submitit_finetune.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# A script to run multinode training with submitit.
# --------------------------------------------------------

import argparse
import datetime
import os
import uuid
from pathlib import Path

import submitit

import main_finetune as classification


def parse_args():
    classification_parser = classification.get_args_parser()
    parser = argparse.ArgumentParser(
        "Submitit for MAE finetune", parents=[classification_parser]
    )
    parser.add_argument(
        "--ngpus", default=8, type=int, help="Number of gpus to request on each node"
    )
    parser.add_argument(
        "--nodes", default=2, type=int, help="Number of nodes to request"
    )
    parser.add_argument("--timeout", default=4320, type=int, help="Duration of the job")
    parser.add_argument(
        "--job_dir", default="", type=str, help="Job dir. Leave empty for automatic."
    )

    parser.add_argument(
        "--partition", default="learnfair", type=str, help="Partition where to submit"
    )
    parser.add_argument(
        "--use_volta32", action="store_true", help="Request 32G V100 GPUs"
    )
    parser.add_argument(
        "--comment", default="", type=str, help="Comment to pass to scheduler"
    )
    return parser.parse_args()


def get_shared_folder() -> Path:
    home = os.getenv("HOME")
    p = Path(f"{home}/experiments")
    p.mkdir(exist_ok=True)
    return p


def get_init_file():
    # Init file must not exist, but it's parent dir must exist.
    os.makedirs(str(get_shared_folder()), exist_ok=True)
    init_file = get_shared_folder() / f"{uuid.uuid4().hex}_init"
    if init_file.exists():
        os.remove(str(init_file))
    return init_file


class Trainer(object):
    def __init__(self, args):
        self.args = args

    def __call__(self):
        import main_finetune as classification

        self._setup_gpu_args()
        classification.main(self.args)

    def checkpoint(self):
        import os

        import submitit

        self.args.dist_url = get_init_file().as_uri()
        checkpoint_file = os.path.join(self.args.output_dir, "checkpoint.pth")
        if os.path.exists(checkpoint_file):
            self.args.resume = checkpoint_file
        print("Requeuing ", self.args)
        empty_trainer = type(self)(self.args)
        return submitit.helpers.DelayedSubmission(empty_trainer)

    def _setup_gpu_args(self):
        from pathlib import Path

        import submitit

        job_env = submitit.JobEnvironment()
        self.args.output_dir = Path(
            str(self.args.output_dir).replace("%j", str(job_env.job_id))
        )
        self.args.log_dir = self.args.output_dir
        self.args.gpu = job_env.local_rank
        self.args.rank = job_env.global_rank
        self.args.world_size = job_env.num_tasks
        print(f"Process group: {job_env.num_tasks} tasks, rank: {job_env.global_rank}")


def main():
    args = parse_args()
    if args.job_dir == "":
        args.job_dir = get_shared_folder() / datetime.datetime.now().strftime(
            "%Y_%m_%d_%H_%M"
        )
    link = get_shared_folder() / "latest"
    try:
        os.unlink(link)
    except FileNotFoundError:
        pass
    os.symlink(args.job_dir, link)

    # Note that the folder will depend on the job_id, to easily track experiments
    print(f"writing to folder {args.job_dir}")
    executor = submitit.AutoExecutor(folder=args.job_dir, slurm_max_num_timeout=30)

    num_gpus_per_node = args.ngpus
    nodes = args.nodes
    timeout_min = args.timeout

    partition = args.partition
    kwargs = {}
    if args.use_volta32:
        kwargs["slurm_constraint"] = "volta32gb"
    if args.comment:
        kwargs["slurm_comment"] = args.comment

    executor.update_parameters(
        mem_gb=40 * num_gpus_per_node,
        gpus_per_node=num_gpus_per_node,
        tasks_per_node=num_gpus_per_node,  # one task per GPU
        cpus_per_task=10,
        nodes=nodes,
        timeout_min=timeout_min,
        # Below are cluster dependent parameters
        slurm_partition=partition,
        slurm_signal_delay_s=120,
        **kwargs,
    )

    executor.update_parameters(name="mae")

    args.dist_url = get_init_file().as_uri()
    args.output_dir = args.job_dir

    trainer = Trainer(args)
    job = executor.submit(trainer)

    print("Submitted job_id:", job.job_id)


if __name__ == "__main__":
    main()