ErrLookup › Lightning-AI/pytorch-lightning

Lightning-AI/pytorch-lightning

Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes. · Python · 270 source files

Analyzed at 9fed5c27d2 on 2026-08-28. 526 documented errors.

Code / MessageTypeSeverityTags
Device should be CPU, got {device} instead.
validation error pytorch-lightning, self-log, duplicate-metric, misconfiguration
`devices` selected with `CPUAccelerator` should be an int >
validation error pytorch-lightning, torchmetrics, compute, type-mismatch
Device should be CUDA, got {device} instead.
validation error pytorch-lightning, trainer, fast-dev-run, validation
You requested to find {num_devices} devices but there are no
validation error pytorch-lightning, trainer, limit-batches, validation
You requested to find {num_devices} devices but this machine
validation error pytorch-lightning, trainer, profiler, invalid-argument
You requested to find {num_devices} devices but only {len(av
error_code error pytorch-lightning, trainer, val-check-interval, time-format
Device should be MPS, got {device} instead.
validation error pytorch-lightning, trainer, val-check-interval, parse-error
`name` must be a str, found {name}
validation error pytorch-lightning, trainer, barebones, checkpointing
'{name}' is already present in the registry. HINT: Use `over
validation error pytorch-lightning, trainer, barebones, logger
'{}' not found in registry. Available names: {}
validation error pytorch-lightning, trainer, barebones, progress-bar
Received multiple values for {', '.join(duplicated_plugin_ke
validation error lightning, fabric, plugins, config-validation, duplicate-argument
Received both `precision={precision_input}` and `plugins={se
validation error lightning, fabric, precision, plugins, conflicting-arguments
accelerator set through both strategy class and accelerator
validation error lightning, fabric, strategy, accelerator, conflicting-arguments
precision set through both strategy class and plugins, choos
validation error lightning, fabric, strategy, precision, plugins, conflicting-arguments
checkpoint_io set through both strategy class and plugins, c
validation error lightning, fabric, strategy, checkpointio, plugins, conflicting-arguments
cluster_environment set through both strategy class and plug
validation error lightning, fabric, strategy, cluster-environment, plugins, conflicting-arguments
CPU parallel_devices set through {self._strategy_flag.__clas
validation error lightning, fabric, strategy, accelerator, device-mismatch, cpu
GPU parallel_devices set through {self._strategy_flag.__clas
validation error lightning, fabric, strategy, accelerator, device-mismatch, gpu
`num_nodes` must be a positive integer, but got {num_nodes}.
validation error lightning, fabric, num-nodes, argument-validation, distributed
`Fabric(devices={self._devices_flag!r})` value is not a vali
validation error lightning, fabric, devices, argument-validation, no-gpus
Precision {repr(precision)} is invalid. Allowed precision va
validation error lightning, fabric, precision, config-validation
No models were set up for backward. Did you forget to call `
exception critical lightning, fabric, deepspeed, backward, setup-order
When using multiple models + deepspeed, please provide the m
validation error lightning, fabric, deepspeed, multi-model, backward
Only one of `clip_val` or `max_norm` can be set as this spec
validation error lightning, fabric, gradient-clipping, mutually-exclusive-args
You have to specify either `clip_val` or `max_norm` to do gr
validation error lightning, fabric, gradient-clipping, missing-argument
You need to set up the model first before you can call `fabr
exception error lightning, fabric, no-backward-sync, ddp, setup-order
Filter should be a dictionary, given {filter!r}
validation error lightning, fabric, checkpointing, type-validation
The filter keys {filter.keys() - state} are not present in t
validation error lightning, fabric, checkpointing, key-mismatch
Expected `fabric.save(filter=...)` for key {k!r} to be a cal
validation error lightning, fabric, checkpointing, type-validation
This script was launched through the CLI, and processes have
exception error lightning, fabric, cli, launch, double-launch
`Fabric.launch(...)` needs to be a callable, but got {functi
validation error lightning, fabric, launch, callable, typo
`Fabric.launch(function={function})` needs to take at least
validation error lightning, fabric, launch, signature
To spawn processes with the `{type(self.strategy).__name__}`
validation error lightning, fabric, ddp-spawn, launch, xla
Overriding `Fabric.run()` and launching from the CLI is not
validation error lightning, fabric, cli, subclass, method-override
To use Fabric with more than one device, you must call `.lau
exception critical lightning, fabric, distributed, launch, ddp
A model should be passed only once to the `setup` method.
validation error lightning, fabric, setup, double-setup
An optimizer should be passed only once to the `setup` metho
validation error lightning, fabric, setup, optimizer, double-setup
The optimizer has references to the model's meta-device para
exception error lightning, fabric, fsdp, meta-device, init-module
A model should be passed only once to the `setup_module` met
validation error lightning, fabric, setup-module, double-setup
The `{type(self._strategy).__name__}` requires the model and
exception error lightning, fabric, deepspeed, xla, setup, joint-setup
`setup_optimizers` requires at least one optimizer as input.
validation error pytorch-lightning, fabric, optimizer, validation
An optimizer should be passed only once to the `setup_optimi
validation error pytorch-lightning, fabric, optimizer, double-setup
The optimizer has references to the model's meta-device para
exception error pytorch-lightning, fabric, meta-device, fsdp, lazy-init
`setup_dataloaders` requires at least one dataloader as inpu
validation error pytorch-lightning, fabric, dataloader, validation
A dataloader should be passed only once to the `setup_datalo
validation error pytorch-lightning, fabric, dataloader, double-setup
Only PyTorch DataLoader are currently supported in `setup_da
validation error pytorch-lightning, fabric, dataloader, type-validation
The `CSVLogger` does not yet support logging hyperparameters
exception warning pytorch-lightning, fabric, csv-logger, hyperparameters, not-implemented
Neither `tensorboard` nor `tensorboardX` is available. Try `
exception error pytorch-lightning, tensorboard, missing-dependency, logger
you tried to log {v} which is currently not supported. Try a
validation error pytorch-lightning, tensorboard, logging, type-validation
`{type(self).__name__}` does not own a group. HINT: try `col
exception error pytorch-lightning, distributed, process-group, collectives
`{type(self).__name__}` already owns a group.
exception error pytorch-lightning, distributed, process-group, collectives, double-setup
`{type(self).__name__}` does not own a group to destroy.
exception error pytorch-lightning, distributed, process-group, collectives, teardown
Torch distributed is not available.
exception critical pytorch-lightning, distributed, pytorch-build, platform-support
Unsupported op {op!r} of type {type(op).__name__}
validation error pytorch-lightning, distributed, reduceop, type-validation
op {op!r} is not a member of `ReduceOp`
validation error pytorch-lightning, distributed, reduceop, invalid-value
The Kubeflow environment can't be detected automatically.
exception error pytorch-lightning, kubeflow, cluster-environment, distributed
Cannot determine world size. Environment variable `JSM_NAMES
validation error lsf, jsrun, hpc, missing-env-var, distributed
Cannot determine global rank. Environment variable `JSM_NAME
validation error lsf, jsrun, hpc, missing-env-var, distributed
Cannot determine local rank. Environment variable `JSM_NAMES
validation error lsf, jsrun, hpc, missing-env-var, distributed
Did not find the environment variable `LSB_DJOB_RANKFILE`
validation error lsf, bsub, hpc, missing-env-var, distributed
str(_XLA_AVAILABLE)
exception error xla, tpu, dependency, pytorch-lightning
`Trainer.save_checkpoint(..., storage_options=...)` with `st
validation error xla, checkpoint, storage-options, pytorch-lightning
Passed `{type(self).__name__}(precision={precision!r})`. Pre
validation error amp, precision, validation, pytorch-lightning
`precision='bf16-mixed'` does not use a scaler, found {scale
validation error amp, precision, gradscaler, pytorch-lightning
AMP and the LBFGS optimizer are not compatible.
