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How to use lt_strategy method in pandera

Best Python code snippet using pandera_python

test_strategies.py

Source:test_strategies.py

...115    assert (116        data.draw(strategies.ge_strategy(data_type, min_value=value)) >= value117    )118    assert (119        data.draw(strategies.lt_strategy(data_type, max_value=value)) < value120    )121    assert (122        data.draw(strategies.le_strategy(data_type, max_value=value)) <= value123    )124def value_ranges(data_type: pa.DataType):125    """Strategy to generate value range based on PandasDtype"""126    kwargs = dict(127        allow_nan=False,128        allow_infinity=False,129        exclude_min=False,130        exclude_max=False,131    )132    return (133        st.tuples(...

test_cli.py

Source:test_cli.py

1# Copyright The PyTorch Lightning team.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#     http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14import os15from unittest import mock16from unittest.mock import Mock17import pytest18from tests_lite.helpers.runif import RunIf19from lightning_lite.cli import main as cli_main20from lightning_lite.utilities.imports import _IS_WINDOWS, _TORCH_GREATER_EQUAL_1_1321if not (_IS_WINDOWS and _TORCH_GREATER_EQUAL_1_13):22    import torch.distributed.run23def skip_windows_pt_1_13():24    # https://github.com/pytorch/pytorch/issues/8542725    return pytest.mark.skipif(26        condition=(_IS_WINDOWS and _TORCH_GREATER_EQUAL_1_13),27        reason="Torchelastic import bug in 1.13 affecting Windows",28    )29@skip_windows_pt_1_13()30@mock.patch.dict(os.environ, os.environ.copy(), clear=True)31def test_cli_env_vars_defaults(monkeypatch):32    monkeypatch.setattr(torch.distributed, "run", Mock())33    with mock.patch("sys.argv", ["cli.py", "script.py"]):34        cli_main()35    assert os.environ["LT_CLI_USED"] == "1"36    assert os.environ["LT_ACCELERATOR"] == "cpu"37    assert "LT_STRATEGY" not in os.environ38    assert os.environ["LT_DEVICES"] == "1"39    assert os.environ["LT_NUM_NODES"] == "1"40    assert os.environ["LT_PRECISION"] == "32"41@skip_windows_pt_1_13()42@pytest.mark.parametrize("accelerator", ["cpu", "gpu", "cuda", pytest.param("mps", marks=RunIf(mps=True))])43@mock.patch.dict(os.environ, os.environ.copy(), clear=True)44@mock.patch("lightning_lite.accelerators.cuda.num_cuda_devices", return_value=2)45def test_cli_env_vars_accelerator(_, accelerator, monkeypatch):46    monkeypatch.setattr(torch.distributed, "run", Mock())47    with mock.patch("sys.argv", ["cli.py", "script.py", "--accelerator", accelerator]):48        cli_main()49    assert os.environ["LT_ACCELERATOR"] == accelerator50@skip_windows_pt_1_13()51@pytest.mark.parametrize("strategy", ["dp", "ddp", "deepspeed"])52@mock.patch.dict(os.environ, os.environ.copy(), clear=True)53@mock.patch("lightning_lite.accelerators.cuda.num_cuda_devices", return_value=2)54def test_cli_env_vars_strategy(_, strategy, monkeypatch):55    monkeypatch.setattr(torch.distributed, "run", Mock())56    with mock.patch("sys.argv", ["cli.py", "script.py", "--strategy", strategy]):57        cli_main()58    assert