Source code for catalyst.utils.scripts

# flake8: noqa
# @TODO: code formatting issue for 20.07 release
from typing import Callable
from importlib.util import module_from_spec, spec_from_file_location
import os
import pathlib
import shutil
import subprocess
import sys
import warnings

import torch
import torch.distributed

from catalyst.utils.distributed import (
from catalyst.utils.misc import get_utcnow_time

[docs]def import_module(expdir: pathlib.Path): """ @TODO: Docs. Contribution is welcome """ # @TODO: better PYTHONPATH handling if not isinstance(expdir, pathlib.Path): expdir = pathlib.Path(expdir) sys.path.insert(0, str(expdir.absolute())) sys.path.insert(0, os.path.dirname(str(expdir.absolute()))) s = spec_from_file_location(, str(expdir.absolute() / ""), submodule_search_locations=[expdir.absolute()], ) m = module_from_spec(s) s.loader.exec_module(m) sys.modules[] = m return m
def _tricky_dir_copy(dir_from, dir_to): os.makedirs(dir_to, exist_ok=True) shutil.rmtree(dir_to) shutil.copytree(dir_from, dir_to)
[docs]def dump_code(expdir, logdir): """ @TODO: Docs. Contribution is welcome """ expdir = expdir[:-1] if expdir.endswith("/") else expdir new_src_dir = "code" # @TODO: hardcoded old_pro_dir = os.path.dirname(os.path.abspath(__file__)) + "/../" new_pro_dir = os.path.join(logdir, new_src_dir, "catalyst") _tricky_dir_copy(old_pro_dir, new_pro_dir) old_expdir = os.path.abspath(expdir) new_expdir = os.path.basename(old_expdir) new_expdir = os.path.join(logdir, new_src_dir, new_expdir) _tricky_dir_copy(old_expdir, new_expdir)
[docs]def dump_python_files(src, dst): """ @TODO: Docs. Contribution is welcome """ py_files = list(src.glob("*.py")) ipynb_files = list(src.glob("*.ipynb")) py_files += ipynb_files py_files = list(set(py_files)) for py_file in py_files: shutil.copy2(f"{str(py_file.absolute())}", f"{dst}/{}")
[docs]def import_experiment_and_runner(expdir: pathlib.Path): """ @TODO: Docs. Contribution is welcome """ if not isinstance(expdir, pathlib.Path): expdir = pathlib.Path(expdir) m = import_module(expdir) runner_fn = m.Runner if hasattr(m, "Experiment"): experiment_fn = m.Experiment else: experiment_fn = None return experiment_fn, runner_fn
[docs]def dump_base_experiment_code(src: pathlib.Path, dst: pathlib.Path): """ @TODO: Docs. Contribution is welcome """ utcnow = get_utcnow_time() dst = dst.joinpath("code") dst = dst.joinpath(f"code-{utcnow}") if dst.exists() else dst os.makedirs(dst, exist_ok=True) dump_python_files(src, dst)
[docs]def distributed_cmd_run( worker_fn: Callable, distributed: bool = True, *args, **kwargs ) -> None: """ Distributed run Args: worker_fn (Callable): worker fn to run in distributed mode distributed (bool): distributed flag args: additional parameters for worker_fn kwargs: additional key-value parameters for worker_fn """ distributed_params = get_distributed_params() local_rank = distributed_params["local_rank"] world_size = distributed_params["world_size"] if distributed and torch.distributed.is_initialized(): warnings.warn( "Looks like you are trying to call distributed setup twice, " "switching to normal run for correct distributed training." ) if ( not distributed or torch.distributed.is_initialized() or world_size <= 1 ): worker_fn(*args, **kwargs) elif local_rank is not None: torch.cuda.set_device(int(local_rank)) torch.distributed.init_process_group( backend="nccl", init_method="env://" ) worker_fn(*args, **kwargs) else: workers = [] try: for local_rank in range(torch.cuda.device_count()): rank = distributed_params["start_rank"] + local_rank env = get_distributed_env(local_rank, rank, world_size) cmd = [sys.executable] + sys.argv.copy() workers.append(subprocess.Popen(cmd, env=env)) for worker in workers: worker.wait() finally: for worker in workers: worker.kill()
__all__ = [ "import_module", "dump_code", "dump_python_files", "import_experiment_and_runner", "dump_base_experiment_code", "distributed_cmd_run", ]