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+/aihwkit-0.7.1.tar.gz
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+%global _empty_manifest_terminate_build 0
+Name:		python-aihwkit
+Version:	0.7.1
+Release:	1
+Summary:	IBM Analog Hardware Acceleration Kit
+License:	Apache 2.0
+URL:		https://github.com/IBM/aihwkit
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/45/37/9090df4bfa50b1ce6924052c99a5d6b6b91d6ee58b27df4089213e431e61/aihwkit-0.7.1.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-torch
+Requires:	python3-torchvision
+Requires:	python3-scipy
+Requires:	python3-requests
+Requires:	python3-numpy
+Requires:	python3-protobuf
+Requires:	python3-transformers
+Requires:	python3-evaluate
+Requires:	python3-datasets
+Requires:	python3-wandb
+Requires:	python3-tensorboard
+Requires:	python3-lmfit
+Requires:	python3-matplotlib
+
+%description
+# IBM Analog Hardware Acceleration Kit
+
+![PyPI](https://img.shields.io/pypi/v/aihwkit)
+[![Documentation Status](https://readthedocs.org/projects/aihwkit/badge/?version=latest)](https://aihwkit.readthedocs.io/en/latest/?badge=latest)
+[![Build Status](https://travis-ci.com/IBM/aihwkit.svg?branch=master)](https://travis-ci.com/IBM/aihwkit)
+![PyPI - License](https://img.shields.io/pypi/l/aihwkit)
+[![arXiv](https://img.shields.io/badge/arXiv-2104.02184-green.svg)](https://arxiv.org/abs/2104.02184)
+
+## Description
+
+_IBM Analog Hardware Acceleration Kit_ is an open source Python toolkit for
+exploring and using the capabilities of in-memory computing devices in the
+context of artificial intelligence.
+
+> :warning: This library is currently in beta and under active development.
+> Please be mindful of potential issues and keep an eye for improvements,
+> new features and bug fixes in upcoming versions.
+
+The toolkit consists of two main components:
+
+### Pytorch integration
+
+A series of primitives and features that allow using the toolkit within
+[`PyTorch`]:
+
+* Analog neural network modules (fully connected layer, 1d/2d/3d convolution
+  layers, LSTM layer, sequential container).
+* Analog training using torch training workflow:
+  * Analog torch optimizers (SGD).
+  * Analog in-situ training using customizable device models and algorithms
+    (Tiki-Taka).
+* Analog inference using torch inference workflow:
+  * State-of-the-art statistical model of a phase-change memory (PCM) array
+    calibrated on hardware measurements from a 1 million PCM devices chip.
+  * Hardware-aware training with hardware non-idealities and noise
+    included in the forward pass to make the trained models more
+    robust during inference on Analog hardware.
+
+### Analog devices simulator
+
+A high-performant (CUDA-capable) C++ simulator that allows for
+simulating a wide range of analog devices and crossbar configurations
+by using abstract functional models of material characteristics with
+adjustable parameters. Features include:
+
+* Forward pass output-referred noise and device fluctuations, as well
+  as adjustable ADC and DAC discretization and bounds
+* Stochastic update pulse trains for rows and columns with finite
+  weight update size per pulse coincidence
+* Device-to-device systematic variations, cycle-to-cycle noise and
+  adjustable asymmetry during analog update
+* Adjustable device behavior for exploration of material specifications for
+  training and inference
+* State-of-the-art dynamic input scaling, bound management, and update
+  management schemes
+
+### Other features
+
+Along with the two main components, the toolkit includes other
+functionalities such as:
+
+* A library of device presets that are calibrated to real hardware data and
+  based on models in the literature, along with configuration that specifies a particular device and optimizer choice.
+* A module for executing high-level use cases ("experiments"), such as neural
+  network training with minimal code overhead.
+* A utility to automatically convert a downloaded model (e.g., pre-trained) to its equivalent Analog
+  model by replacing all linear/conv layers to Analog layers (e.g., for convenient hardware-aware training).
