Giorgio Gilestro
84124de143
Clarity pass over the main text (36-item audit), Discussion rewrite and cut, acknowledgements, Souly et al. as ref 62, lettered SI panels, model section moved under Results; plus the untracked curriculum/society/compose/smol configs, runners, figures, stats and tests that the SI already cites. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Y64o8FKP7rCuXzC48pxpMm
45 lines
1.8 KiB
Bash
Executable file
45 lines
1.8 KiB
Bash
Executable file
#!/bin/bash
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# The curriculum society campaign (prereg tasks/prereg-llm-society-v4.md, v5 families): one (seed, arm)
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# per array element, seeds 2-3 x 4 arms = 8 elements; seed 1 runs locally as the hedge. Baselines run
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# once per seed in the `isolated` element (they are cheap and need no partner). Each element checkpoints
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# every generation and resumes if requeued.
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# submit: qsub hpc/llm_curriculum.pbs status: qstat -u $USER -t
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# index -> seed = 2 + (i-1)/4, arm = (isolated society society_dry seed_bank)[(i-1)%4]
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#PBS -l select=1:ncpus=8:mem=64gb:ngpus=1:gpu_type=L40S
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#PBS -l walltime=06:00:00
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#PBS -N lam_curriculum
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#PBS -J 1-8
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cd "$PBS_O_WORKDIR"
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export HF_HOME="$EPHEMERAL/hf_cache"
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export HF_DATASETS_CACHE="$EPHEMERAL/hf_cache/datasets"
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export TOKENIZERS_PARALLELISM=false
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export UV_CACHE_DIR="$EPHEMERAL/uvcache"
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export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
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ARMS=(isolated society society_dry seed_bank)
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I=$((PBS_ARRAY_INDEX - 1))
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SEED=$((2 + I / 4))
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ARM=${ARMS[$((I % 4))]}
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source .venv/bin/activate
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nvidia-smi --query-gpu=name,memory.total,driver_version --format=csv,noheader
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echo "seed=$SEED arm=$ARM start=$(date)"
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CFG="configs/llm/_gen/curriculum_v5_s${SEED}_${ARM}.yaml"
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mkdir -p configs/llm/_gen
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python - "$SEED" "$ARM" "$CFG" <<'EOF'
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import sys, yaml
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seed, arm, out = int(sys.argv[1]), sys.argv[2], sys.argv[3]
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cfg = yaml.safe_load(open("configs/llm/curriculum_v5_s1.yaml"))
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cfg["seed"] = seed
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cfg["arms"] = [arm]
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cfg["baselines"] = ["sequential", "single_shot_merge", "joint"] if arm == "isolated" else []
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cfg["batch_size"] = 48 # L40S: 46 GB
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cfg["train_batch_size"] = 4
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cfg["output"] = {"dir": f"results/llm_curriculum_v5/s{seed}_{arm}"}
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yaml.safe_dump(cfg, open(out, "w"), sort_keys=False)
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EOF
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python -m llm.experiment "$CFG"
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echo "done: $(date)"
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