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@@ -0,0 +1 @@ +/tensornetwork-0.4.6.tar.gz diff --git a/python-tensornetwork.spec b/python-tensornetwork.spec new file mode 100644 index 0000000..04b8f71 --- /dev/null +++ b/python-tensornetwork.spec @@ -0,0 +1,587 @@ +%global _empty_manifest_terminate_build 0 +Name: python-tensornetwork +Version: 0.4.6 +Release: 1 +Summary: A high level tensor network API for accelerated tensor network calculations. +License: Apache 2.0 +URL: http://github.com/google/TensorNetwork +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/93/32/55c2af4e7a6257a55e6598dd199ff3aa3ca567785c67948b2dfeb5019ae7/tensornetwork-0.4.6.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-graphviz +Requires: python3-opt-einsum +Requires: python3-h5py +Requires: python3-scipy + +%description +<img src="https://user-images.githubusercontent.com/8702042/67589472-5a1d0e80-f70d-11e9-8812-64647814ae96.png" width="60%" height="60%"> + +[](https://travis-ci.org/google/TensorNetwork) + + +A tensor network wrapper for TensorFlow, JAX, PyTorch, and Numpy. + +For an overview of tensor networks please see the following: + +- [Matrices as Tensor Network Diagrams](https://www.math3ma.com/blog/matrices-as-tensor-network-diagrams) + + +- [Crash Course in Tensor Networks (video)](https://www.youtube.com/watch?v=YN2YBB0viKo) + +- [Hand-waving and interpretive dance: an introductory course on tensor networks](https://iopscience.iop.org/article/10.1088/1751-8121/aa6dc3) + +- [Tensor Networks in a Nutshell](https://arxiv.org/abs/1708.00006) + +- [A Practical Introduction to Tensor Networks](https://arxiv.org/abs/1306.2164) + +More information can be found in our TensorNetwork papers: + +- [TensorNetwork: A Library for Physics and Machine Learning](https://arxiv.org/abs/1905.01330) + +- [TensorNetwork on TensorFlow: A Spin Chain Application Using Tree Tensor Networks](https://arxiv.org/abs/1905.01331) + +- [TensorNetwork on TensorFlow: Entanglement Renormalization for quantum critical lattice models](https://arxiv.org/abs/1906.12030) + +- [TensorNetwork for Machine Learning](https://arxiv.org/abs/1906.06329) + + +## Installation +``` +pip3 install tensornetwork +``` + +## Documentation + +For details about the TensorNetwork API, see the [reference documentation.](https://tensornetwork.readthedocs.io) + + +## Tutorials + +[Basic API tutorial](https://colab.research.google.com/drive/1Fp9DolkPT-P_Dkg_s9PLbTOKSq64EVSu) + +[Tensor Networks inside Neural Networks using Keras](https://colab.research.google.com/github/google/TensorNetwork/blob/master/colabs/Tensor_Networks_in_Neural_Networks.ipynb) +## Basic Example + +Here, we build a simple 2 node contraction. +```python +import numpy as np +import tensornetwork as tn + +# Create the nodes +a = tn.Node(np.ones((10,))) +b = tn.Node(np.ones((10,))) +edge = a[0] ^ b[0] # Equal to tn.connect(a[0], b[0]) +final_node = tn.contract(edge) +print(final_node.tensor) # Should print 10.0 +``` + +## Optimized Contractions. +Usually, it is more computationally effective to flatten parallel edges before contracting them in order to avoid trace edges. +We have `contract_between` and `contract_parallel` that do this automatically for your convenience. + +```python +# Contract all of the edges between a and b +# and create a new node `c`. +c = tn.contract_between(a, b) +# This is the same as above, but much shorter. +c = a @ b + +# Contract all of edges that are parallel to edge +# (parallel means connected to the same nodes). +c = tn.contract_parallel(edge) +``` + +## Split Node +You can split a node by doing a singular value decomposition. +```python +# This will return two nodes and a tensor of the truncation error. +# The two nodes are the unitary matrices multiplied by the square root of the +# singular values. +# The `left_edges` are the edges that will end up on the `u_s` node, and `right_edges` +# will be on the `vh_s` node. +u_s, vh_s, trun_error = tn.split_node(node, left_edges, right_edges) +# If you want the singular values in it's own node, you can use `split_node_full_svd`. +u, s, vh, trun_error = tn.split_node_full_svd(node, left_edges, right_edges) +``` + +## Node and Edge names. +You can optionally name your nodes/edges. This can be useful for debugging, +as all error messages will print the name of the broken edge/node. +```python +node = tn.Node(np.eye(2), name="Identity Matrix") +print("Name of node: {}".format(node.name)) +edge = tn.connect(node[0], node[1], name="Trace