os.environ["LT_STRATEGY"] == strategy59@skip_windows_pt_1_13()60@pytest.mark.parametrize("devices", ["1", "2", "0,", "1,0", "-1"])61@mock.patch.dict(os.environ, os.environ.copy(), clear=True)62@mock.patch("lightning_lite.accelerators.cuda.num_cuda_devices", return_value=2)63def test_cli_env_vars_devices_cuda(_, devices, monkeypatch):64    monkeypatch.setattr(torch.distributed, "run", Mock())65    with mock.patch("sys.argv", ["cli.py", "script.py", "--accelerator", "cuda", "--devices", devices]):66        cli_main()67    assert os.environ["LT_DEVICES"] == devices68@RunIf(mps=True)69@skip_windows_pt_1_13()70@pytest.mark.parametrize("accelerator", ["mps", "gpu"])71@mock.patch.dict(os.environ, os.environ.copy(), clear=True)72def test_cli_env_vars_devices_mps(accelerator, monkeypatch):73    monkeypatch.setattr(torch.distributed, "run", Mock())74    with mock.patch("sys.argv", ["cli.py", "script.py", "--accelerator", accelerator]):75        cli_main()76    assert os.environ["LT_DEVICES"] == "1"77@skip_windows_pt_1_13()78@pytest.mark.parametrize("num_nodes", ["1", "2", "3"])79@mock.patch.dict(os.environ, os.environ.copy(), clear=True)80def test_cli_env_vars_num_nodes(num_nodes, monkeypatch):81    monkeypatch.setattr(torch.distributed, "run", Mock())82    with mock.patch("sys.argv", ["cli.py", "script.py", "--num-nodes", num_nodes]):83        cli_main()84    assert os.environ["LT_NUM_NODES"] == num_nodes85@skip_windows_pt_1_13()86@pytest.mark.parametrize("precision", ["64", "32", "16", "bf16"])87@mock.patch.dict(os.environ, os.environ.copy(), clear=True)88def test_cli_env_vars_precision(precision, monkeypatch):89    monkeypatch.setattr(torch.distributed, "run", Mock())90    with mock.patch("sys.argv", ["cli.py", "script.py", "--precision", precision]):91        cli_main()92    assert os.environ["LT_PRECISION"] == precision93@skip_windows_pt_1_13()94@mock.patch.dict(os.environ, os.environ.copy(), clear=True)95def test_cli_torchrun_defaults(monkeypatch):96    torchrun_mock = Mock()97    monkeypatch.setattr(torch.distributed, "run", torchrun_mock)98    with mock.patch("sys.argv", ["cli.py", "script.py"]):99        cli_main()100    torchrun_mock.main.assert_called_with(101        [102            "--nproc_per_node=1",103            "--nnodes=1",104            "--node_rank=0",105            "--master_addr=127.0.0.1",106            "--master_port=29400",107            "script.py",108        ]109    )110@skip_windows_pt_1_13()111@pytest.mark.parametrize(112    "devices,expected",113    [114        ("1", 1),115        ("2", 2),116        ("0,", 1),117        ("1,0,2", 3),118        ("-1", 5),119    ],120)121@mock.patch.dict(os.environ, os.environ.copy(), clear=True)122@mock.patch("lightning_lite.accelerators.cuda.num_cuda_devices", return_value=5)123def test_cli_torchrun_num_processes_launched(_, devices, expected, monkeypatch):124    torchrun_mock = Mock()125    monkeypatch.setattr(torch.distributed, "run", torchrun_mock)126    with mock.patch("sys.argv", ["cli.py", "script.py", "--accelerator", "cuda", "--devices", devices]):127        cli_main()128    torchrun_mock.main.assert_called_with(129        [130            f"--nproc_per_node={expected}",131            "--nnodes=1",132            "--node_rank=0",133            "--master_addr=127.0.0.1",134            "--master_port=29400",135            "script.py",136        ]...

