feat(downloader): added downloading of artifacts + moved script to tool directory
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2 changed files with 84 additions and 15 deletions
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@ -45,19 +45,22 @@ class Columns(str, Enum):
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"""Column names expected in every evaluation CSV."""
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ARCH = "architecture"
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TIMESTEPS = "total_trained_timesteps"
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REWARD = "accumulated_reward"
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TIMESTEPS = "trained_timesteps"
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REWARD = "eval_return"
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VELOCITY = "velocity"
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EVAL_STEPS = "eval_steps"
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FINAL_XY_DIST = "final_xy_dist"
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INITIAL_XY_DIST = "initial_xy_dist"
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REACHED_TARGET = "reached_target"
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# Maps architecture display names to the path of their evaluation CSV.
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# Update these paths once real evaluation data is available.
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FILE_MAPPING: dict[str, str] = {
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"centralized 2 arms": "runs/dummy/dummy_centralized_2_arms.csv",
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"centralized 5 arms": "runs/dummy/dummy_centralized_5_arms.csv",
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"decentralized fully connected": "runs/dummy/dummy_decentralized_fully_connected.csv",
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"decentralized ring-level": "runs/dummy/dummy_decentralized_ring-level.csv",
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"decentralized segment-level": "runs/dummy/dummy_decentralized_segment-level.csv",
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# "centralized 2 arms": "runs/dummy/dummy_centralized_2_arms.csv",
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"centralized 5 arms": "runs/final-v2-centralized/checkpoint_evaluation.csv",
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"decentralized fully connected": "runs/final-v2-fully-conn/checkpoint_evaluation.csv",
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"decentralized ring-level": "runs/final-v2-ring/checkpoint_evaluation.csv",
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}
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# Architecture profiles for dummy data generation: (max_reward, max_velocity, sigmoid_speed)
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@ -108,7 +111,13 @@ def load_metrics(file_mapping: dict[str, str]) -> pd.DataFrame:
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Loads one CSV per architecture, injects the architecture name as a column,
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and returns the combined DataFrame with only the required columns.
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"""
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required = [Columns.TIMESTEPS, Columns.REWARD, Columns.VELOCITY]
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required = [
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Columns.TIMESTEPS,
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Columns.REWARD,
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Columns.INITIAL_XY_DIST,
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Columns.FINAL_XY_DIST,
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Columns.EVAL_STEPS,
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]
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dfs = []
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for arch_name, filepath in file_mapping.items():
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@ -125,6 +134,10 @@ def load_metrics(file_mapping: dict[str, str]) -> pd.DataFrame:
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df = df[required].copy()
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df[Columns.ARCH] = arch_name
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df[Columns.VELOCITY] = (df[Columns.INITIAL_XY_DIST] - df[Columns.FINAL_XY_DIST]) / df[
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Columns.EVAL_STEPS
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]
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dfs.append(df)
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return pd.concat(dfs, ignore_index=True) if dfs else pd.DataFrame()
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@ -303,6 +316,7 @@ def plot_results(df: pd.DataFrame, results: pd.DataFrame, output_dir: str, **kwa
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def obtain_data() -> pd.DataFrame:
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"""Resolves the file mapping, falling back to generated dummy CSVs if needed."""
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global USING_DUMMY_DATA
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if not any(os.path.exists(p) for p in FILE_MAPPING.values()):
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logger.info("No real evaluation files found. Generating dummy CSVs at expected locations.")
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generate_dummy_csvs(FILE_MAPPING)
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@ -7,6 +7,7 @@ import argparse
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# tune these depending on network / W&B limits
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MAX_RUN_WORKERS = 8
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MAX_FILE_WORKERS = 16
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MAX_ARTIFACT_WORKERS = 8
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api = wandb.Api()
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@ -20,19 +21,42 @@ def safe_print(*args, **kwargs):
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def download_file(file, run_dir):
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target = run_dir / file.name
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try:
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# skip existing files
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if target.exists():
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return f"SKIP {target}"
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return f"SKIP FILE {target}"
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target.parent.mkdir(parents=True, exist_ok=True)
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file.download(root=run_dir, replace=False)
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return f"DONE {target}"
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return f"DONE FILE {target}"
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except Exception as e:
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return f"FAIL {target}: {e}"
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return f"FAIL FILE {target}: {e}"
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def sanitize_artifact_name(name: str):
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return name.replace(":", "_")
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def download_artifact(artifact, artifact_root):
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try:
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artifact_name = sanitize_artifact_name(artifact.name)
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artifact_dir = artifact_root / artifact_name
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if artifact_dir.exists() and any(artifact_dir.iterdir()):
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return f"SKIP ARTIFACT {artifact.name}"
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artifact_dir.mkdir(parents=True, exist_ok=True)
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artifact.download(root=artifact_dir)
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return f"DONE ARTIFACT {artifact.name}"
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except Exception as e:
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return f"FAIL ARTIFACT {artifact.name}: {e}"
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def download_run(run, root):
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@ -41,6 +65,9 @@ def download_run(run, root):
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safe_print(f"\n=== {run.name} ({run.id}) ===")
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# -------------------------
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# Download regular run files
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# -------------------------
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files = list(run.files())
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with ThreadPoolExecutor(max_workers=MAX_FILE_WORKERS) as executor:
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@ -49,10 +76,38 @@ def download_run(run, root):
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for future in as_completed(futures):
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safe_print(future.result())
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# OPTIONAL: download artifacts too
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# for artifact in run.logged_artifacts():
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# artifact_dir = run_dir / "artifacts" / artifact.name
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# artifact.download(root=artifact_dir)
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# -------------------------
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# Download logged artifacts
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# -------------------------
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artifact_root = run_dir / "artifacts"
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try:
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artifacts = list(run.logged_artifacts())
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safe_print(f"Found {len(artifacts)} artifacts for {run.name}")
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with ThreadPoolExecutor(max_workers=MAX_ARTIFACT_WORKERS) as executor:
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futures = [
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executor.submit(download_artifact, artifact, artifact_root)
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for artifact in artifacts
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]
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for future in as_completed(futures):
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safe_print(future.result())
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except Exception as e:
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safe_print(f"Artifact download failed for {run.name}: {e}")
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# -------------------------
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# OPTIONAL: download used/input artifacts
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# -------------------------
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# try:
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# used_artifacts = list(run.used_artifacts())
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# used_root = run_dir / "used_artifacts"
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#
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# for artifact in used_artifacts:
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# download_artifact(artifact, used_root)
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# except Exception as e:
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# safe_print(f"Used artifact download failed: {e}")
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safe_print(f"Finished {run.name}")
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