+* Integration with the [AIHW Composer] platform, a no-code web experience, that allows executing
+  experiments in the cloud.
+
+## Example
+
+### Training example
+
+```python
+from torch import Tensor
+from torch.nn.functional import mse_loss
+
+# Import the aihwkit constructs.
+from aihwkit.nn import AnalogLinear
+from aihwkit.optim import AnalogSGD
+
+x = Tensor([[0.1, 0.2, 0.4, 0.3], [0.2, 0.1, 0.1, 0.3]])
+y = Tensor([[1.0, 0.5], [0.7, 0.3]])
+
+# Define a network using a single Analog layer.
+model = AnalogLinear(4, 2)
+
+# Use the analog-aware stochastic gradient descent optimizer.
+opt = AnalogSGD(model.parameters(), lr=0.1)
+opt.regroup_param_groups(model)
+
+# Train the network.
+for epoch in range(10):
+    pred = model(x)
+    loss = mse_loss(pred, y)
+    loss.backward()
+
+    opt.step()
+    print('Loss error: {:.16f}'.format(loss))
+```
+
+You can find more examples in the [`examples/`] folder of the project, and
+more information about the library in the [documentation]. Please note that
+the examples have some additional dependencies - you can install them via
+`pip install -r requirements-examples.txt`.
+
+
+## What is Analog AI?
+
+In traditional hardware architecture, computation and memory are siloed in
+different locations. Information is moved back and forth between computation
+and memory units every time an operation is performed, creating a limitation
+called the [von Neumann bottleneck].
+
+Analog AI delivers radical performance improvements by combining compute and
+memory in a single device, eliminating the von Neumann bottleneck. By leveraging
+the physical properties of memory devices, computation happens at the same place
+where the data is stored. Such in-memory computing hardware increases the speed
+and energy-efficiency needed for next generation AI workloads.
+
+## What is an in-memory computing chip?
+
+An in-memory computing chip typically consists of multiple arrays of memory
+devices that communicate with each other. Many types of memory devices such as
+[phase-change memory] (PCM), [resistive random-access memory] (RRAM), and
+[Flash memory] can be used for in-memory computing.
+
+Memory devices have the ability to store synaptic weights in their analog
+charge (Flash) or conductance (PCM, RRAM) state. When these devices are arranged
+in a crossbar configuration, it allows to perform an analog matrix-vector
+multiplication in a single time step, exploiting the advantages of analog
+storage capability and [Kirchhoff’s circuits laws]. You can learn more about
+it in our [online demo].
+
+In deep learning, data propagation through multiple layers of a neural network
+involves a sequence of matrix multiplications, as each layer can be represented
+as a matrix of synaptic weights. The devices are arranged in multiple crossbar
+arrays, creating an artificial neural network where all matrix multiplications
+are performed in-place in an analog manner. This structure allows to run deep
+learning models at reduced energy consumption.
+
+## How to cite?
+
+In case you are using the _IBM Analog Hardware Acceleration Kit_ for
+your research, please cite the AICAS21 paper that describes the toolkit:
+
+> Malte J. Rasch, Diego Moreda, Tayfun Gokmen, Manuel Le Gallo, Fabio Carta,
+> Cindy Goldberg, Kaoutar El Maghraoui, Abu Sebastian, Vijay Narayanan.
+> "A flexible and fast PyTorch toolkit for simulating training and inference on
+> analog crossbar arrays" (2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems)
+>
+> https://ieeexplore.ieee.org/abstract/document/9458494
+
+## Installation
+
+### Installing from PyPI
+
+The preferred way to install this package is by using the
+[Python package index]:
+
+```bash
+$ pip install aihwkit
+```
+
+> :warning: Note that currently we provide CPU-only pre-built packages for
+> specific combinations of architectures and versions, and in some cases a
+> pre-built package might still not be available.
+
+If you encounter any issues during download or want to compile the package
+for your environment, please refer to the [advanced installation] guide.
+That section describes the additional libraries and tools required for
+compiling the sources, using a build system based on `cmake`.