Edge") +print("Name of the edge: {}".format(edge.name)) +# Adding name to a contraction will add the name to the new edge created. +final_result = tn.contract(edge, name="Trace Of Identity") +print("Name of new node after contraction: {}".format(final_result.name)) +``` + +## Named axes. +To make remembering what an axis does easier, you can optionally name a node's axes. +```python +a = tn.Node(np.zeros((2, 2)), axis_names=["alpha", "beta"]) +edge = a["beta"] ^ a["alpha"] +``` + +## Edge reordering. +To assert that your result's axes are in the correct order, you can reorder a node at any time during computation. +```python +a = tn.Node(np.zeros((1, 2, 3))) +e1 = a[0] +e2 = a[1] +e3 = a[2] +a.reorder_edges([e3, e1, e2]) +# If you already know the axis values, you can equivalently do +# a.reorder_axes([2, 0, 1]) +print(a.tensor.shape) # Should print (3, 1, 2) +``` + +## NCON interface. +For a more compact specification of a tensor network and its contraction, there is `ncon()`. For example: +```python +from tensornetwork import ncon +a = np.ones((2, 2)) +b = np.ones((2, 2)) +c = ncon([a, b], [(-1, 1), (1, -2)]) +print(c) +``` + +## Different backend support. +Currently, we support JAX, TensorFlow, PyTorch and NumPy as TensorNetwork backends. +We also support tensors with Abelian symmetries via a `symmetric` backend, see the [reference +documentation](https://tensornetwork.readthedocs.io/en/latest/block_sparse_tutorial.html) for more details. + +To change the default global backend, you can do: +```python +tn.set_default_backend("jax") # tensorflow, pytorch, numpy, symmetric +``` +Or, if you only want to change the backend for a single `Node`, you can do: +```python +tn.Node(tensor, backend="jax") +``` + +If you want to run your contractions on a GPU, we highly recommend using JAX, as it has the closet API to NumPy. + +## Disclaimer +This library is in *alpha* and will be going through a lot of breaking changes. While releases will be stable enough for research, we do not recommend using this in any production environment yet. + +TensorNetwork is not an official Google product. Copyright 2019 The TensorNetwork Developers. + +## Citation +If you are using TensorNetwork for your research please cite this work using the following bibtex entry: + +``` +@misc{roberts2019tensornetwork, + title={TensorNetwork: A Library for Physics and Machine Learning}, + author={Chase Roberts and Ashley Milsted and Martin Ganahl and Adam Zalcman and Bruce Fontaine and Yijian Zou and Jack Hidary and Guifre Vidal and Stefan Leichenauer}, + year={2019}, + eprint={1905.01330}, + archivePrefix={arXiv}, + primaryClass={physics.comp-ph} +} +``` + + + + + +%package -n python3-tensornetwork +Summary: A high level tensor network API for accelerated tensor network calculations. +Provides: python-tensornetwork +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-tensornetwork +<img src="https://user-images.githubusercontent.com/8702042/67589472-5a1d0e80-f70d-11e9-8812-64647814ae96.png" width="60%" height="60%"> + +[](https://travis-ci.org/google/TensorNetwork) + + +A tensor network wrapper for TensorFlow, JAX, PyTorch, and Numpy. + +For an overview of tensor networks please see the following: + +- [Matrices as Tensor Network Diagrams](https://www.math3ma.com/blog/matrices-as-tensor-network-diagrams) + + +- [Crash Course in Tensor Networks (video)](https://www.youtube.com/watch?v=YN2YBB0viKo) + +- [Hand-waving and interpretive dance: an introductory course on tensor networks](https://iopscience.iop.org/article/10.1088/1751-8121/aa6dc3) + +- [Tensor Networks in a Nutshell](https://arxiv.org/abs/1708.00006) + +- [A Practical Introduction to Tensor Networks](https://arxiv.org/abs/1306.2164) + +More information can be found in our TensorNetwork papers: + +- [TensorNetwork: A Library for Physics and Machine Learning](https://arxiv.org/abs/1905.01330) + +- [TensorNetwork on TensorFlow: A Spin Chain Application Using Tree Tensor Networks](https://arxiv.org/abs/1905.01331) + +- [TensorNetwork on TensorFlow: Entanglement Renormalization for quantum critical lattice models](https://arxiv.org/abs/1906.12030) + +- [TensorNetwork for Machine Learning](https://arxiv.org/abs/1906.06329) + + +## Installation +``` +pip3 install tensornetwork +``` + +## Documentation + +For details about the TensorNetwork API, see the [reference documentation.](https://tensornetwork.readthedocs.io) + + +## Tutorials + +[Basic API tutorial](https://colab.research.google.com/drive/1Fp9DolkPT-P_Dkg_s9PLbTOKSq64EVSu) + +[Tensor