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+/continuum-1.2.7.tar.gz
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+%global _empty_manifest_terminate_build 0
+Name:		python-continuum
+Version:	1.2.7
+Release:	1
+Summary:	A clean and simple library for Continual Learning in PyTorch.
+License:	MIT License
+URL:		https://github.com/Continvvm/continuum
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/39/86/f3ab27dd5d2169e58a92fbf08717fbd584217342a4d5248cfe03fa3297fc/continuum-1.2.7.tar.gz
+BuildArch:	noarch
+
+
+%description
+<div align="center">
+
+# Continuum: Simple Management of Complex Continual Learning Scenarios
+
+[![PyPI version](https://badge.fury.io/py/continuum.svg)](https://badge.fury.io/py/continuum) [![Build Status](https://travis-ci.com/Continvvm/continuum.svg?branch=master)](https://travis-ci.com/Continvvm/continuum) [![Codacy Badge](https://api.codacy.com/project/badge/Grade/c3a31475bebc4036a13e6048c24eb3e0)](https://www.codacy.com/gh/Continvvm/continuum?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=Continvvm/continuum&amp;utm_campaign=Badge_Grade) [![DOI](https://zenodo.org/badge/254864913.svg)](https://zenodo.org/badge/latestdoi/254864913) [![Documentation Status](https://readthedocs.org/projects/continuum/badge/?version=latest)](https://continuum.readthedocs.io/en/latest/?badge=latest)
+[![coverage](coverage.svg)]()
+
+[![Doc](https://img.shields.io/badge/Documentation-link-blue)](https://continuum.readthedocs.io/)
+[![Paper](https://img.shields.io/badge/arXiv-2102.06253-brightgreen)](https://arxiv.org/abs/2102.06253)
+[![Youtube](https://img.shields.io/badge/Youtube-link-purple)](https://www.youtube.com/watch?v=ntSR5oYKyhM)
+</div>
+
+## A library for PyTorch's loading of datasets in the field of Continual Learning
+
+Aka Continual Learning, Lifelong-Learning, Incremental Learning, etc.
+
+Read the [documentation](https://continuum.readthedocs.io/en/latest/). <br>
+Test Continuum on [Colab](https://colab.research.google.com/drive/1bRx3M1YFcol9RZxBZ51brxqGWrf4-Bzn?usp=sharing) !
+
+### Example:
+
+Install from and PyPi:
+```bash
+pip3 install continuum
+```
+
+And run!
+```python
+from torch.utils.data import DataLoader
+
+from continuum import ClassIncremental
+from continuum.datasets import MNIST
+from continuum.tasks import split_train_val
+
+dataset = MNIST("my/data/path", download=True, train=True)
+scenario = ClassIncremental(
+    dataset,
+    increment=1,
+    initial_increment=5
+)
+
+print(f"Number of classes: {scenario.nb_classes}.")
+print(f"Number of tasks: {scenario.nb_tasks}.")
+
+for task_id, train_taskset in enumerate(scenario):
+    train_taskset, val_taskset = split_train_val(train_taskset, val_split=0.1)
+    train_loader = DataLoader(train_taskset, batch_size=32, shuffle=True)
+    val_loader = DataLoader(val_taskset, batch_size=32, shuffle=True)
+
+    for x, y, t in train_loader:
+        # Do your cool stuff here
+```
+
+### Supported Types of Scenarios
+
+|Name | Acronym | Supported | Scenario |
+|:----|:---|:---:|:---:|
+| **New Instances** | NI | :white_check_mark: | [Instances Incremental](https://continuum.readthedocs.io/en/latest/_tutorials/scenarios/scenarios.html#instance-incremental)|
+| **New Classes** | NC | :white_check_mark: |[Classes Incremental](https://continuum.readthedocs.io/en/latest/_tutorials/scenarios/scenarios.html#classes-incremental)|
+| **New Instances & Classes** | NIC | :white_check_mark: | [Data Incremental](https://continuum.readthedocs.io/en/latest/_tutorials/scenarios/scenarios.html#new-class-and-instance-incremental)|
+
+### Supported Datasets:
+
+Most dataset from [torchvision.dasasets](https://pytorch.org/docs/stable/torchvision/datasets.html) are supported, for the complete list, look at the documentation page on datasets [here](https://continuum.readthedocs.io/en/latest/_tutorials/datasets/dataset.html).