cli.py

Source:cli.py

1# Copyright The PyTorch Lightning team.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#     http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14import logging15import os16from argparse import ArgumentParser, Namespace17from typing import List, Tuple18from lightning_lite.accelerators import CPUAccelerator, CUDAAccelerator, MPSAccelerator19from lightning_lite.utilities.device_parser import _parse_gpu_ids20from lightning_lite.utilities.imports import _IS_WINDOWS, _TORCH_GREATER_EQUAL_1_1321_log = logging.getLogger(__name__)22_SUPPORTED_ACCELERATORS = ("cpu", "gpu", "cuda", "mps", "tpu")23_SUPPORTED_STRATEGIES = (None, "ddp", "dp", "deepspeed")24_SUPPORTED_PRECISION = ("64", "32", "16", "bf16")25def _parse_args() -> Tuple[Namespace, List[str]]:26    parser = ArgumentParser(description="Launch your script with the Lightning Lite CLI.")27    parser.add_argument("script", type=str, help="Path to the Python script with Lightning Lite inside.")28    parser.add_argument(29        "--accelerator",30        type=str,31        default="cpu",32        choices=_SUPPORTED_ACCELERATORS,33        help="The hardware accelerator to run on.",34    )35    parser.add_argument(36        "--strategy",37        type=str,38        default=None,39        choices=_SUPPORTED_STRATEGIES,40        help="Strategy for how to run across multiple devices.",41    )42    parser.add_argument(43        "--devices",44        type=str,45        default="1",46        help=(47            "Number of devices to run on (``int``), which devices to run on (``list`` or ``str``), or ``'auto'``."48            " The value applies per node."49        ),50    )51    parser.add_argument(52        "--num-nodes",53        "--num_nodes",54        type=int,55        default=1,56        help="Number of machines (nodes) for distributed execution.",57    )58    parser.add_argument(59        "--node-rank",60        "--node_rank",61        type=int,62        default=0,63        help=(64            "The index of the machine (node) this command gets started on. Must be a number in the range"65            " 0, ..., num_nodes - 1."66        ),67    )68    parser.add_argument(69        "--main-address",70        "--main_address",71        type=str,72        default="127.0.0.1",73        help="The hostname or IP address of the main machine (usually the one with node_rank = 0).",74    )75    parser.add_argument(76        "--main-port",77        "--main_port",78        type=int,79        default=29400,80        help="The main port to connect to the main machine.",81    )82    parser.add_argument(83        "--precision",84        type=str,85        default="32",86        choices=_SUPPORTED_PRECISION,87        help=(88            "Double precision (``64``), full precision (``32``), half precision (``16``) or bfloat16 precision"89            " (``'bf16'``)"90        ),91    )92    args, script_args = parser.parse_known_args()93    return args, script_args94def _set_env_variables(args: Namespace) -> None:95    """Set the environment variables for the new processes.96    The Lite connector will parse the arguments set here.97    """98    os.environ["LT_CLI_USED"] = "1"99    os.environ["LT_ACCELERATOR"] = str(args.accelerator)100    if args.strategy is not None:101        os.environ["LT_STRATEGY"] = str(args.strategy)102    os.environ["LT_DEVICES"] = str(args.devices)103    os.environ["LT_NUM_NODES"] = str(args.num_nodes)104    os.environ["LT_PRECISION"] = str(args.precision)105def _get_num_processes(accelerator: str, devices: str) -> int:106    """Parse the `devices` argument to determine how many processes need to be launched on the current machine."""107    if accelerator == "gpu":108        parsed_devices = _parse_gpu_ids(devices, include_cuda=True, include_mps=True)109    elif accelerator == "cuda":110        parsed_devices = CUDAAccelerator.parse_devices(devices)111    elif accelerator == "mps":112        parsed_devices = MPSAccelerator.parse_devices(devices)113    elif accelerator == "tpu":114        raise ValueError("Launching processes for TPU through the CLI is not supported.")115    else:116        return CPUAccelerator.parse_devices(devices)117    return len(parsed_devices) if parsed_devices is not None else 0118def _torchrun_launch(args: Namespace, script_args: List[str]) -> None:119    """This will invoke `torchrun` programmatically to launch the given script in new processes."""120    if _IS_WINDOWS and _TORCH_GREATER_EQUAL_1_13:121        # TODO: remove once import issue is resolved: https://github.com/pytorch/pytorch/issues/85427122        _log.error(123            "On the Windows platform, this launcher is currently only supported on torch < 1.13 due to a bug"124            " upstream: https://github.com/pytorch/pytorch/issues/85427"125        )126        exit(1)127    import torch.distributed.run as torchrun128    if args.strategy == "dp":129        num_processes = 1130    else:131        num_processes = _get_num_processes(args.accelerator, args.devices)132    torchrun_args = [133        f"--nproc_per_node={num_processes}",134        f"--nnodes={args.num_nodes}",135        f"--node_rank={args.node_rank}",136        f"--master_addr={args.main_address}",137        f"--master_port={args.main_port}",138        args.script,139    ]140    torchrun_args.extend(script_args)141    # set a good default number of threads for OMP to avoid warnings being emitted to the user142    os.environ.setdefault("OMP_NUM_THREADS", str(max(1, (os.cpu_count() or 1) // num_processes)))143    torchrun.main(torchrun_args)144def main() -> None:145    args, script_args = _parse_args()146    _set_env_variables(args)147    _torchrun_launch(args, script_args)148if __name__ == "__main__":...

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