config_template.yaml

experiment: # Name of the expirement 
    name: "experiment_name" # This will be used to save the best model weights
    base_path: "/absolute/path/to/the/model/code" 

data_generation:
    train_dataset_file: "/path/to/the/training/dataset"  # training / validation set
    valid_dataset_file: "/path/to/the/validation/dataset"
    benchmark_dataset_file: "/path/to/the/benchmarks/dataset"
    batch_size: 2048
    nb_processes: 4 # Number of processes to use when loading the data in parallel
    min_functions_per_tree_footprint: 2 # Minimum number of functions accepted in a batch. Set to 0 if you are using a small data sample

training: 
    log_file: "logs.txt" # Just the name
    lr: 0.001
    max_epochs: 1000 
    training_gpu: "cuda:x" # GPU to train on. Example: cuda:2
    validation_gpu: "cpu" # GPU to validate on. Usually the CPU is enough
    continue_training: False # Continue training from saved model checkpoint
    model_weights_path: "/path/to/model/weights" # Model weights to use for finetuning

testing:
    testing_model_weights_path: "/path/to/model/weights" # Model weights to evaluate
    gpu: "cuda:x" # GPU to validate on

wandb: 
    use_wandb: False # Track model progress using the Weights & Biases platform
    project: "release_model" # Name of the project to add this expirement to
    
model: 
    input_size: 846 # Size of the input. Here we specify the size of the computation vector.
    comp_embed_layer_sizes: 
        - 600
        - 350
        - 200
        - 180
    drops: # Dropout layers probabilities
        - 0.050
        - 0.050
        - 0.050
        - 0.050
        - 0.050

defaults:
  - override hydra/job_logging: disabled