validation error amp, lbfgs, optimizer, pytorch-lightning
Gradient clipping is not implemented for optimizers handling
exception error amp, gradient-clipping, fused-optimizer, pytorch-lightning
{mode!r} only works with `dtype=torch.float16`, but you chos
validation error bitsandbytes, quantization, int8, dtype, pytorch-lightning
You are using the bitsandbytes precision plugin, but your mo
validation error bitsandbytes, quantization, model-structure, pytorch-lightning
Instantiating your model under the `init_module` context man
validation error bitsandbytes, quantization, init-module, pytorch-lightning
str(_BITSANDBYTES_AVAILABLE)
exception error bitsandbytes, dependency, quantization, pytorch-lightning
`precision={precision!r})` is not supported in DeepSpeed. `p
validation error deepspeed, precision, config-validation, pytorch-lightning
`precision={precision!r})` is not supported in FSDP. `precis
validation error fsdp, precision, config-validation, pytorch-lightning
`precision={precision!r}` does not use a scaler, found {scal
validation error fsdp, precision, grad-scaler, pytorch-lightning
Gradient clipping is not implemented for optimizers handling
exception error fsdp, grad-scaler, optimizer-incompatibility, pytorch-lightning
str(_TRANSFORMER_ENGINE_AVAILABLE)
exception error transformer-engine, fp8, missing-dependency, gpu, pytorch-lightning
str(_XLA_AVAILABLE)
exception error xla, tpu, missing-dependency, pytorch-lightning
`precision={precision!r})` is not supported in XLA. `precisi
validation error xla, precision, config-validation, pytorch-lightning
Blocking backward sync is only possible if the module passed
validation error ddp, gradient-accumulation, type-mismatch, pytorch-lightning
To use the `DeepSpeedStrategy`, you must have DeepSpeed inst
exception critical deepspeed, missing-dependency, import-error, pytorch-lightning
PyTorch >= 2.6 requires DeepSpeed >= 0.16.0. Detected DeepSp
exception critical deepspeed, version-mismatch, pytorch-upgrade, pytorch-lightning
Currently only one optimizer is supported with DeepSpeed. Go
validation error deepspeed, multiple-optimizers, unsupported-operation, pytorch-lightning
The `{type(self).__name__}` does not support setting up the
validation error deepspeed, api-misuse, optimizer-setup, pytorch-lightning
`{empty_init=}` is not a valid choice with `DeepSpeedStrateg
validation error deepspeed, zero-3, model-init, pytorch-lightning
`DeepSpeedStrategy.save_checkpoint(..., storage_options=...)
validation error deepspeed, checkpointing, unsupported-argument, pytorch-lightning
`DeepSpeedStrategy.save_checkpoint(..., filter=...)` is not
validation error deepspeed, checkpointing, unsupported-argument, pytorch-lightning
Could not find a DeepSpeed model in the provided checkpoint
validation error deepspeed, checkpointing, state-validation, pytorch-lightning
Found multiple DeepSpeed engine modules in the given state.
validation error deepspeed, checkpointing, multiple-models, pytorch-lightning
Got DeepSpeedStrategy.load_checkpoint(..., state={state!r})
validation error deepspeed, checkpointing, resume, state-validation, pytorch-lightning
Could not find a DeepSpeed model in the provided checkpoint
validation error deepspeed, checkpointing, resume, state-validation, pytorch-lightning
Found multiple DeepSpeed engine modules in the given state.
validation error deepspeed, checkpointing, resume, multiple-models, pytorch-lightning
DeepSpeed was unable to load the checkpoint. Ensure you pass
error_code critical deepspeed, checkpoint, load
DeepSpeed handles gradient clipping automatically within the
validation error deepspeed, gradient-clipping, not-implemented
The DeepSpeed strategy is only supported on CUDA GPUs but `{
validation critical deepspeed, accelerator, cuda, gpu-required
To use DeepSpeed you must pass in a DeepSpeed config dict, o
validation error deepspeed, config, missing-argument
You passed in a path to a DeepSpeed config but the path does
error_code error deepspeed, config, file-not-found, path
The selected device indices {selected_device_indices!r} don'
error_code critical deepspeed, cuda, device-selection, multi-gpu
{default_message}. It looks like you passed the path to a su
error_code error deepspeed, checkpoint, path, load
{default_message}. It looks like you passed the path to a fi
error_code error deepspeed, checkpoint, path, load
The provided path is not a valid DeepSpeed checkpoint: {path
error_code error deepspeed, checkpoint, path, load
The `{type(self).__name__}` does not use the `CheckpointIO`
validation error fsdp, checkpoint, not-implemented, api-misuse
The `{type(self).__name__}` does not support setting a `Chec
validation error fsdp, checkpoint, not-implemented, setter
The FSDP strategy can only work with the `FSDPPrecision` plu
validation error fsdp, precision, type-error
You set `{type(self).__name__}(use_orig_params=False)` but t
validation error fsdp, use-orig-params, optimizer, setup
The optimizer does not seem to reference any FSDP parameters
validation error fsdp, optimizer, flat-params, setup-order
Gradient clipping with FSDP is only possible if the module p
validation error fsdp, gradient-clipping, type-error, wrapping
`FSDPStrategy.save_checkpoint(..., storage_options=...)` is
validation error fsdp, checkpoint, storage-options, save
Could not find a FSDP model in the provided checkpoint state
validation error fsdp, checkpoint, state, save
Found multiple FSDP models in the given state. Saving checkp
validation error fsdp, checkpoint, save, pytorch-lightning, distributed
Unknown state_dict_type: {self._state_dict_type}
validation error fsdp, internal, state-dict, pytorch-lightning
Got FSDPStrategy.load_checkpoint(..., state={state!r}) but a
validation error fsdp, checkpoint, load, validation, pytorch-lightning
Loading a single optimizer object from a checkpoint is not s
validation error fsdp, optimizer, checkpoint, not-implemented, pytorch-lightning
Could not find a FSDP model in the provided checkpoint state
validation error fsdp, checkpoint, load, setup, pytorch-lightning
Found multiple FSDP models in the given state. Loading check
validation error fsdp, checkpoint, load, distributed, pytorch-lightning
The path {str(path)!r} does not point to a valid checkpoint.
error_code error fsdp, checkpoint, load, file-validation, distributed
You cannot set both `activation_checkpointing` and `activati
validation error fsdp, activation-checkpointing, config, mutually-exclusive, pytorch-lightning
The hybrid sharding strategy requires you to pass at least o
exception error pytorch-lightning, fabric, fsdp, distributed, sharding
The start method '{self._start_method}' is not available on
validation error pytorch-lightning, multiprocessing, platform, start-method, windows
Cannot re-initialize CUDA in forked subprocess. To use CUDA
exception critical pytorch-lightning, cuda, multiprocessing, fork, spawn
Lightning can't create new processes if CUDA is already init
exception critical pytorch-lightning, cuda, multiprocessing, pytorch-version
Launching multiple processes with the 'spawn' start method r
exception error pytorch-lightning, multiprocessing, spawn, entry-point, guard
The launcher can only create subprocesses once.
exception error pytorch-lightning, subprocess, launcher, ddp, lifecycle
Lightning attempted to launch new distributed processes with
exception error pytorch-lightning, local-rank, environment-variables, distributed, launcher
{str(_XLA_AVAILABLE)}
exception error pytorch-lightning, xla, tpu, missing-dependency, torch-xla
Accessing the device mesh before processes have initialized
exception error pytorch-lightning, model-parallel, device-mesh, initialization-order
The `{type(self).__name__}` does not use the `CheckpointIO`
exception error pytorch-lightning, model-parallel, checkpoint-io, not-implemented, api-contract
Could not find a distributed model in the provided checkpoin
validation error lightning, fabric, model-parallel, checkpoint, dtensor
Found multiple distributed models in the given state. Loadin
validation error lightning, fabric, model-parallel, checkpoint, multiple-models
The path {str(path)!r} does not point to a valid checkpoint.