+
+Furthermore some "Meta"-datasets are can be create or used from numpy array or any torchvision.datasets or from a folder for datasets having a tree-like structure or by combining several dataset and creating dataset fellowships!
+
+### Indexing
+
+All our continual loader are iterable (i.e. you can for loop on them), and are
+also indexable.
+
+Meaning that `clloader[2]` returns the third task (index starts at 0). Likewise,
+if you want to evaluate after each task, on all seen tasks do `clloader_test[:n]`.
+
+### Example of Sample Images from a Continuum scenario
+
+**CIFAR10**:
+
+|<img src="images/cifar10_0.jpg" width="150">|<img src="images/cifar10_1.jpg" width="150">|<img src="images/cifar10_2.jpg" width="150">|<img src="images/cifar10_3.jpg" width="150">|<img src="images/cifar10_4.jpg" width="150">|
+|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|
+|Task 0 | Task 1 | Task 2 | Task 3 | Task 4|
+
+**MNIST Fellowship (MNIST + FashionMNIST + KMNIST)**:
+
+|<img src="images/mnist_fellowship_0.jpg" width="150">|<img src="images/mnist_fellowship_1.jpg" width="150">|<img src="images/mnist_fellowship_2.jpg" width="150">|
+|:-------------------------:|:-------------------------:|:-------------------------:|
+|Task 0 | Task 1 | Task 2 |
+
+
+**PermutedMNIST**:
+
+|<img src="images/mnist_permuted_0.jpg" width="150">|<img src="images/mnist_permuted_1.jpg" width="150">|<img src="images/mnist_permuted_2.jpg" width="150">|<img src="images/mnist_permuted_3.jpg" width="150">|<img src="images/mnist_permuted_4.jpg" width="150">|
+|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|
+|Task 0 | Task 1 | Task 2 | Task 3 | Task 4|
+
+**RotatedMNIST**:
+
+|<img src="images/mnist_rotated_0.jpg" width="150">|<img src="images/mnist_rotated_1.jpg" width="150">|<img src="images/mnist_rotated_2.jpg" width="150">|<img src="images/mnist_rotated_3.jpg" width="150">|<img src="images/mnist_rotated_4.jpg" width="150">|
+|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|
+|Task 0 | Task 1 | Task 2 | Task 3 | Task 4|
+
+**TransformIncremental + BackgroundSwap**:
+
+|<img src="images/background_0.jpg" width="250">|<img src="images/background_1.jpg" width="250">|<img src="images/background_2.jpg" width="250">|
+|:-------------------------:|:-------------------------:|:-------------------------:|
+|Task 0 | Task 1 | Task 2 |
+
+### Citation
+
+If you find this library useful in your work, please consider citing it:
+
+```
+@misc{douillardlesort2021continuum,
+  author={Douillard, Arthur and Lesort, Timothée},
+  title={Continuum: Simple Management of Complex Continual Learning Scenarios},
+  publisher={arXiv: 2102.06253},
+  year={2021}
+}
+```
+
+
+### Maintainers
+
+This project was started by a joint effort from [Arthur Douillard](https://arthurdouillard.com/) &
+[Timothée Lesort](https://tlesort.github.io/), and we are currently the two maintainers.
+
+Feel free to contribute! If you want to propose new features, please create an issue.
+
+Contributors: [Lucas Caccia](https://github.com/pclucas14) [Lucas Cecchi](https://github.com/Lucasc-99) [Pau Rodriguez](https://github.com/prlz77), [Yury Antonov](https://github.com/yantonov),
+[psychicmario](https://github.com/psychicmario), [fcld94](https://github.com/fcdl94), [Ashok Arjun](https://github.com/ashok-arjun), [Md Rifat Arefin](https://github.com/rarefin), [DanieleMugnai](https://github.com/mugnaidaniele), [Xiaohan Zou](https://github.com/Renovamen), [Umberto Cappellazzo](https://github.com/umbertocappellazzo).
+
+
+### On PyPi
+
+Our project is available on PyPi!