+
+## Authors
+
+IBM Analog Hardware Acceleration Kit has been developed by IBM Research,
+with Malte Rasch, Tayfun Gokmen, Diego Moreda, Manuel Le Gallo-Bourdeau, and Kaoutar El Maghraoui
+as the initial core authors, along with many [contributors].
+
+You can contact us by opening a new issue in the repository, or alternatively
+at the ``aihwkit@us.ibm.com`` email address.
+
+## License
+
+This project is licensed under [Apache License 2.0].
+
+[Apache License 2.0]: LICENSE.txt
+[`CUDA Toolkit`]: https://developer.nvidia.com/accelerated-computing-toolkit
+[`OpenBLAS`]: https://www.openblas.net/
+[Python package index]: https://pypi.org/project/aihwkit
+[`PyTorch`]: https://pytorch.org/
+
+[`examples/`]: examples/
+[documentation]: https://aihwkit.readthedocs.io/
+[contributors]: https://github.com/IBM/aihwkit/graphs/contributors
+[advanced installation]: https://aihwkit.readthedocs.io/en/latest/advanced_install.html
+
+[von Neumann bottleneck]: https://en.wikipedia.org/wiki/Von_Neumann_architecture#Von_Neumann_bottleneck
+[phase-change memory]: https://en.wikipedia.org/wiki/Phase-change_memory
+[resistive random-access memory]: https://en.wikipedia.org/wiki/Resistive_random-access_memory
+[Flash memory]: https://en.wikipedia.org/wiki/Flash_memory
+[Kirchhoff’s circuits laws]: https://en.wikipedia.org/wiki/Kirchhoff%27s_circuit_laws
+[online demo]: https://analog-ai-demo.mybluemix.net/
+[AIHW Composer]: https://aihw-composer.draco.res.ibm.com
+
+
+
+%package -n python3-aihwkit
+Summary:	IBM Analog Hardware Acceleration Kit
+Provides:	python-aihwkit
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-aihwkit
+# IBM Analog Hardware Acceleration Kit
+
+![PyPI](https://img.shields.io/pypi/v/aihwkit)
+[![Documentation Status](https://readthedocs.org/projects/aihwkit/badge/?version=latest)](https://aihwkit.readthedocs.io/en/latest/?badge=latest)
+[![Build Status](https://travis-ci.com/IBM/aihwkit.svg?branch=master)](https://travis-ci.com/IBM/aihwkit)
+![PyPI - License](https://img.shields.io/pypi/l/aihwkit)
+[![arXiv](https://img.shields.io/badge/arXiv-2104.02184-green.svg)](https://arxiv.org/abs/2104.02184)
+
+## Description
+
+_IBM Analog Hardware Acceleration Kit_ is an open source Python toolkit for
+exploring and using the capabilities of in-memory computing devices in the
+context of artificial intelligence.
+
+> :warning: This library is currently in beta and under active development.
+> Please be mindful of potential issues and keep an eye for improvements,
+> new features and bug fixes in upcoming versions.
+
+The toolkit consists of two main components:
+
+### Pytorch integration
+
+A series of primitives and features that allow using the toolkit within
+[`PyTorch`]:
+
+* Analog neural network modules (fully connected layer, 1d/2d/3d convolution
+  layers, LSTM layer, sequential container).
+* Analog training using torch training workflow:
+  * Analog torch optimizers (SGD).
+  * Analog in-situ training using customizable device models and algorithms
+    (Tiki-Taka).
+* Analog inference using torch inference workflow:
+  * State-of-the-art statistical model of a phase-change memory (PCM) array
+    calibrated on hardware measurements from a 1 million PCM devices chip.
+  * Hardware-aware training with hardware non-idealities and noise
+    included in the forward pass to make the trained models more
+    robust during inference on Analog hardware.
