composer.trainer.trainer#

Train models!

The trainer supports models with ComposerModel instances. The Trainer is highly customizable and can support a wide variety of workloads.

Example

Train a model and save a checkpoint:

import os
from composer import Trainer

### Create a trainer
trainer = Trainer(model=model,
                  train_dataloader=train_dataloader,
                  max_duration="1ep",
                  eval_dataloader=eval_dataloader,
                  optimizers=optimizer,
                  schedulers=scheduler,
                  device="cpu",
                  validate_every_n_epochs=1,
                  save_folder="checkpoints",
                  save_interval="1ep")

### Fit and run evaluation for 1 epoch.
### Save a checkpoint after 1 epocch as specified during trainer creation.
trainer.fit()

Load the checkpoint and resume training:

### Get the saved checkpoint folder
### By default, the checkpoint folder is of the form runs/<timestamp>/rank_0/checkpoints
### Alternatively, if you set the run directory environment variable as follows:
### os.environ["COMPOSER_RUN_DIRECTORY"] = "my_run_directory", then the checkpoint path
### will be of the form my_run_directory/rank_0/checkpoints
checkpoint_folder = trainer.checkpoint_folder

### If the save_interval was in terms of epochs like above then by default,
### checkpoint filenames are of the form "ep{EPOCH_NUMBER}.pt".
checkpoint_path = os.path.join(checkpoint_folder, "ep1.pt")

### Create a new trainer with the load_path argument set to the checkpoint path.
### This will automatically load the checkpoint on trainer creation.
trainer = Trainer(model=model,
                  train_dataloader=train_dataloader,
                  max_duration="2ep",
                  eval_dataloader=eval_dataloader,
                  optimizers=optimizer,
                  schedulers=scheduler,
                  device="cpu",
                  validate_every_n_epochs=1,
                  load_path=checkpoint_path)

### Continue training and running evaluation where the previous trainer left off
### until the new max_duration is reached.
### In this case it will be one additional epoch to reach 2 epochs total.
trainer.fit()

Classes

Trainer

Trainer for training a models with Composer algorithms.

class composer.trainer.trainer.Trainer(*, model, train_dataloader, max_duration, eval_dataloader=None, algorithms=None, optimizers=None, schedulers=None, device=None, grad_accum=1, grad_clip_norm=None, validate_every_n_batches=- 1, validate_every_n_epochs=1, compute_training_metrics=False, precision=Precision.FP32, scale_schedule_ratio=1.0, step_schedulers_every_batch=None, dist_timeout=300.0, ddp_sync_strategy=None, seed=None, deterministic_mode=False, loggers=None, callbacks=(), load_path=None, load_object_store=None, load_weights_only=False, load_strict=False, load_chunk_size=1048576, load_progress_bar=True, save_folder=None, save_interval='1ep', save_compression=None, train_subset_num_batches=None, eval_subset_num_batches=None, deepspeed_config=False, profiler_trace_file=None, prof_event_handlers=(), prof_skip_first=0, prof_wait=0, prof_warmup=1, prof_active=4, prof_repeat=1, sys_prof_cpu=True, sys_prof_memory=False, sys_prof_disk=False, sys_prof_net=False, sys_prof_stats_thread_interval_seconds=0.5, torch_profiler_trace_dir=None, torch_prof_use_gzip=False, torch_prof_record_shapes=False, torch_prof_profile_memory=True, torch_prof_with_stack=False, torch_prof_with_flops=True)[source]#

Bases: object

Trainer for training a models with Composer algorithms. See the Trainer guide for more information.

Parameters

model (ComposerModel) –
The model to train. Can be user-defined or one of the models included with Composer.

See also

composer.models for models built into Composer.
train_dataloader (DataLoader, DataSpec, or dict) –
The DataLoader, DataSpec, or dict of DataSpec kwargs for the training data. In order to specify custom preprocessing steps on each data batch, specify a DataSpec instead of a DataLoader.