+
+```bash
+pip3 install continuum
+```
+
+Note that previously another project, a CI tool, was using that name. It is now
+there [continuum_ci](https://pypi.org/project/continuum_ci/).
+
+
+%package -n python3-continuum
+Summary:	A clean and simple library for Continual Learning in PyTorch.
+Provides:	python-continuum
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-continuum
+<div align="center">
+
+# Continuum: Simple Management of Complex Continual Learning Scenarios
+
+[![PyPI version](https://badge.fury.io/py/continuum.svg)](https://badge.fury.io/py/continuum) [![Build Status](https://travis-ci.com/Continvvm/continuum.svg?branch=master)](https://travis-ci.com/Continvvm/continuum) [![Codacy Badge](https://api.codacy.com/project/badge/Grade/c3a31475bebc4036a13e6048c24eb3e0)](https://www.codacy.com/gh/Continvvm/continuum?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=Continvvm/continuum&amp;utm_campaign=Badge_Grade) [![DOI](https://zenodo.org/badge/254864913.svg)](https://zenodo.org/badge/latestdoi/254864913) [![Documentation Status](https://readthedocs.org/projects/continuum/badge/?version=latest)](https://continuum.readthedocs.io/en/latest/?badge=latest)
+[![coverage](coverage.svg)]()
+
+[![Doc](https://img.shields.io/badge/Documentation-link-blue)](https://continuum.readthedocs.io/)
+[![Paper](https://img.shields.io/badge/arXiv-2102.06253-brightgreen)](https://arxiv.org/abs/2102.06253)
+[![Youtube](https://img.shields.io/badge/Youtube-link-purple)](https://www.youtube.com/watch?v=ntSR5oYKyhM)
+</div>
+
+## A library for PyTorch's loading of datasets in the field of Continual Learning
+
+Aka Continual Learning, Lifelong-Learning, Incremental Learning, etc.
+
+Read the [documentation](https://continuum.readthedocs.io/en/latest/). <br>
+Test Continuum on [Colab](https://colab.research.google.com/drive/1bRx3M1YFcol9RZxBZ51brxqGWrf4-Bzn?usp=sharing) !
+
+### Example:
+
+Install from and PyPi:
+```bash
+pip3 install continuum
+```
+
+And run!
+```python
+from torch.utils.data import DataLoader
+
+from continuum import ClassIncremental
+from continuum.datasets import MNIST
+from continuum.tasks import split_train_val
+
+dataset = MNIST("my/data/path", download=True, train=True)
+scenario = ClassIncremental(
+    dataset,
+    increment=1,
+    initial_increment=5
+)
+
+print(f"Number of classes: {scenario.nb_classes}.")
+print(f"Number of tasks: {scenario.nb_tasks}.")
+
+for task_id, train_taskset in enumerate(scenario):
+    train_taskset, val_taskset = split_train_val(train_taskset, val_split=0.1)
+    train_loader = DataLoader(train_taskset, batch_size=32, shuffle=True)
+    val_loader = DataLoader(val_taskset, batch_size=32, shuffle=True)
+
+    for x, y, t in train_loader:
+        # Do your cool stuff here
+```
+
+### Supported Types of Scenarios
+
+|Name | Acronym | Supported | Scenario |
+|:----|:---|:---:|:---:|
+| **New Instances** | NI | :white_check_mark: | [Instances Incremental](https://continuum.readthedocs.io/en/latest/_tutorials/scenarios/scenarios.html#instance-incremental)|
+| **New Classes** | NC | :white_check_mark: |[Classes Incremental](https://continuum.readthedocs.io/en/latest/_tutorials/scenarios/scenarios.html#classes-incremental)|
+| **New Instances & Classes** | NIC | :white_check_mark: | [Data Incremental](https://continuum.readthedocs.io/en/latest/_tutorials/scenarios/scenarios.html#new-class-and-instance-incremental)|
+
+### Supported Datasets:
+
+Most dataset from [torchvision.dasasets](https://pytorch.org/docs/stable/torchvision/datasets.html) are supported, for the complete list, look at the documentation page on datasets [here](https://continuum.readthedocs.io/en/latest/_tutorials/datasets/dataset.html).
