Ray tune self terminates at 98 trials consistently

How severe does this issue affect your experience of using Ray?

  • Medium: It contributes to significant difficulty to complete my task, but I can work around it.

Currently when I run my ray tune hyperparameter tuning script on windows it self terminates at exactly 98 trials every time before it has reached running through the num_samples. I have tested that it is not time dependent with different complexity of models

The error I seem to get is:

2022-07-27 15:15:46,750 WARNING worker.py:1404 -- The log monitor on node DESKTOP-N18AJ6S failed with the following error:
OSError: [WinError 87] The parameter is incorrect

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "C:\Users\Steven\anaconda3\envs\GRF_hip_outcomes\lib\site-packages\ray\_private\log_monitor.py", line 451, in <module>
  File "C:\Users\Steven\anaconda3\envs\GRF_hip_outcomes\lib\site-packages\ray\_private\log_monitor.py", line 376, in run
  File "C:\Users\Steven\anaconda3\envs\GRF_hip_outcomes\lib\site-packages\ray\_private\log_monitor.py", line 218, in open_closed_files
  File "C:\Users\Steven\anaconda3\envs\GRF_hip_outcomes\lib\site-packages\ray\_private\log_monitor.py", line 130, in close_all_files
    os.kill(file_info.worker_pid, 0)
SystemError: <built-in function kill> returned a result with an error set

then it’d exit:

2022-07-27 15:15:47,590 WARNING tune.py:682 -- Stop signal received (e.g. via SIGINT/Ctrl+C), ending Ray Tune run. This will try to checkpoint the experiment state one last time. Press CTRL+C (or send SIGINT/SIGKILL/SIGTERM) to skip.

I am using ray 1.13.0 with pytorch. This only happens on Windows and the same script does not have this problem on Ubuntu 20.04 or Pop OS

My train function is adapted from the ray tune pytorch example.

This seems to be an issue with Ray core:

2022-07-27 15:15:46,750 WARNING worker.py:1404 -- The log monitor on node DESKTOP-N18AJ6S failed with the following error:
OSError: [WinError 87] The parameter is incorrect

Basically Ray crashes (for unknown reasons from the excerpt you posted), taking down all other processes with it. The log output you see is just from this process killing.

How many trials are you running in total? 100?

What happens if you increase this to say 120 - does it stop after 118?

Hi Kai, thanks for the quick response!

I have tried this with a number of sample sizes (150, 250, 500 and 1000), they all stop at precisely 98 trials

any ideas on work around or solution or what I should try next?

It’s odd - you said you tested with different trial lengths and model sizes, so it’s probably not resource utilization. I’m wondering if there is some kind of internal memory limit going on (number of open threads or so) that we don’t catch in windows.

Just fyi, windows support is not well-tested - we build windows wheels and the core library is usable, but we don’t test the libraries.

Is using WSL an alternative for you?

In any case, if you could paste (parts of) your training code, that could give us an indication of what might be going wrong.

Hi Kai, thank you for your help! I understand that ray does work better on linux and indeed my script runs perfectly on linux.

I’ve tried using WSL2 in the past however it results in the GPU not recognised by ray (even though other libraries like pytorch recognises there are GPU’s) which is a problem for large scale experiments that exceed 100 trials.

I’ve spent a little time to produce a “simple” script that doesn’t take 4 hours to reach the error message (previous attempts have resulted in the error message not showing anymore):

main script to run from command line:

import logging
import os
from functools import partial
from importlib import import_module
from pathlib import Path

import hydra
import ray
import ray.tune as tune
from hydra.utils import instantiate
from omegaconf import OmegaConf
from ray.tune import CLIReporter

from srcs.utils import set_seed, open_struct
from srcs.utils.tune import trial_name, get_metric_names

logger = logging.getLogger(__name__)

