save_checkpoint#

composer.utils.save_checkpoint(state, filename='ep{epoch}-ba{batch}-rank{rank}', *, weights_only=False)[source]#

Checkpoint the training state.

Parameters

  • state (State) โ€“ The training state.

  • logger (Logger) โ€“ The logger.

  • filename (str) โ€“

    A format string describing how to name checkpoints. (default: 'ep{epoch}-ba{batch}-rank{rank}')

    The following format variables are available:

    Variable

    Description

    {run_name}

    The name of the training run. See Logger.run_name.

    {rank}

    The global rank, as returned by get_global_rank().

    {local_rank}

    The local rank of the process, as returned by get_local_rank().

    {world_size}

    The world size, as returned by get_world_size().

    {local_world_size}

    The local world size, as returned by get_local_world_size().

    {node_rank}

    The node rank, as returned by get_node_rank().

    {epoch}

    The total epoch count, as returned by epoch().

    {batch}

    The total batch count, as returned by batch().

    {batch_in_epoch}

    The batch count in the current epoch, as returned by batch_in_epoch().

    {sample}

    The total sample count, as returned by sample().

    {sample_in_epoch}

    The sample count in the current epoch, as returned by sample_in_epoch().

    {token}

    The total token count, as returned by token().

    {token_in_epoch}

    The token count in the current epoch, as returned by token_in_epoch().

    {total_wct}

    The total training duration in seconds, as returned by total_wct().

    {epoch_wct}

    The epoch duration in seconds, as returned by epoch_wct().

    {batch_wct}

    The batch duration in seconds, as returned by batch_wct().

    Note

    • By default, only the rank zero process will save a checkpoint file.

    • When using DeepSpeed, each rank will save a checkpoint file in tarball format. DeepSpeed requires tarball format, as it saves model and optimizer states in separate files. Ensure that '{rank}' appears within the filename. Otherwise, multiple ranks may attempt to write to the same file(s), leading to corrupted checkpoints. If no tarball file extension is specified, .tar will be used.

    • To use compression (regardless of whether DeepSpeed is enabled), set the file extension to '.tar.gz', '.tgz', '.tar.bzip', or '.tar.lzma' (depending on the desired compression algorithm).

    Warning

    Using compression will block the training loop while checkpoints are being compressed. As such, we recommend saving checkpoints without compression.

    Consider the following scenario, where:

    • The default name='ep{epoch}-ba{batch}-rank{rank}' is used.

    • The current epoch count is 1.

    • The current batch count is 42.

    When DeepSpeed is not being used, the rank zero process will save the checkpoint to 'ep1-ba42-rank0'. When DeepSpeed is being used, each rank (process) will save checkpoints to:

    ep1-ba42-rank0.tar
ep1-ba42-rank1.tar
ep1-ba42-rank2.tar
...

  • weights_only (bool, optional) โ€“

    If True, save only the model weights instead of the entire training state. (default: False)

    Note

    When using DeepSpeed, this parameter must be False. Weights-only checkpointing is not currently compatible with DeepSpeed,

  • Returns โ€“

    List[pathlib.Path]: The list of checkpoint files saved, indexed by the rank of the process.

    Note

    When using DeepSpeed, each process (rank) saves its own checkpoint file. When doing multi-node training, the filepaths are valid only on each processโ€™s node; Composer does not move checkpoint files between nodes.

    Otherwise, when not using DeepSpeed, each list will contain only one filepath, since only the rank zero process saves checkpoints.