+
+Furthermore some "Meta"-datasets are can be create or used from numpy array or any torchvision.datasets or from a folder for datasets having a tree-like structure or by combining several dataset and creating dataset fellowships!
+
+### Indexing
+
+All our continual loader are iterable (i.e. you can for loop on them), and are
+also indexable.
+
+Meaning that `clloader[2]` returns the third task (index starts at 0). Likewise,
+if you want to evaluate after each task, on all seen tasks do `clloader_test[:n]`.
+
+### Example of Sample Images from a Continuum scenario
+
+**CIFAR10**:
+
+|<img src="images/cifar10_0.jpg" width="150">|<img src="images/cifar10_1.jpg" width="150">|<img src="images/cifar10_2.jpg" width="150">|<img src="images/cifar10_3.jpg" width="150">|<img src="images/cifar10_4.jpg" width="150">|
+|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|
+|Task 0 | Task 1 | Task 2 | Task 3 | Task 4|
+
+**MNIST Fellowship (MNIST + FashionMNIST + KMNIST)**:
+
+|<img src="images/mnist_fellowship_0.jpg" width="150">|<img src="images/mnist_fellowship_1.jpg" width="150">|<img src="images/mnist_fellowship_2.jpg" width="150">|
+|:-------------------------:|:-------------------------:|:-------------------------:|
+|Task 0 | Task 1 | Task 2 |
+
+
+**PermutedMNIST**:
+
+|<img src="images/mnist_permuted_0.jpg" width="150">|<img src="images/mnist_permuted_1.jpg" width="150">|<img src="images/mnist_permuted_2.jpg" width="150">|<img src="images/mnist_permuted_3.jpg" width="150">|<img src="images/mnist_permuted_4.jpg" width="150">|
+|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|
+|Task 0 | Task 1 | Task 2 | Task 3 | Task 4|
+
+**RotatedMNIST**:
+
+|<img src="images/mnist_rotated_0.jpg" width="150">|<img src="images/mnist_rotated_1.jpg" width="150">|<img src="images/mnist_rotated_2.jpg" width="150">|<img src="images/mnist_rotated_3.jpg" width="150">|<img src="images/mnist_rotated_4.jpg" width="150">|
+|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|
+|Task 0 | Task 1 | Task 2 | Task 3 | Task 4|
+
+**TransformIncremental + BackgroundSwap**:
+
+|<img src="images/background_0.jpg" width="250">|<img src="images/background_1.jpg" width="250">|<img src="images/background_2.jpg" width="250">|
+|:-------------------------:|:-------------------------:|:-------------------------:|
+|Task 0 | Task 1 | Task 2 |
+
+### Citation
+
+If you find this library useful in your work, please consider citing it:
+
+```
+@misc{douillardlesort2021continuum,
+  author={Douillard, Arthur and Lesort, Timothée},
+  title={Continuum: Simple Management of Complex Continual Learning Scenarios},
+  publisher={arXiv: 2102.06253},
+  year={2021}
+}
+```
+
+
+### Maintainers
+
+This project was started by a joint effort from [Arthur Douillard](https://arthurdouillard.com/) &
+[Timothée Lesort](https://tlesort.github.io/), and we are currently the two maintainers.
+
+Feel free to contribute! If you want to propose new features, please create an issue.
+
+Contributors: [Lucas Caccia](https://github.com/pclucas14) [Lucas Cecchi](https://github.com/Lucasc-99) [Pau Rodriguez](https://github.com/prlz77), [Yury Antonov](https://github.com/yantonov),
+[psychicmario](https://github.com/psychicmario), [fcld94](https://github.com/fcdl94), [Ashok Arjun](https://github.com/ashok-arjun), [Md Rifat Arefin](https://github.com/rarefin), [DanieleMugnai](https://github.com/mugnaidaniele), [Xiaohan Zou](https://github.com/Renovamen), [Umberto Cappellazzo](https://github.com/umbertocappellazzo).
+
+
+### On PyPi
+
+Our project is available on PyPi!
+
+```bash
+pip3 install continuum
+```
+
+Note that previously another project, a CI tool, was using that name. It is now
+there [continuum_ci](https://pypi.org/project/continuum_ci/).