+
+### Analog devices simulator
+
+A high-performant (CUDA-capable) C++ simulator that allows for
+simulating a wide range of analog devices and crossbar configurations
+by using abstract functional models of material characteristics with
+adjustable parameters. Features include:
+
+* Forward pass output-referred noise and device fluctuations, as well
+  as adjustable ADC and DAC discretization and bounds
+* Stochastic update pulse trains for rows and columns with finite
+  weight update size per pulse coincidence
+* Device-to-device systematic variations, cycle-to-cycle noise and
+  adjustable asymmetry during analog update
+* Adjustable device behavior for exploration of material specifications for
+  training and inference
+* State-of-the-art dynamic input scaling, bound management, and update
+  management schemes
+
+### Other features
+
+Along with the two main components, the toolkit includes other
+functionalities such as:
+
+* A library of device presets that are calibrated to real hardware data and
+  based on models in the literature, along with configuration that specifies a particular device and optimizer choice.
+* A module for executing high-level use cases ("experiments"), such as neural
+  network training with minimal code overhead.
+* A utility to automatically convert a downloaded model (e.g., pre-trained) to its equivalent Analog
+  model by replacing all linear/conv layers to Analog layers (e.g., for convenient hardware-aware training).
+* Integration with the [AIHW Composer] platform, a no-code web experience, that allows executing
+  experiments in the cloud.
+
+## Example
+
+### Training example
+
+```python
+from torch import Tensor
+from torch.nn.functional import mse_loss
+
+# Import the aihwkit constructs.
+from aihwkit.nn import AnalogLinear
+from aihwkit.optim import AnalogSGD
+
+x = Tensor([[0.1, 0.2, 0.4, 0.3], [0.2, 0.1, 0.1, 0.3]])
+y = Tensor([[1.0, 0.5], [0.7, 0.3]])
+
+# Define a network using a single Analog layer.
+model = AnalogLinear(4, 2)
+
+# Use the analog-aware stochastic gradient descent optimizer.
+opt = AnalogSGD(model.parameters(), lr=0.1)
+opt.regroup_param_groups(model)
+
+# Train the network.
+for epoch in range(10):
+    pred = model(x)
+    loss = mse_loss(pred, y)
+    loss.backward()
+
+    opt.step()
+    print('Loss error: {:.16f}'.format(loss))
+```
+
+You can find more examples in the [`examples/`] folder of the project, and
+more information about the library in the [documentation]. Please note that
+the examples have some additional dependencies - you can install them via
+`pip install -r requirements-examples.txt`.
+
+
+## What is Analog AI?
+
+In traditional hardware architecture, computation and memory are siloed in
+different locations. Information is moved back and forth between computation
+and memory units every time an operation is performed, creating a limitation
+called the [von Neumann bottleneck].
+
+Analog AI delivers radical performance improvements by combining compute and
+memory in a single device, eliminating the von Neumann bottleneck. By leveraging
+the physical properties of memory devices, computation happens at the same place
+where the data is stored. Such in-memory computing hardware increases the speed
+and energy-efficiency needed for next generation AI workloads.
+
+## What is an in-memory computing chip?
+
+An in-memory computing chip typically consists of multiple arrays of memory
+devices that communicate with each other. Many types of memory devices such as
+[phase-change memory] (PCM), [resistive random-access memory] (RRAM), and
+[Flash memory] can be used for in-memory computing.
+
+Memory devices have the ability to store synaptic weights in their analog
+charge (Flash) or conductance (PCM, RRAM) state. When these devices are arranged
+in a crossbar configuration, it allows to perform an analog matrix-vector
+multiplication in a single time step, exploiting the advantages of analog
+storage capability and [Kirchhoff’s circuits laws]. You can learn more about
+it in our [online demo].
+
+In deep learning, data propagation through multiple layers of a neural network
+involves a sequence of matrix multiplications, as each layer can be represented
+as a matrix of synaptic weights. The devices are arranged in multiple crossbar
+arrays, creating an artificial neural network where all matrix multiplications
+are performed in-place in an analog manner. This structure allows to run deep
+learning models at reduced energy consumption.
+
+## How to cite?
