def test_random_search_smoke_test():
with tempfile.TemporaryDirectory() as tmp:
outdir = Path(tmp)
flags.FLAGS.unparse_flags()
flags.FLAGS(["argv0"])
random_search(
make_env=make_env,
outdir=outdir,
patience=50,
total_runtime=3,
nproc=1,
skip_done=False,
)
assert (outdir / "random_search.json").is_file()
assert (outdir / "random_search_progress.csv").is_file()
assert (outdir / "random_search_best_actions.txt").is_file()
assert (outdir / "optimized.bc").is_file()
env = make_env()
try:
replay_actions_from_logs(env, Path(outdir))
assert (outdir /
"random_search_best_actions_progress.csv").is_file()
assert (outdir /
"random_search_best_actions_commandline.txt").is_file()
finally:
env.close()
def main(argv):
"""Main entry point."""
argv = FLAGS(argv)
if len(argv) != 1:
raise app.UsageError(f"Unknown command line arguments: {argv[1:]}")
if FLAGS.ls_benchmark:
env = env_from_flags()
print("\n".join(sorted(env.benchmarks)))
env.close()
return
if FLAGS.ls_reward:
env = env_from_flags()
print("\n".join(sorted(env.reward.indices.keys())))
env.close()
return
assert FLAGS.patience >= 0, "--patience must be >= 0"
def make_env():
return env_from_flags(benchmark=benchmark_from_flags())
env = make_env()
try:
env.reset()
if not env.benchmark:
raise app.UsageError("No benchmark specified.")
finally:
env.close()
best_reward, _ = random_search(
make_env=make_env,
outdir=Path(FLAGS.output_dir) if FLAGS.output_dir else None,
patience=FLAGS.patience,
total_runtime=FLAGS.runtime,
nproc=FLAGS.nproc,
skip_done=FLAGS.skip_done,
)
# Exit with error if --fail_threshold was set and the best reward does not
# meet this value.
if FLAGS.fail_threshold is not None and best_reward < FLAGS.fail_threshold:
print(
f"Best reward {best_reward:.3f} below threshold of {FLAGS.fail_threshold}",
file=sys.stderr,
)
sys.exit(1)
def main(argv):
"""Main entry point."""
argv = FLAGS(argv)
if len(argv) != 1:
raise app.UsageError(f"Unknown command line arguments: {argv[1:]}")
if FLAGS.ls_reward:
with env_from_flags() as env:
print("\n".join(sorted(env.reward.indices.keys())))
return
assert FLAGS.patience >= 0, "--patience must be >= 0"
# Create an environment now to catch a startup time error before we launch
# a bunch of workers.
with env_from_flags() as env:
env.reset(benchmark=benchmark_from_flags())
env = random_search(
make_env=lambda: env_from_flags(benchmark=benchmark_from_flags()),
outdir=Path(FLAGS.output_dir) if FLAGS.output_dir else None,
patience=FLAGS.patience,
total_runtime=FLAGS.runtime,
nproc=FLAGS.nproc,
skip_done=FLAGS.skip_done,
)
try:
# Exit with error if --fail_threshold was set and the best reward does not
# meet this value.
if (
FLAGS.fail_threshold is not None
and env.episode_reward < FLAGS.fail_threshold
):
print(
f"Best reward {env.episode_reward:.3f} below threshold of {FLAGS.fail_threshold}",
file=sys.stderr,
)
sys.exit(1)
finally:
env.close()