config.yaml

---
# @package _global_
_target_: l2hmc.configs.ExperimentConfig
#
# ----------------------------------------------------------------------------
framework: pytorch      # ML framework to use: one of 'pytorch', 'tensorflow'
backend: 'DDP'          # Backend to use for distributed training
profile: false          # Flag for profiling in pytorch
precision: 'float32'    # Default floating point precision
width: 200              # Setting controlling terminal width for printing
seed: 9992              # Seed for random number
compile: true           # Compile network in tensorflow? (True by default)
restore: true           # try restoring weights from previous checkpoint
save: true              # Save model w/ checkpoints for restoring later?
init_aim: false          # Use aim for experiment / metric tracking
init_wandb: true        # Use WandB for experiment / metric tracking
use_wandb: true
use_tb: false
compression: false      # Compression for Horovod
nchains: null          # Number of chains to use when evaluating model
ds_config_path: null
# optional num_threads: 8
# optional outdir: outputs/${now:%Y-%m-%d_%H-%M-%S}
# ----------------------------------------------------------------------------

# ----------------------------------------------------------------------------
# pretty print config at the start of the run using Rich library
print_config: true
# ----------------------------------------------------------------------------
# disable python warnings if they annoy you
ignore_warnings: true
# name of the run, should be used along with experiment mode
name: null
# ----------------------------------------------------------------------------

defaults:
  - _self_
  # ------------------------------------------------------------------------------------------
  #                                 DEFAULTS
  # ------------------------------------------------------------------------------------------
  - steps: default.yaml               # Defines num_era, num_epoch, num_test, etc.
  - dynamics: default.yaml            # Defines gauge group, nleapfrog, lattice volume, etc.
  - wandb: default.yaml               # Weights & Biases config
  - logdir: default.yaml              # Defines where to run experiment using info from cfg
  - loss: default.yaml                # Defines weights of various terms in loss function
  - network: default.yaml             # Defines network architecture, activation fns, etc.
  - conv: default.yaml                # Defines arch of Conv block to prepend to xnetwork
  - net_weights: default.yaml         # Weights for controlling rel contribution of net fns
  - learning_rate: default.yaml       # Defines initial lr, optimizer type, lr schedule, etc.
  - annealing_schedule: default.yaml  # Defines annealing schedule to use for training
  # - accelerator: default.yaml       # Defines options for HuggingFace Accelerator
  # ------------------------------------------------------------------------------------------
  # modes are special collections of config options for different purposes, e.g. debugging
  - mode: default.yaml
  # experiment configs allow for version control of specific configurations
  # for example, use them to store best hyperparameters for each model configuration
  # - experiment: null
  # - hydra/run: default.yaml
  # - optional local: default.yaml
  #
  - override hydra/hydra_logging: colorlog
  - override hydra/job_logging: rich
  # - override hydra/launcher: joblib

  # https://hydra.cc/docs/tutorials/basic/running_your_app/logging/
  # use this to set level of only chosen command line loggers to 'DEBUG'
  # verbose: [src.train, src.utils]

hydra:
  verbose: l2hmc
  job:
    chdir: true
...