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Sequence-to-sequence framework with a focus on Neural Machine Translation based on PyTorch
| Date | Stars |
|---|---|
| 2026-07-24 | 1215 |
| 2026-07-25 | 1215 |
| 2026-07-28 | 1215 |
| 2026-07-30 | 1215 |
| 2026-08-06 | 1215 |
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# Sockeye
**Sockeye has entered maintenance mode and is no longer adding new features. We are grateful to everyone who has contributed to Sockeye throughout its development with pull requests, issue reports, and more.**
[](https://badge.fury.io/py/sockeye)
[](https://github.com/awslabs/sockeye/blob/main/LICENSE)
[](https://github.com/awslabs/sockeye/issues)
[](http://sockeye.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/awslabs/sockeye/actions/workflows/torch_nightly.yml)
Sockeye is an open-source sequence-to-sequence framework for Neural Machine Translation built on [PyTorch](https://pytorch.org/). It implements distributed training and optimized inference for state-of-the-art models, powering [Amazon Translate](https://aws.amazon.com/translate/) and other MT applications. Recent developments and changes are tracked in our [CHANGELOG](https://github.com/awslabs/sockeye/blob/master/CHANGELOG.md).
For a quickstart guide to training a standard NMT model on any size of data, see the [WMT 2014 English-German tutorial](docs/tutorials/wmt_large.md).
For questions and issue reports, please [file an issue](https://github.com/awslabs/sockeye/issues/new) on GitHub.
### Version 3.1.x: PyTorch only
With version 3.1.x, we remove support for MXNet 2.x. Models trained with PyTorch and Sockeye 3.0.x remain compatible
with Sockeye 3.1.x. Models trained with 2.3.x (using MXNet) and converted to PyTorch with Sockeye 3.0.x's conversion
tool can NOT be used with Sockeye 3.1.x.
### Version 3.0.0: Concurrent PyTorch and MXNet support
Starting with version 3.0.0, Sockeye is also based on PyTorch. We maintain backwards compatibility with
MXNet models of version 2.3.x with 3.0.x. If MXNet 2.x is installed, Sockeye can run both with PyTorch or MXNet.
All models trained with 2.3.x (using MXNet)
can be converted to models running with PyTorch using the converter CLI (`sockeye.mx_to_pt`). This will
create a PyTorch parameter file (`<model>/params.best`) and backup the existing MXNet parameter
file to `<model>/params.best.mx`. Note that this only applies to fully-trained models that are to be used
for inference. Continued training of an MXNet model with PyTorch is not supported
(because we do not convert training and optimizer states).
`sockeye.mx_to_pt` requires MXNet to be installed into the environment.
All CLIs of Version 3.0.0 now use PyTorch by default, e.g. `sockeye-{train,translate,score}`.
MXNet-based CLIs/modules are still operational and accessible via `sockeye-{train,translate,score}-mx`.
Sockeye 3 can be installed and run without MXNet, but if installed, an extended test suite is executed to ensure
equivalence between PyTorch and MXNet models. Note that running Sockeye 3.0.0 with MXNet requires MXNet 2.x to be
installed (`pip install --pre -f https://dist.mxnet.io/python 'mxnet>=2.0.0b2021'`)
## Installation
Download the current version of Sockeye:
```bash
git clone https://github.com/awslabs/sockeye.git
```
Install the sockeye module and its dependencies:
```bash
cd sockeye && pip3 install --editable .
```
For faster GPU training, install [NVIDIA Apex](https://github.com/NVIDIA/apex). NVIDIA also provides [PyTorch Docker containers](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) that include Apex.
## Documentation
- For information on how to use Sockeye, please visit [our documentation](https://awslabs.github.io/sockeye/).
- Developers may be interested in our [developer guidelines](https://awslabs.github.io/sockeye/development.html).
### Older versions
- Sockeye 3.0, based on PyTorch & MXNet 2.x is available in the `sExcerpt of 18,537 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:0f0eb713d54cd0df, topic:deep-learning, topic:pytorch, readme:distributed training
matched fp:0f0eb713d54cd0df, topic:transformer
matched fp:0f0eb713d54cd0df, topic:machine-translation, desc:machine translation, readme:machine translation