name: DEFAULT
project: Spikey

slurm:
  name: 'Spikey_{config[name]}'
  partitions:
    - single
  standard_output: ./reports/slurm/out_%A_%a.log
  standard_error: ./reports/slurm/err_%A_%a.log
  num_parallel_jobs: 50
  cpus_per_task: 8
  memory_per_cpu: 4000
  time_limit: 1440  # in minutes
  ntasks: 1
  venv: '.venv/bin/activate'
  sh_lines:
    - 'mkdir -p {tmp}/wandb'
    - 'mkdir -p {tmp}/local_pycache'
    - 'export PYTHONPYCACHEPREFIX={tmp}/local_pycache'

runner: spikey

scheduler:
  reps_per_version: 1
  agents_per_job: 1
  reps_per_agent: 1

wandb:
  project: '{config[project]}'
  group: '{config[name]}'
  job_type: '{delta_desc}'
  name: '{job_id}_{task_id}:{run_id}:{rand}={config[name]}_{delta_desc}'
  #tags:
  #  - '{config[env][name]}'
  #  - '{config[algo][name]}'
  sync_tensorboard: False
  monitor_gym: False
  save_code: False

---
name: Test
import: $

latent_projector:
  type: rnn  # Options: 'fc', 'rnn'
  input_size: 19531  # =1s Input size for the Latent Projector (length of snippets).
  latent_size: 8  # Size of the latent representation before message passing.
  #layer_shapes: [256, 32]  # List of layer sizes for the latent projector (if type is 'fc').
  #activations: ['ReLU', 'ReLU']  # Activation functions for the latent projector layers (if type is 'fc').
  rnn_hidden_size: 12  # Hidden size for the RNN projector (if type is 'rnn').
  rnn_num_layers: 1  # Number of layers for the RNN projector (if type is 'rnn').

middle_out:
  output_size: 8  # Size of the latent representation after message passing.
  num_peers: 3  # Number of most correlated peers to consider.

predictor:
  layer_shapes: [8, 4]  # List of layer sizes for the predictor.
  activations: ['ReLU', 'None']  # Activation functions for the predictor layers.

training:
  epochs: 128  # Number of training epochs.
  batch_size: 64  # Batch size for training.
  num_batches: 16 # Batches per epoch
  learning_rate: 0.001  # Learning rate for the optimizer.
  eval_freq: -1 # 8  # Frequency of evaluation during training (in epochs).
  save_path: models  # Directory to save the best model and encoder.
  num_points: 1000  # Number of data points to visualize

evaluation:
  full_compression: false  # Perform full compression during evaluation

bitstream_encoding:
  type: identity  # Options: 'arithmetic', 'identity', 'bzip2'

data:
  url: https://content.neuralink.com/compression-challenge/data.zip  # URL to download the dataset.
  directory: data  # Directory to extract and store the dataset.
  split_ratio: 0.8  # Ratio to split the data into train and test sets.
  cut_length: null  # Optional length to cut sequences to.

profiler:
  enable: false