Lightning-AI/pytorch-lightning · error · ValueError

GPU parallel_devices set through {self._strategy_flag.__clas

Error message

GPU parallel_devices set through {self._strategy_flag.__class__.__name__} class, but accelerator set to {self._accelerator_flag}, please choose one device type

What it means

The passed Strategy instance has `parallel_devices` whose first device is a CUDA torch.device, but the `accelerator=` flag was explicitly set to something other than "auto", "cuda", or "gpu" (e.g. "cpu" or "tpu"). The connector refuses contradictory device specifications.

Source

Thrown at src/lightning/fabric/connector.py:291

                if self.checkpoint_io:
                    raise ValueError("checkpoint_io set through both strategy class and plugins, choose one")
                self.checkpoint_io = self._strategy_flag._checkpoint_io
            if getattr(self._strategy_flag, "cluster_environment", None):
                if self._cluster_environment_flag:
                    raise ValueError("cluster_environment set through both strategy class and plugins, choose one")
                self._cluster_environment_flag = getattr(self._strategy_flag, "cluster_environment")

            if hasattr(self._strategy_flag, "parallel_devices") and self._strategy_flag.parallel_devices:
                if self._strategy_flag.parallel_devices[0].type == "cpu":
                    if self._accelerator_flag and self._accelerator_flag not in ("auto", "cpu"):
                        raise ValueError(
                            f"CPU parallel_devices set through {self._strategy_flag.__class__.__name__} class,"
                            f" but accelerator set to {self._accelerator_flag}, please choose one device type"
                        )
                    self._accelerator_flag = "cpu"
                if self._strategy_flag.parallel_devices[0].type == "cuda":
                    if self._accelerator_flag and self._accelerator_flag not in ("auto", "cuda", "gpu"):
                        raise ValueError(
                            f"GPU parallel_devices set through {self._strategy_flag.__class__.__name__} class,"
                            f" but accelerator set to {self._accelerator_flag}, please choose one device type"
                        )
                    self._accelerator_flag = "cuda"
                self._parallel_devices = self._strategy_flag.parallel_devices

    def _check_device_config_and_set_final_flags(self, devices: Union[list[int], str, int], num_nodes: int) -> None:
        if not isinstance(num_nodes, int) or num_nodes < 1:
            raise ValueError(f"`num_nodes` must be a positive integer, but got {num_nodes}.")

        self._num_nodes_flag = num_nodes
        self._devices_flag = devices

        if self._devices_flag in ([], 0, "0"):
            accelerator_name = (
                self._accelerator_flag.__class__.__qualname__
                if isinstance(self._accelerator_flag, Accelerator)
                else self._accelerator_flag

View on GitHub (pinned to 9fed5c27d2)

Solutions

Drop the accelerator flag (it will be inferred as "cuda" from parallel_devices)
Or set parallel_devices to CPU devices if you truly want CPU execution
Ensure the strategy object matches the target hardware when reusing configs

Example fix

# before
strategy = DDPStrategy(parallel_devices=[torch.device("cuda", 0)])
fabric = Fabric(strategy=strategy, accelerator="cpu")

# after
strategy = DDPStrategy(parallel_devices=[torch.device("cuda", 0)])
fabric = Fabric(strategy=strategy)

Defensive patterns

Strategy: validation

Validate before calling

devs = getattr(strategy, "parallel_devices", None)
if devs and devs[0].type == "cuda" and accelerator not in (None, "auto", "cuda", "gpu"):
    raise SystemExit(f"GPU parallel_devices conflict with accelerator={accelerator}")

Prevention

For CPU debug runs, rebuild the strategy with CPU parallel_devices instead of forcing accelerator="cpu"
Derive the accelerator flag from the strategy's devices rather than hardcoding

When it happens

Trigger: DDPStrategy(parallel_devices=[torch.device("cuda", 0)]) with Fabric(strategy=strategy, accelerator="cpu") — a GPU-configured strategy combined with a non-GPU accelerator flag.

Common situations: Forcing accelerator="cpu" for debugging while the strategy was auto-built with CUDA devices; reusing a GPU strategy object in CPU-only tests; cluster scripts that override accelerator without rebuilding the strategy.

Related errors

AI-assisted analysis of Lightning-AI/pytorch-lightning@9fed5c27d2 (2026-08-28). Data as JSON: /api/errors/a803aca4a212acd1. Report an issue: GitHub.