validation error lightning, fabric, checkpoint, invalid-path
The sizes `data_parallel_size={data_parallel_size}` and `ten
exception error lightning, fabric, model-parallel, device-mesh, world-size
Failed to load checkpoint directly into the model. The given
validation error lightning, fabric, checkpoint, full-checkpoint, shards
The model contains a key '{full_param_name}' that does not e
validation error lightning, fabric, checkpoint, state-dict, strict-loading
`name` must be a str, found {name}
validation error lightning, fabric, registry, type-error
'{name}' is already present in the registry. HINT: Use `over
validation error lightning, fabric, registry, duplicate-registration
'{}' not found in registry. Available names: {}
exception error lightning, fabric, registry, unknown-strategy, key-error
{str(_XLA_AVAILABLE)}
exception error lightning, fabric, xla, tpu, missing-dependency
Could not find a XLAFSDP model in the provided checkpoint st
exception error xla, fsdp, checkpoint, save, model-wrapping, lightning-fabric
Found multiple XLAFSDP modules in the given state. Saving ch
exception error xla, fsdp, checkpoint, multiple-models, lightning-fabric
Multihost setups do not have a shared filesystem, so the che
exception error xla, fsdp, tpu, multihost, checkpoint, consolidation, lightning-fabric
Got `XLAFSDPStrategy.load_checkpoint(..., state={state!r})`
exception error xla, fsdp, checkpoint, load, state-required, lightning-fabric
Loading a single module or optimizer object from a checkpoin
exception error xla, fsdp, checkpoint, load, not-implemented, lightning-fabric
The path {str(file)!r} does not point to valid sharded check
exception error xla, fsdp, checkpoint, load, sharded, file-not-found, lightning-fabric
Could not find a XLAFSDP model in the provided checkpoint st
exception error xla, fsdp, checkpoint, load, model-wrapping, lightning-fabric
Found multiple XLAFSDP modules in the given state. Loading c
exception error xla, fsdp, checkpoint, load, multiple-models, lightning-fabric
The path {str(path)!r} does not point to a valid full checkp
exception error xla, fsdp, checkpoint, load, full-checkpoint, file-not-found, lightning-fabric
Found a XLAFSDP model in the provided checkpoint state. Plea
exception error xla, fsdp, checkpoint, load, full-checkpoint, model-wrapping, lightning-fabric
XLAFSDP only supports a single model instance with 'model' a
exception error xla, fsdp, checkpoint, load, full-checkpoint, not-implemented, lightning-fabric
Unknown state_dict_type: {self._state_dict_type}
exception error xla, fsdp, config, invalid-value, lightning-fabric
You cannot set both `auto_wrapper_callable` and `activation_
exception error xla, fsdp, activation-checkpointing, config-conflict, lightning-fabric
`activation_checkpointing_policy` must be a set, found {poli
exception error xla, fsdp, activation-checkpointing, type-error, lightning-fabric
Blocking backward sync is only possible if the module passed
exception error xla, fsdp, gradient-accumulation, no-sync, type-error, lightning-fabric
The metric `{value}` does not contain a single element, thus
exception error metrics, tensor, scalar, logging, value-error, lightning-fabric
Upgrade fsspec to enable cross-device local checkpoints: pip
exception error fsspec, checkpoint, cross-device, exdev, atomic-save, environment, lightning-fabric
Remote (fsspec) distributed checkpoints require `torch.distr
exception error distributed-checkpoint, fsspec, remote-storage, import-error, pytorch-version, lightning-fabric
The dataloader {dataloader} needs to subclass `torch.utils.d
exception error dataloader, distributed, sampler, type-error, lightning-fabric
Trying to inject custom `Sampler` into the `{dataloader_cls_
exception error dataloader, distributed, sampler, misconfiguration, lightning-fabric
Trying to inject parameters into the `{dataloader_cls_name}`
exception error pytorch-lightning, dataloader, distributed, kwargs, init-signature
Trying to inject a modified sampler into the batch sampler;
exception error pytorch-lightning, batch-sampler, distributed, ddp
Lightning can't inject a (distributed) sampler into your ba
exception error pytorch-lightning, batch-sampler, distributed-sampler, ddp
The {constructor.__name__} implementation has an error where
exception error pytorch-lightning, kwargs, duplicate-argument, dataloader
`local_world_size` should be >= 1, got {local_world_size}.
exception error pytorch-lightning, validation, num-workers, world-size
'{type(self).__name__}' object has no attribute '{key}'
exception error pytorch-lightning, attribute-dict, config, keyerror
Cannot set the dtype explicitly. Please use module.to(new_dt
exception error pytorch-lightning, dtype, module, property-setter
GPUs should be a list
exception error pytorch-lightning, gpu, device-parser, internal-api
GPUs requested but none are available.
exception critical pytorch-lightning, cuda, gpu, environment, device-availability
At least one gpu type should be specified!
exception error gpu, device-configuration, lightning, cuda, mps
You requested gpu: {gpus} But your machine only has: {all_a
exception error gpu, device-configuration, cuda, multi-node, lightning
Device ID's (GPU) must be unique.
exception error gpu, duplicate-ids, device-configuration, validation
Device IDs (GPU/TPU) must be an int, a string, a sequence of
validation error devices, typeerror, null-config, lightning
Device IDs (GPU/TPU) must be an int, a string, a sequence of
validation error devices, typeerror, numpy, sequence-validation
Device IDs (GPU/TPU) must be an int, a string, a sequence of
validation error devices, typeerror, config, validation
Unable to determine if the path belongs to a shared filesyst
exception error distributed, filesystem, checkpointing, shared-storage
torch.distributed is not available. Cannot initialize distri
exception critical distributed, pytorch, environment, runtimeerror
You seem to have configured a sampler in your DataLoader whi
validation error dataloader, sampler, distributed-training, lightning
You seem to have configured a sampler in your DataLoader whi
validation error dataloader, sampler, infinite-iterator, distributed-training
`num_processes` should be >= 1, got {num_processes}.
validation error lightning, multiprocessing, configuration
Materialization requires that the `{type(module).__name__}.r
validation error lightning, meta-device, model-initialization
Expected `torch.nn.Module` or `torch.optim.Optimizer`, got:
validation error lightning, type-error, setup
'{type(self).__name__}' object has no attribute '{name}'
exception error lightning, lazy-loading, checkpoints
Path {str(filename)!r} does not exist or is not a file.
exception error lightning, checkpoints, file-not-found
Invalid seed specified via PL_GLOBAL_SEED: {repr(env_seed)}
validation error lightning, seeding, environment-variables
{seed} is not in bounds, numpy accepts from {min_seed_value}
validation error lightning, seeding, validation
SpikeDetection requires `torchmetrics>=1.0.0` Please upgrade
exception error lightning, torchmetrics, version-mismatch
Invalid mode. Has to be min or max, found {self.mode}
validation error lightning, spike-detection, configuration
Expected samples ({samples}) to be greater or equal than bat
validation error lightning, throughput, validation
Expected lengths ({lengths}) to be greater or equal than sam
validation error pytorch-lightning, throughput, validation, argument-mismatch
If lengths are passed ({len(self._lengths)}), there needs to
exception error pytorch-lightning, throughput, state-consistency
Expected a precision plugin, got {plugin}
exception error pytorch-lightning, fabric, precision, type-mismatch
Expected the value to increase, last: {last}, current: {x}
validation error pytorch-lightning, throughput, monotonic, timing
__setitem__ is not supported
exception warning pytorch-lightning, not-implemented, internal-api
Expected a method or a string, but got: {type(method).__name
validation error pytorch-lightning, fabric, type-mismatch, method-binding
You cannot mark the forward method itself as a forward metho
validation error pytorch-lightning, fabric, invalid-argument
You marked '{name}' as a forward method, but `{type(self._or
exception error pytorch-lightning, fabric, attribute-error, method-lookup
You are calling the method `{type(self._original_module).__n
exception error pytorch-lightning, fabric, ddp, gradient-sync, method-bypass
Failed to determine the arguments that were used to compile
exception error pytorch-lightning, torch-compile, import-order
`mode` can be {', '.join(self.mode_dict.keys())}, got {self.