+
+
+%package help
+Summary:	Development documents and examples for continuum
+Provides:	python3-continuum-doc
+%description help
+<div align="center">
+
+# Continuum: Simple Management of Complex Continual Learning Scenarios
+
+[![PyPI version](https://badge.fury.io/py/continuum.svg)](https://badge.fury.io/py/continuum) [![Build Status](https://travis-ci.com/Continvvm/continuum.svg?branch=master)](https://travis-ci.com/Continvvm/continuum) [![Codacy Badge](https://api.codacy.com/project/badge/Grade/c3a31475bebc4036a13e6048c24eb3e0)](https://www.codacy.com/gh/Continvvm/continuum?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=Continvvm/continuum&amp;utm_campaign=Badge_Grade) [![DOI](https://zenodo.org/badge/254864913.svg)](https://zenodo.org/badge/latestdoi/254864913) [![Documentation Status](https://readthedocs.org/projects/continuum/badge/?version=latest)](https://continuum.readthedocs.io/en/latest/?badge=latest)
+[![coverage](coverage.svg)]()
+
+[![Doc](https://img.shields.io/badge/Documentation-link-blue)](https://continuum.readthedocs.io/)
+[![Paper](https://img.shields.io/badge/arXiv-2102.06253-brightgreen)](https://arxiv.org/abs/2102.06253)
+[![Youtube](https://img.shields.io/badge/Youtube-link-purple)](https://www.youtube.com/watch?v=ntSR5oYKyhM)
+</div>
+
+## A library for PyTorch's loading of datasets in the field of Continual Learning
+
+Aka Continual Learning, Lifelong-Learning, Incremental Learning, etc.
+
+Read the [documentation](https://continuum.readthedocs.io/en/latest/). <br>
+Test Continuum on [Colab](https://colab.research.google.com/drive/1bRx3M1YFcol9RZxBZ51brxqGWrf4-Bzn?usp=sharing) !
+
+### Example:
+
+Install from and PyPi:
+```bash
+pip3 install continuum
+```
+
+And run!
+```python
+from torch.utils.data import DataLoader
+
+from continuum import ClassIncremental
+from continuum.datasets import MNIST
+from continuum.tasks import split_train_val
+
+dataset = MNIST("my/data/path", download=True, train=True)
+scenario = ClassIncremental(
+    dataset,
+    increment=1,
+    initial_increment=5
+)
+
+print(f"Number of classes: {scenario.nb_classes}.")
+print(f"Number of tasks: {scenario.nb_tasks}.")
+
+for task_id, train_taskset in enumerate(scenario):
+    train_taskset, val_taskset = split_train_val(train_taskset, val_split=0.1)
+    train_loader = DataLoader(train_taskset, batch_size=32, shuffle=True)
+    val_loader = DataLoader(val_taskset, batch_size=32, shuffle=True)
+
+    for x, y, t in train_loader:
+        # Do your cool stuff here
+```
+
+### Supported Types of Scenarios
+
+|Name | Acronym | Supported | Scenario |
+|:----|:---|:---:|:---:|
+| **New Instances** | NI | :white_check_mark: | [Instances Incremental](https://continuum.readthedocs.io/en/latest/_tutorials/scenarios/scenarios.html#instance-incremental)|
+| **New Classes** | NC | :white_check_mark: |[Classes Incremental](https://continuum.readthedocs.io/en/latest/_tutorials/scenarios/scenarios.html#classes-incremental)|
+| **New Instances & Classes** | NIC | :white_check_mark: | [Data Incremental](https://continuum.readthedocs.io/en/latest/_tutorials/scenarios/scenarios.html#new-class-and-instance-incremental)|
+
+### Supported Datasets:
+
+Most dataset from [torchvision.dasasets](https://pytorch.org/docs/stable/torchvision/datasets.html) are supported, for the complete list, look at the documentation page on datasets [here](https://continuum.readthedocs.io/en/latest/_tutorials/datasets/dataset.html).
+
+Furthermore some "Meta"-datasets are can be create or used from numpy array or any torchvision.datasets or from a folder for datasets having a tree-like structure or by combining several dataset and creating dataset fellowships!
