mirror of
https://github.com/velocitatem/PHANTOM.git
synced 2026-05-31 08:33:36 +00:00
117 lines
3.3 KiB
Bash
Executable File
117 lines
3.3 KiB
Bash
Executable File
#!/bin/bash
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# Submits PHANTOM training to a Ray cluster with .env injection.
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# Modes:
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# RAY_MODE=single -> one run (default)
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# RAY_MODE=distributed -> one run per TPU node (experimental)
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# RAY_MODE=benchmark -> one benchmark run per TPU node (overnight)
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set -euo pipefail
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ROOT="/home/velocitatem/Documents/Projects/PHANTOM"
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RAY_BIN="${RAY_BIN:-ray}"
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if ! command -v "$RAY_BIN" >/dev/null 2>&1; then
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if [ -x "$ROOT/.venv-ray/bin/ray" ]; then
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RAY_BIN="$ROOT/.venv-ray/bin/ray"
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else
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echo "ray CLI not found. Activate .venv-ray or set RAY_BIN." >&2
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exit 1
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fi
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fi
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# 1. Parse .env and generate the JSON payload for Ray
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export RUNTIME_ENV_JSON=$(python -c '
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import json
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import os
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from dotenv import dotenv_values
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env = dotenv_values(".env")
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# Filter out empty/None values
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env_vars = {k: v for k, v in env.items() if v}
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env_vars.setdefault("CLOUD_TPU_TASK_ID", os.getenv("CLOUD_TPU_TASK_ID", "0"))
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for k in ("WANDB_ENTITY", "WANDB_PROJECT", "PHANTOM_BENCHMARK_COMPARE_ROBUST"):
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if os.getenv(k):
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env_vars[k] = os.getenv(k)
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print(json.dumps({
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"pip": [
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"stable-baselines3>=2.2.0",
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"gymnasium>=0.29.0",
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"wandb",
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"tensorboard",
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"python-dotenv",
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"pandas",
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"pydantic",
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"graphviz",
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"huggingface_hub",
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"matplotlib"
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],
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"env_vars": env_vars
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}))
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')
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RAY_MODE="${RAY_MODE:-single}"
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TRAIN_ARGS="${TRAIN_ARGS:---algo ppo --total-timesteps 1000000}"
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BENCHMARK_ARGS="${BENCHMARK_ARGS:---project capstone_tpu --tiers static,surge,linear,qtable,ppo --alpha-values 0.0,0.1,0.25,0.4,0.6,0.8 --episodes 12 --total-timesteps 30000 --max-steps 100 --robust-radius 0.2 --robust-points 7 --robust-rollouts 1 --lambda-coi 0.2 --eta-ux 0.5 --reward-profit-weight 1.0 --device cpu}"
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SUBMIT_ARGS=()
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if [ "${RAY_NO_WAIT:-0}" = "1" ]; then
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SUBMIT_ARGS+=(--no-wait)
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fi
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if [ -n "${SUBMISSION_ID:-}" ]; then
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SUBMIT_ARGS+=(--submission-id "$SUBMISSION_ID")
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fi
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COMMON_ARGS=(
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job submit
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--address http://localhost:8265
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--working-dir "$ROOT"
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--runtime-env-json "$RUNTIME_ENV_JSON"
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"${SUBMIT_ARGS[@]}"
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--
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)
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if [ "$RAY_MODE" = "single" ]; then
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read -r -a TRAIN_TOKENS <<< "$TRAIN_ARGS"
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"$RAY_BIN" "${COMMON_ARGS[@]}" python -m engine.train "${TRAIN_TOKENS[@]}"
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exit 0
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fi
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if [ "$RAY_MODE" = "distributed" ]; then
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DIST_ARGS=(
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python
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scripts/ray_distributed_train.py
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--train-args "$TRAIN_ARGS"
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--num-nodes "${NUM_NODES:-4}"
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--tpu-per-task "${TPU_PER_TASK:-8}"
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--base-seed "${BASE_SEED:-42}"
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)
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if [ "${SYNC_JAX:-0}" = "1" ]; then
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DIST_ARGS+=(--sync-jax)
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fi
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"$RAY_BIN" "${COMMON_ARGS[@]}" "${DIST_ARGS[@]}"
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exit 0
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fi
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if [ "$RAY_MODE" = "benchmark" ]; then
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DIST_ARGS=(
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python
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scripts/ray_distributed_train.py
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--run-kind benchmark
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--entry-args "$BENCHMARK_ARGS"
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--num-nodes "${NUM_NODES:-4}"
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--tpu-per-task "${TPU_PER_TASK:-8}"
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--base-seed "${BASE_SEED:-42}"
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--output-root "${OUTPUT_ROOT:-engine/studies/results/overnight}"
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--wandb-entity "${WANDB_ENTITY:-lusiana}"
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--wandb-project "${WANDB_PROJECT:-capstone_tpu}"
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)
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if [ "${COMPARE_ROBUST:-1}" = "1" ]; then
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DIST_ARGS+=(--compare-robust)
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fi
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"$RAY_BIN" "${COMMON_ARGS[@]}" "${DIST_ARGS[@]}"
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exit 0
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fi
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echo "Unsupported RAY_MODE='$RAY_MODE' (expected 'single', 'distributed', or 'benchmark')." >&2
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exit 1
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