coolguy/coorl-lost-cities
1
0
Fork 0
Code Issues Pull Requests Actions 1 Packages Projects Releases Wiki Activity

Auto-derive run dir from experiment_name + timestamp

Browse Source 
Drop checkpoint.directory from config — config defines what an experiment
is, not where its outputs go. The CLI now computes the run directory from
run.experiment_name plus a timestamp, defaulting to runs/tmp/ for
throwaway runs and runs/ when --keep is passed.

- Remove CheckpointConfig.directory and DeepCFRConfig.checkpoint_path
- DeepCFRTrainer takes run_dir: Path explicitly
- CLI: add --keep boolean; --resume requires an explicit path (no shortcut)
- Auto path: runs/[tmp/]<YYYY-MM-DD_HHMMSS>_<experiment_name-kebab>/
- Rename 13 configs to kebab-case; strip directory: lines; kebab their
  experiment_name values
- Rewrite AGENTS.md training/run sections; document
  archive/tmp/<flat> layout, --keep, kebab-case scope
- Update tests for new run_dir flow and dropped --resume shortcut

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
coolguy
2026-05-07 16:32:44 +09:00
co-authored by Claude Opus 4.7
parent a177031963
commit acb664c873
19 changed files with 144 additions and 143 deletions
Download Patch File Download Diff File Expand all files Collapse all files
tests/games/classic/test_deep_cfr_trainer.py
+35 -56
View File
@@ -1,6 +1,7 @@
from __future__ import annotations
import re
from pathlib import Path
import numpy as np
import torch
@@ -14,8 +15,8 @@ from coolrl_lost_cities.games.classic.deep_cfr.benchmark import (
)
from coolrl_lost_cities.games.classic.deep_cfr.checkpoints import load_checkpoint
from coolrl_lost_cities.games.classic.deep_cfr.cli import (
_RESUME_LATEST,
_resolve_resume_path,
_kebab_slug,
_resolve_run_dir,
_train_overrides_from_args,
_with_overrides,
)
@@ -36,13 +37,13 @@ def test_deep_cfr_loads_smoke_yaml_config() -> None:
assert config.run.max_iterations == 1
assert config.network.hidden_size == 16
assert config.traversal.traversals_per_player == 1
assert config.checkpoint.directory == "runs/deep_cfr/smoke"
assert config.run.experiment_name == "smoke"
def test_deep_cfr_loads_mapped_legacy_reproduction_config() -> None:
config = load_config("configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability.yaml")
config = load_config("configs/deep_cfr/deep-cfr-selfplay-full-depth-slot-playability.yaml")
assert config.run.experiment_name.endswith("slot_playability")
assert config.run.experiment_name.endswith("slot-playability")
assert config.run.seed == 79
assert config.run.max_iterations is None
assert config.run.max_minutes == 240
@@ -69,9 +70,6 @@ def test_deep_cfr_loads_mapped_legacy_reproduction_config() -> None:
assert config.regret_matching.all_negative_fallback == "uniform"
assert config.training_weighting.mode == "none"
assert config.checkpoint.save_every == 10
assert (
config.checkpoint.directory == "runs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability"
)
def test_deep_cfr_train_cli_accepts_run_and_traversal_config_overrides() -> None:
@@ -91,7 +89,7 @@ def test_deep_cfr_train_cli_accepts_run_and_traversal_config_overrides() -> None
],
},
)()
config = load_config("configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability.yaml")
config = load_config("configs/deep_cfr/deep-cfr-selfplay-full-depth-slot-playability.yaml")
overridden = _with_overrides(config, _train_overrides_from_args(args))
@@ -216,25 +214,18 @@ def test_deep_cfr_batched_evaluation_matches_batch_size_one() -> None:
assert np.isclose(batched["policy_entropy"], batch_one["policy_entropy"])
def test_deep_cfr_resume_latest_resolution_uses_config_checkpoint_dir(tmp_path) -> None:
config = _deep_cfr_config({"checkpoint": {"directory": str(tmp_path)}})
latest = tmp_path / "latest.pt"
latest.write_bytes(b"checkpoint")
def test_deep_cfr_resolve_run_dir_uses_keep_flag_and_kebab_slug() -> None:
config = _deep_cfr_config({"run": {"experiment_name": "Color Shared Attn v2"}})
assert _resolve_resume_path(config, _RESUME_LATEST) == str(latest)
assert _resolve_resume_path(config, "custom.pt") == "custom.pt"
assert _resolve_resume_path(config, None) is None
tmp_path = _resolve_run_dir(config, keep=False)
keep_path = _resolve_run_dir(config, keep=True)