+
+In case you are using the _IBM Analog Hardware Acceleration Kit_ for
+your research, please cite the AICAS21 paper that describes the toolkit:
+
+> Malte J. Rasch, Diego Moreda, Tayfun Gokmen, Manuel Le Gallo, Fabio Carta,
+> Cindy Goldberg, Kaoutar El Maghraoui, Abu Sebastian, Vijay Narayanan.
+> "A flexible and fast PyTorch toolkit for simulating training and inference on
+> analog crossbar arrays" (2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems)
+>
+> https://ieeexplore.ieee.org/abstract/document/9458494
+
+## Installation
+
+### Installing from PyPI
+
+The preferred way to install this package is by using the
+[Python package index]:
+
+```bash
+$ pip install aihwkit
+```
+
+> :warning: Note that currently we provide CPU-only pre-built packages for
+> specific combinations of architectures and versions, and in some cases a
+> pre-built package might still not be available.
+
+If you encounter any issues during download or want to compile the package
+for your environment, please refer to the [advanced installation] guide.
+That section describes the additional libraries and tools required for
+compiling the sources, using a build system based on `cmake`.
+
+## Authors
+
+IBM Analog Hardware Acceleration Kit has been developed by IBM Research,
+with Malte Rasch, Tayfun Gokmen, Diego Moreda, Manuel Le Gallo-Bourdeau, and Kaoutar El Maghraoui
+as the initial core authors, along with many [contributors].
+
+You can contact us by opening a new issue in the repository, or alternatively
+at the ``aihwkit@us.ibm.com`` email address.
+
+## License
+
+This project is licensed under [Apache License 2.0].
+
+[Apache License 2.0]: LICENSE.txt
+[`CUDA Toolkit`]: https://developer.nvidia.com/accelerated-computing-toolkit
+[`OpenBLAS`]: https://www.openblas.net/
+[Python package index]: https://pypi.org/project/aihwkit
+[`PyTorch`]: https://pytorch.org/
+
+[`examples/`]: examples/
+[documentation]: https://aihwkit.readthedocs.io/
+[contributors]: https://github.com/IBM/aihwkit/graphs/contributors
+[advanced installation]: https://aihwkit.readthedocs.io/en/latest/advanced_install.html
+
+[von Neumann bottleneck]: https://en.wikipedia.org/wiki/Von_Neumann_architecture#Von_Neumann_bottleneck
+[phase-change memory]: https://en.wikipedia.org/wiki/Phase-change_memory
+[resistive random-access memory]: https://en.wikipedia.org/wiki/Resistive_random-access_memory
+[Flash memory]: https://en.wikipedia.org/wiki/Flash_memory
+[Kirchhoff’s circuits laws]: https://en.wikipedia.org/wiki/Kirchhoff%27s_circuit_laws
+[online demo]: https://analog-ai-demo.mybluemix.net/
+[AIHW Composer]: https://aihw-composer.draco.res.ibm.com
+
+
+
+%package help
+Summary:	Development documents and examples for aihwkit
+Provides:	python3-aihwkit-doc
+%description help
+# IBM Analog Hardware Acceleration Kit
+
+![PyPI](https://img.shields.io/pypi/v/aihwkit)
+[![Documentation Status](https://readthedocs.org/projects/aihwkit/badge/?version=latest)](https://aihwkit.readthedocs.io/en/latest/?badge=latest)
+[![Build Status](https://travis-ci.com/IBM/aihwkit.svg?branch=master)](https://travis-ci.com/IBM/aihwkit)
+![PyPI - License](https://img.shields.io/pypi/l/aihwkit)
+[![arXiv](https://img.shields.io/badge/arXiv-2104.02184-green.svg)](https://arxiv.org/abs/2104.02184)
+
+## Description
+
+_IBM Analog Hardware Acceleration Kit_ is an open source Python toolkit for
+exploring and using the capabilities of in-memory computing devices in the
+context of artificial intelligence.
+
+> :warning: This library is currently in beta and under active development.
+> Please be mindful of potential issues and keep an eye for improvements,
+> new features and bug fixes in upcoming versions.
