diff --git a/docs/backbones.md b/docs/backbones.md index ecab32a..35494db 100644 --- a/docs/backbones.md +++ b/docs/backbones.md @@ -364,5 +364,5 @@ BoardOCR では未実装だが、モバイル配信で精度を詰めたい場 各 backbone の val cell_acc / sfen_full_acc / wall-clock は sweep 完了後に W&B で並び、ここに引用予定。 -- W&B project: `mito-train-board-ocr` +- W&B project: `mito-train-board-ocr-w{image_size}-v{package_version}` (例: `mito-train-board-ocr-w224-v0.2.0`) - run 名は各 backbone 名そのまま（例: `mobilenet_v3_small`, `convnext_tiny`） diff --git a/docs/ocr-scaling-outlook.md b/docs/ocr-scaling-outlook.md index a4fc19f..3eb6844 100644 --- a/docs/ocr-scaling-outlook.md +++ b/docs/ocr-scaling-outlook.md @@ -257,7 +257,7 @@ EPOCHS=50 IMAGE_SIZE=288 BACKBONES="mobilenet_v3_small convnext_atto convnext_ti RESUME_INCOMPLETE=1 EPOCHS=100 IMAGE_SIZE=288 ./scripts/train_backbones.sh ``` -W&B project: `mito-train-board-ocr`。今回の sweep run 一覧： +W&B project: `mito-train-board-ocr`（当時。以降は `mito-train-board-ocr-w{image_size}-v{package_version}` に分割）。今回の sweep run 一覧： | Backbone | Run ID | |---|---| diff --git a/mito_train/training/train_board_ocr.py b/mito_train/training/train_board_ocr.py index 6287f47..f6bd809 100644 --- a/mito_train/training/train_board_ocr.py +++ b/mito_train/training/train_board_ocr.py @@ -17,12 +17,16 @@ from __future__ import annotations import argparse +from importlib.metadata import version as _pkg_version from pathlib import Path +from dotenv import load_dotenv + import torch import torch.nn as nn import torch.nn.functional as F from torch.nn.parallel import DistributedDataParallel as DDP +from torch.optim.lr_scheduler import CosineAnnealingLR, LinearLR, SequentialLR from torch.utils.data import DataLoader from torch.utils.data.distributed import DistributedSampler from tqdm.auto import tqdm @@ -101,7 +105,7 @@ def compute_loss( METRIC_KEYS = ( "board/cell_acc", "board/full_acc", - "hand/slot_acc", "hand/full_acc", "sfen/full_acc", + "hand/slot_acc", "hand/full_acc", "hand/mae", "sfen/full_acc", ) @@ -130,6 +134,9 @@ def compute_metrics( hand_slot_acc = hand_ok.float().mean() hand_all = hand_ok.all(dim=1) hand_full = hand_all.float().mean() + # MAE over all 14 slots — treats the count head as ordinal so + # "off by 1" is much better than "off by 9". + hand_mae = (hand_pred - hand_target).abs().float().mean() sfen_full = (board_all & hand_all).float().mean() return { @@ -137,6 +144,7 @@ def compute_metrics( "board/full_acc": board_full, "hand/slot_acc": hand_slot_acc, "hand/full_acc": hand_full, + "hand/mae": hand_mae, "sfen/full_acc": sfen_full, } @@ -275,25 +283,61 @@ def log_main(msg: str) -> None: model.parameters(), lr=args.lr, weight_decay=1e-4, fused=use_cuda, ) + # Per-step scheduler: linear warmup for --warmup-epochs, then cosine anneal + # to lr * --min-lr-ratio over the remaining steps. Every rank steps in + # lockstep (same len(train_loader) shard size), so DDP stays in sync. + steps_per_epoch = len(train_loader) + total_steps = max(1, args.epochs * steps_per_epoch) + warmup_steps = max(0, args.warmup_epochs * steps_per_epoch) + scheduler: torch.optim.lr_scheduler.LRScheduler | None = None + if args.scheduler == "cosine": + cosine_steps = max(1, total_steps - warmup_steps) + eta_min = args.lr * args.min_lr_ratio + if warmup_steps > 0: + warmup = LinearLR( + optimizer, start_factor=1e-3, end_factor=1.0, total_iters=warmup_steps, + ) + cosine = CosineAnnealingLR(optimizer, T_max=cosine_steps, eta_min=eta_min) + scheduler = SequentialLR( + optimizer, schedulers=[warmup, cosine], milestones=[warmup_steps], + ) + else: + scheduler = CosineAnnealingLR(optimizer, T_max=cosine_steps, eta_min=eta_min) + log_main( + f"[board_ocr] scheduler=cosine peak_lr={args.lr:.2e} " + f"warmup={args.warmup_epochs}ep ({warmup_steps} steps) " + f"anneal={cosine_steps} steps -> min_lr={eta_min:.2e}" + ) + else: + log_main(f"[board_ocr] scheduler=none lr={args.lr:.2e} (constant)") + + ckpt_dir = args.ckpt_root / args.model_name if is_main(): - args.ckpt_dir.mkdir(parents=True, exist_ok=True) + ckpt_dir.mkdir(parents=True, exist_ok=True) start_epoch = 1 resumed_from: str | None = None resumed_wandb_id: str | None = None if args.resume is not None: - ckpt_path = args.ckpt_dir / "latest.pt" if str(args.resume) == "latest" else args.resume - log_main(f"[board_ocr] resume from {ckpt_path}") - ckpt = torch.load(ckpt_path, map_location=device, weights_only=False) - _unwrap(model).load_state_dict(ckpt["model"]) - optimizer.load_state_dict(ckpt["optimizer"]) - if ckpt.get("scaler") is not None and scaler.is_enabled(): - scaler.load_state_dict(ckpt["scaler"]) - start_epoch = ckpt["epoch"] + 1 - resumed_from = str(ckpt_path) - resumed_wandb_id = ckpt.get("wandb_run_id") - log_main(f"[board_ocr] resumed at epoch={start_epoch} (ckpt was epoch {ckpt['epoch']})") - if resumed_wandb_id: - log_main(f"[board_ocr] will resume wandb run id={resumed_wandb_id}") + ckpt_path = ckpt_dir / "latest.pt" if str(args.resume) == "latest" else args.resume + # `--resume latest` on a fresh model_name has no checkpoint to load — treat + # as fresh start so sweep scripts can pass --resume latest unconditionally. + if str(args.resume) == "latest" and not ckpt_path.exists(): + log_main(f"[board_ocr] --resume latest requested but {ckpt_path} not found — starting fresh") + else: + log_main(f"[board_ocr] resume from {ckpt_path}") + ckpt = torch.load(ckpt_path, map_location=device, weights_only=False) + _unwrap(model).load_state_dict(ckpt["model"]) + optimizer.load_state_dict(ckpt["optimizer"]) + if ckpt.get("scaler") is not None and scaler.is_enabled(): + scaler.load_state_dict(ckpt["scaler"]) + if ckpt.get("scheduler") is not None and scheduler is not None: + scheduler.load_state_dict(ckpt["scheduler"]) + start_epoch = ckpt["epoch"] + 1 + resumed_from = str(ckpt_path) + resumed_wandb_id = ckpt.get("wandb_run_id") + log_main(f"[board_ocr] resumed at epoch={start_epoch} (ckpt was epoch {ckpt['epoch']})") + if resumed_wandb_id: + log_main(f"[board_ocr] will resume wandb run id={resumed_wandb_id}") if args.wandb_run_id: resumed_wandb_id = args.wandb_run_id @@ -301,8 +345,9 @@ def log_main(msg: str) -> None: # Only rank 0 talks to W&B; other ranks keep wandb_run=None and log nothing. run_name = args.backbone + wandb_project = f"mito-train-board-ocr-w{args.image_size}-v{_pkg_version('mito-train')}" wandb_run = _init_wandb( - project="mito-train-board-ocr", + project=wandb_project, run_name=run_name, run_id=resumed_wandb_id, resume="allow" if resumed_wandb_id else None, @@ -315,6 +360,9 @@ def log_main(msg: str) -> None: "effective_batch_size": args.batch_size * world_size, "world_size": world_size, "lr": args.lr, + "scheduler": args.scheduler, + "warmup_epochs": args.warmup_epochs, + "min_lr_ratio": args.min_lr_ratio, "epochs": args.epochs, "start_epoch": start_epoch, "resumed_from": resumed_from, @@ -366,6 +414,8 @@ def log_main(msg: str) -> None: scaler.scale(total).backward() scaler.step(optimizer) scaler.update() + if scheduler is not None: + scheduler.step() running_loss += total.detach() running_board_loss += bl.detach() running_hand_loss += hl.detach() @@ -387,6 +437,7 @@ def log_main(msg: str) -> None: "step/hand_loss": hl.item(), "step/board/cell_acc": m["board/cell_acc"].item(), "step/hand/slot_acc": m["hand/slot_acc"].item(), + "step/lr": optimizer.param_groups[0]["lr"], "step/global_step": global_step, "step/epoch_frac": epoch - 1 + n_batches / len(train_loader), }) @@ -406,6 +457,9 @@ def log_main(msg: str) -> None: f"[board_ocr] epoch={epoch:3d} " f"loss={avg_loss:.4f} (board={avg_bl:.4f} hand={avg_hl:.4f}) " f"cell_acc={avg['board/cell_acc']:.3f} " + f"board_acc={avg['board/full_acc']:.3f} " + f"hand_acc={avg['hand/full_acc']:.3f} " + f"hand_mae={avg['hand/mae']:.3f} " f"sfen_acc={avg['sfen/full_acc']:.3f}" ) @@ -446,6 +500,9 @@ def log_main(msg: str) -> None: } log_main( f"[board_ocr] val cell_acc={v_avg['board/cell_acc']:.3f} " + f"board_acc={v_avg['board/full_acc']:.3f} " + f"hand_acc={v_avg['hand/full_acc']:.3f} " + f"hand_mae={v_avg['hand/mae']:.3f} " f"sfen_acc={v_avg['sfen/full_acc']:.3f}" ) wandb_log.update({f"val/{k}": v for k, v in v_avg.items()}) @@ -464,14 +521,15 @@ def log_main(msg: str) -> None: "model": _unwrap(model).state_dict(), "optimizer": optimizer.state_dict(), "scaler": scaler.state_dict() if scaler.is_enabled() else None, + "scheduler": scheduler.state_dict() if scheduler is not None else None, "backbone": args.backbone, "image_size": args.image_size, "hand_weight": args.hand_weight, "wandb_run_id": wandb_run.id if wandb_run is not None else None, } - torch.save(ckpt_payload, args.ckpt_dir / "latest.pt") + torch.save(ckpt_payload, ckpt_dir / "latest.pt") if epoch % args.save_every == 0 or epoch == args.epochs: - torch.save(ckpt_payload, args.ckpt_dir / f"epoch-{epoch:03d}.pt") + torch.save(ckpt_payload, ckpt_dir / f"epoch-{epoch:03d}.pt") if wandb_run is not None: wandb_run.finish() @@ -488,6 +546,11 @@ def log_main(msg: str) -> None: def main() -> None: + # Pull HF_TOKEN / WANDB_API_KEY / CF_* out of .env into os.environ before + # any HF or wandb call resolves credentials. No-op if .env is missing. + # override=True so devcontainer.json's ${localEnv:...