture from
+[PVNet1.0](https://github.com/openclimatefix/predict_pv_yield). The NWP and
+satellite data are sent through some neural network which encodes them down to
+1D intermediate representations. These are concatenated together with the GSP
+output history, the calculated solar coordinates (azimuth and elevation) and the
+GSP ID which has been put through an embedding layer. This 1D concatenated
+feature vector is put through an output network which outputs predictions of the
+future GSP yield. National forecasts are made by adding all the GSP forecasts
+together.
+
+## Setup / Installation
 
-## Setup
 ```bash
 git clone https://github.com/openclimatefix/PVNet.git
 cd PVNet
 pip install -r requirements.txt
-pip install git+https://github.com/SheffieldSolar/PV_Live-API
 ```
 
-## Running
+### Additional development dependencies
+
 ```bash
-python run.py
+pip install -r requirements-dev.txt
 ```
 
-## Development
-```bash
-pip install -r requirements.txt -r requirements-dev.txt
-pytest
+## Getting started with running PVNet
+
+Before running any code in within PVNet, copy the example configuration to a
+configs directory:
+
+```
+cp -r configs.example configs
+```
+
+You will be making local amendments to these configs
+
+### Datasets
+
+As a minimum, in order to create batches of data/run PVNet, you will need to
+supply paths to NWP and GSP data. PV data can also be used. We list some
+suggested locations for downloading such datasets below:
+
+**GSP (Grid Supply Point)** - Regional PV generation data\
+The University of Sheffield provides API access to download this data:
+https://www.solar.sheffield.ac.uk/pvlive/api/
+
+Documentation for querying generation data aggregated by GSP region can be found
+here:
+https://docs.google.com/document/d/e/2PACX-1vSDFb-6dJ2kIFZnsl-pBQvcH4inNQCA4lYL9cwo80bEHQeTK8fONLOgDf6Wm4ze_fxonqK3EVBVoAIz/pub#h.9d97iox3wzmd
+
+**NWP (Numerical weather predictions)**\
+OCF maintains a Zarr formatted version the German Weather Service's (DWD)
+ICON-EU NWP model here:
+https://huggingface.co/datasets/openclimatefix/dwd-icon-eu which includes the UK
+
+**PV**\
+OCF maintains a dataset of PV generation from 1311 private PV installations
+here: https://huggingface.co/datasets/openclimatefix/uk_pv
+
+### Generating pre-made batches of data for training/validation of PVNet
+
+PVNet contains a script for generating batches of data suitable for training the
+PVNet models.
+
+To run the script you will need to make some modifications to the datamodule
+configuration.
+
+1. First, create your new configuration file in
+   `./configs/datamodule/configiration/local_configuration.yaml` and paste the
+   sample config (shown below)
+2. Duplicate the `./configs/datamodule/ocf_datapipes.yaml` to
+   `./configs/datamodule/_local_ocf_datapipes.yaml` and ensure the
+   `configuration` key points to your newly created configuration file in
+   step 1.
+3. Also in this file, update the train, val & test periods to cover the data you
+   have access to.