validation error lightning, early-stopping, callback-config, invalid-argument
Early stopping conditioned on metric `{self.monitor}` which
exception error lightning, early-stopping, metric-not-found, monitor
The Finetuning callback does not support running with the De
exception error lightning, finetuning, deepspeed, strategy-incompatible
The LightningModule should have a nn.Module `backbone` attri
validation error lightning, finetuning, backbone, attribute-missing
Empty dict cannot be interpreted correct
validation error lightning, gradient-accumulation, empty-argument, callback-config
Epoch should be an int greater than or equal to 0. Got {list
validation error lightning, gradient-accumulation, invalid-key, type-validation
Accumulation factor should be an int greater than 0. Got {li
validation error lightning, gradient-accumulation, invalid-value, type-validation
Epochs indexing from 1, epoch {minimal_epoch} cannot be inte
validation error lightning, gradient-accumulation, unreachable, defensive-code
Automatic gradient accumulation and the `GradientAccumulatio
exception error lightning, gradient-accumulation, manual-optimization, incompatible-config
The `{type(trainer.strategy).__name__}` does not support `ac
exception error lightning, deepspeed, gradient-accumulation, strategy-incompatible
You have set `accumulate_grad_batches` and are using the `Gr
validation error lightning, gradient-accumulation, config-conflict, trainer-options
`mode` should be either of {self.SUPPORTED_MODES}
validation error lightning, lr-finder, invalid-argument, hyperparameter-tuning
logging_interval should be `step` or `epoch` or `None`.
validation error lightning, lr-monitor, invalid-argument, callback-config
Cannot use `LearningRateMonitor` callback with `Trainer` tha
validation error lightning, lr-monitor, missing-logger, trainer-config
A single `Optimizer` cannot have multiple parameter groups w
validation error lightning, lr-monitor, optimizer, duplicate-name
`ModelCheckpoint(save_last='link')` is only supported for lo
validation error lightning, model-checkpoint, remote-storage, symlink
`ModelCheckpoint(monitor={self.monitor!r})` could not find t
validation error lightning, model-checkpoint, metric-not-found, monitor
Invalid value for save_top_k={self.save_top_k}. Must be >= -
validation error lightning, model-checkpoint, invalid-argument, callback-config
Invalid value for every_n_train_steps={self._every_n_train_s
validation error pytorch-lightning, modelcheckpoint, config-validation, argument-validation
Invalid value for every_n_epochs={self._every_n_epochs}. Mus
validation error pytorch-lightning, modelcheckpoint, config-validation, argument-validation
Combination of parameters every_n_train_steps={self._every_n
validation error pytorch-lightning, modelcheckpoint, mutually-exclusive-args, config-validation
ModelCheckpoint(save_top_k={self.save_top_k}, monitor=None)
validation error pytorch-lightning, modelcheckpoint, monitor, config-validation
`mode` can be {', '.join(mode_dict.keys())} but got {mode}
exception error pytorch-lightning, modelcheckpoint, mode, argument-validation
The filename cannot be empty
exception error pytorch-lightning, on-exception-checkpoint, filename, argument-validation
`write_interval` should be one of {[i.value for i in WriteIn
exception error pytorch-lightning, prediction-writer, write-interval, argument-validation
The `PredictionWriterCallback` does not support using `datal
exception error pytorch-lightning, prediction-writer, dataloader-iter, unsupported-operation
`RichProgressBar` requires `rich` >= 10.2.2. Install it by r
exception error pytorch-lightning, rich, missing-dependency, progress-bar
The provided `parameter_names` name: {name} isn't in {self.P
exception error pytorch-lightning, pruning, parameter-names, argument-validation
The provided `pruning_fn` {pruning_fn} isn't available in Py
exception error pytorch-lightning, pruning, pruning-fn, argument-validation
When requesting `structured` pruning, the `pruning_dim` shou
exception error pytorch-lightning, pruning, structured-pruning, missing-argument
When requesting `ln_structured` pruning, the `pruning_norm`
exception error pytorch-lightning, pruning, ln-structured, missing-argument
PyTorch `BasePruningMethod` is currently only supported with
exception error pytorch-lightning, pruning, base-pruning-method, unsupported-operation
`pruning_fn` is expected to be a str in {list(_PYTORCH_PRUNI
exception error pytorch-lightning, pruning, pruning-fn, type-error
Only the "unstructured" PRUNING_TYPE is supported with `use_
exception error pytorch-lightning, pruning, global-unstructured, unsupported-operation
`amount` should be provided and be either an int, a float or
exception error pytorch-lightning, pruning, amount, type-error
`verbose` must be any of (0, 1, 2)
exception error pytorch-lightning, pruning, verbose, argument-validation
Some provided `parameters_to_prune` don't exist in the model
exception error pytorch-lightning, pruning, parameters-to-prune, model-mismatch
The provided `parameters_to_prune` should either be list of
exception error pytorch-lightning, pruning, parameters-to-prune, argument-validation
`RichModelSummary` requires `rich` to be installed. Install
exception error rich, optional-dependency, model-summary, callback
outputs have to be of type torch.Tensor or Mapping, got {typ
exception error spike-detection, training-step, typeerror, callback
swa_epoch_start should be a >0 integer or a float between 0
exception error swa, stochastic-weight-averaging, validation, callback
The `swa_lrs` should a positive float, or a list of positive
exception error swa, swa-lrs, validation, learning-rate
The `avg_fn` should be callable.
exception error swa, avg-fn, validation, callback
device is expected to be a torch.device or a str. Found {dev
exception error swa, device, validation, callback
SWA does not currently support sharded models.
exception error swa, fsdp, deepspeed, sharding, incompatible-strategy
SWA currently works with 1 `optimizer`.
exception error swa, multiple-optimizers, configure-optimizers, callback
SWA currently not supported for more than 1 `lr_scheduler`.
exception error swa, lr-scheduler, multiple-schedulers, callback
SWA with `swa_epoch_start` as a float is not supported when
exception error swa, max-epochs, open-ended-training, callback
`Timer(duration={duration!r})` is not a valid duration. Expe
exception error timer, duration-format, validation, callback
Unsupported parameter value `Timer(interval={interval})`. Po
exception error timer, interval, enum-validation, callback
{_JSONARGPARSE_SIGNATURES_AVAILABLE}
exception error lightning-cli, jsonargparse, dependency-version, module-not-found
Cannot add arguments from: {lightning_class}. You should pro
exception error lightning-cli, add-arguments, type-validation, parser
`save_to_log_dir=False` only makes sense when subclassing Sa
exception error lightning-cli, save-config, save-to-log-dir, misuse-guard
{self.__class__.__name__} expected {config_path} to NOT exis
exception error lightning-cli, save-config, overwrite, file-exists, idempotent-rerun
`{self.__class__.__name__}.add_configure_optimizers_method_t
exception error lightning-cli, configure-optimizers, multiple-optimizers, automatic-mode
`train_dataloader` must be implemented to be used with the L
exception error train-dataloader, not-implemented, lightning-module, required-method
`test_dataloader` must be implemented to be used with the Li
exception error test-dataloader, not-implemented, lightning-module, required-method
`val_dataloader` must be implemented to be used with the Lig
exception critical pytorch-lightning, lightning-module, dataloader, validation, hook-not-implemented
`predict_dataloader` must be implemented to be used with the
exception error pytorch-lightning, lightning-module, dataloader, inference, hook-not-implemented
Primitives {_PRIMITIVE_TYPES} are not allowed.
exception error pytorch-lightning, hparams, save-hyperparameters, type-validation
Unsupported config type of {type(hp)}.
exception error pytorch-lightning, hparams, save-hyperparameters, type-validation, unsupported-type
{self.__class__.__qualname__} is not attached to a `Trainer`
exception error pytorch-lightning, lightning-module, trainer, lifecycle, not-attached
You are trying to `self.log()` but the loop's result collect
exception error pytorch-lightning, self-log, predict-step, logging, misconfiguration
You are trying to `self.log()` but it is not managed by the
exception error pytorch-lightning, self-log, control-flow, logging, misconfiguration
You called `self.log` with the key `{name}` but it should no
exception error pytorch-lightning, self-log, multi-dataloader, key-validation
Could not find the `LightningModule` attribute for the `torc
exception error pytorch-lightning, self-log, torchmetrics, metric-registration
Could not find the `LightningModule` attribute for the `torc
exception error pytorch-lightning, self-log, torchmetrics, metric-attribute
With `def training_step(self, dataloader_iter)`, `self.log(.