+
+### Indexing
+
+All our continual loader are iterable (i.e. you can for loop on them), and are
+also indexable.
+
+Meaning that `clloader[2]` returns the third task (index starts at 0). Likewise,
+if you want to evaluate after each task, on all seen tasks do `clloader_test[:n]`.
+
+### Example of Sample Images from a Continuum scenario
+
+**CIFAR10**:
+
+|<img src="images/cifar10_0.jpg" width="150">|<img src="images/cifar10_1.jpg" width="150">|<img src="images/cifar10_2.jpg" width="150">|<img src="images/cifar10_3.jpg" width="150">|<img src="images/cifar10_4.jpg" width="150">|
+|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|
+|Task 0 | Task 1 | Task 2 | Task 3 | Task 4|
+
+**MNIST Fellowship (MNIST + FashionMNIST + KMNIST)**:
+
+|<img src="images/mnist_fellowship_0.jpg" width="150">|<img src="images/mnist_fellowship_1.jpg" width="150">|<img src="images/mnist_fellowship_2.jpg" width="150">|
+|:-------------------------:|:-------------------------:|:-------------------------:|
+|Task 0 | Task 1 | Task 2 |
+
+
+**PermutedMNIST**:
+
+|<img src="images/mnist_permuted_0.jpg" width="150">|<img src="images/mnist_permuted_1.jpg" width="150">|<img src="images/mnist_permuted_2.jpg" width="150">|<img src="images/mnist_permuted_3.jpg" width="150">|<img src="images/mnist_permuted_4.jpg" width="150">|
+|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|
+|Task 0 | Task 1 | Task 2 | Task 3 | Task 4|
+
+**RotatedMNIST**:
+
+|<img src="images/mnist_rotated_0.jpg" width="150">|<img src="images/mnist_rotated_1.jpg" width="150">|<img src="images/mnist_rotated_2.jpg" width="150">|<img src="images/mnist_rotated_3.jpg" width="150">|<img src="images/mnist_rotated_4.jpg" width="150">|
+|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|
+|Task 0 | Task 1 | Task 2 | Task 3 | Task 4|
+
+**TransformIncremental + BackgroundSwap**:
+
+|<img src="images/background_0.jpg" width="250">|<img src="images/background_1.jpg" width="250">|<img src="images/background_2.jpg" width="250">|
+|:-------------------------:|:-------------------------:|:-------------------------:|
+|Task 0 | Task 1 | Task 2 |
+
+### Citation
+
+If you find this library useful in your work, please consider citing it:
+
+```
+@misc{douillardlesort2021continuum,
+  author={Douillard, Arthur and Lesort, Timothée},
+  title={Continuum: Simple Management of Complex Continual Learning Scenarios},
+  publisher={arXiv: 2102.06253},
+  year={2021}
+}
+```
+
+
+### Maintainers
+
+This project was started by a joint effort from [Arthur Douillard](https://arthurdouillard.com/) &
+[Timothée Lesort](https://tlesort.github.io/), and we are currently the two maintainers.
+
+Feel free to contribute! If you want to propose new features, please create an issue.
+
+Contributors: [Lucas Caccia](https://github.com/pclucas14) [Lucas Cecchi](https://github.com/Lucasc-99) [Pau Rodriguez](https://github.com/prlz77), [Yury Antonov](https://github.com/yantonov),
+[psychicmario](https://github.com/psychicmario), [fcld94](https://github.com/fcdl94), [Ashok Arjun](https://github.com/ashok-arjun), [Md Rifat Arefin](https://github.com/rarefin), [DanieleMugnai](https://github.com/mugnaidaniele), [Xiaohan Zou](https://github.com/Renovamen), [Umberto Cappellazzo](https://github.com/umbertocappellazzo).
+
+
+### On PyPi
+
+Our project is available on PyPi!
+
+```bash
+pip3 install continuum
+```
+
+Note that previously another project, a CI tool, was using that name. It is now
+there [continuum_ci](https://pypi.org/project/continuum_ci/).
+
+
+%prep
+%autosetup -n continuum-1.2.7
+
+%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-continuum -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.2.7-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..89f3d71
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+296344c53a87b9fb24782190b7aaddc8  continuum-1.2.7.tar.gz