# when on windows, scipy bug causes ray tune to not save trials properly, see https://stackoverflow.com/questions/15457786/ctrl-c-crashes-python-after-importing-scipy-stats
if os.name == 'nt':
    import _thread
    import win32api

    def handler(dwCtrlType, hook_sigint=_thread.interrupt_main):
        if dwCtrlType == 0:  # CTRL_C_EVENT
            return 1  # don't chain to the next handler
        return 0  # chain to the next handler

    win32api.SetConsoleCtrlHandler(handler, 1)

@hydra.main(config_path='conf/', config_name='scratch', version_base='1.2')
def main(config):
    output_dir = Path(hydra.utils.HydraConfig.get().run.dir)
    if config.resume is not None:
        output_dir = output_dir.parent / config.resume

    ray.init(runtime_env={"env_vars": OmegaConf.to_container(hydra.utils.HydraConfig.get().job.env_set)},

    further_train = False
    if not config.train_func.get("keep_pth", True):
        logger.warning("Checkpoint pth files are not saved!")
        further_train = True

    best_trial, best_checkpoint_dir = main_worker(config, output_dir)

    print("I finished!")

def main_worker(config, output_dir, _post_tune=False):

    if config.get("seed"):

    train_met_names = get_metric_names(config.metrics, prefix="train/", include_loss=True)
    val_met_names = get_metric_names(config.metrics, prefix="val/", include_loss=True)

    reporter = CLIReporter(metric_columns=["training_iteration"] + val_met_names)

    assert config.run.config.get("wandb",
                                 False), "This pipeline requires wandb to be configured with at least project name in config"

    wandb_cfg = config.run.config.wandb
    if wandb_cfg.get("group") is None:
        with open_struct(wandb_cfg):
            wandb_cfg["group"] = f"{config.name}-{str(output_dir.name).replace('-', '')}"

    module_name, func_name = config.train_func._target_.rsplit('.', 1)
    train_func = getattr(import_module(module_name), func_name)

    train_func_args = OmegaConf.to_container(config.train_func)

    partial_func = partial(train_func, **train_func_args, arch_cfg=config)

    if hasattr(train_func, "__mixins__"):
        partial_func.__mixins__ = train_func.__mixins__

    analysis = tune.run(
        **(instantiate(config.run, _convert_="partial")),
        name=output_dir.name if config.resume is None else config.resume,
        resume=config.resume is not None

    monitor = config.monitor
    mode = config.mode

    best_trial = analysis.get_best_trial(metric=monitor, mode=mode, scope='all')

    best_checkpoint_dir = analysis.get_best_checkpoint(best_trial, metric=monitor, mode=mode).local_path

    return best_trial, best_checkpoint_dir

if __name__ == '__main__':

train function passed to tune.run (referenced as srcs.trainer.tune_trainer.train_func:

import os
from pathlib import Path

import torch
from omegaconf import OmegaConf
from ray import tune
from ray.tune.utils.util import flatten_dict
from ray.tune.integration.wandb import wandb_mixin
from hydra.utils import instantiate

from srcs.loggers.logger import MetricCollection
from srcs.utils import prepare_devices, set_seed, open_struct
from srcs.utils.files import change_directory
import wandb

def train_func(config, arch_cfg, checkpoint_dir=None, keep_pth=True, epochs=100, checkpoint_name=None):
    # cwd is changed to the trial folder
    project_dir = os.getenv('TUNE_ORIG_WORKING_DIR')

    config = OmegaConf.create(config)
    arch_cfg = OmegaConf.merge(arch_cfg, config)

    wandb.log_artifact("config.yaml", type="config")

    _config = arch_cfg.copy()

    with open_struct(_config):
        _config = OmegaConf.to_container(_config)
        _config = flatten_dict(_config, delimiter='-')

    wandb.config.update(_config, allow_val_change=True)

    # set seed for each run
    if arch_cfg.get("seed"):

    if checkpoint_name is None:
        checkpoint_name = "model_checkpoint.pth"

    with change_directory(project_dir):
        # setup code
        device, device_ids = prepare_devices(arch_cfg.n_gpu)

