diff --git a/.github/workflows/update_hpc_requirements.yml b/.github/workflows/update_hpc_requirements.yml index 38dbc42..969b433 100644 --- a/.github/workflows/update_hpc_requirements.yml +++ b/.github/workflows/update_hpc_requirements.yml @@ -14,6 +14,7 @@ jobs: runs-on: ubuntu-latest permissions: contents: write + pull-requests: write steps: - uses: actions/checkout@v4 with: @@ -30,9 +31,13 @@ jobs: - name: Regenerate env/hpc/requirements.txt run: uv run scripts/hpc/export_requirements.py - - name: Commit updated requirements if changed - uses: stefanzweifel/git-auto-commit-action@v5 + - name: Create Pull Request with updated requirements + uses: peter-evans/create-pull-request@v6 with: - commit_message: "chore(hpc): update env/hpc/requirements.txt from pyproject.toml [skip ci]" - file_pattern: env/hpc/requirements.txt - commit_author: "github-actions[bot] " + token: ${{ secrets.GITHUB_TOKEN }} + commit-message: "chore(hpc): update env/hpc/requirements.txt from pyproject.toml" + title: "chore(hpc): update HPC requirements" + body: "Automatically generated pull request to update `env/hpc/requirements.txt` based on recent changes to `pyproject.toml`." + branch: chore/auto-update-hpc-requirements + base: ${{ github.ref_name }} + author: "github-actions[bot] " diff --git a/configs/logging/default.yaml b/configs/logging/default.yaml index 4a0cb92..1ecf7fd 100644 --- a/configs/logging/default.yaml +++ b/configs/logging/default.yaml @@ -6,6 +6,8 @@ wandb_project_name: "PPO-Modularity" wandb_entity: "SEL3-2026-Groep-4" capture_video: false save_model: true +save_checkpoints: true checkpoint_frequency: 100 -upload_model: false +upload_final_model: false +upload_checkpoints: false hf_entity: "" diff --git a/configs/logging/hpc.yaml b/configs/logging/hpc.yaml new file mode 100644 index 0000000..3c03918 --- /dev/null +++ b/configs/logging/hpc.yaml @@ -0,0 +1,9 @@ +track: true +wandb_project_name: "hpc-default" +wandb_entity: "SEL3-2026-Groep-4" +save_model: true +save_checkpoints: true +upload_final_model: true +upload_checkpoints: true +checkpoint_frequency: 100 +hf_entity: "" diff --git a/configs/logging/wandb_enabled.yaml b/configs/logging/wandb_enabled.yaml index 40c52bf..7ddf95f 100644 --- a/configs/logging/wandb_enabled.yaml +++ b/configs/logging/wandb_enabled.yaml @@ -2,10 +2,12 @@ # For production/cloud experiments with weights synced. track: true -wandb_project_name: "PPO-Modularity - reward engineering" +wandb_project_name: "default-project" wandb_entity: "SEL3-2026-Groep-4" capture_video: false save_model: true +save_checkpoints: true checkpoint_frequency: 100 -upload_model: false +upload_final_model: true +upload_checkpoints: false hf_entity: "" diff --git a/env/hpc/requirements.txt b/env/hpc/requirements.txt index 84515f9..c85ce2d 100644 --- a/env/hpc/requirements.txt +++ b/env/hpc/requirements.txt @@ -15,6 +15,6 @@ optax>=0.2.6 pyopengl>=3.1.10 pyopengl-accelerate>=3.1.10 pyyaml>=6.0 -tyro>=1.0.10 +hydra-core>=1.3.2 wandb==0.24.2 torch>=2.4.0 diff --git a/scripts/hpc/train.pbs b/scripts/hpc/train.pbs index 60ba4d8..0f95b18 100644 --- a/scripts/hpc/train.pbs +++ b/scripts/hpc/train.pbs @@ -67,7 +67,7 @@ fi python scripts/train.py \ hydra.run.dir="$SCRATCH_RUNDIR" \ ppo=stable \ - logging=wandb_enabled + logging=hpc echo ">>> Staging out results to $DATA_RUNDIR..." cp -r "$SCRATCH_RUNDIR/." "$DATA_RUNDIR/" diff --git a/scripts/simulate.py b/scripts/simulate.py index 666d048..3c4aa42 100644 --- a/scripts/simulate.py +++ b/scripts/simulate.py @@ -57,6 +57,19 @@ def main(dict_cfg: DictConfig) -> None: nu = int(state.mj_model.nu) if model_path is not None: + # TODO: Refactoring Notice - The .flax checkpoint payload no longer encapsulates the config + # and no longer wraps parameters into a hardcoded list. + # Now natively contains solely the pure raw Jax 'agent_state.params' FrozenDict mapping. + # The entire BrittleStarConfig is safely exported alongside it down at '..._metadata.yaml'. + # + # Example parsed layout from flax.serialization.from_bytes(): + # { + # 'sensor_params': FrozenDict({...