exception error pytorch-lightning, self-log, dataloader-iter, batch-size, training-step
`self.log_dict({dictionary})` was called, but nested diction
exception error pytorch-lightning, fabric, log-dict, nested-dict, validation
`self.log({name}, {value})` was called, but nested dictionar
exception error pytorch-lightning, fabric, self-log, nested-dict, validation
`self.log({name}, {value})` was called, but `{type(v).__name
exception error pytorch-lightning, fabric, self-log, type-validation, non-numeric
`self.log({name}, {value})` was called, but the tensor must
exception error pytorch-lightning, self-log, tensor, scalar, shape-validation
You have set `Trainer(gradient_clip_val={self.trainer.gradie
exception error pytorch-lightning, gradient-clipping, trainer-config, conflict
You have set `Trainer(gradient_clip_algorithm={self.trainer.
exception error pytorch-lightning, gradient-clipping, trainer-config, conflict
`gradient_clip_val` should be an int or a float. Got {gradie
exception error pytorch-lightning, gradient-clipping, type-error, validation
`gradient_clip_algorithm` {gradient_clip_algorithm} is inval
exception error pytorch-lightning, gradient-clipping, invalid-argument, enum-validation
to use {fn_name}, please disable automatic optimization: set
exception error pytorch-lightning, manual-optimization, manual-backward, misconfiguration
`{type(self).__name__}.to_onnx()` requires `onnx` to be inst
exception error onnx, export, missing-dependency, lightning
`{type(self).__name__}.to_onnx(dynamo=True)` requires `onnxs
exception error onnx, dynamo, onnxscript, missing-dependency, lightning
Could not export to ONNX since neither `input_sample` nor `m
exception error onnx, export, example-input, lightning
Choosing method=`trace` requires either `example_inputs` or
exception error torchscript, trace, example-input, lightning
The 'method' parameter only supports 'script' or 'trace', bu
exception error torchscript, invalid-argument, lightning
`{type(self).__name__}.to_tensorrt` requires `torch_tensorrt
exception error tensorrt, export, missing-dependency, lightning
TensorRT only supports CUDA devices. The current device is {
exception error tensorrt, cuda, device-mismatch, lightning
Could not export to TensorRT since neither `input_sample` no
exception error tensorrt, example-input, lightning
TensorRT with IR mode 'ts' only supports output format 'torc
exception error tensorrt, invalid-combination, torchscript, lightning
Your LightningModule code tried to access `self.trainer.{ite
exception error fabric, trainer, attribute-error, lightning
When `optimizer.step(closure)` is called, the closure should
exception error optimizer, closure, manual-optimization, lightning
Unknown configuration for model optimizers. Output from `mod
exception critical configure-optimizers, validation, lightning, misconfiguration
The lr scheduler dict must have the key "scheduler" with its
exception critical lr-scheduler, configure-optimizers, validation, lightning
The "interval" key in lr scheduler dict must be "step" or "e
exception critical lr-scheduler, interval, validation, lightning
`configure_optimizers` must include a monitor when a `Reduce
exception critical lr-scheduler, reduce-lr-on-plateau, monitor, lightning
The provided lr scheduler `{scheduler.__class__.__name__}` i
exception error lr-scheduler, checkpointing, type-error, lightning
The provided lr scheduler `{scheduler.__class__.__name__}` d
exception error lr-scheduler, custom-scheduler, hook, lightning
Training with multiple optimizers is only supported with man
exception critical optimizer, manual-optimization, migration, lightning
Training with multiple optimizers is only supported with man
exception critical optimizer, manual-optimization, gan, lightning
Some schedulers are attached with an optimizer that wasn't r
exception critical lr-scheduler, optimizer, validation, lightning
.csv, .yml or .yaml is required for `hparams_file`
exception error lightning, checkpoint, hparams, file-extension
Unsupported {cls}
exception error lightning, checkpoint, not-implemented, lightningmodule
The instantiator {instantiator_path!r} from the checkpoint i
exception critical lightning, checkpoint, security, arbitrary-code-execution, allowlist
You set `.load_from_checkpoint(..., strict={strict!r})` whic
exception error lightning, checkpoint, strict-loading, config-conflict
Missing folder: {os.path.dirname(tags_csv)}.
exception error lightning, hparams, csv, missing-directory, fsspec
Missing folder: {os.path.dirname(config_yaml)}.
exception error lightning, hparams, yaml, missing-directory, fsspec
hparams must be dictionary
exception error lightning, hparams, yaml, type-error, serialization
Experiment is not initialized
exception error litlogger, lightning, logger, lazy-initialization
`synchronous` requires mlflow>=2.8.0
exception error mlflow, lightning, logger, version-mismatch, dependency
NeptuneLogger is no longer supported. Neptune has been sunse
exception error neptune, lightning, logger, removed-api, sunset
Error while merging hparams: the keys {inconsistent_keys} ar
exception error lightning, hparams, merge-conflict, datamodule
Providing log_model={log_model} and offline={offline} is an
exception error wandb, lightning, logger, offline, config-conflict
Expected a list as "images", found {type(images)}
exception error wandb, lightning, logging, images, type-error
Expected {n} items but only found {len(v)} for {k}
exception error wandb, lightning, logging, images, length-mismatch
Expected a list as "audios", found {type(audios)}
exception error wandb, lightning, logging, audio, type-error
Expected a list as "videos", found {type(videos)}
exception error wandb, lightning, logging, video, type-error
`{self.__class__.__name__}` should have been `setup` with a
exception error lightning, loops, fetchers, setup-order, internal-api
`prefetch_batches` should at least be 0.
exception error lightning, data-loading, prefetch, validation
`max_epochs` must be a non-negative integer or -1. You passe
exception error pytorch-lightning, trainer, max-epochs, configuration, validation
`val_check_interval` ({trainer.val_check_interval}) must be
exception error pytorch-lightning, validation, val-check-interval, scheduling
When using an IterableDataset for `train_dataloader`, `Train
exception error pytorch-lightning, iterable-dataset, streaming, validation
`{type(self).__name__}` does not support the `CombinedLoader
exception error pytorch-lightning, combined-loader, multi-dataloader, training
In automatic_optimization, when `training_step` returns a di
exception error pytorch-lightning, training-step, loss, automatic-optimization
In automatic optimization, `training_step` must return a Ten
exception error pytorch-lightning, training-step, return-type, automatic-optimization
Skipping the `training_step` by returning None in distribute
exception critical pytorch-lightning, distributed, ddp, training-step, sync
The closure hasn't been executed. HINT: did you call `optimi
exception error pytorch-lightning, optimizer-step, closure, automatic-optimization
In manual optimization, `training_step` must either return a
exception error pytorch-lightning, manual-optimization, training-step, return-type
`return_predictions` should be set to `False` when using the
exception error pytorch-lightning, predict, ddp-spawn, return-predictions
`trainer.predict()` only supports the `CombinedLoader(mode="
exception error pytorch-lightning, predict, combined-loader, multi-dataloader
`max_steps` must be a non-negative integer or -1 (infinite s
exception error pytorch-lightning, trainer, max-steps, validation
ReduceLROnPlateau conditioned on metric {monitor_key} which
exception error pytorch-lightning, lr-scheduler, reducelronplateau, monitor-metric, logging
The loss returned in `training_step` is {loss}.
exception critical pytorch-lightning, nan-loss, numerical-stability, training
DataFetcher is unsupported for {trainer.state.stage}
exception error pytorch-lightning, internal, running-stage, data-fetcher
You provided only a single `{stage.dataloader_prefix}_datalo
exception error pytorch-lightning, dataloader-idx, hook-signature, single-dataloader
You provided multiple `{stage.dataloader_prefix}_dataloader`
exception error pytorch-lightning, dataloader-idx, hook-signature, multi-dataloader
The given dataset must implement the `__len__` method.
exception error pytorch-lightning, distributed, distributed-sampler, iterable-dataset
`Passed `{type(self).__name__}(precision={precision!r})`. Pr
exception error pytorch-lightning, precision, amp, mixed-precision, version-migration
`precision='bf16-mixed'` does not use a scaler, found {scale
exception error pytorch-lightning, amp, bf16, mixed-precision, gradscaler, plugin
AMP and the LBFGS optimizer are not compatible.