+
+The toolkit consists of two main components:
+
+### Pytorch integration
+
+A series of primitives and features that allow using the toolkit within
+[`PyTorch`]:
+
+* Analog neural network modules (fully connected layer, 1d/2d/3d convolution
+  layers, LSTM layer, sequential container).
+* Analog training using torch training workflow:
+  * Analog torch optimizers (SGD).
+  * Analog in-situ training using customizable device models and algorithms
+    (Tiki-Taka).
+* Analog inference using torch inference workflow:
+  * State-of-the-art statistical model of a phase-change memory (PCM) array
+    calibrated on hardware measurements from a 1 million PCM devices chip.
+  * Hardware-aware training with hardware non-idealities and noise
+    included in the forward pass to make the trained models more
+    robust during inference on Analog hardware.
+
+### Analog devices simulator
+
+A high-performant (CUDA-capable) C++ simulator that allows for
+simulating a wide range of analog devices and crossbar configurations
+by using abstract functional models of material characteristics with
+adjustable parameters. Features include:
+
+* Forward pass output-referred noise and device fluctuations, as well
+  as adjustable ADC and DAC discretization and bounds
+* Stochastic update pulse trains for rows and columns with finite
+  weight update size per pulse coincidence
+* Device-to-device systematic variations, cycle-to-cycle noise and
+  adjustable asymmetry during analog update
+* Adjustable device behavior for exploration of material specifications for
+  training and inference
+* State-of-the-art dynamic input scaling, bound management, and update
+  management schemes
+
+### Other features
+
+Along with the two main components, the toolkit includes other
+functionalities such as:
+
+* A library of device presets that are calibrated to real hardware data and
+  based on models in the literature, along with configuration that specifies a particular device and optimizer choice.
+* A module for executing high-level use cases ("experiments"), such as neural
+  network training with minimal code overhead.
+* A utility to automatically convert a downloaded model (e.g., pre-trained) to its equivalent Analog
+  model by replacing all linear/conv layers to Analog layers (e.g., for convenient hardware-aware training).
+* Integration with the [AIHW Composer] platform, a no-code web experience, that allows executing
+  experiments in the cloud.
+
+## Example
+
+### Training example
+
+```python
+from torch import Tensor
+from torch.nn.functional import mse_loss
+
+# Import the aihwkit constructs.
+from aihwkit.nn import AnalogLinear
+from aihwkit.optim import AnalogSGD
+
+x = Tensor([[0.1, 0.2, 0.4, 0.3], [0.2, 0.1, 0.1, 0.3]])
+y = Tensor([[1.0, 0.5], [0.7, 0.3]])
+
+# Define a network using a single Analog layer.
+model = AnalogLinear(4, 2)
+
+# Use the analog-aware stochastic gradient descent optimizer.
+opt = AnalogSGD(model.parameters(), lr=0.1)
+opt.regroup_param_groups(model)
+
+# Train the network.
+for epoch in range(10):
+    pred = model(x)
+    loss = mse_loss(pred, y)
+    loss.backward()
+
+    opt.step()
+    print('Loss error: {:.16f}'.format(loss))
+```
+
+You can find more examples in the [`examples/`] folder of the project, and
+more information about the library in the [documentation]. Please note that
+the examples have some additional dependencies - you can install them via
+`pip install -r requirements-examples.txt`.
+
+
+## What is Analog AI?
+
+In traditional hardware architecture, computation and memory are siloed in
+different locations. Information is moved back and forth between computation
+and memory units every time an operation is performed, creating a limitation
+called the [von Neumann bottleneck].
+
+Analog AI delivers radical performance improvements by combining compute and
+memory in a single device, eliminating the von Neumann bottleneck. By leveraging
+the physical properties of memory devices, computation happens at the same place
+where the data is stored. Such in-memory computing hardware increases the speed
+and energy-efficiency needed for next generation AI workloads.
+
+## What is an in-memory computing chip?
+
+An in-memory computing chip typically consists of multiple arrays of memory
+devices that communicate with each other. Many types of memory devices such as
+[phase-change memory] (PCM), [resistive random-access memory] (RRAM), and
+[Flash memory] can be used for in-memory computing.