Note

The train_dataloader should yield per-rank batches. Each per-rank batch will then be further divided based on the grad_accum parameter. For example, if the desired optimization batch size is 2048 and training is happening across 8 GPUs, then each train_dataloader should yield a batch of size 2048 / 8 = 256. If grad_accum = 2, then the per-rank batch will be divided into microbatches of size 256 / 2 = 128.
max_duration (int, str, or Time) – The maximum duration to train. Can be an integer, which will be interpreted to be epochs, a str (e.g. 1ep, or 10ba), or a Time object.
eval_dataloader (DataLoader, DataSpec, or Evaluators, optional) – The DataLoader, DataSpec, or Evaluators for the evaluation data. In order to evaluate one or more specific metrics across one or more datasets, pass in an Evaluator. If a DataSpec or DataLoader is passed in, then all metrics returned by model.metrics() will be used during evaluation. None results in no evaluation. (default: None)
algorithms (List[Algorithm], optional) –
The algorithms to use during training. If None, then no algorithms will be used. (default: None)

See also

composer.algorithms for the different algorithms built into Composer.
optimizers (Optimizers, optional) –
The optimizer. If None, will be set to DecoupledSGDW(model.parameters(), lr=0.1). (default: None)

See also

composer.optim for the different optimizers built into Composer.
schedulers (Schedulers, optional) –
The learning rate schedulers. If [] or None, will be set to [constant_scheduler]. (default: None).

See also

composer.optim.scheduler for the different schedulers built into Composer.
device (str or Device, optional) – The device to use for training. Either cpu or gpu. (default: cpu)
grad_accum (int, optional) –
The number of microbatches to split a per-device batch into. Gradients are summed over the microbatches per device. (default: 1)

Note

This is implemented by taking the batch yielded by the train_dataloader and splitting it into grad_accum sections. Each section is of size train_dataloader // grad_accum. If the batch size of the dataloader is not divisible by grad_accum, then the last section will be of size batch_size % grad_accum.
grad_clip_norm (float, optional) – The norm to clip gradient magnitudes to. Set to None for no gradient clipping. (default: None)
validate_every_n_batches (int, optional) – Compute metrics on evaluation data every N batches. Set to -1 to never validate on a batchwise frequency. (default: -1)
validate_every_n_epochs (int, optional) – Compute metrics on evaluation data every N epochs. Set to -1 to never validate on a epochwise frequency. (default: 1)
compute_training_metrics (bool, optional) – True to compute metrics on training data and False to not. (default: False)
precision (str or Precision, optional) –
Numerical precision to use for training. One of fp32, fp16 or amp (recommended). (default: Precision.FP32)

Note

fp16 only works if deepspeed_config is also provided.
scale_schedule_ratio (float, optional) –
Ratio by which to scale the training duration and learning rate schedules. E.g., 0.5 makes the schedule take half as many epochs and 2.0 makes it take twice as many epochs. 1.0 means no change.

Training for less time is a strong baseline approach to speeding up training, provided that the training still gets through the entire learning rate schedule. E.g., training for half as long often yields little accuracy degredation, provided that the learning rate schedule is rescaled to take half as long as well. In contrast, if the schedule is not rescaled, training for half as long would mean simply stopping halfway through the training curve, which does reach nearly as high an accuracy.

To see the difference, consider training for half as long using a cosine annealing learning rate schedule. If the schedule is not rescaled, training ends while the learning rate is still ~0.5. If the schedule is rescaled, training ends after passing through the full cosine curve, at a learning rate near 0. (default: 1.0)
step_schedulers_every_batch (bool, optional) – By default, native PyTorch schedulers are updated every epoch, while composer schedulers are updated every step. Setting this to True will cause any scheduler passed to the trainer to be stepped every batch, while setting this to False will cause all schedulers to be stepped every epoch. If this parameter is None, then if any composer scheduler is provided, schedulers will be stepped every batch. Otherwise, schedulers will be stepped every epoch. (default: None)
dist_timeout (float, optional) – Timeout, in seconds, for initializing the distributed process group. (default: 15.0)
ddp_sync_strategy (str or DDPSyncStrategy, optional) – The strategy to use for synchronizing gradients. Leave unset to let the trainer auto-configure this. See DDPSyncStrategy for more details.
seed (int, optional) –
The seed used in randomization. If None, then a random seed will be created. (default: None)

Note

In order to get reproducible results, call the seed_all() function at the start of your script with the seed passed to the trainer. This will ensure any initialization done before the trainer init (ex. model weight initialization) also uses the provided seed.