exception error pytorch-lightning, amp, lbfgs, optimizer, gradscaler, optimizer-step
The current optimizer, {type(optimizer).__qualname__}, does
exception error pytorch-lightning, amp, fused-optimizer, gradient-clipping, adamw, gradscaler
`Trainer(strategy='deepspeed', precision={precision!r})` is
exception error pytorch-lightning, deepspeed, precision, config-validation
DeepSpeed and the LBFGS optimizer are not compatible.
exception error pytorch-lightning, deepspeed, lbfgs, optimizer, distributed
Skipping backward by returning `None` from your `training_st
exception error pytorch-lightning, deepspeed, training-step, backward, automatic-optimization
`precision={precision!r})` is not supported in FSDP. `precis
exception error pytorch-lightning, fsdp, precision, config-validation, distributed
`precision={precision!r}` does not use a scaler, found {scal
exception error pytorch-lightning, fsdp, shardedgradscaler, mixed-precision, bf16
`gradient_clip_algorithm='norm'` is currently not supported
exception error pytorch-lightning, fsdp, gradient-clipping, clip-norm, distributed
Was unable to infer precision type, received {self.precision
exception error pytorch-lightning, fsdp, mixed-precision, dtype-mapping, config-validation
{_XLA_AVAILABLE}
exception critical xla, tpu, dependency-missing, pytorch-lightning
`precision={precision!r})` is not supported in XLA. `precisi
exception error xla, precision, invalid-argument, pytorch-lightning
Skipping backward by returning `None` from your `training_st
exception error xla, training-step, backward, pytorch-lightning, misconfiguration
Attempting to stop recording an action ({action_name}) which
exception error profiler, advanced-profiler, api-misuse, pytorch-lightning
You are trying to use `ScheduleWrapper` which require kineto
exception error profiler, kineto, dependency-missing, pytorch
Found sort_by_key: {self._sort_by_key}. Should be within {se
exception error profiler, invalid-argument, pytorch-lightning
Found invalid table_kwargs key: {key}. This is already a pos
exception error profiler, invalid-argument, kwargs, pytorch-lightning
Found invalid table_kwargs key: {key}. Should be within {val
exception error profiler, invalid-argument, kwargs, version-compat
Schedule should be a callable. Found: {schedule}
exception error profiler, schedule, type-error, pytorch-lightning
Schedule should return a `torch.profiler.ProfilerAction`. Fo
exception error profiler, schedule, type-error, pytorch
The `configure_payload` method needs to be overridden.
exception error lightning, serving, not-implemented, pytorch
The `configure_serialization` method needs to be overridden.
exception error lightning, serving, serialization, not-implemented
The `serve_step` method needs to be overridden.
exception error lightning, serving, inference, not-implemented
The server didn't start within {self.timeout} seconds.
exception error lightning, serving, timeout, server-startup
Your provided payload {payload} should have a field named "b
exception error lightning, serving, payload, schema
The expected response {response} doesn't match the generated
exception error lightning, serving, response-mismatch, assertion
The model isn't servable. Investigate the traceback and try
exception error lightning, serving, misconfiguration, pipeline-gate
Please, return your outputs as a dictionary. Found {output}
exception error lightning, serving, return-type, contract
Post-localSGD algorithm is used, but model averaging period
exception error pytorch, lightning, ddp, distributed, post-local-sgd
Currently model averaging cannot work with a distributed opt
exception error pytorch, lightning, ddp, distributed-optimizer, incompatibility
To use the `DeepSpeedStrategy`, you must have DeepSpeed inst
exception critical deepspeed, strategy, dependencies, installation
PyTorch >= 2.6 requires DeepSpeed >= 0.16.0. Detected DeepSp
exception critical deepspeed, version-mismatch, pytorch-2-6, compatibility
The DeepSpeed strategy is only supported on CUDA GPUs but `{
exception critical deepspeed, accelerator, cuda, hardware-requirement
Currently only one optimizer is supported with DeepSpeed. Go
exception error deepspeed, optimizers, multiple-optimizers, configure-optimizers
DeepSpeed does not support clipping gradients by value.
exception error deepspeed, gradient-clipping, trainer-flags
DeepSpeed currently only supports single optimizer, single o
exception error deepspeed, lr-scheduler, configure-optimizers
`{empty_init=}` is not a valid choice with `DeepSpeedStrateg
exception error deepspeed, zero-stage-3, weight-initialization
`Trainer.save_checkpoint(..., storage_options=...)` with `st
exception error deepspeed, save-checkpoint, storage-options, checkpointio
DeepSpeed was unable to load the checkpoint. Ensure you pass
exception critical deepspeed, load-checkpoint, resume, load-full-weights
You passed in a path to a DeepSpeed config but the path does
exception error deepspeed, config-file, file-not-found, paths
To use DeepSpeed you must pass in a DeepSpeed config dict, o
exception critical deepspeed, missing-config, zero-optimization
Do not set `gradient_accumulation_steps` in the DeepSpeed co
exception error deepspeed, gradient-accumulation, config-conflict
The FSDP strategy can only work with the `FSDPPrecision` plu
exception error fsdp, precision-plugin, mixed-precision, type-mismatch
The optimizer does not seem to reference any FSDP parameters
exception critical fsdp, optimizer, empty-parameter-list, use-orig-params
Unknown state_dict_type: {self._state_dict_type}
exception error fsdp, state-dict, invalid-argument, checkpointing
`FSDPStrategy.save_checkpoint(..., storage_options=...)` is
exception error fsdp, save-checkpoint, storage-options
The checkpoint path exists and is a directory: {path}
exception error fsdp, save-checkpoint, is-a-directory, paths
You have configured {len(self.optimizers)} optimizers but th
exception critical fsdp, resume, optimizer-states, checkpoint-mismatch
The path {str(path)!r} does not point to a valid checkpoint.
exception error fsdp, checkpoint, load-checkpoint, pytorch-lightning
The start method '{self._start_method}' is not available on
exception error multiprocessing, start-method, platform, pytorch-lightning
Calling `trainer.fit()` twice on the same Trainer instance u
exception error trainer, spawn, fit-twice, pytorch-lightning
The launcher can only create subprocesses once.
exception error subprocess, launcher, distributed, pytorch-lightning
Lightning attempted to launch new distributed processes with
exception error local-rank, distributed, subprocess, cluster-environment
raise ModuleNotFoundError(str(_XLA_AVAILABLE))
exception critical xla, tpu, missing-dependency, pytorch-lightning
Calling `trainer.fit()` twice on the same Trainer instance u
exception error xla, tpu, fit-twice, trainer
Accessing the device mesh before processes have initialized
exception error device-mesh, model-parallel, lifecycle, pytorch-lightning
When using the {type(self).__name__}, you are required to ov
exception error model-parallel, fsdp2, configure-model, hook-required
Found modules that are wrapped with `torch.distributed.fsdp.
