%global _empty_manifest_terminate_build 0
Name: python-spotty
Version: 1.3.3
Release: 1
Summary: Training deep learning models on AWS and GCP instances
License: MIT
URL: https://github.com/spotty-cloud/spotty
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/8d/28/8fcb680613f400e68059d62b45e8fabb2d77caadf3ef40f3153535daab4d/spotty-1.3.3.tar.gz
BuildArch: noarch
Requires: python3-boto3
Requires: python3-google-api-python-client
Requires: python3-google-cloud-storage
Requires: python3-cfn-flip
Requires: python3-schema
Requires: python3-chevron
%description
[](https://spotty.cloud)
[](https://pypi.org/project/spotty/)
Spotty drastically simplifies training of deep learning models on [AWS](https://aws.amazon.com/)
and [GCP](https://cloud.google.com/):
- it makes training on GPU instances as simple as training on your local machine
- it automatically manages all necessary cloud resources including images, volumes, snapshots and SSH keys
- it makes your model trainable in the cloud by everyone with a couple of commands
- it uses [tmux](https://en.wikipedia.org/wiki/Tmux) to easily detach remote processes from their terminals
- it saves you up to 70% of the costs by using [AWS Spot Instances](https://aws.amazon.com/ec2/spot/)
and [GCP Preemtible VMs](https://cloud.google.com/preemptible-vms/)
## Documentation
- See the [documentation page](https://spotty.cloud).
- Read [this](https://medium.com/@apls/how-to-train-deep-learning-models-on-aws-spot-instances-using-spotty-8d9e0543d365)
article on Medium for a real-world example.
## Installation
Requirements:
* Python >=3.6
* AWS CLI (see [Installing the AWS Command Line Interface](http://docs.aws.amazon.com/cli/latest/userguide/installing.html))
if you're using AWS
* Google Cloud SDK (see [Installing Google Cloud SDK](https://cloud.google.com/sdk/install))
if you're using GCP
Use [pip](http://www.pip-installer.org/en/latest/) to install or upgrade Spotty:
$ pip install -U spotty
## Get Started
1. Prepare a `spotty.yaml` file and put it to the root directory of your project:
- See the file specification [here](https://spotty.cloud/docs/user-guide/configuration-file.html).
- Read [this](https://medium.com/@apls/how-to-train-deep-learning-models-on-aws-spot-instances-using-spotty-8d9e0543d365)
article for a real-world example.
2. Start an instance:
```bash
$ spotty start
```
It will run a Spot Instance, restore snapshots if any, synchronize the project with the running instance
and start the Docker container with the environment.
3. Train a model or run notebooks.
To connect to the running container via SSH, use the following command:
```bash
$ spotty sh
```
It runs a [tmux](https://github.com/tmux/tmux/wiki) session, so you can always detach this session using
__`Ctrl + b`__, then __`d`__ combination of keys. To be attached to that session later, just use the
`spotty sh` command again.
Also, you can run your custom scripts inside the Docker container using the `spotty run ` command. Read more
about custom scripts in the documentation:
[Configuration: "scripts" section](https://spotty.cloud/docs/configuration-file/#scripts-section-optional).
## Contributions
Any feedback or contributions are welcome! Please check out the [guidelines](CONTRIBUTING.md).
## License
[MIT License](LICENSE)
%package -n python3-spotty
Summary: Training deep learning models on AWS and GCP instances
Provides: python-spotty
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-spotty
[](https://spotty.cloud)
[](https://pypi.org/project/spotty/)
Spotty drastically simplifies training of deep learning models on [AWS](https://aws.amazon.com/)
and [GCP](https://cloud.google.com/):
- it makes training on GPU instances as simple as training on your local machine
- it automatically manages all necessary cloud resources including images, volumes, snapshots and SSH keys
- it makes your model trainable in the cloud by everyone with a couple of commands
- it uses [tmux](https://en.wikipedia.org/wiki/Tmux) to easily detach remote processes from their terminals
- it saves you up to 70% of the costs by using [AWS Spot Instances](https://aws.amazon.com/ec2/spot/)
and [GCP Preemtible VMs](https://cloud.google.com/preemptible-vms/)
## Documentation
- See the [documentation page](https://spotty.cloud).
- Read [this](https://medium.com/@apls/how-to-train-deep-learning-models-on-aws-spot-instances-using-spotty-8d9e0543d365)
article on Medium for a real-world example.