        # setup dataloaders
        data_loader = instantiate(arch_cfg.data_loader)
        valid_data_loader = data_loader.split_validation()

        output_size = len(data_loader.categories)
        channel_n = data_loader.channel_n
        # setup model
        model = instantiate(arch_cfg.arch, output_size=output_size, channel_n=channel_n)

        wandb.watch(model, log="all", log_freq=1)

        wandb.define_metric(name=arch_cfg.monitor, summary=arch_cfg.mode)
        trainable_params = filter(lambda p: p.requires_grad, model.parameters())
        trainable_params = sum([p.numel() for p in trainable_params])
        # logger.info(f'Trainable parameters: {sum([p.numel() for p in trainable_params])}')
        model = model.to(device)

        if len(device_ids) > 1:
            model = torch.nn.DataParallel(model, device_ids=device_ids)

        criterion = instantiate(arch_cfg.loss)

        optimizer = instantiate(arch_cfg.optimizer, model.parameters())

        lr_scheduler = None
        if arch_cfg.get("lr_scheduler"):
            lr_scheduler = instantiate(arch_cfg.lr_scheduler, optimizer)

        train_metrics = MetricCollection(arch_cfg.metrics, prefix="train/")
        valid_metrics = MetricCollection(arch_cfg.metrics, prefix="val/")

    # later changed if checkpoint
    start_epoch = 0
    if checkpoint_dir:
        checkpoint = torch.load(checkpoint_dir)
        start_epoch = checkpoint["epoch"] + 1

    for epoch in range(start_epoch, epochs):  # loop over the dataset multiple times
        train_log = one_epoch(data_loader, criterion, model, device, train_metrics, optimizer)

        with torch.no_grad():
            val_log = one_epoch(valid_data_loader, criterion, model, device, valid_metrics)

        if lr_scheduler is not None:

        state = {
            'arch': type(model).__name__,
            'epoch': epoch,
            'state_dict': model.state_dict(),
            "optimizer": optimizer.state_dict(),
            "config": arch_cfg

        # create checkpoint
        with tune.checkpoint_dir(f"epoch-{epoch}") as checkpoint_dir:
            if keep_pth:
                filename = Path(checkpoint_dir) / checkpoint_name
                torch.save(state, filename)

            # log metrics, log in checkpoint in case actor dies half way
            wandb.log(val_log, step=epoch)
            # tune.report(**train_log)

def one_epoch(data_loader, criterion, model, device, metric_tracker: MetricCollection, optimizer=None) -> dict:
    for i, data in enumerate(data_loader, 0):
        # get the inputs; data is a list of [inputs, labels]
        inputs, targets = data
        inputs, targets = inputs.to(device), targets.to(device)

        if optimizer is not None:
            # zero the parameter gradients

        # forward + backward
        outputs = model(inputs)
        loss = criterion(outputs, targets)

        if optimizer is not None:
        metric_tracker.update(outputs, targets, loss)

    return metric_tracker.result()

custom utility functions used in above code:

import contextlib
import numpy as np
import torch
from omegaconf import OmegaConf
from pathlib import Path

def open_struct(config):
    OmegaConf.set_struct(config, False)
        OmegaConf.set_struct(config, True)

def set_seed(seed):
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False

def prepare_devices(n_gpu_use):
    setup GPU device if available, move model into configured device
    n_gpu = torch.cuda.device_count()
    if n_gpu_use > 0 and n_gpu == 0:
        logger.warning("Warning: There\'s no GPU available on this machine,"
                       "training will be performed on CPU.")
        n_gpu_use = 0
    if n_gpu_use > n_gpu:
        logger.warning("Warning: The number of GPU\'s configured to use is {}, but only {} are available "
                       "on this machine.".format(n_gpu_use, n_gpu))
        n_gpu_use = n_gpu
    device = torch.device('cuda:0' if n_gpu_use > 0 else 'cpu')
    list_ids = list(range(n_gpu_use))
    return device, list_ids

def change_directory(path):
    """Changes working directory and returns to previous on exit."""
    prev_cwd = Path.cwd()
    if path is None:
        path = prev_cwd


