Janis Eccarius
1c80abdb8a
Build & Test (NowChessSystems) TeamCity build finished
Add an easy local data pipeline feeding GPU training on Colab. - SelfPlayMain: standalone NNUEBot self-play (no microservices) writing FENs for labeling; randomised openings for game diversity, sequential due to the shared EvaluationNNUE accumulator. Exposed via the `selfPlay` Gradle task and selfplay.sh. - NNUEBot: optional fixedMoveTimeMs so self-play runs fast (default unchanged). - NbaiLoader: honor `-Dnnue.weights=<path>` to load weights from a file before falling back to the bundled resource. - build_dataset.py / dataset.sh: one command builds the entire dataset (Lichess eval-DB backbone + self-play + tactical + random filler), dedups, balances the eval histogram, writes append-only zstd shards + manifest, and rclone-pushes to Drive. - train.py: NNUEDataset reads a directory of .jsonl.zst shards (streaming) in addition to a single file. - NNUETraining.ipynb: clone to ephemeral /content, sync shards from Drive (cache-aware), train on the shards dir; removed Colab generation/upload steps. - Concept + implementation plan docs. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
24 lines
671 B
Bash
24 lines
671 B
Bash
#!/usr/bin/env bash
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# Standalone bot self-play -> FENs for labeling. No microservices.
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#
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# ./selfplay.sh # 500 games with the bundled net
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# ./selfplay.sh 2000 # more games
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# ./selfplay.sh 2000 path.nbai # play with a specific net
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set -euo pipefail
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GAMES="${1:-500}"
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WEIGHTS="${2:-}"
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SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
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REPO_ROOT="$(cd "$SCRIPT_DIR/../../.." && pwd)"
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OUT="$SCRIPT_DIR/data/selfplay.txt"
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cd "$REPO_ROOT"
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SP_ARGS="--games $GAMES --out $OUT"
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if [[ -n "$WEIGHTS" ]]; then
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SP_ARGS="$SP_ARGS --weights $WEIGHTS"
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fi
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./gradlew -q :modules:official-bots:selfPlay -PspArgs="$SP_ARGS"
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echo "Self-play FENs -> $OUT"
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