See also

composer.utils.reproducibility for more details on reproducibility.
deterministic_mode (bool, optional) –
Run the model deterministically. (default: False)

Note

This is an experimental feature. Performance degradations expected. Certain Torch modules may not have deterministic implementations, which will result in a crash.

Note

In order to get reproducible results, call the configure_deterministic_mode() function at the start of your script. This will ensure any initialization done before the trainer init also runs deterministically.

See also

composer.utils.reproducibility for more details on reproducibility.
loggers (Sequence[LoggerCallback], optional) –
The destinations to log training information to. If None, will be set to [TQDMLogger()]. (default: None)

See also

composer.loggers for the different loggers built into Composer.
callbacks (Sequence[Callback], optional) –
The callbacks to run during training. If None, then no callbacks will be run. (default: None).

See also

composer.callbacks for the different callbacks built into Composer.
load_path (str, optional) –
The template path to an existing checkpoint file. It can be a path to a file on local disk, a URL, or if load_object_store is set, the object name for a checkpoint in a cloud bucket.

When using Deepspeed ZeRO, saved checkpoints are sharded rank. To load deepspeed checkpoints, specify {RANK} in this load_path parameter, and the RANK variable will be substituted with the global rank, thus allowing the correct checkpoints to be loaded per-rank.

For example, suppose that checkpoints are stored in the following structure:
```
my_model/rank_0/ep1.tar
my_model/rank_1/ep1.tar
my_model/rank_2/ep1.tar
...
```
Then, load_path should be set to my_model/rank_{RANK}/ep1.tar, and all ranks will load the correct data.

If None then no checkpoint will be loaded. (default: None)

load_object_store (ObjectStoreProvider, optional) –

If the load_path is in an object store (i.e. AWS S3 or Google Cloud Storage), an instance of ObjectStoreProvider which will be used to retreive the checkpoint. Otherwise, if the checkpoint is a local filepath, set to None. Ignored if load_path is None. (default: None)

Example:

from composer import Trainer
from composer.utils import ObjectStoreProvider

# Create the object store provider with the specified credentials
creds = {"key": "object_store_key",
         "secret": "object_store_secret"}
store = ObjectStoreProvider(provider="s3",
                            container="my_container",
                            provider_init_kwargs=creds)

checkpoint_path = "/path_to_the_checkpoint_in_object_store"

# Create a trainer which will load a checkpoint from the specified object store
trainer = Trainer(model=model,
                  train_dataloader=train_dataloader,
                  max_duration="10ep",
                  eval_dataloader=eval_dataloader,
                  optimizers=optimizer,
                  schedulers=scheduler,
                  device="cpu",
                  validate_every_n_epochs=1,
                  load_path=checkpoint_path,
                  load_object_store=store)