Networks inside Neural Networks using Keras](https://colab.research.google.com/github/google/TensorNetwork/blob/master/colabs/Tensor_Networks_in_Neural_Networks.ipynb) +## Basic Example + +Here, we build a simple 2 node contraction. +```python +import numpy as np +import tensornetwork as tn + +# Create the nodes +a = tn.Node(np.ones((10,))) +b = tn.Node(np.ones((10,))) +edge = a[0] ^ b[0] # Equal to tn.connect(a[0], b[0]) +final_node = tn.contract(edge) +print(final_node.tensor) # Should print 10.0 +``` + +## Optimized Contractions. +Usually, it is more computationally effective to flatten parallel edges before contracting them in order to avoid trace edges. +We have `contract_between` and `contract_parallel` that do this automatically for your convenience. + +```python +# Contract all of the edges between a and b +# and create a new node `c`. +c = tn.contract_between(a, b) +# This is the same as above, but much shorter. +c = a @ b + +# Contract all of edges that are parallel to edge +# (parallel means connected to the same nodes). +c = tn.contract_parallel(edge) +``` + +## Split Node +You can split a node by doing a singular value decomposition. +```python +# This will return two nodes and a tensor of the truncation error. +# The two nodes are the unitary matrices multiplied by the square root of the +# singular values. +# The `left_edges` are the edges that will end up on the `u_s` node, and `right_edges` +# will be on the `vh_s` node. +u_s, vh_s, trun_error = tn.split_node(node, left_edges, right_edges) +# If you want the singular values in it's own node, you can use `split_node_full_svd`. +u, s, vh, trun_error = tn.split_node_full_svd(node, left_edges, right_edges) +``` + +## Node and Edge names. +You can optionally name your nodes/edges. This can be useful for debugging, +as all error messages will print the name of the broken edge/node. +```python +node = tn.Node(np.eye(2), name="Identity Matrix") +print("Name of node: {}".format(node.name)) +edge = tn.connect(node[0], node[1], name="Trace Edge") +print("Name of the edge: {}".format(edge.name)) +# Adding name to a contraction will add the name to the new edge created. +final_result = tn.contract(edge, name="Trace Of Identity") +print("Name of new node after contraction: {}".format(final_result.name)) +``` + +## Named axes. +To make remembering what an axis does easier, you can optionally name a node's axes. +```python +a = tn.Node(np.zeros((2, 2)), axis_names=["alpha", "beta"]) +edge = a["beta"] ^ a["alpha"] +``` + +## Edge reordering. +To assert that your result's axes are in the correct order, you can reorder a node at any time during computation. +```python +a = tn.Node(np.zeros((1, 2, 3))) +e1 = a[0] +e2 = a[1] +e3 = a[2] +a.reorder_edges([e3, e1, e2]) +# If you already know the axis values, you can equivalently do +# a.reorder_axes([2, 0, 1]) +print(a.tensor.shape) # Should print (3, 1, 2) +``` + +## NCON interface. +For a more compact specification of a tensor network and its contraction, there is `ncon()`. For example: +```python +from tensornetwork import ncon +a = np.ones((2, 2)) +b = np.ones((2, 2)) +c = ncon([a, b], [(-1, 1), (1, -2)]) +print(c) +``` + +## Different backend support. +Currently, we support JAX, TensorFlow, PyTorch and NumPy as TensorNetwork backends. +We also support tensors with Abelian symmetries via a `symmetric` backend, see the [reference +documentation](https://tensornetwork.readthedocs.io/en/latest/block_sparse_tutorial.html) for more details. + +To change the default global backend, you can do: +```python +tn.set_default_backend("jax") # tensorflow, pytorch, numpy, symmetric +``` +Or, if you only want to change the backend for a single `Node`, you can do: +```python +tn.Node(tensor, backend="jax") +``` + +If you want to run your contractions on a GPU, we highly recommend using JAX, as it has the closet API to NumPy. + +## Disclaimer +This library is in *alpha* and will be going through a lot of breaking changes. While releases will be stable enough for research, we do not recommend using this in any production environment yet. + +TensorNetwork is not an official Google product. Copyright 2019 The TensorNetwork Developers. + +## Citation +If you are using TensorNetwork for your research please cite this work using the following bibtex entry: + +``` +@misc{roberts2019tensornetwork, + title={TensorNetwork: A Library for Physics and Machine Learning}, + author={Chase Roberts and Ashley