YannAhlgrim
107 lines
2.8 KiB
Python
107 lines
2.8 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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#
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import argparse
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import logging
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import os
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import pprint
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import sys
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import yaml
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import submitit
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from src.train_supervised import main as app_main
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logging.basicConfig(stream=sys.stdout, level=logging.INFO)
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logger = logging.getLogger()
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parser = argparse.ArgumentParser()
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parser.add_argument("--folder", type=str, help="location to save submitit logs")
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parser.add_argument(
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"--batch-launch",
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action="store_true",
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help="whether fname points to a file to batch-lauch several config files",
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)
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parser.add_argument(
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"--fname",
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type=str,
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help="yaml file containing config file names to launch",
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default="configs.yaml",
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)
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parser.add_argument("--partition", type=str, help="cluster partition to submit jobs on")
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parser.add_argument(
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"--nodes", type=int, default=1, help="num. nodes to request for job"
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)
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parser.add_argument(
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"--tasks-per-node", type=int, default=1, help="num. procs to per node"
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)
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parser.add_argument("--time", type=int, default=4300, help="time in minutes to run job")
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class Trainer:
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def __init__(self, fname="configs.yaml", load_model=None):
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self.fname = fname
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self.load_model = load_model
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def __call__(self):
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fname = self.fname
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load_model = self.load_model
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logger.info(f"called-params {fname}")
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# -- load script params
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params = None
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with open(fname, "r") as y_file:
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params = yaml.load(y_file, Loader=yaml.FullLoader)
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logger.info("loaded params...")
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pp = pprint.PrettyPrinter(indent=4)
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pp.pprint(params)
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resume_preempt = False if load_model is None else load_model
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app_main(args=params, resume_preempt=resume_preempt)
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def checkpoint(self):
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fb_trainer = Trainer(self.fname, True)
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return submitit.helpers.DelayedSubmission(
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fb_trainer,
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)
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def launch():
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executor = submitit.SlurmExecutor(
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folder=os.path.join(args.folder, "job_%j"), max_num_timeout=20
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)
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executor.update_parameters(
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partition=args.partition,
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mem_per_gpu="180G",
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time=args.time,
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nodes=args.nodes,
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ntasks_per_node=args.tasks_per_node,
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cpus_per_task=10,
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gpus_per_node=args.tasks_per_node,
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)
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config_fnames = [args.fname]
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jobs, trainers = [], []
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with executor.batch():
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for cf in config_fnames:
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fb_trainer = Trainer(cf)
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job = executor.submit(
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fb_trainer,
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)
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trainers.append(fb_trainer)
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jobs.append(job)
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for job in jobs:
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print(job.job_id)
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if __name__ == "__main__":
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args = parser.parse_args()
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launch()
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