+
+Memory devices have the ability to store synaptic weights in their analog
+charge (Flash) or conductance (PCM, RRAM) state. When these devices are arranged
+in a crossbar configuration, it allows to perform an analog matrix-vector
+multiplication in a single time step, exploiting the advantages of analog
+storage capability and [Kirchhoff’s circuits laws]. You can learn more about
+it in our [online demo].
+
+In deep learning, data propagation through multiple layers of a neural network
+involves a sequence of matrix multiplications, as each layer can be represented
+as a matrix of synaptic weights. The devices are arranged in multiple crossbar
+arrays, creating an artificial neural network where all matrix multiplications
+are performed in-place in an analog manner. This structure allows to run deep
+learning models at reduced energy consumption.
+
+## How to cite?
+
+In case you are using the _IBM Analog Hardware Acceleration Kit_ for
+your research, please cite the AICAS21 paper that describes the toolkit:
+
+> Malte J. Rasch, Diego Moreda, Tayfun Gokmen, Manuel Le Gallo, Fabio Carta,
+> Cindy Goldberg, Kaoutar El Maghraoui, Abu Sebastian, Vijay Narayanan.
+> "A flexible and fast PyTorch toolkit for simulating training and inference on
+> analog crossbar arrays" (2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems)
+>
+> https://ieeexplore.ieee.org/abstract/document/9458494
+
+## Installation
+
+### Installing from PyPI
+
+The preferred way to install this package is by using the
+[Python package index]:
+
+```bash
+$ pip install aihwkit
+```
+
+> :warning: Note that currently we provide CPU-only pre-built packages for
+> specific combinations of architectures and versions, and in some cases a
+> pre-built package might still not be available.
+
+If you encounter any issues during download or want to compile the package
+for your environment, please refer to the [advanced installation] guide.
+That section describes the additional libraries and tools required for
+compiling the sources, using a build system based on `cmake`.
+
+## Authors
+
+IBM Analog Hardware Acceleration Kit has been developed by IBM Research,
+with Malte Rasch, Tayfun Gokmen, Diego Moreda, Manuel Le Gallo-Bourdeau, and Kaoutar El Maghraoui
+as the initial core authors, along with many [contributors].
+
+You can contact us by opening a new issue in the repository, or alternatively
+at the ``aihwkit@us.ibm.com`` email address.
+
+## License
+
+This project is licensed under [Apache License 2.0].
+
+[Apache License 2.0]: LICENSE.txt
+[`CUDA Toolkit`]: https://developer.nvidia.com/accelerated-computing-toolkit
+[`OpenBLAS`]: https://www.openblas.net/
+[Python package index]: https://pypi.org/project/aihwkit
+[`PyTorch`]: https://pytorch.org/
+
+[`examples/`]: examples/
+[documentation]: https://aihwkit.readthedocs.io/
+[contributors]: https://github.com/IBM/aihwkit/graphs/contributors
+[advanced installation]: https://aihwkit.readthedocs.io/en/latest/advanced_install.html
+
+[von Neumann bottleneck]: https://en.wikipedia.org/wiki/Von_Neumann_architecture#Von_Neumann_bottleneck
+[phase-change memory]: https://en.wikipedia.org/wiki/Phase-change_memory
+[resistive random-access memory]: https://en.wikipedia.org/wiki/Resistive_random-access_memory
+[Flash memory]: https://en.wikipedia.org/wiki/Flash_memory
+[Kirchhoff’s circuits laws]: https://en.wikipedia.org/wiki/Kirchhoff%27s_circuit_laws
+[online demo]: https://analog-ai-demo.mybluemix.net/
+[AIHW Composer]: https://aihw-composer.draco.res.ibm.com
+
+
+
+%prep
+%autosetup -n aihwkit-0.7.1
+
+%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-aihwkit -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 17 2023 Python_Bot <Python_Bot@openeuler.org> - 0.7.1-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..216fe94
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+7ef9bdeed21479831251fdadec23fa96  aihwkit-0.7.1.tar.gz