run_dino_with_submitit.py

# Copyright (c) Facebook, Inc. and its affiliates.
# 
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# 
#     http://www.apache.org/licenses/LICENSE-2.0
# 
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
A script to run multinode training with submitit.
Almost copy-paste from https://github.com/facebookresearch/deit/blob/main/run_with_submitit.py
"""
import argparse
import os
import uuid
from pathlib import Path
import main_dino_mc
import submitit


def parse_args():
    parser = argparse.ArgumentParser("Submitit for DINO-MC", parents=[main_dino.get_args_parser()])
    parser.add_argument("--ngpus", default=2, type=int, help="Number of gpus to request on each node")
    parser.add_argument("--nodes", default=1, type=int, help="Number of nodes to request")
    parser.add_argument("--timeout", default=5600, type=int, help="Duration of the job")

    parser.add_argument("--partition", default="feit-gpu-a100", type=str, help="Partition where to submit")
    parser.add_argument("--qos", default="feit", type=str, help="Partition where to submit")
    parser.add_argument("--mem", default="12gb", type=str)
    parser.add_argument("--use_volta32", action='store_true', help="Big models? Use this")
    parser.add_argument('--comment', default="", type=str,
                        help='Comment to pass to scheduler, e.g. priority message')
    parser.add_argument("--job_name", default="dino", type=str, help="Name of the job.")
    
    return parser.parse_args()


def get_shared_folder() -> Path:
    user = os.getenv("USER")
    if Path("outputs").is_dir():
        p = Path(f"outputs/experiments")
        p.mkdir(exist_ok=True)
        return p
    raise RuntimeError("No shared folder available")


def get_init_file():
    # Init file must not exist, but its 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_dino

        self._setup_gpu_args()
        main_dino.train_dino(self.args)

    def checkpoint(self):
        import os
        import submitit

        self.args.dist_url = get_init_file().as_uri()
        print("Requeuing ", self.args)
        empty_trainer = type(self)(self.args)
        return submitit.helpers.DelayedSubmission(empty_trainer)

    def _setup_gpu_args(self):
        import submitit
        from pathlib import Path

        job_env = submitit.JobEnvironment()
        self.args.output_dir = Path(str(self.args.output_dir).replace("%j", str(job_env.job_id)))
        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.output_dir == "":
        args.output_dir = get_shared_folder() / "%j"
    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
    executor = submitit.AutoExecutor(folder=args.output_dir, slurm_max_num_timeout=60)

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

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

    executor.update_parameters(
        # mem_gb=10 * 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=args.num_workers,
        nodes=nodes,
        timeout_min=timeout_min,  # max is 60 * 72
        # Below are cluster dependent parameters
        slurm_partition=partition,
        slurm_signal_delay_s=120,
        slurm_additional_parameters = {
            'qos': args.qos
        },
        **kwargs
    )
    
    executor.update_parameters(name=args.job_name)
    # print(executor.parameters)
    args.dist_url = get_init_file().as_uri()

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

    print(f"Submitted job_id: {job.job_id}")
    print(f"Logs and checkpoints will be saved at: {args.output_dir}")


if __name__ == "__main__":
    main()