} forwards that expand + # to an empty string on hosts without those vars don't win over .env. + load_dotenv(override=True) p = argparse.ArgumentParser() p.add_argument("--mode", choices=["smoke", "full"], default="smoke") p.add_argument("--backbone", default="mobilenet_v3_small", @@ -525,6 +588,12 @@ def main() -> None: help="torch.compile the model. Worth measuring for full runs; " "compile overhead usually not worth it for smoke runs.") p.add_argument("--lr", type=float, default=3e-4) + p.add_argument("--scheduler", choices=["none", "cosine"], default="cosine", + help="Per-step LR schedule. cosine = linear warmup then cosine anneal.") + p.add_argument("--warmup-epochs", type=int, default=5, + help="Epochs of linear warmup (0 -> lr). Ignored when --scheduler=none.") + p.add_argument("--min-lr-ratio", type=float, default=0.01, + help="Cosine anneal floor as a fraction of --lr (min_lr = lr * ratio).") p.add_argument("--hand-mode", choices=["classification", "regression"], default="classification", help="classification: 14x19 logits + CE. regression: 14 scalars + SmoothL1.") @@ -537,14 +606,20 @@ def main() -> None: p.add_argument("--val-every", type=int, default=2) p.add_argument("--log-every", type=int, default=20, help="Log per-step train metrics to W&B every N batches.") - p.add_argument("--ckpt-dir", type=Path, default=Path("./runs/board-ocr")) + p.add_argument("--model-name", type=str, default=None, + help="Run identifier. Checkpoints go to //. " + "Defaults to 'board-ocr-'.") + p.add_argument("--ckpt-root", type=Path, default=Path("./runs"), + help="Directory under which each run gets its own / subdir.") p.add_argument("--save-every", type=int, default=5, help="Interval for saving epoch-{N}.pt snapshots. latest.pt is saved every epoch.") p.add_argument("--resume", type=Path, default=None, - help="Checkpoint path. Pass 'latest' to load --ckpt-dir/latest.pt.") + help="Checkpoint path. Pass 'latest' to load ./runs//latest.pt.") p.add_argument("--wandb-run-id", type=str, default=None, help="Force resume this W&B run id (overrides ckpt's stored id).") args = p.parse_args() + if args.model_name is None: + args.model_name = f"board-ocr-{args.backbone}" run(args) diff --git a/mito_train/training/train_detector.py b/mito_train/training/train_detector.py index d95fcb4..c539c14 100644 --- a/mito_train/training/train_detector.py +++ b/mito_train/training/train_detector.py @@ -20,6 +20,8 @@ import argparse from pathlib import Path +from dotenv import load_dotenv + import torch import torch.nn as nn import torch.nn.functional as F @@ -74,6 +76,11 @@ def evaluate(model: nn.Module, loader: DataLoader, device: str) -> dict[str, flo def main() -> None: + # Pull HF_TOKEN / WANDB_API_KEY / CF_* out of .env into os.environ before + # any wandb or HF call resolves credentials. No-op if .env is missing. + # override=True so devcontainer.json's ${localEnv:...