## Installation
Requirements:
* Python >=3.6
* AWS CLI (see [Installing the AWS Command Line Interface](http://docs.aws.amazon.com/cli/latest/userguide/installing.html))
if you're using AWS
* Google Cloud SDK (see [Installing Google Cloud SDK](https://cloud.google.com/sdk/install))
if you're using GCP
Use [pip](http://www.pip-installer.org/en/latest/) to install or upgrade Spotty:
$ pip install -U spotty
## Get Started
1. Prepare a `spotty.yaml` file and put it to the root directory of your project:
- See the file specification [here](https://spotty.cloud/docs/user-guide/configuration-file.html).
- Read [this](https://medium.com/@apls/how-to-train-deep-learning-models-on-aws-spot-instances-using-spotty-8d9e0543d365)
article for a real-world example.
2. Start an instance:
```bash
$ spotty start
```
It will run a Spot Instance, restore snapshots if any, synchronize the project with the running instance
and start the Docker container with the environment.
3. Train a model or run notebooks.
To connect to the running container via SSH, use the following command:
```bash
$ spotty sh
```
It runs a [tmux](https://github.com/tmux/tmux/wiki) session, so you can always detach this session using
__`Ctrl + b`__, then __`d`__ combination of keys. To be attached to that session later, just use the
`spotty sh` command again.
Also, you can run your custom scripts inside the Docker container using the `spotty run ` command. Read more
about custom scripts in the documentation:
[Configuration: "scripts" section](https://spotty.cloud/docs/configuration-file/#scripts-section-optional).
## Contributions
Any feedback or contributions are welcome! Please check out the [guidelines](CONTRIBUTING.md).
## License
[MIT License](LICENSE)
%package help
Summary: Development documents and examples for spotty
Provides: python3-spotty-doc
%description help
[](https://spotty.cloud)
[](https://pypi.org/project/spotty/)
Spotty drastically simplifies training of deep learning models on [AWS](https://aws.amazon.com/)
and [GCP](https://cloud.google.com/):
- it makes training on GPU instances as simple as training on your local machine
- it automatically manages all necessary cloud resources including images, volumes, snapshots and SSH keys
- it makes your model trainable in the cloud by everyone with a couple of commands
- it uses [tmux](https://en.wikipedia.org/wiki/Tmux) to easily detach remote processes from their terminals
- it saves you up to 70% of the costs by using [AWS Spot Instances](https://aws.amazon.com/ec2/spot/)
and [GCP Preemtible VMs](https://cloud.google.com/preemptible-vms/)
## Documentation
- See the [documentation page](https://spotty.cloud).
- Read [this](https://medium.com/@apls/how-to-train-deep-learning-models-on-aws-spot-instances-using-spotty-8d9e0543d365)
article on Medium for a real-world example.
## Installation
Requirements:
* Python >=3.6
* AWS CLI (see [Installing the AWS Command Line Interface](http://docs.aws.amazon.com/cli/latest/userguide/installing.html))
if you're using AWS
* Google Cloud SDK (see [Installing Google Cloud SDK](https://cloud.google.com/sdk/install))
if you're using GCP
Use [pip](http://www.pip-installer.org/en/latest/) to install or upgrade Spotty:
$ pip install -U spotty
## Get Started
1. Prepare a `spotty.yaml` file and put it to the root directory of your project:
- See the file specification [here](https://spotty.cloud/docs/user-guide/configuration-file.html).
- Read [this](https://medium.com/@apls/how-to-train-deep-learning-models-on-aws-spot-instances-using-spotty-8d9e0543d365)
article for a real-world example.
2. Start an instance:
```bash
$ spotty start
```
It will run a Spot Instance, restore snapshots if any, synchronize the project with the running instance
and start the Docker container with the environment.
3. Train a model or run notebooks.
To connect to the running container via SSH, use the following command:
```bash
$ spotty sh
```
It runs a [tmux](https://github.com/tmux/tmux/wiki) session, so you can always detach this session using
__`Ctrl + b`__, then __`d`__ combination of keys. To be attached to that session later, just use the
`spotty sh` command again.
Also, you can run your custom scripts inside the Docker container using the `spotty run ` command. Read more
about custom scripts in the documentation:
[Configuration: "scripts" section](https://spotty.cloud/docs/configuration-file/#scripts-section-optional).
## Contributions
Any feedback or contributions are welcome! Please check out the [guidelines](CONTRIBUTING.md).
## License
[MIT License](LICENSE)
%prep
%autosetup -n spotty-1.3.3
%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-spotty -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue May 30 2023 Python_Bot - 1.3.3-1
- Package Spec generated