def test_deep_cfr_resume_latest_resolution_requires_latest(tmp_path) -> None:
config = _deep_cfr_config({"checkpoint": {"directory": str(tmp_path)}})
try:
_resolve_resume_path(config, _RESUME_LATEST)
except FileNotFoundError as exc:
assert "latest checkpoint does not exist" in str(exc)
else: # pragma: no cover
raise AssertionError("expected FileNotFoundError")
assert tmp_path.parent == Path("runs/tmp")
assert keep_path.parent == Path("runs")
assert tmp_path.name.endswith("_color-shared-attn-v2")
assert keep_path.name.endswith("_color-shared-attn-v2")
assert _kebab_slug("Foo BAR_baz!!") == "foo-bar-baz"
assert _kebab_slug(" ") == "run"
def test_deep_cfr_playability_encoding_extends_input_shape() -> None:
@@ -321,13 +312,11 @@ def test_deep_cfr_trainer_forwards_metrics_to_extra_trackers(tmp_path) -> None:
"max_nodes_per_traversal": 16,
},
"optimization": {"advantage_batch_size": 2, "strategy_batch_size": 2},
"checkpoint": {
"directory": str(tmp_path / "extra-tracker"),
"save_every": 0,
},
"checkpoint": {"save_every": 0},
}
),
LostCitiesConfig(seed=71),
run_dir=tmp_path / "extra-tracker",
extra_trackers=[_CaptureTracker()],
)
@@ -643,14 +632,12 @@ def test_deep_cfr_trainer_saves_loads_and_evaluates_checkpoint(tmp_path) -> None
"max_nodes_per_traversal": 32,
},
"optimization": {"advantage_batch_size": 2, "strategy_batch_size": 2},
"checkpoint": {
"directory": str(checkpoint_dir),
"save_every": 1,
},
"checkpoint": {"save_every": 1},
"evaluation": {"eval_every": 1, "games": 2, "opponents": ("random",)},
}
),
LostCitiesConfig(seed=41),
run_dir=checkpoint_dir,
)
metrics = trainer.train()
@@ -660,13 +647,11 @@ def test_deep_cfr_trainer_saves_loads_and_evaluates_checkpoint(tmp_path) -> None
{
"run": {"seed": 41},
"network": {"hidden_size": 16},
"checkpoint": {
"directory": str(checkpoint_dir),
"save_every": 0,
},
"checkpoint": {"save_every": 0},
}
),
LostCitiesConfig(seed=41),
run_dir=checkpoint_dir,
)
restored.load_checkpoint(latest)
@@ -701,13 +686,11 @@ def test_deep_cfr_trainer_always_saves_latest_checkpoint(tmp_path) -> None:
"run": {"max_iterations": 1, "seed": 42},
"network": {"hidden_size": 16},
"traversal": {"traversals_per_player": 1, "max_depth": 1},
"checkpoint": {
"directory": str(checkpoint_dir),
"save_every": 10,
},
"checkpoint": {"save_every": 10},
}
),
LostCitiesConfig(seed=42),
run_dir=checkpoint_dir,
)
trainer.train()
@@ -724,20 +707,22 @@ def test_deep_cfr_exact_resume_is_explicitly_not_implemented(tmp_path) -> None:
"run": {"max_iterations": 1, "seed": 44},
"network": {"hidden_size": 16},
"traversal": {"traversals_per_player": 1, "max_depth": 1},
"checkpoint": {"directory": str(checkpoint_dir), "save_every": 1},
"checkpoint": {"save_every": 1},
}
),
LostCitiesConfig(seed=44),
run_dir=checkpoint_dir,
)
trainer.train()
exact = DeepCFRTrainer(
_deep_cfr_config(
{
"network": {"hidden_size": 16},
"checkpoint": {"directory": str(checkpoint_dir), "exact_resume": True},
"checkpoint": {"exact_resume": True},
}
),
LostCitiesConfig(seed=44),
run_dir=checkpoint_dir,
)
try:
@@ -763,13 +748,11 @@ def test_deep_cfr_trainer_multiprocessing_smoke_run(tmp_path) -> None:
"progress_every_traversals": 1,
},
"optimization": {"advantage_batch_size": 2, "strategy_batch_size": 2},
"checkpoint": {
"directory": str(tmp_path / "mp"),
"save_every": 0,
},
"checkpoint": {"save_every": 0},
}
),
LostCitiesConfig(seed=43),
run_dir=tmp_path / "mp",
)
metrics = trainer.train()
@@ -827,13 +810,11 @@ def test_deep_cfr_self_play_league_records_snapshots(tmp_path) -> None:
"anchor_probability": 1.0,
},
"optimization": {"advantage_batch_size": 2, "strategy_batch_size": 2},
"checkpoint": {
"directory": str(tmp_path / "league"),
"save_every": 0,
},
"checkpoint": {"save_every": 0},
}
),
LostCitiesConfig(seed=53),
run_dir=tmp_path / "league",
)
metrics = trainer.train()
@@ -864,13 +845,11 @@ def test_deep_cfr_weighted_self_play_league_uses_snapshot_bucket(tmp_path) -> No
"recent_window": 1,
},
"optimization": {"advantage_batch_size": 2, "strategy_batch_size": 2},
"checkpoint": {
"directory": str(tmp_path / "weighted-league"),
"save_every": 0,
},
"checkpoint": {"save_every": 0},
}
),
LostCitiesConfig(seed=59),
run_dir=tmp_path / "weighted-league",
)
metrics = trainer.train()