} forwards that expand + # to an empty string on hosts without those vars don't win over .env. + load_dotenv(override=True) p = argparse.ArgumentParser() p.add_argument("--train-manifest", type=Path, default=Path("data/detector/train.jsonl")) p.add_argument("--val-manifest", type=Path, default=Path("data/detector/val.jsonl")) diff --git a/mito_train/training/train_piece.py b/mito_train/training/train_piece.py index 911b997..a94013e 100644 --- a/mito_train/training/train_piece.py +++ b/mito_train/training/train_piece.py @@ -18,6 +18,8 @@ import os from pathlib import Path +from dotenv import load_dotenv + import torch import torch.nn as nn from torch.utils.data import DataLoader @@ -219,6 +221,11 @@ def run_manifest(args: argparse.Namespace) -> None: def main() -> None: + # Pull WANDB_API_KEY / CF_* / HF_TOKEN out of .env into os.environ before + # any wandb or HF call resolves credentials. No-op if .env is missing. + # override=True so devcontainer.json's ${localEnv:...} forwards that expand + # to an empty string on hosts without those vars don't win over .env. + load_dotenv(override=True) p = argparse.ArgumentParser() p.add_argument("--mode", choices=["smoke", "manifest"], default="smoke") # smoke-mode args diff --git a/pyproject.toml b/pyproject.toml index ef2cf36..5aadc78 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "mito-train" -version = "0.2.0" +version = "0.3.3" description = "ぴよ将棋OCR 3モデル (board-detector / piece-classifier / hand-classifier) の学習・ONNX出力" requires-python = ">=3.11" dependencies = [ @@ -15,6 +15,7 @@ dependencies = [ "onnxscript>=0.7.1", "opencv-python-headless>=5.0.0.93", "pillow>=12.3.0", + "python-dotenv>=1.1.1", "python-shogi>=1.1.1", "pyyaml>=6.0.3", "timm>=1.0.28", diff --git a/scripts/train.sh b/scripts/train.sh index a422457..64116e8 100755 --- a/scripts/train.sh +++ b/scripts/train.sh @@ -1,7 +1,7 @@ #!/usr/bin/env bash # board OCR full 学習: mobilenet_v3_small + Apple Silicon MPS 前提。 # 環境変数で上書き可: EPOCHS, BATCH_SIZE, NUM_WORKERS, LR, BACKBONE, IMAGE_SIZE, -# CKPT_DIR, SAVE_EVERY, RESUME, WANDB_RUN_ID, HF_REPO_ID +# MODEL_NAME, SAVE_EVERY, RESUME, WANDB_RUN_ID, HF_REPO_ID # 途中再開したいときは: RESUME=latest EPOCHS=30 ./scripts/train.sh # 過去の W&B run に強制的に紐づけたいときは: # WANDB_RUN_ID=xxxxxxxx RESUME=latest EPOCHS=30 ./scripts/train.sh @@ -15,7 +15,7 @@ NUM_WORKERS=${NUM_WORKERS:-8} LR=${LR:-6e-4} BACKBONE=${BACKBONE:-mobilenet_v3_small} IMAGE_SIZE=${IMAGE_SIZE:-288} -CKPT_DIR=${CKPT_DIR:-./runs/board-ocr-v2} +MODEL_NAME=${MODEL_NAME:-board-ocr-v2} SAVE_EVERY=${SAVE_EVERY:-5} extra_args=() @@ -37,6 +37,6 @@ uv run python -m mito_train.training.train_board_ocr \ --batch-size "${BATCH_SIZE}" \ --num-workers "${NUM_WORKERS}" \ --lr "${LR}" \ - --ckpt-dir "${CKPT_DIR}" \ + --model-name "${MODEL_NAME}" \ --save-every "${SAVE_EVERY}" \ "${extra_args[@]}" diff --git a/scripts/train_backbones.sh b/scripts/train_backbones.sh index 7c753ad..1aa74f7 100755 --- a/scripts/train_backbones.sh +++ b/scripts/train_backbones.sh @@ -139,7 +139,8 @@ resolve_run_state() { # and DDP modes. Returns