+4. To get you started with your own configuration file, see the sample config
+   below. Update the data paths to the location of your local GSP, NWP and PV
+   datasets:
+
+```yaml
+general:
+  description: Demo config
+  name: demo_datamodule_config
+
+input_data:
+  default_history_minutes: 60
+  default_forecast_minutes: 120
+
+  gsp:
+    gsp_zarr_path: /path/to/gsp-data.zarr
+    history_minutes: 60
+    forecast_minutes: 120
+    time_resolution_minutes: 30
+    start_datetime: "2019-01-01T00:00:00"
+    end_datetime: "2019-01-08T00:00:00"
+    metadata_only: false
+
+  nwp:
+    ukv:
+      nwp_zarr_path: /path/to/nwp-data.zarr 
+      history_minutes: 60
+      forecast_minutes: 120
+      time_resolution_minutes: 60
+      nwp_channels: # comment out channels as appropriate
+        - t # live = t2m
+        - dswrf
+        - dlwrf
+        - hcc
+        - MCC
+        - lcc
+        - vis
+        - r # live = r2
+        - prate # live ~= rprate
+        - si10 # 10-metre wind speed | live = unknown
+      nwp_image_size_pixels_height: 24
+      nwp_image_size_pixels_width: 24
+      nwp_provider: ukv
+
+  pv:
+    pv_files_groups:
+      - label: pvoutput.org
+        pv_filename: /path/to/pv-data/pv.netcdf 
+        pv_metadata_filename: /path/to/pv-data/metadata.csv
+    history_minutes: 60
+    forecast_minutes: 0 # PVNet assumes no future PV generation
+    time_resolution_minutes: 5
+    start_datetime: "2019-01-01T00:00:00"
+    end_datetime: "2019-01-08T00:00:00"
+    pv_image_size_meters_height: 24
+    pv_image_size_meters_width: 24
+    pv_ml_ids: [154,155,156,158,159,160,162,164,165,166,167,168,169,171,173,177,178,179,181,182,185,186,187,188,189,190,191,192,193,197,198,199,200,202,204,205,206,208,209,211,214,215,216,217,218,219,220,221,225,229,230,232,233,234,236,242,243,245,252,254,255,256,257,258,260,261,262,265,267,268,272,273,275,276,277,280,281,282,283,287,289,291,292,293,294,295,296,297,298,301,302,303,304,306,307,309,310,311,317,318,319,320,321,322,323,325,326,329,332,333,335,336,338,340,342,344,345,346,348,349,352,354,355,356,357,360,362,363,368,369,370,371,372,374,375,376,378,380,382,384,385,388,390,391,393,396,397,398,399,400,401,403,404,405,406,407,409,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,429,431,435,437,438,440,441,444,447,450,451,453,456,457,458,459,464,465,466,467,468,470,471,473,474,476,477,479,480,481,482,485,486,488,490,491,492,493,496,498,501,503,506,507,508,509,510,511,512,513,515,516,517,519,520,521,522,524,526,527,528,531,532,536,537,538,540,541,542,543,544,545,549,550,551,552,553,554,556,557,560,561,563,566,568,571,572,575,576,577,579,580,581,582,584,585,588,590,594,595,597,600,602,603,604,606,611,613,614,616,618,620,622,623,624,625,626,628,629,630,631,636,637,638,640,641,642,644,645,646,650,651,652,653,654,655,657,660,661,662,663,666,667,668,670,675,676,679,681,683,684,685,687,696,698,701,702,703,704,706,710,722,723,724,725,727,728,729,730,732,733,734,735,736,737,]
+    n_pv_systems_per_example: 128
+    get_center: false
+    is_live: false
+
+  satellite:
+    satellite_zarr_path: "" # Left empty to avoid using satellite data
+    history_minutes: 60
+    forecast_minutes: 0
+    live_delay_minutes: 30
+    time_resolution_minutes: 5
+    satellite_channels:
+      - IR_016
+      - IR_039
+      - IR_087
+      - IR_097
+      - IR_108
+      - IR_120
+      - IR_134
+      - VIS006
+      - VIS008
+      - WV_062
+      - WV_073
+    satellite_image_size_pixels_height: 24
+    satellite_image_size_pixels_width: 24
+```
+
+With your configuration in place, you can proceed to create batches. PVNet uses
+[hydra](https://hydra.cc/) which enables us to pass variables via the command
+line that will override the configuration defined in the `./configs` directory.
+
+Run the save_batches.py script to create batches with the following arguments as
+a minimum:
+
+```
+python scripts/save_batches.py datamodule=local_ocf_datapipes +batch_output_dir="./output" +num_train_batches=10 +num_val_batches=5
+```
+
+### Training PVNet
+
+How PVNet is run is determined by the extensive configuration in the config
+files. The following configs have been tested to work using batches of data
+created using the steps and batch creation config mentioned above.
