File size: 2,852 Bytes
96b4304 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | #!/usr/bin/env bash
# 12-stage latent curriculum, instance-wise superposition targets.
#
# Same architecture and curriculum as w12_latent, except:
# - the output prompt is one sampled stage-k assignment, not the unique
# solution (input clues stay fixed)
# - the candidate-head BCE is off (SUDOKU_AUX_WEIGHT=0)
# - promotion is gated on the in-set rate (emitted digit is a stage-k
# candidate), not on candidate-set exact match
#
# Usage:
# bash launch_instance_latent.sh # GPU 0, full 800k run
# GPU=6 bash launch_instance_latent.sh # pick another GPU
# SUDOKU_MAX_STEPS=200 bash launch_instance_latent.sh # smoke
set -u
_SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
_CODE_DIR="$(cd "${_SCRIPT_DIR}/.." && pwd)"
export SUDOKU_RUN_DIR="${SUDOKU_RUN_DIR:-${_SCRIPT_DIR}}"
# shellcheck source=env_paths.sh
source "${_SCRIPT_DIR}/env_paths.sh"
cd "$RUN_DIR" || exit 1
if [ -n "${CUDNN_LIB}" ]; then export LD_LIBRARY_PATH="${CUDNN_LIB}:${LD_LIBRARY_PATH:-}"; fi
export XLA_PYTHON_CLIENT_MEM_FRACTION=0.9
GPU="${GPU:-0}"
name="${NAME:-w12_inst_latent}"
mkdir -p logs
export SUDOKU_TRAIN_PATH="${_CODE_DIR}/datasets/train_sudoku_puzzles.npy"
export SUDOKU_TEST_PATH="${_CODE_DIR}/datasets/test_sudoku_puzzles.npy"
# Masks are a metric only (in-set rate / promotion). Not a training target.
export SUDOKU_TRAIN_CAND="${_CODE_DIR}/datasets_multicandidate_s12/train_cand_masks.npy"
export SUDOKU_TEST_CAND="${_CODE_DIR}/datasets_multicandidate_s12/test_cand_masks.npy"
export SUDOKU_INSTANCE_DIR="${_CODE_DIR}/datasets_superposition"
export SUDOKU_RESUME=0
export SUDOKU_LATENT_SLOTS=12
export SUDOKU_RECURRENT=1
export SUDOKU_BACKTRACK=0
export SUDOKU_START_STAGE=1
export SUDOKU_MAX_STAGE=12
export SUDOKU_CAND_SLOT_MODE=depth
export SUDOKU_PASSES_PER_STAGE=1
export SUDOKU_AUX_WEIGHT=0.0
export SUDOKU_LEVEL_BALANCED=0
export SUDOKU_DATA_CURRICULUM=none
export SUDOKU_PLATEAU_STEPS="${SUDOKU_PLATEAU_STEPS:-20000}"
export SUDOKU_PLATEAU_DELTA=0.005
export SUDOKU_PATIENCE="${SUDOKU_PATIENCE:-80000}"
export SUDOKU_MIN_STAGE_STEPS="${SUDOKU_MIN_STAGE_STEPS:-8000}"
export SUDOKU_PROMOTE_ACC="${SUDOKU_PROMOTE_ACC:-0.90}"
export SUDOKU_MAX_STEPS="${SUDOKU_MAX_STEPS:-800000}"
export SUDOKU_EVAL_EVERY="${SUDOKU_EVAL_EVERY:-2000}"
export SUDOKU_SAVE_EVERY="${SUDOKU_SAVE_EVERY:-10000}"
export SUDOKU_CKPT_KEEP=100
export SUDOKU_LR=0.0002
export SUDOKU_DROPOUT=0.2
export SUDOKU_WD=0.005
echo "launching ${name} on GPU ${GPU}"
echo " K=12 recurrent=1 aux=0 instance_dir=${SUDOKU_INSTANCE_DIR}"
echo " max_steps=${SUDOKU_MAX_STEPS} plateau=${SUDOKU_PLATEAU_STEPS} patience=${SUDOKU_PATIENCE}"
CUDA_VISIBLE_DEVICES="${GPU}" \
nohup "$PY" -u -m train.main \
--workdir="./logs/${name}" \
--exp_name="${name}" \
> "logs/${name}.log" 2>&1 &
echo " pid $! log=${RUN_DIR}/logs/${name}.log"
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