# Default configs for inference through generate.py
# The defaults are geared more towards ease-of-use by designers, so running with
# the existing analysis scripts will necessitate overrides

defaults:
  - _self_
  - inference_experiment: inference_on_user_inputs # Default for design
  - recycle_strategy: thinking_medium

# NOTE: The experiment configs can override values specified in this config

run_name_: inference
verbose: false # Set true for routine warnings and detailed diagnostic output.
hf_ckpt_path: nvidia/RFD4-Proteina  # Optional local model bundle or HF repo; overrides ckpt_path/name when set.
hf_ckpt_revision: a06e2509c7033b010fc43614e9fd840303588502  # Pin a Hub commit for reproducible model/AE loading.

out_dir: null  # If null, defaults to ./runs/<run_name>. For production, set to an external
               # path (e.g. /shared/runs/experiment_name). SLURM scripts set this
               # automatically. All outputs (samples, metrics) go under this directory.

# Current SOTA; generation and regression tooling resolve the immutable checkpoint from here.
# Regenerate the inference baselines when advancing it; use CLI overrides for one-off checkpoints.
ckpt_name: rfd4-proteina-flow-release-2026-10-19.ckpt
ckpt_path: ${oc.env:DATA_PATH}/flow_checkpoints
adapter_path: null  # Local PEFT adapter directory; load onto the exact base/AE pair used for fine-tuning.

# Only applies when a pre-oracle design filter is active (i.e. its tag gate matched).
# Determines whether to delete structure files for designs that fail this filter
delete_failing_pre_oracle: True

autoencoder_ckpt_path: ${oc.env:DATA_PATH}/ae_checkpoints/rfd4-proteina-ae-release-2026-10-19.ckpt

num_replicates: 1 # Number of times to replicate each example

# Token counts to pad batches to for torch.compile. Multiples of 16; null disables.
compile_lengths: [128, 160, 192, 224, 256, 288, 320, 352, 384, 448, 512, 640, 768, 896, 1024]

# Network settings that may change after training without touching the weights; null keeps the checkpoint's
# value. Example: nn.torch_compile_mode=reduce-overhead nn.tri_mult_backend=cuequivariance
nn:
  torch_compile_mode: null
  torch_compile_fullgraph: null
  tri_mult_backend: null
  attention_type: null

precision: bf16-mixed

batch_size: 1
subset_to_examples: null # If specified, only run inference on these example indices
prevalidate_inputs: false # Whether to sanity-check each unique input once prior to generation (fail fast on bad inputs).
seed: null # Will set a random seed if null
torch_deterministic: false  # Locks cuDNN/cuBLAS to deterministic algorithms; significantly reduces throughput
skip_existing: true # If true, skip examples whose output structure files already exist in the output directory
save_chain_of_interest: false # If true, write per-design chain-of-interest CIFs (only needed for foldseek clustering).
compress_outputs: false # Compress persistent structure outputs, including refolds and CIF trajectories.
metrics_tags: [] # If specified, determines which metrics are computed during inference

search:
  checkpoints: null # Set checkpoint policies to enable beam search.
  save_states: false
  resume_from: null
  resume_seed: null

num_workers: 4  # Number of dataloader workers

hardware:
  accelerator: gpu
  ngpus_per_node_: 1
  nnodes_: 1

# Cluster job settings (used by launch_infer_from_local.sh)
ncpus_: 24  # Number of CPUs per task
gen_njobs: 1  # Number of parallel generation jobs per config
eval_njobs: 1  # Number of parallel evaluation jobs per config
job_id: null  # Set by launch_generate.sh; used to disambiguate per-job output files

hydra:
  run:
    dir: ./hydra_outputs