MetricCollection class:

import logging
import pandas as pd
import torch
from hydra.utils import instantiate

logger = logging.getLogger('logger')

class BatchMetrics:
    def __init__(self, *keys, postfix=''):
        self.postfix = postfix
        if postfix:
            keys = [k + postfix for k in keys]
        self._data = pd.DataFrame(index=keys, columns=['total', 'counts', 'average'])

    def reset(self):
        for col in self._data.columns:
            self._data[col].values[:] = 0

    def update(self, key, value, n=1):
        if self.postfix:
            key = key + self.postfix
        self._data.total[key] += value * n
        self._data.counts[key] += n
        self._data.average[key] = self._data.total[key] / self._data.counts[key]

    def avg(self, key):
        if self.postfix:
            key = key + self.postfix
        return self._data.average[key]

    def result(self):
        return dict(self._data.average)

class MetricCollection:
    def __init__(self, metric_dict, prefix='', postfix=''):
        self.metric_ftns=[instantiate(met, _partial_=True) for met in metric_dict]
        self.met_names=[self.prefix+met.func.__name__+self.postfix for met in self.metric_ftns] + [self.prefix+'loss'+self.postfix]
        self.metric_tracker = BatchMetrics(*self.met_names)

    def update(self, output, target, loss):
        self.metric_tracker.update(self.prefix+'loss'+self.postfix, loss.item())
        pred = torch.argmax(output, dim=1)
        assert pred.shape[0] == len(target)
        target = target.detach().cpu().numpy()
        with torch.no_grad():
            for met in self.metric_ftns:
                result = met(target, pred)
                self.metric_tracker.update(self.prefix+met.func.__name__+self.postfix, result)

    def result(self):
        return self.metric_tracker.result()

    def reset(self):

accuracy function:

def accuracy(target, output):
    correct = 0
    correct += np.sum(output == target)
    return correct / len(target)

Then finally, the yaml file used to run hydra:

  - _self_
  - optional local_env: env

  _target_: srcs.data_loader.dummy_loader.DummyLoader
  batch_size: 50
  validation_split: 0.15
  test_split: 0.1
  num_workers: ${num_workers}
  _target_: torch.nn.NLLLoss
  _target_: torch.optim.AdamW
  lr: 0.001
  weight_decay: 0.01
  amsgrad: true
name: dummy
  _target_: srcs.models.dummy_model.DummyModel
  a: 3
  _target_: srcs.trainer.tune_trainer.train_func
  epochs: 5
  keep_pth: false
  checkpoint_name: model_checkpoint.pth
  metric: ${monitor}
  mode: ${mode}
    cpu: ${num_workers}
    gpu: 0.2
  verbose: 2
  keep_checkpoints_num: 8
  checkpoint_score_attr: ${monitor}
    _target_: ray.tune.schedulers.ASHAScheduler
    max_t: ${train_func.epochs}
    grace_period: 1
    reduction_factor: 2
      project: GRF_hip_outcomes
      mode: disabled
        _target_: ray.tune.loguniform
        - 0.0001
        - 0.01
        _target_: ray.tune.loguniform
        - 0.001
        - 0.01
        _target_: ray.tune.choice
        - - 128
          - 256
        _target_: ray.tune.randint
        - 1
        - 30
  raise_on_failed_trial: false
  num_samples: 250
status: tune
n_gpu: 1
n_cpu: 10
num_workers: 4
resume: null
seed: 122
output_root: ./outputs
- _target_: srcs.metrics.accuracy
monitor: val/accuracy
mode: max
topk_num: 5

the dummy model used:

from torch import nn

class DummyModel(nn.Module):
    def __init__(self, a, output_size, channel_n=9):