}), + # 'actor_params': FrozenDict({...}), + # 'critic_params': FrozenDict({...}), + # ... + # } + # Update to support this raw dictionary natively. policy = RLModel.load(Path(model_path)) if hasattr(policy, "nu"): policy.nu = nu diff --git a/scripts/train.py b/scripts/train.py index 409f050..c367000 100644 --- a/scripts/train.py +++ b/scripts/train.py @@ -36,11 +36,9 @@ def main(dict_cfg: DictConfig): cfg_dict = OmegaConf.to_container(dict_cfg, resolve=True, throw_on_missing=True) init_logger( run_name=run_name, - config=cfg_dict, - project_name=config.logging.wandb_project_name, - entity=config.logging.wandb_entity, + full_config=cfg_dict, + logging_cfg=config.logging, base_dir=os.path.dirname(run_dir), - use_wandb=config.logging.track, ) logger = get_logger() logger.info(f"Hydra-initialized run: {run_name}") diff --git a/src/brittle_star_project/trainers/PPOTrainer.py b/src/brittle_star_project/trainers/PPOTrainer.py index 3b102d4..7cdc1ff 100644 --- a/src/brittle_star_project/trainers/PPOTrainer.py +++ b/src/brittle_star_project/trainers/PPOTrainer.py @@ -575,23 +575,13 @@ class PPOTrainer: def _save_model(self, model_path: str): self.logger.info("[SAVE]: Saving the final model...") + self.logger.save_final_model(params=self.agent_state.params, metadata=asdict(self.cfg)) - from dataclasses import asdict as _asdict - - config_dict = { - "experiment": _asdict(self.experiment), - "ppo": _asdict(self.ppo), - } - params = [ - config_dict, - [ - self.agent_state.params["sensor_params"], - self.agent_state.params["actor_params"], - self.agent_state.params["critic_params"], - self.agent_state.params["feature_extractor_params"], - ], - ] - self.logger.save_final_model(params=params) + def _save_checkpoint(self, iteration: int): + self.logger.info(f"[SAVE]: Saving checkpoint at iteration {iteration}...") + self.logger.save_checkpoint( + params=self.agent_state.params, step=iteration, metadata=asdict(self.cfg) + ) def train(self): """ @@ -622,8 +612,6 @@ class PPOTrainer: self._update_obs_stats(next_obs) next_obs = _normalize_obs(next_obs, self.obs_mean, self.obs_var) - xy_distance = _get_xy_distance_to_target(env_state.observations) - global_step += self.ppo.num_steps * self.ppo.num_envs self._log( global_step, @@ -647,6 +635,10 @@ class PPOTrainer: f"ETA {eta_str}" ) + if self.logging_cfg.save_checkpoints and self.logging_cfg.checkpoint_frequency > 0: + if iteration % self.logging_cfg.checkpoint_frequency == 0: + self._save_checkpoint(iteration) + if getattr(self.cfg.experiment, "debug_sanity", False): self.logger.info("\n[SANITY CHECK] Successfully completed 1 epoch") break diff --git a/src/experiment_logger/__init__.py b/src/experiment_logger/__init__.py index 53e57c0..e1b2d09 100644 --- a/src/experiment_logger/__init__.py +++ b/src/experiment_logger/__init__.py @@ -4,7 +4,6 @@ This package provides a unified interface for logging to multiple backends (WandB, disk, stdout) simultaneously, ensuring no data loss. """ -from experiment_logger.config_utils import load_yaml_config from experiment_logger.unified_logger import UnifiedLogger, get_logger, init_logger from experiment_logger.simple_logger import SimpleLogger from experiment_logger.wandb_utils import finish_wandb, init_wandb @@ -16,6 +15,5 @@ __all__ = [ "init_logger", "init_wandb", "finish_wandb", - "load_yaml_config", ] __version__ = "0.1.0" diff --git a/src/experiment_logger/config_logger.py b/src/experiment_logger/config_logger.py index 5d39b8e..c34c162 100644 --- a/src/experiment_logger/config_logger.py +++ b/src/experiment_logger/config_logger.py @@ -5,10 +5,29 @@ from typing import Optional @dataclass class LoggingConfig: track: bool = False - wandb_project_name: str = "PPO-Modularity" + wandb_project_name: str = "default-project" wandb_entity: Optional[str] = "SEL3-2026-Groep-4" capture_video: bool = False - save_model: bool = True + + # Local Saving + save_model: bool = True # Final model + save_checkpoints: bool = True # Intermediate checkpoints checkpoint_frequency: int = 100 - upload_model: bool = False + + # Remote Uploading (WandB Artifacts) + upload_final_model: bool = False + upload_checkpoints: bool = False + hf_entity: str = "" + + def __post_init__(self): + if self.upload_final_model and not (self.track and self.save_model): + raise ValueError( + "Configuration Error: 'upload_final_model' is True, but it requires " + "both 'track' and 'save_model' to also be True." + ) + if self.upload_checkpoints and not (self.track and self.save_checkpoints): + raise ValueError( + "Configuration Error: 'upload_checkpoints' is True, but it requires " + "both 'track' and 'save_checkpoints' to also be True." + ) diff --git a/src/experiment_logger/config_utils.py b/src/experiment_logger/config_utils.py deleted file mode 100644 index 80d1748..0000000 --- a/src/experiment_logger/config_utils.py +++ /dev/null @@ -1,83 +0,0 @@ -"""Configuration utilities for loading YAML configs and merging with CLI args.""" - -import os -from typing import Dict, Any, Type, TypeVar -import yaml -from dataclasses import fields, is_dataclass - -from experiment_logger.unified_logger import get_logger - -T = TypeVar("T") - - -def load_yaml_config(config_path: str) -> Dict[str, Any]: - """Load configuration from YAML file.""" - if not os.path.exists(config_path): - raise FileNotFoundError(f"Config file not found: {config_path}") - - with open(config_path, "r") as f: - config = yaml.safe_load(f) - - if config is None: - return {} - - get_logger().info(f"Loaded configuration from: {config_path}") - return config - - -def save_yaml_config(config: Dict[str, Any], config_path: str): - """Save configuration to YAML file.""" - os.makedirs(os.path.dirname(config_path), exist_ok=True) - - with open(config_path, "w") as f: - yaml.dump(config, f, default_flow_style=False, indent=2, sort_keys=False) - - get_logger().info(f"Saved configuration to: {config_path}") - - -def dataclass_from_dict(cls: Type[T], config_dict: Dict[str, Any]) -> T: - """Create dataclass instance from dictionary, handling type conversions.""" - if not is_dataclass(cls): - raise ValueError(f"{cls} is not a dataclass") - - # Get field names and types - field_map = {f.name: f for f in fields(cls)} # type: ignore - - # Filter config to only include valid fields - filtered_config: Dict[str, Any] = {} - for key, value in config_dict.items(): - if key in field_map: - field = field_map[key] - # Handle type conversion if needed - try: - # Handle None values and optional types - if value is None: - filtered_config[key] = None - elif hasattr(field.type, "__origin__") and field.type.__origin__ is type(None): - # Optional type (Union[X, None]) - filtered_config[key] = value - else: - # Try to convert to the expected type - if field.type is bool and isinstance(value, str): - filtered_config[key] = value.lower() in ("true", "1", "yes", "on") - else: - filtered_config[key] = field.type(value) if value is not None else None # type: ignore - except (ValueError, TypeError) as e: - get_logger().warning(f"Could not convert {key}={value} to {field.type}: {e}") - filtered_config[key] = value - else: - get_logger().warning(f"Unknown configuration parameter: {key}") - - return cls(**filtered_config) - - -def print_config(config: Any, title: str = "Configuration"): - """Pretty print configuration.""" - get_logger().info(f"{title}:") - if is_dataclass(config): - for field in fields(config): - value = getattr(config, field.name) - get_logger().info(f" {field.name}: {value}") - else: - for key, value in vars(config).items(): - get_logger().info(f" {key}: {value}") diff --git a/src/experiment_logger/simple_logger.py b/src/experiment_logger/simple_logger.py index 0ed1ab5..7e4a816 100644 --- a/src/experiment_logger/simple_logger.py +++ b/src/experiment_logger/simple_logger.py @@ -14,18 +14,16 @@ class SimpleLogger: def __init__( self, run_name: str = "simple_run", - config: Optional[Dict[str, Any]] = None, - project_name: str = "none", - entity: Optional[str] = None, + full_config: Optional[Dict[str, Any]] = None, + logging_cfg: Optional[Any] = None, base_dir: str = "runs", - use_wandb: bool = False, save_code: bool = False, log_level: int = logging.INFO, _set_as_global: bool = False, ): self.is_interactive = True self.run_name = run_name - self.config = config or {} + self.full_config = full_config or {} print(f"[INIT] SimpleLogger initialized for run: {run_name}") def set_level(self, level: int): diff --git a/src/experiment_logger/unified_logger.py b/src/experiment_logger/unified_logger.py index 2f5d06f..634638d 100644 --- a/src/experiment_logger/unified_logger.py +++ b/src/experiment_logger/unified_logger.py @@ -18,6 +18,7 @@ import jax.numpy as jnp import numpy as np from experiment_logger.wandb_utils import finish_wandb, init_wandb +from experiment_logger.config_logger import LoggingConfig # Global storage for the active logger and the proxy singleton _active_logger: Optional[Any] = None @@ -88,11 +89,9 @@ class UnifiedLogger: def __init__( self, run_name: str, - config: Dict[str, Any], - project_name: str = "PPO-Modularity", - entity: Optional[str] = None, + full_config: Dict[str, Any], + logging_cfg: LoggingConfig, base_dir: str = "runs", - use_wandb: bool = True, save_code: bool = True, log_level: int = logging.INFO, ): @@ -100,16 +99,16 @@ class UnifiedLogger: Args: run_name: Unique name for this run - config: Configuration dictionary with hyperparameters - project_name: WandB project name - entity: WandB entity (team/user name) + full_config: Full configuration dictionary with hyperparameters to be saved + logging_cfg: Structured logging configuration dataclass base_dir: Base directory for local storage - use_wandb: Whether to use WandB logging save_code: Whether to save code to WandB """ self.run_name = run_name - self.config = config - self.use_wandb = use_wandb + self.full_config = full_config + self.use_wandb = logging_cfg.track + self.upload_final_model = logging_cfg.upload_final_model + self.upload_checkpoints = logging_cfg.upload_checkpoints self.wandb_available = False self.wandb_run = None self.is_interactive = sys.stdout.isatty() @@ -159,7 +158,7 @@ class UnifiedLogger: # Initialize WandB if requested if self.use_wandb: - self._init_wandb(project_name, entity, save_code) + self._init_wandb(logging_cfg.wandb_project_name, logging_cfg.wandb_entity, save_code) # Initialize metrics storage self.metrics_buffer: List[Dict[str, Any]] = [] @@ -207,7 +206,7 @@ class UnifiedLogger: project=project_name, entity=entity, name=self.run_name, - config=self.config, + config=self.full_config, save_code=save_code, resume="allow", ) @@ -217,7 +216,7 @@ class UnifiedLogger: """Save configuration to disk.""" try: with open(self.config_file, "w") as f: - yaml.dump(self.config, f, default_flow_style=False, indent=2, sort_keys=False) + yaml.dump(self.full_config, f, default_flow_style=False, indent=2, sort_keys=False) self.info(f"Config saved to {self.config_file}") except Exception as e: self.error(f"Error saving config: {e}") @@ -327,7 +326,7 @@ class UnifiedLogger: self.info(f"Checkpoint saved: {checkpoint_path}") # Log to WandB as artifact - if self.wandb_run is not None: + if self.wandb_run is not None and self.upload_checkpoints: try: import wandb @@ -363,7 +362,7 @@ class UnifiedLogger: self.info(f"Final model saved: {final_model_path}") # Log to WandB - if self.wandb_run is not None: + if self.wandb_run is not None and self.upload_final_model: try: import wandb