exception error fsdp, fsdp2, legacy-api, pytorch-version
`{type(self).__name__}.save_checkpoint(..., storage_options=
exception error checkpoint, storage-options, model-parallel, unsupported-argument
The checkpoint path exists and is a directory: {path}
exception error checkpoint, is-a-directory, model-parallel, path
raise ModuleNotFoundError(str(_XLA_AVAILABLE))
exception critical xla, tpu, missing-dependency
The XLA strategy can only work with the `XLACheckpointIO` pl
exception error xla, checkpoint-io, plugin-mismatch
The XLA strategy can only work with the `XLAPrecision` plugi
exception error xla, precision-plugin, plugin-mismatch
raise ModuleNotFoundError(str(_XLA_AVAILABLE))
exception critical xla, tpu, missing-dependency
The XLA strategy can only work with the `XLACheckpointIO` pl
exception error xla, checkpoint-io, plugin-mismatch
The XLA strategy can only work with the `XLAPrecision` plugi
exception error xla, precision-plugin, plugin-mismatch
Accessing the XLA device before processes have spawned is no
exception error xla, root-device, lifecycle
Currently, the XLAStrategy only supports `sum`, `mean`, `avg
exception error xla, reduce, reduce-op, distributed
No `{step_name}()` method defined to run `Trainer.{trainer_m
validation error pytorch-lightning, trainer, validation, missing-hook, configuration
Support for `{epoch_end_name}` has been removed in v2.0.0. `
exception error pytorch-lightning, migration, v2-breaking-change, legacy-hooks
Automatic gradient clipping is not supported for manual opti
validation error pytorch-lightning, manual-optimization, gradient-clipping, configuration
Automatic gradient accumulation is not supported for manual
validation error pytorch-lightning, manual-optimization, gradient-accumulation, configuration
Both `{name}.configure_model`, and `{name}.configure_sharded
validation error pytorch-lightning, fsdp, deprecated-hook, configuration
You selected an invalid strategy name: `strategy={strategy!r
validation error pytorch-lightning, trainer, strategy, invalid-argument, ddp
You selected an invalid accelerator name: `accelerator={acce
validation error pytorch-lightning, trainer, accelerator, invalid-argument
You set `strategy={strategy}` but strategies from the DDP fa
validation error pytorch-lightning, mps, apple-silicon, ddp, strategy-mismatch
You set `Trainer(sync_batchnorm=True)` and provided a `{plug
validation error pytorch-lightning, sync-batchnorm, plugins, configuration-conflict
Found invalid type for plugin {plugin}. Expected one of: Pre
validation error pytorch-lightning, trainer, plugins, config-validation
Received multiple values for {', '.join(duplicated_plugin_ke
validation error pytorch-lightning, plugins, duplicate-config
Received both `precision={precision_flag}` and `plugins={sel
validation error pytorch-lightning, precision, conflicting-config
accelerator set through both strategy class and accelerator
validation error pytorch-lightning, strategy, accelerator, conflicting-config
precision set through both strategy class and plugins, choos
validation error pytorch-lightning, strategy, precision, conflicting-config
checkpoint_io set through both strategy class and plugins, c
validation error pytorch-lightning, strategy, checkpoint-io, conflicting-config
cluster_environment set through both strategy class and plug
validation error pytorch-lightning, strategy, cluster-environment, conflicting-config
CPU parallel_devices set through {self._strategy_flag.__clas
validation error pytorch-lightning, strategy, parallel-devices, device-mismatch
GPU parallel_devices set through {self._strategy_flag.__clas
validation error pytorch-lightning, strategy, parallel-devices, device-mismatch
`num_nodes` must be a positive integer, but got {num_nodes}.
validation error pytorch-lightning, num-nodes, input-validation
`Trainer(devices={self._devices_flag!r})` value is not a val
validation error pytorch-lightning, devices, config-validation
No supported gpu backend found!
validation error pytorch-lightning, gpu, cuda, environment
`{accelerator_cls.__qualname__}` can not run on your system
validation error pytorch-lightning, accelerator, hardware-availability
HPU is currently not supported. Please contact developer@lig
validation error pytorch-lightning, hpu, unsupported-feature
The strategy `{FSDPStrategy.strategy_name}` requires a GPU a
validation error pytorch-lightning, fsdp, strategy, cuda-only
You selected `Trainer(strategy='{strategy_flag}')` but proce
validation error pytorch-lightning, ddp-fork, windows, platform-unsupported
No precision set
exception error pytorch-lightning, precision, config-validation
Bitsandbytes is only supported on CUDA GPUs.
validation error pytorch-lightning, bitsandbytes, quantization, cuda-only
The `ModelParallelStrategy` does not support `Fabric(..., pr
validation error pytorch-lightning, model-parallel, precision, unsupported-combination
`Trainer(strategy={self._strategy_flag!r})` is not compatibl
validation error pytorch-lightning, ddp, jupyter, interactive-environment
The `XLAAccelerator` can only be used with a `SingleDeviceXL
validation error lightning, tpu, xla, strategy, trainer-init, accelerator
Trainer was configured with `enable_checkpointing=False` but
validation error lightning, trainer-init, checkpointing, callbacks, config-conflict
Unable to determine the source of the trainer.
exception info lightning, litmodels, inspect, dynamic-import, checkpointing
You added multiple progress bar callbacks to the Trainer, bu
validation error lightning, progress-bar, callbacks, trainer-init, duplicate-callbacks
Trainer was configured with `enable_progress_bar=False` but
validation error lightning, progress-bar, trainer-init, config-conflict, callbacks
Found more than one stateful callback of type `{type(callbac
validation error lightning, callbacks, state-dict, checkpointing, trainer-init
`.{fn}(ckpt_path="best")` is set but `ModelCheckpoint` is no
validation error lightning, checkpoint, best-model, validate, test, predict
You cannot execute `.{fn}(ckpt_path="best")` with `fast_dev_
validation error lightning, fast-dev-run, checkpoint, best-model, smoke-test
`.{fn}(ckpt_path="best")` is set but `ModelCheckpoint` is no
validation error lightning, model-checkpoint, monitor, best-model, test
`.{fn}(ckpt_path="hpc")` is set but no HPC checkpoint was fo
validation error lightning, hpc, resume, checkpoint, slurm
`.{fn}()` found no path for the best weights: {ckpt_path!r}.
validation error lightning, checkpoint, resume, path-resolution, test
You restored a checkpoint with current_epoch={self.trainer.c
validation error lightning, resume, max-epochs, epoch, trainer-state
Trying to restore optimizer state but checkpoint contains on
validation error lightning, resume, optimizer-state, weights-only, checkpoint
Trying to restore learning rate scheduler state but checkpoi
validation error lightning, resume, lr-scheduler, weights-only, checkpoint
f"`check_val_every_n_epoch` should be an integer, found {che
validation error lightning, trainer-init, validation-interval, type-error, config
"`val_check_interval` should be an integer or a time-based d
validation error lightning, val-check-interval, validation, trainer-init, config
f"`reload_dataloaders_every_n_epochs` should be an int >= 0,
validation error lightning, trainer-init, dataloader-reload, type-error, config
f"`{hook_name}` is not a shared hook within `LightningModule
validation error lightning, internal-api, hooks, data-transfer, misuse
f"An invalid dataloader was passed to `Trainer.{trainer_fn.v
validation error lightning, dataloader, iterable, type-error, fit
f"An invalid dataloader was passed to `Trainer.{trainer_fn.v
validation error lightning, dataloader, iterable, lightning-module, hook-return
f"An invalid dataloader was returned from `{type(source.inst
validation error pytorch-lightning, dataloader, validation, typeerror
f"When using an `IterableDataset`, `Trainer(limit_{stage.dat
validation error pytorch-lightning, iterable-dataset, limit-batches, misconfiguration
f"You requested to check {limit_batches} of the `{stage.data
validation error pytorch-lightning, limit-batches, validation-loop, misconfiguration
f"Logging inside `{fx_name}` is not implemented." " Please,
exception error pytorch-lightning, logging, internal-error, runtimeerror
f"You can't `self.log()` inside `{fx_name}`. HINT: You can s
validation error pytorch-lightning, logging, hook-validation, misconfiguration
m.format("on_step", on_step, fx_name, fx_config["allowed_on_
validation error pytorch-lightning, logging, on-step, misconfiguration
m.format("on_epoch", on_epoch, fx_name, fx_config["allowed_o
validation error pytorch-lightning, logging, on-epoch, misconfiguration
"`self.log(on_step=False, on_epoch=False)` is not useful."