        self.layers = nn.Sequential(nn.Linear(1,a),

        self.softmax = nn.LogSoftmax(dim=1)

    def forward(self, x):
        x = x.view(-1, 1)
        out = self.layers(x)
        return self.softmax(out)

the dummy dataloader used:

import torch
from torch.utils.data import DataLoader, TensorDataset, random_split

class1 = [2,4,6,8,10,12,14,16]
class2 = [3,6,9,12,15,18,21,24,27]
class3 = [1,5,7,11,13,17,19]

class DummyLoader(DataLoader):
    def __init__(self, batch_size=50, validation_split=0.1, test_split=0.1, num_workers=4):
        dataset to identify whether number is multiple of 2 or 3 or something else

        :param batch_size:
        :param validation_split:
        :param test_split:
        :param num_workers:
        data_x = torch.tensor(class1+class2+class3, dtype=torch.float)
        data_y = torch.tensor(len(class1)*[0]+len(class2)*[1]+len(class3)*[2])
        self.dataset = TensorDataset(data_x, data_y)
        self.n_samples = len(self.dataset)
        self.train_set, self.val_set, self.test_set = self.__sample_split(validation_split, test_split)

        self.loader_kwargs = {
            'batch_size': batch_size,
            'num_workers': num_workers

        super().__init__(self.train_set, **self.loader_kwargs, shuffle=True)

    def __sample_split(self, va_split, te_split):
        val_n = int(self.n_samples * va_split)
        test_n = int(self.n_samples * te_split)
        train_n = self.n_samples - val_n - test_n

        return random_split(self.dataset, [train_n, val_n, test_n])

    def split_validation(self):
        return DataLoader(self.val_set, shuffle=False,**self.loader_kwargs)

    def split_test(self):
        return DataLoader(self.test_set, shuffle=False, **self.loader_kwargs)

    def categories(self):
        return [0,1,2]

    def channel_n(self):
        return 1

when tested on partial GPU’s, it won’t end exactly on 98 trials anymore but ends around 100, the exact number seems to fluctuate. Regardless, it never reaches the total number of trials.

I’ve ran this on windows 11 machine with 32 gb of RAM, 1050ti, Ryzen 3700X

Hi @kai, @RayAdmin

after much experimentation, I have arrived at the most trimmed down script that still reproduces the error:

import logging
import os
import ray
import torch
from ray import tune
from ray.tune import CLIReporter
from torch.utils.data import TensorDataset, DataLoader

from srcs.models.dummy_model import DummyModel


logger = logging.getLogger(__name__)

def get_dataloader(batch_size=50, num_workers=4):
    class1 = [2, 4, 6, 8, 10, 12, 14, 16]
    class2 = [3, 6, 9, 12, 15, 18, 21, 24, 27]
    class3 = [1, 5, 7, 11, 13, 17, 19]

    data_x = torch.tensor(class1 + class2 + class3, dtype=torch.float)
    data_y = torch.tensor(len(class1) * [0] + len(class2) * [1] + len(class3) * [2])
    dataset = TensorDataset(data_x, data_y)

    return DataLoader(dataset, batch_size=batch_size, num_workers=num_workers)

def train_func(config, epochs=2):
    # cwd is changed to the trial folder
    batch_size = config["batch_size"]
    weight_decay = config['weight_decay']
    a = config["a"]
    lr = config['lr']

    # setup code
    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

    # setup dataloaders
    data_loader = get_dataloader(batch_size, num_workers=4)

    output_size = 3
    channel_n = 9
    # setup model
    model = DummyModel(a=a, output_size=output_size, channel_n=channel_n)

    model = model.to(device)

    criterion = torch.nn.NLLLoss()

    optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay, amsgrad=True)

    for epoch in range(0, epochs):  # loop over the dataset multiple times
        log = {}
        for i, data in enumerate(data_loader, 0):
            # get the inputs; data is a list of [inputs, labels]
            inputs, targets = data
            inputs, targets = inputs.to(device), targets.to(device)