+
+You should create the following configs before trying to train a model locally,
+as so:
+
+In `configs/datamodule/local_premade_batches.yaml`:
+
+```yaml
+_target_: pvnet.data.datamodule.DataModule
+configuration: null
+batch_dir: "./output" # where the batches are saved
+num_workers: 20
+prefetch_factor: 2
+batch_size: 8
 ```
 
-Might need to install PVLive
+In `configs/model/local_multimodal.yaml`:
+
+```yaml
+_target_: pvnet.models.multimodal.multimodal.Model
+
+output_quantiles: [0.02, 0.1, 0.25, 0.5, 0.75, 0.9, 0.98]
+
+#--------------------------------------------
+# NWP encoder
+#--------------------------------------------
+
+nwp_encoders_dict:
+  ukv:
+    _target_: pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet
+    _partial_: True
+    in_channels: 10
+    out_features: 256
+    number_of_conv3d_layers: 6
+    conv3d_channels: 32
+    image_size_pixels: 24
+
+#--------------------------------------------
+# Sat encoder settings
+#--------------------------------------------
+
+# Ignored as premade batches were created without satellite data
+# sat_encoder:
+#   _target_: pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet
+#   _partial_: True
+#   in_channels: 11
+#   out_features: 256
+#   number_of_conv3d_layers: 6
+#   conv3d_channels: 32
+#   image_size_pixels: 24
+
+add_image_embedding_channel: False
+
+#--------------------------------------------
+# PV encoder settings
+#--------------------------------------------
+
+pv_encoder:
+  _target_: pvnet.models.multimodal.site_encoders.encoders.SingleAttentionNetwork
+  _partial_: True
+  num_sites: 349
+  out_features: 40
+  num_heads: 4
+  kdim: 40
+  pv_id_embed_dim: 20
+
+#--------------------------------------------
+# Tabular network settings
+#--------------------------------------------
+
+output_network:
+  _target_: pvnet.models.multimodal.linear_networks.networks.ResFCNet2
+  _partial_: True
+  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
+
+#--------------------------------------------
+# Times
+#--------------------------------------------
+
+# Foreast and time settings
+history_minutes: 60
+forecast_minutes: 120
+
+min_sat_delay_minutes: 60
+
+sat_history_minutes: 90
+pv_history_minutes: 60
+
+# These must be set for each NWP encoder
+nwp_history_minutes:
+  ukv: 60
+nwp_forecast_minutes:
+  ukv: 120
+
+# ----------------------------------------------
+# Optimizer
+# ----------------------------------------------
+optimizer:
+  _target_: pvnet.optimizers.EmbAdamWReduceLROnPlateau
+  lr: 0.0001
+  weight_decay: 0.01
+  amsgrad: True
+  patience: 5
+  factor: 0.1
+  threshold: 0.002
+```
+
+In `configs/local_trainer.yaml`:
+
+```yaml
+_target_: lightning.pytorch.trainer.trainer.Trainer
+
+accelerator: cpu # Important if running on a system without a supported GPU
+devices: auto
+
+min_epochs: null
+max_epochs: null
+reload_dataloaders_every_n_epochs: 0
+num_sanity_val_steps: 8
+fast_dev_run: false
+accumulate_grad_batches: 4
+log_every_n_steps: 50
+```
+
+And finally update `defaults` in the main `./configs/config.yaml` file to use
+your customised config files:
+
+```yaml
+defaults:
+  - trainer: local_trainer.yaml
+  - model: local_multimodal.yaml
+  - datamodule: local_premade_batches.yaml
+  - callbacks: null
+  - logger: csv.yaml
+  - experiment: null
+  - hparams_search: null
+  - hydra: default.yaml
 ```
-pip install git+https://github.com/SheffieldSolar/PV_Live-API#pvlive_api
+
+Assuming you ran the `save_batches.py` script to generate some premade train and
+val data batches, you can now train PVNet by running:
+
+```
+python run.py
 ```
 
 ## Testing
 
-You can use `pytest` to run tests
+You can use `python -m pytest tests` to run tests
 
 ## Experiments
 
-Notes on these experiments are [here](https://docs.google.com/document/d/1fbkfkBzp16WbnCg7RDuRDvgzInA6XQu3xh4NCjV-WDA/edit?usp=sharing).
+Notes on these experiments are
+[here](https://docs.google.com/document/d/1fbkfkBzp16WbnCg7RDuRDvgzInA6XQu3xh4NCjV-WDA/edit?usp=sharing).

configs/model/multimodal.yaml → configs.example/model/multimodal.yaml

@@ -6,7 +6,7 @@ output_quantiles: [0.02, 0.1, 0.25, 0.5, 0.75, 0.9, 0.98]
 # NWP encoder
 #--------------------------------------------
 
-nwp_encoder:
+nwp_encoders_dict:
   ukv:
     _target_: pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet
     _partial_: True