| Name | dmlcloud JSON |
| Version |
0.3.3
JSON |
| download |
| home_page | None |
| Summary | Distributed torch training using horovod and slurm |
| upload_time | 2024-04-23 12:37:55 |
| maintainer | None |
| docs_url | None |
| author | Sebastian Hoffmann |
| requires_python | >=3.10 |
| license | BSD 3-Clause License Copyright (c) 2023, Sebastian Hoffmann Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
pytorch
torch.distributed
slurm
distributed training
deep learning
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
torch
numpy
xarray
progress_table
omegaconf
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# dmlcloud
[](https://pypi.org/project/dmlcloud/)
[](https://github.com/sehoffmann/dmlcloud/actions/workflows/run_tests.yml)
[](https://github.com/sehoffmann/dmlcloud/actions/workflows/run_linting.yml)
*Flexibel, easy-to-use, opinionated*
*dmlcloud* is a library for **distributed training** of deep learning models with *torch*. Unlike other similar frameworks, dmcloud adds as little additional complexity and abstraction as possible. It is tailored towards a carefully selected set of libraries and workflows.
## Installation
```
pip install dmlcloud
```
## Why dmlcloud?
- Easy initialization of `torch.distributed` (supports *slurm* and *MPI*).
- Simple, yet powerful, API. No unnecessary abstractions and complications.
- Checkpointing and metric tracking (distributed)
- Extensive logging and diagnostics out-of-the-box. Greatly improve reproducability and traceability.
- A wealth of useful utility functions required for distributed training (e.g. for data set sharding)
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"description": "# dmlcloud\n[](https://pypi.org/project/dmlcloud/)\n[](https://github.com/sehoffmann/dmlcloud/actions/workflows/run_tests.yml)\n[](https://github.com/sehoffmann/dmlcloud/actions/workflows/run_linting.yml)\n\n*Flexibel, easy-to-use, opinionated*\n\n*dmlcloud* is a library for **distributed training** of deep learning models with *torch*. Unlike other similar frameworks, dmcloud adds as little additional complexity and abstraction as possible. It is tailored towards a carefully selected set of libraries and workflows.\n\n## Installation\n```\npip install dmlcloud\n```\n\n## Why dmlcloud?\n- Easy initialization of `torch.distributed` (supports *slurm* and *MPI*).\n- Simple, yet powerful, API. No unnecessary abstractions and complications.\n- Checkpointing and metric tracking (distributed)\n- Extensive logging and diagnostics out-of-the-box. Greatly improve reproducability and traceability.\n- A wealth of useful utility functions required for distributed training (e.g. for data set sharding)\n",
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