| Checkpoints |
| =========== |
|
|
| In this section, we present the checkpoint formats supported by NVIDIA NeMo. |
|
|
| NeMo Checkpoints (.nemo) |
| ------------------------- |
|
|
| A ``.nemo`` checkpoint is a tar archive that bundles model configurations (YAML), model weights (``.ckpt``), |
| and other artifacts like tokenizer models or vocabulary files. This consolidated design streamlines |
| sharing, loading, tuning, evaluating, and inference. |
|
|
| Because ``.nemo`` files are standard tar archives, you can unpack them, inspect or modify their contents, |
| and repack them: |
|
|
| .. code-block:: bash |
|
|
| # Unpack |
| mkdir model_contents && tar xf model.nemo -C model_contents/ |
|
|
| # Inspect / edit files inside |
| ls model_contents/ |
|
|
| # Repack |
| cd model_contents && tar cf ../model_modified.nemo * && cd .. |
|
|
| This is useful for inspecting model configs, swapping tokenizer files, or modifying configuration |
| without reloading the model in Python. |
|
|
| ``.nemo`` checkpoints are the primary format for ASR, TTS, and Audio pretrained models. |
|
|
| PyTorch Lightning Checkpoints (.ckpt) |
| -------------------------------------- |
|
|
| During training, PyTorch Lightning saves ``.ckpt`` files that contain model weights, optimizer |
| states, and training metadata (epoch, step, scheduler state). These are used to resume training |
| from where it left off. |
|
|
| SafeTensors (.safetensors) |
| -------------------------- |
|
|
| `SafeTensors <https://huggingface.co/docs/safetensors>`_ is a format for storing tensors that is |
| safe (no arbitrary code execution, unlike pickle-based formats), fast (supports zero-copy and |
| lazy loading of individual tensors), and widely adopted across the HuggingFace ecosystem. |
|
|
| SpeechLM2 models use ``.safetensors`` as their primary checkpoint format, following the HuggingFace |
| model conventions. SpeechLM2 models are saved and loaded via HuggingFace Hub integration |
| (``save_pretrained`` / ``from_pretrained``), and their weights are stored in ``.safetensors`` files. |
|
|
| .. note:: |
|
|
| SpeechLM2 models do not use the ``.nemo`` format for their own checkpoints. The ``.nemo`` format |
| is only used in the SpeechLM2 collection to load pretrained ASR checkpoints that initialize |
| the speech encoder component. |
|
|
| Distributed Checkpoints |
| ----------------------- |
|
|
| When training with ``ModelParallelStrategy`` (FSDP2 / Tensor Parallelism), PyTorch Lightning |
| automatically saves **distributed checkpoints**. Instead of gathering all shards onto a single |
| process, each process saves its own shard to a directory. This is significantly faster and uses |
| less memory than consolidating into a single file. |
|
|
| Distributed checkpoints are saved as a directory containing: |
|
|
| - A ``.metadata`` file describing the tensor layout across shards |
| - Numbered ``.distcp`` files with per-rank weight shards |
|
|
| PyTorch Lightning handles loading distributed checkpoints transparently -- you resume training |
| with the same ``ckpt_path`` argument regardless of whether the checkpoint is a single file or a |
| sharded directory. |
|
|
| .. code-block:: python |
|
|
| # Resuming from a distributed checkpoint works the same as a regular checkpoint |
| trainer.fit(model, ckpt_path="path/to/distributed_checkpoint_dir") |
|
|