validation error pytorch-lightning, logging, aggregation, misconfiguration
error
validation error pytorch-lightning, logging, reduce-fx, misconfiguration
f"`Trainer(barebones=True, log_every_n_steps={log_every_n_st
validation error trainer, barebones, logging, misconfiguration, pytorch-lightning
f"`Trainer(barebones=True, enable_model_summary={enable_mode
validation error trainer, barebones, model-summary, misconfiguration, pytorch-lightning
f"`Trainer(barebones=True, num_sanity_val_steps={num_sanity_
validation error trainer, barebones, sanity-check, misconfiguration, pytorch-lightning
f"`Trainer(barebones=True, fast_dev_run={fast_dev_run!r})` w
validation error trainer, barebones, fast-dev-run, misconfiguration, pytorch-lightning
f"`Trainer(barebones=True, detect_anomaly={detect_anomaly!r}
validation error trainer, barebones, anomaly-detection, misconfiguration, pytorch-lightning
f"`Trainer(barebones=True, profiler={profiler!r})` was passe
validation error trainer, barebones, profiler, misconfiguration, pytorch-lightning
f"`gradient_clip_val` should be an int or a float. Got {grad
validation error trainer, gradient-clipping, type-error, hyperparameters, pytorch-lightning
f"`gradient_clip_algorithm` {gradient_clip_algorithm} is inv
validation error trainer, gradient-clipping, invalid-argument, misconfiguration, pytorch-lightning
"You cannot pass `train_dataloader` or `val_dataloaders` to
validation error trainer, fit, datamodule, dataloader, misconfiguration, pytorch-lightning
"`Trainer.validate()` requires a `LightningModule` when it h
validation error trainer, validate, missing-model, pytorch-lightning
"You cannot pass both `trainer.validate(dataloaders=..., dat
validation error trainer, validate, datamodule, dataloader, misconfiguration, pytorch-lightning
"`Trainer.test()` requires a `LightningModule` when it hasn'
validation error trainer, test, missing-model, checkpoint, pytorch-lightning
You cannot pass both `trainer.test(dataloaders=..., datamodu
validation error trainer, test, datamodule, dataloader, misconfiguration, pytorch-lightning
`Trainer.predict()` requires a `LightningModule` when it has
validation error trainer, predict, inference, missing-model, pytorch-lightning
You cannot pass both `trainer.predict(dataloaders=..., datam
validation error trainer, predict, datamodule, dataloader, misconfiguration, pytorch-lightning
Saving a checkpoint is only possible if a model is attached
validation error trainer, checkpoint, save, no-model-attached, pytorch-lightning
The attribute name for the learning rate was set to {attr_na
validation error lr-finder, tuner, attr-name, hyperparameters, pytorch-lightning
When using the learning rate finder, either `model` or `mode
validation error lr-finder, tuner, learning-rate, hyperparameters, pytorch-lightning
`model.configure_optimizers()` returned {len(optimizers)}, b
validation error lr-finder, tuner, multiple-optimizers, configure-optimizers, pytorch-lightning
To use the `plot` method, you must have Matplotlib installed
validation warning lr-finder, plot, matplotlib, missing-dependency, optional-install, pytorch-lightning
method='fit' is the only valid configuration to run lr finde
validation error lr-finder, tuner, misconfiguration, lightning
method {method!r} is invalid. Should be one of {supported_me
validation error tuner, validation, method-argument
In tuner with method={method!r}, `dataloaders` argument shou
validation error tuner, dataloaders, misconfiguration
In tuner with `method`={method!r}, `train_dataloaders` and `
validation error tuner, dataloaders, misconfiguration
Trainer is already configured with a `LearningRateFinder` ca
validation error lr-finder, callbacks, tuner, duplicate-config
Tuning the batch size is currently not supported with distri
validation error batch-size-finder, distributed, ddp, tuner
Trainer is already configured with a `BatchSizeFinder` callb
validation error batch-size-finder, callbacks, tuner, duplicate-config
Mismatch in number of limits ({len(limits)}) and number of i
validation error combined-loader, limits, validation
Unsupported mode {mode!r}, please select one of: {list(_SUPP
validation error combined-loader, mode, invalid-argument
Mismatch in flattened length ({len(flattened)}) and existing
validation error combined-loader, mutation, length-mismatch
Mismatch in number of limits ({len(limits)}) and number of i
validation error combined-loader, limits, length-mismatch
Please call `iter(combined_loader)` first.
validation error combined-loader, iteration-order, runtime-state
All datasets are iterable-style datasets.
validation error combined-loader, iterable-dataset, not-implemented
The CombinedLoader has {len(stateful_loaders)} stateful load
validation critical combined-loader, checkpoint, state-mismatch, resume
`model` is required to be a `OptimizedModule`. Found a `{typ
validation error torch-compile, type-mismatch, unwrap
`model` is expected to be a compiled LightningModule. Found
validation error torch-compile, mixed-imports, type-mismatch
Unexpected error, the wrapped model should be a LightningMod
validation error torch-compile, unwrap, invariant-violation
`model` is required to be a compiled LightningModule. Found
validation error torch-compile, state-check
`model` must either be an instance of OptimizedModule or Lig
validation error torch-compile, type-mismatch
`model` must be a `LightningModule` or `torch._dynamo.Optimi
validation error torch-compile, mixed-imports, trainer, type-mismatch
Using a compiled model is incompatible with the current stra
validation error pytorch-lightning, torch-compile, strategy, distributed-training
`{dataloader_cls_name}` within local rank has zero length. P
validation error pytorch-lightning, dataloader, distributed-training, empty-dataset
The dataloader {dataloader} needs to subclass `torch.utils.d
validation error pytorch-lightning, dataloader, type-validation
Trying to inject custom `Sampler` into the `{dataloader_cls_
exception error pytorch-lightning, dataloader, sampler, introspection
Trying to inject parameters into the `{dataloader_cls_name}`
exception error pytorch-lightning, dataloader, kwargs, signature-mismatch
Trying to inject a modified sampler into the batch sampler;
exception error pytorch-lightning, batch-sampler, sampler, distributed-training
Lightning can't inject a (distributed) sampler into your ba
exception error pytorch-lightning, batch-sampler, distributed-training, typeerror
Lightning can't inject a (distributed) sampler into your ba
exception error pytorch-lightning, batch-sampler, distributed-training, typeerror
Unable to find 'latest' file at {latest_path}
exception error pytorch-lightning, deepspeed, checkpoint, conversion
Directory '{ds_checkpoint_dir}' doesn't exist
exception error pytorch-lightning, deepspeed, checkpoint, file-not-found
Download model failed - {model_registry}
exception critical model-registry, download, lightning, checkpoint
`max_depth` can be -1, 0 or > 0, got {max_depth}.
exception error model-summary, validation, lightning, valueerror
There is no `frame` available while being required.
exception error hyperparameters, introspection, lightning, attributeerror
{attribute} is neither stored in the model namespace nor the
exception error attribute-lookup, batch-size, tuner, lightning
You have overridden the `LightningModule.backward` hook but
console warning deepspeed, backward-hook, precision, lightning
The PyTorch Profiler default schedule will be overridden as
console warning profiler, pytorch-profiler, schedule, lightning
You have overridden `{hook_name}` in both `LightningModule`
console warning datamodule, hook-conflict, data-loading, lightning
You have overridden `{hook_name}` in `LightningModule` but h
console warning datamodule, hook-conflict, data-loading, lightning
The dirpath has changed from {dirpath_from_ckpt!r} to {self.
console warning model-checkpoint, resume, dirpath, lightning
Skipping '{k}' parameter because it is not possible to safel
console warning hyperparameters, yaml-serialization, saving, lightning
LitLogger does not support `log_graph`
console warning logger, log-graph, litlogger, lightning
`training_step` returned `None`. If this was on purpose, ign
console warning training-step, loss, automatic-optimization, lightning
predict returned None if it was on purpose, ignore this warn
console warning predict-step, inference, lightning
Couldn't infer the batch indices fetched from your dataloade
console warning prediction, dataloader, batch-indices, lightning
train_dataloader yielded None. If this was on purpose, ignor
console warning training, dataloader, none-batch, lightning
When saving the DeepSpeed Stage 3 checkpoint, each worker wi
console warning deepspeed, zero-stage-3, checkpoint, sharding
Starting from v1.9.0, `tensorboardX` has been removed as a d
console warning logger, tensorboard, csv-fallback, lightning, version-change
You called `self.log({self.meta.name!r}, ...)` in your `{sel
console warning logging, dtype, metrics, lightning
It is recommended to use `self.log({result_metric.meta.name!
console warning pytorch-lightning, distributed, ddp, logging, sync-dist, metrics
Trying to infer the `batch_size` from an ambiguous collectio
console warning pytorch-lightning, batch-size, logging, data-loading
Redirecting import of {module}.{name} to {new_module}.{name}
console info pytorch-lightning, migration, pickle, checkpoint, version-upgrade
The total number of parameters detected may be inaccurate be
console warning pytorch-lightning, model-summary, lazy-layers, uninitialized-parameter, parameter-counting