            # forward + backward
            outputs = model(inputs)
            loss = criterion(outputs, targets)

            log = {"loss": loss.item()}

        # log metrics, log in checkpoint in case actor dies half way

def main():
        resources_per_trial={"cpu": 4, "gpu": 0.15},
            "lr": ray.tune.loguniform(0.0001, 0.01),
            "weight_decay": ray.tune.loguniform(0.001, 0.01),
            "batch_size": ray.tune.choice([128, 256]),
            "a": ray.tune.randint(1, 30)
        progress_reporter=CLIReporter(metric_columns=["training_iteration", "loss"])

    print("I finished!")

if __name__ == '__main__':

Am honestly at a loss of what to do next

Just wanted to substantiate that I am having the same issue at exactly 99 trials on windows 10. Even when I try to resume the run from ~/ray_results, it immediately fails again with a different error message @kai @RayAdmin

2022-08-18 11:23:50,088 ERROR trial_runner.py:1358 -- Trial objective_fun_b34f0221: Error stopping trial.
Traceback (most recent call last):
  File "C:\Users\danie\.conda\envs\clam_dan\lib\site-packages\ray\tune\trial_runner.py", line 1348, in stop_trial
  File "C:\Users\danie\.conda\envs\clam_dan\lib\site-packages\ray\tune\suggest\search_generator.py", line 140, in on_trial_complete
    self.searcher.on_trial_complete(trial_id=trial_id, result=result, error=error)
  File "C:\Users\danie\.conda\envs\clam_dan\lib\site-packages\ray\tune\suggest\ax.py", line 292, in on_trial_complete
    self._process_result(trial_id, result)
  File "C:\Users\danie\.conda\envs\clam_dan\lib\site-packages\ray\tune\suggest\ax.py", line 296, in _process_result
    ax_trial_index = self._live_trial_mapping[trial_id]
KeyError: 'b34f0221'
Traceback (most recent call last):
  File "C:\Users\danie\.conda\envs\clam_dan\lib\site-packages\ray\tune\trial_runner.py", line 1348, in stop_trial
  File "C:\Users\danie\.conda\envs\clam_dan\lib\site-packages\ray\tune\suggest\search_generator.py", line 140, in on_trial_complete
    self.searcher.on_trial_complete(trial_id=trial_id, result=result, error=error)
  File "C:\Users\danie\.conda\envs\clam_dan\lib\site-packages\ray\tune\suggest\ax.py", line 292, in on_trial_complete
    self._process_result(trial_id, result)
  File "C:\Users\danie\.conda\envs\clam_dan\lib\site-packages\ray\tune\suggest\ax.py", line 296, in _process_result
    ax_trial_index = self._live_trial_mapping[trial_id]
KeyError: 'b34f0221'

@pcmoritz Any thoughts on how to debug this?

I’m wondering if there is some kind of internal memory limit going on (number of open threads or so) that we don’t catch in windows.

I added a concurrency limiter which seems to have resolved it. I’m not entirely sure why this would matter though given that 98/99 of my runs had already been terminated. Could anyone explain?

@stephano41 does this resolve your issue as well?

what were your settings for the concurrency limitter? I’ll try it, have not tried that yet

setting the max_concurrent_trials of tune.run to 4 still seems to have the same issue


I got the same 98 limit at Windows11, Python 3.10, ray 2.2.0, pytorch 2.0.0.dev20230206, optuna 3.1.0, using ASHAScheduler.

Any solution yet?

@kai @xwjiang2010 @pcmoritz Seems like this issues fell off the cliff. Any resolution. A few of the community members seem to have hit is magic 99 number on Windows?

@kai did point out our testing on Windows is a bit sketchy, and so some of the more extreme cases with load and memory might tip this over.

Any next steps perhaps? May be have the original create of this question file an GH issue?