Files
scenarionet/scenarionet_training/wandb_utils/our_wandb_callbacks_ray100.py

81 lines
2.6 KiB
Python
Raw Permalink Normal View History

Add come updates for Neurips paper (#4) * scenarionet training * wandb * train utils * fix callback * run PPO * use pg test * save path * use torch * add dependency * update ignore * update training * large model * use curriculum training * add time to exp name * storage_path * restore * update training * use my key * add log message * check seed * restore callback * restore call bacl * add log message * add logging message * restore ray1.4 * length 500 * ray 100 * wandb * use tf * more levels * add callback * 10 worker * show level * no env horizon * callback result level * more call back * add diffuculty * add mroen stat * mroe stat * show levels * add callback * new * ep len 600 * fix setup * fix stepup * fix to 3.8 * update setup * parallel worker! * new exp * add callback * lateral dist * pg dataset * evaluate * modify config * align config * train single RL * update training script * 100w eval * less eval to reveal * 2000 env eval * new trianing * eval 1000 * update eval * more workers * more worker * 20 worker * dataset to database * split tool! * split dataset * try fix * train 003 * fix mapping * fix test * add waymo tqdm * utils * fix bug * fix bug * waymo * int type * 8 worker read * disable * read file * add log message * check existence * dist 0 * int * check num * suprass warning * add filter API * filter * store map false * new * ablation * filter * fix * update filyter * reanme to from * random select * add overlapping checj * fix * new training sceheme * new reward * add waymo train script * waymo different config * copy raw data * fix bug * add tqdm * update readme * waymo * pg * max lateral dist 3 * pg * crash_done instead of penalty * no crash done * gpu * update eval script * steering range penalty * evaluate * finish pg * update setup * fix bug * test * fix * add on line * train nuplan * generate sensor * udpate training * static obj * multi worker eval * filx bug * use ray for testing * eval! * filter senario * id filter * fox bug * dist = 2 * filter * eval * eval ret * ok * update training pg * test before use * store data=False * collect figures * capture pic --------- Co-authored-by: Quanyi Li <quanyi@bolei-gpu02.cs.ucla.edu>
2023-06-10 18:56:33 +01:00
from multiprocessing import Queue
from ray.tune.integration.wandb import WandbLogger, _clean_log, _set_api_key
class OurWandbLogger(WandbLogger):
def __init__(self, config, logdir, trial):
self.exp_name = config["logger_config"]["wandb"].pop("exp_name")
super(OurWandbLogger, self).__init__(config, logdir, trial)
def _init(self):
config = self.config.copy()
config.pop("callbacks", None) # Remove callbacks
try:
if config.get("logger_config", {}).get("wandb"):
logger_config = config.pop("logger_config")
wandb_config = logger_config.get("wandb").copy()
else:
wandb_config = config.pop("wandb").copy()
except KeyError:
raise ValueError(
"Wandb logger specified but no configuration has been passed. "
"Make sure to include a `wandb` key in your `config` dict "
"containing at least a `project` specification.")
_set_api_key(wandb_config)
exclude_results = self._exclude_results.copy()
# Additional excludes
additional_excludes = wandb_config.pop("excludes", [])
exclude_results += additional_excludes
# Log config keys on each result?
log_config = wandb_config.pop("log_config", False)
if not log_config:
exclude_results += ["config"]
# Fill trial ID and name
trial_id = self.trial.trial_id if self.trial else None
trial_name = str(self.trial) if self.trial else None
# Project name for Wandb
try:
wandb_project = wandb_config.pop("project")
except KeyError:
raise ValueError(
"You need to specify a `project` in your wandb `config` dict.")
# Grouping
wandb_group = wandb_config.pop(
"group", self.trial.trainable_name if self.trial else None)
# remove unpickleable items!
config = _clean_log(config)
assert trial_id is not None
run_name = "{}_{}".format(self.exp_name, trial_id)
wandb_init_kwargs = dict(
id=trial_id,
name=run_name,
resume=True,
reinit=True,
allow_val_change=True,
group=wandb_group,
project=wandb_project,
config=config)
wandb_init_kwargs.update(wandb_config)
self._queue = Queue()
self._wandb = self._logger_process_cls(
queue=self._queue,
exclude=exclude_results,
to_config=self._config_results,
**wandb_init_kwargs)
self._wandb.start()