%global _empty_manifest_terminate_build 0 Name: python-torchelastic Version: 0.2.2 Release: 1 Summary: PyTorch Elastic Training License: BSD-3 URL: https://github.com/pytorch/elastic Source0: https://mirrors.nju.edu.cn/pypi/web/packages/4f/b5/6b598fe8881a2de40e5a01100ab5932c8b791b9249ccc99c0d5006443c93/torchelastic-0.2.2.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-etcd Requires: python3-torch %description [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](LICENSE)[![CircleCI](https://circleci.com/gh/pytorch/elastic.svg?style=svg&circle-token=9bea46e94adbe2f3e0fb2d4054b1b655f2e208c2)](https://circleci.com/gh/pytorch/elastic) # TorchElastic TorchElastic allows you to launch distributed PyTorch jobs in a fault-tolerant and elastic manner. For the latest documentation, please refer to our [website](https://pytorch.org/elastic). ## Requirements torchelastic requires * python3 (3.8+) * torch * etcd ## Installation ```bash pip install torchelastic ``` ## Quickstart **Fault-tolerant** on `4` nodes, `8` trainers/node, total `4 * 8 = 32` trainers. Run the following on all nodes. ```bash python -m torchelastic.distributed.launch --nnodes=4 --nproc_per_node=8 --rdzv_id=JOB_ID --rdzv_backend=etcd --rdzv_endpoint=ETCD_HOST:ETCD_PORT YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...) ``` **Elastic on** `1 ~ 4` nodes, `8` trainers/node, total `8 ~ 32` trainers. Job starts as soon as `1` node is healthy, you may add up to `4` nodes. ```bash python -m torchelastic.distributed.launch --nnodes=1:4 --nproc_per_node=8 --rdzv_id=JOB_ID --rdzv_backend=etcd --rdzv_endpoint=ETCD_HOST:ETCD_PORT YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...) ``` ## Contributing We welcome PRs. See the [CONTRIBUTING](CONTRIBUTING.md) file. ## License torchelastic is BSD licensed, as found in the [LICENSE](LICENSE) file. %package -n python3-torchelastic Summary: PyTorch Elastic Training Provides: python-torchelastic BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-torchelastic [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](LICENSE)[![CircleCI](https://circleci.com/gh/pytorch/elastic.svg?style=svg&circle-token=9bea46e94adbe2f3e0fb2d4054b1b655f2e208c2)](https://circleci.com/gh/pytorch/elastic) # TorchElastic TorchElastic allows you to launch distributed PyTorch jobs in a fault-tolerant and elastic manner. For the latest documentation, please refer to our [website](https://pytorch.org/elastic). ## Requirements torchelastic requires * python3 (3.8+) * torch * etcd ## Installation ```bash pip install torchelastic ``` ## Quickstart **Fault-tolerant** on `4` nodes, `8` trainers/node, total `4 * 8 = 32` trainers. Run the following on all nodes. ```bash python -m torchelastic.distributed.launch --nnodes=4 --nproc_per_node=8 --rdzv_id=JOB_ID --rdzv_backend=etcd --rdzv_endpoint=ETCD_HOST:ETCD_PORT YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...) ``` **Elastic on** `1 ~ 4` nodes, `8` trainers/node, total `8 ~ 32` trainers. Job starts as soon as `1` node is healthy, you may add up to `4` nodes. ```bash python -m torchelastic.distributed.launch --nnodes=1:4 --nproc_per_node=8 --rdzv_id=JOB_ID --rdzv_backend=etcd --rdzv_endpoint=ETCD_HOST:ETCD_PORT YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...) ``` ## Contributing We welcome PRs. See the [CONTRIBUTING](CONTRIBUTING.md) file. ## License torchelastic is BSD licensed, as found in the [LICENSE](LICENSE) file. %package help Summary: Development documents and examples for torchelastic Provides: python3-torchelastic-doc %description help [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](LICENSE)[![CircleCI](https://circleci.com/gh/pytorch/elastic.svg?style=svg&circle-token=9bea46e94adbe2f3e0fb2d4054b1b655f2e208c2)](https://circleci.com/gh/pytorch/elastic) # TorchElastic TorchElastic allows you to launch distributed PyTorch jobs in a fault-tolerant and elastic manner. For the latest documentation, please refer to our [website](https://pytorch.org/elastic). ## Requirements torchelastic requires * python3 (3.8+) * torch * etcd ## Installation ```bash pip install torchelastic ``` ## Quickstart **Fault-tolerant** on `4` nodes, `8` trainers/node, total `4 * 8 = 32` trainers. Run the following on all nodes. ```bash python -m torchelastic.distributed.launch --nnodes=4 --nproc_per_node=8 --rdzv_id=JOB_ID --rdzv_backend=etcd --rdzv_endpoint=ETCD_HOST:ETCD_PORT YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...) ``` **Elastic on** `1 ~ 4` nodes, `8` trainers/node, total `8 ~ 32` trainers. Job starts as soon as `1` node is healthy, you may add up to `4` nodes. ```bash python -m torchelastic.distributed.launch --nnodes=1:4 --nproc_per_node=8 --rdzv_id=JOB_ID --rdzv_backend=etcd --rdzv_endpoint=ETCD_HOST:ETCD_PORT YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...) ``` ## Contributing We welcome PRs. See the [CONTRIBUTING](CONTRIBUTING.md) file. ## License torchelastic is BSD licensed, as found in the [LICENSE](LICENSE) file. %prep %autosetup -n torchelastic-0.2.2 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-torchelastic -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue May 30 2023 Python_Bot - 0.2.2-1 - Package Spec generated