load_weights_only (bool, optional) – Whether or not to only restore the weights from the checkpoint without restoring the associated state. Ignored if load_path is None. (default: False)
load_strict (bool, optional) – Ensure that the set of weights in the checkpoint and model must exactly match. Ignored if load_path is None. (default: False)
load_chunk_size (int, optional) – Chunk size (in bytes) to use when downloading checkpoints. Ignored if load_path is either None or a local file path. (default: 1,048,675)
load_progress_bar (bool, optional) – Display the progress bar for downloading the checkpoint. Ignored if load_path is either None or a local file path. (default: True)
save_folder (str, optional) – The folder to store checkpoints in. If an absolute path is specified, then that path will be used. Otherwise, the save_folder will be relative to the folder returned by get_run_directory(). If the save_folder does not exist, it will be created. If None, then no checkpoints will be saved. (default: None)
save_interval (str or int, optional) – How often to save checkpoints. For example, set to 1ep to save checkpoints every epoch, or 10ba to save checkpoints every 10 batches. An integer will be assumed to be epochs. Ignored if save_folder is None. (default: 1ep)
save_compression (str, optional) – Compression algorithm to run on checkpoints. Can be gzip, bzip2, lzma, or None for no compression. Ignored if save_folder is None. (default: None)
train_subset_num_batches (int, optional) – If specified, finish every epoch early after training on this many batches. This parameter has no effect if it is greater than len(train_dataloader). If None, then the entire dataloader will be iterated over. (default: None)
eval_subset_num_batches (int, optional) – If specified, evaluate on this many batches. This parameter has no effect if it is greater than len(eval_dataloader). If None, then the entire dataloader will be iterated over. (default: None)
deepspeed_config (bool or Dict[str, Any], optional) – Configuration for DeepSpeed, formatted as a JSON according to DeepSpeed’s documentation. If True is provided, the trainer will initialize the DeepSpeed engine with an empty config {}. If False is provided, deepspeed will not be used. (default: False)
profiler_trace_file (str, optional) –
Name of the trace file, relative to the run directory. Setting this parameter activates the profiler. (default: None).

See also

composer.profiler for more details on profiling with the trainer.
prof_event_handlers (List[ProfilerEventHandler], optional) – Trace event handler. Ignored if profiler_trace_file is not specified. (default: [JSONTraceHandler()]).
prof_skip_first (int, optional) – Number of batches to skip at epoch start. Ignored if profiler_trace_file is not specified. (default: 0).
prof_wait (int, optional) – Number of batches to skip at the beginning of each cycle. Ignored if profiler_trace_file is not specified. (default: 0).
prof_warmup (int, optional) – Number of warmup batches in a cycle. Ignored if profiler_trace_file is not specified. (default: 1).
prof_active (int, optional) – Number of batches to profile in a cycle. Ignored if profiler_trace_file is not specified. (default: 4).
prof_repeat (int, optional) – Maximum number of profiling cycle repetitions per epoch (0 for no maximum). Ignored if profiler_trace_file is not specified. (default: 1).
sys_prof_cpu (bool, optional) – Whether to record cpu statistics. Ignored if profiler_trace_file is not specified. (default: True).
sys_prof_memory (bool, optional) – Whether to record memory statistics. Ignored if profiler_trace_file is not specified. (default: False).
sys_prof_disk (bool, optional) – Whether to record disk statistics. Ignored if profiler_trace_file is not specified. (default: False).
sys_prof_net (bool, optional) – Whether to record network statistics. Ignored if profiler_trace_file is not specified. (default: False).
sys_prof_stats_thread_interval_seconds (float, optional) – Interval to record stats, in seconds. Ignored if profiler_trace_file is not specified. (default: 0.5).
torch_profiler_trace_dir (str, optional) – Directory to store trace results relative to the run directory. Must be specified to activate the Torch profiler. Ignored if profiler_trace_file is not specified. See profiler. (default: None).
torch_prof_use_gzip (bool) – Whether to use gzip for trace. Ignored if torch_profiler_trace_dir and profiler_trace_file are not specified. (default: False).
torch_prof_record_shapes (bool, optional) – Whether to record tensor shapes. Ignored if torch_profiler_trace_dir and profiler_trace_file are not specified. (default: False).
torch_prof_profile_memory (bool, optional) – Track tensor memory allocations and frees. Ignored if torch_profiler_trace_dir and profiler_trace_file are not specified. (default: True).
torch_prof_with_stack (bool, optional) – Record stack info. Ignored if torch_profiler_trace_dir and profiler_trace_file are not specified. (default: False).
torch_prof_with_flops (bool, optional) – Estimate flops for operators. Ignored if torch_profiler_trace_dir and profiler_trace_file are not specified. (default: True).

state#

The State object used to store training state.

Type: State

evaluators#

The Evaluator objects to use for validation during training.

Type: List[Evaluator]

logger#

The Logger used for logging.

Type: Logger

engine#

The Engine used for running callbacks and algorithms.

Type: Engine

property checkpoint_folder#

The folder in which checkpoints are stored.