the training exit code. run_single() { local BB="$1" - local ckpt_dir="${CKPT_ROOT}/board-ocr-${BB}" + local model_name="board-ocr-${BB}" + local ckpt_dir="${CKPT_ROOT}/${model_name}" local latest_ckpt="${ckpt_dir}/latest.pt" local final_ckpt="${ckpt_dir}/${final_ckpt_name}" @@ -187,7 +188,7 @@ run_single() { --num-workers "${NUM_WORKERS}" \ --prefetch-factor "${PREFETCH_FACTOR}" \ --lr "${LR}" \ - --ckpt-dir "${ckpt_dir}" \ + --model-name "${model_name}" \ "${extra_args[@]}" \ "${per_run_extra[@]}" then @@ -205,7 +206,8 @@ run_single() { # Its stdout+stderr is redirected to per-backbone log file. Sets $!. launch_parallel() { local BB="$1" gpu="$2" - local ckpt_dir="${CKPT_ROOT}/board-ocr-${BB}" + local model_name="board-ocr-${BB}" + local ckpt_dir="${CKPT_ROOT}/${model_name}" local latest_ckpt="${ckpt_dir}/latest.pt" local final_ckpt="${ckpt_dir}/${final_ckpt_name}" local log_file="${PARALLEL_LOG_DIR}/${BB}.log" @@ -242,7 +244,7 @@ launch_parallel() { --num-workers "${NUM_WORKERS}" \ --prefetch-factor "${PREFETCH_FACTOR}" \ --lr "${LR}" \ - --ckpt-dir "${ckpt_dir}" \ + --model-name "${model_name}" \ "${extra_args[@]}" \ "${per_run_extra[@]}" \ >"${log_file}" 2>&1 & diff --git a/scripts/train_multi_gpu.sh b/scripts/train_multi_gpu.sh index 41ace65..c21c654 100755 --- a/scripts/train_multi_gpu.sh +++ b/scripts/train_multi_gpu.sh @@ -7,11 +7,18 @@ # PER_GPU_WORKERS DataLoader workers per rank (defaults to 4; total across # ranks is NPROC_PER_NODE * PER_GPU_WORKERS) # BACKBONE Model backbone (defaults to mobilenet_v3_small) +# AUTO_FREE_GPUS When 1, restrict training to GPUs whose used memory is +# below FREE_GPU_MEM_MB (default 500). Sets +# CUDA_VISIBLE_DEVICES and NPROC_PER_NODE to the survivors +# so shared boxes don't step on running jobs. +# FREE_GPU_MEM_MB "Free" threshold in MiB (default 500). Only used when +# AUTO_FREE_GPUS=1. # # Examples: # ./scripts/train_multi_gpu.sh # all GPUs, defaults # NPROC_PER_NODE=8 BACKBONE=convnext_tiny ./scripts/train_multi_gpu.sh # NPROC_PER_NODE=4 EPOCHS=100 ./scripts/train_multi_gpu.sh +# AUTO_FREE_GPUS=1 BACKBONE=mobilenet_v3_small ./scripts/train_multi_gpu.sh set -euo pipefail @@ -20,6 +27,34 @@ detect_gpu_count() { uv run python -c "import torch; print(torch.cuda.device_count())" 2>/dev/null || echo 1 } +# Select GPU indices whose used memory is under FREE_GPU_MEM_MB. Prints a +# comma-separated list (or an empty string if none qualify). +detect_free_gpus() { + local threshold="${1:-500}" + nvidia-smi --query-gpu=index,memory.used --format=csv,noheader,nounits 2>/dev/null \ + | awk -F, -v t="${threshold}" '{ + gsub(/ /, "", $1); gsub(/ /, "", $2); + if ($2+0 < t+0) picks[n++] = $1 + } + END { + for (i=0; i&2 + nvidia-smi --query-gpu=index,memory.used --format=csv >&2 + exit 1 + fi + export CUDA_VISIBLE_DEVICES="${free_list}" + # torchrun sees the remapped devices, so nproc is just the count. + NPROC_PER_NODE=$(awk -F, '{print NF}' <<<"${free_list}") + echo "[train_multi_gpu] AUTO_FREE_GPUS: CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES} (threshold=${FREE_GPU_MEM_MB}MiB)" +fi + NPROC_PER_NODE=${NPROC_PER_NODE:-$(detect_gpu_count)} MASTER_PORT=${MASTER_PORT:-29500} PER_GPU_WORKERS=${PER_GPU_WORKERS:-4} @@ -31,7 +66,7 @@ IMAGE_SIZE=${IMAGE_SIZE:-224} LR=${LR:-3e-4} BACKBONE=${BACKBONE:-mobilenet_v3_small} HF_REPO_ID=${HF_REPO_ID:-ultemica/piyoshogi} -CKPT_DIR=${CKPT_DIR:-./runs/board-ocr-${BACKBONE}} +MODEL_NAME=${MODEL_NAME:-board-ocr-${BACKBONE}} SAVE_EVERY=${SAVE_EVERY:-5} extra_args=(--preload) @@ -48,7 +83,7 @@ fi echo "[train_multi_gpu] nproc=${NPROC_PER_NODE} backbone=${BACKBONE} batch=${BATCH_SIZE} (per rank)" echo "[train_multi_gpu] effective batch = ${BATCH_SIZE} x ${NPROC_PER_NODE} = $(( BATCH_SIZE * NPROC_PER_NODE ))" echo "[train_multi_gpu] workers=${PER_GPU_WORKERS} per rank ($(( PER_GPU_WORKERS * NPROC_PER_NODE )) total)" -echo "[train_multi_gpu] ckpt_dir=${CKPT_DIR}" +echo "[train_multi_gpu] model_name=${MODEL_NAME} (ckpt_dir=./runs/${MODEL_NAME})" # --standalone: single-node rendezvous, avoids setting MASTER_ADDR/MASTER_PORT # manually for the common single-host case. @@ -66,6 +101,6 @@ uv run torchrun \ --num-workers "${PER_GPU_WORKERS}" \ --prefetch-factor "${PREFETCH_FACTOR}" \ --lr "${LR}" \ - --ckpt-dir "${CKPT_DIR}" \ + --model-name "${MODEL_NAME}" \ --save-every "${SAVE_EVERY}" \ "${extra_args[@]}" diff --git a/uv.lock b/uv.lock index f017df5..7374b09 100644 --- a/uv.lock +++ b/uv.lock @@ -1119,7 +1119,7 @@ wheels = [ [[package]] name = "mito-train" -version = "0.2.0" +version = "0.3.3" source = { editable = "." } dependencies = [ { name = "albumentations" }, @@ -1134,6 +1134,7 @@ dependencies = [ { name = "onnxscript" }, { name = "opencv-python-headless" }, { name = "pillow" }, + { name = "python-dotenv" }, { name = "python-shogi" }, { name = "pyyaml" }, { name = "timm" }, @@ -1160,6 +1161,7 @@ requires-dist = [ { name = "onnxscript", specifier = ">=0.7.1" }, { name = "opencv-python-headless", specifier = ">=5.0.0.93" }, { name = "pillow", specifier = ">=12.3.0" }, + { name = "python-dotenv", specifier = ">=1.1.1" }, { name = "python-shogi", specifier = ">=1.1.1" }, { name = "pyyaml", specifier = ">=6.0.3" }, { name = "timm", specifier = ">=1.0.28" }, @@ -2269,6 +2271,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892, upload-time = "2024-03-01T18:36:18.57Z" }, ] +[[package]] +name = "python-dotenv" +version = "1.2.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/82/ed/0301aeeac3e5353ef3d94b6ec08bbcabd04a72018415dcb29e588514bba8/python_dotenv-1.2.2.tar.gz", hash = "sha256:2c371a91fbd7ba082c2c1dc1f8bf89ca22564a087c2c287cd9b662adde799cf3", size = 50135, upload-time = "2026-03-01T16:00:26.196Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/0b/d7/1959b9648791274998a9c3526f6d0ec8fd2233e4d4acce81bbae76b44b2a/python_dotenv-1.2.2-py3-none-any.whl", hash = "sha256:1d8214789a24de455a8b8bd8ae6fe3c6b69a5e3d64aa8a8e5d68e694bbcb285a", size = 22101, upload-time = "2026-03-01T16:00:25.09Z" }, +] + [[package]] name = "python-shogi" version = "1.1.1"