Milsted and Martin Ganahl and Adam Zalcman and Bruce Fontaine and Yijian Zou and Jack Hidary and Guifre Vidal and Stefan Leichenauer}, + year={2019}, + eprint={1905.01330}, + archivePrefix={arXiv}, + primaryClass={physics.comp-ph} +} +``` + + + + + +%package help +Summary: Development documents and examples for tensornetwork +Provides: python3-tensornetwork-doc +%description help +<img src="https://user-images.githubusercontent.com/8702042/67589472-5a1d0e80-f70d-11e9-8812-64647814ae96.png" width="60%" height="60%"> + +[](https://travis-ci.org/google/TensorNetwork) + + +A tensor network wrapper for TensorFlow, JAX, PyTorch, and Numpy. + +For an overview of tensor networks please see the following: + +- [Matrices as Tensor Network Diagrams](https://www.math3ma.com/blog/matrices-as-tensor-network-diagrams) + + +- [Crash Course in Tensor Networks (video)](https://www.youtube.com/watch?v=YN2YBB0viKo) + +- [Hand-waving and interpretive dance: an introductory course on tensor networks](https://iopscience.iop.org/article/10.1088/1751-8121/aa6dc3) + +- [Tensor Networks in a Nutshell](https://arxiv.org/abs/1708.00006) + +- [A Practical Introduction to Tensor Networks](https://arxiv.org/abs/1306.2164) + +More information can be found in our TensorNetwork papers: + +- [TensorNetwork: A Library for Physics and Machine Learning](https://arxiv.org/abs/1905.01330) + +- [TensorNetwork on TensorFlow: A Spin Chain Application Using Tree Tensor Networks](https://arxiv.org/abs/1905.01331) + +- [TensorNetwork on TensorFlow: Entanglement Renormalization for quantum critical lattice models](https://arxiv.org/abs/1906.12030) + +- [TensorNetwork for Machine Learning](https://arxiv.org/abs/1906.06329) + + +## Installation +``` +pip3 install tensornetwork +``` + +## Documentation + +For details about the TensorNetwork API, see the [reference documentation.](https://tensornetwork.readthedocs.io) + + +## Tutorials + +[Basic API tutorial](https://colab.research.google.com/drive/1Fp9DolkPT-P_Dkg_s9PLbTOKSq64EVSu) + +[Tensor Networks inside Neural Networks using Keras](https://colab.research.google.com/github/google/TensorNetwork/blob/master/colabs/Tensor_Networks_in_Neural_Networks.ipynb) +## Basic Example + +Here, we build a simple 2 node contraction. +```python +import numpy as np +import tensornetwork as tn + +# Create the nodes +a = tn.Node(np.ones((10,))) +b = tn.Node(np.ones((10,))) +edge = a[0] ^ b[0] # Equal to tn.connect(a[0], b[0]) +final_node = tn.contract(edge) +print(final_node.tensor) # Should print 10.0 +``` + +## Optimized Contractions. +Usually, it is more computationally effective to flatten parallel edges before contracting them in order to avoid trace edges. +We have `contract_between` and `contract_parallel` that do this automatically for your convenience. + +```python +# Contract all of the edges between a and b +# and create a new node `c`. +c = tn.contract_between(a, b) +# This is the same as above, but much shorter. +c = a @ b + +# Contract all of edges that are parallel to edge +# (parallel means connected to the same nodes). +c = tn.contract_parallel(edge) +``` + +## Split Node +You can split a node by doing a singular value decomposition. +```python +# This will return two nodes and a tensor of the truncation error. +# The two nodes are the unitary matrices multiplied by the square root of the +# singular values. +# The `left_edges` are the edges that will end up on the `u_s` node, and `right_edges` +# will be on the `vh_s` node. +u_s, vh_s, trun_error = tn.split_node(node, left_edges, right_edges) +# If you want the singular values in it's own node, you can use `split_node_full_svd`. +u, s, vh, trun_error = tn.split_node_full_svd(node, left_edges, right_edges) +``` + +## Node and Edge names. +You can optionally name your nodes/edges. This can be useful for debugging, +as all error messages will print the name of the broken edge/node. +```python +node = tn.Node(np.eye(2), name="Identity Matrix") +print("Name of node: {}".format(node.name)) +edge = tn.connect(node[0], node[1], name="Trace Edge") +print("Name of the edge: {}".format(edge.name)) +# Adding name to a contraction will add the name to the new edge created. +final_result = tn.contract(edge, name="Trace Of Identity") +print("Name of new node after contraction: {}".format(final_result.name)) +``` + +## Named axes. +To make remembering what an axis does easier, you can optionally name a node's axes. +```python +a = tn.Node(np.zeros((2, 2)), axis_names=["alpha", "beta"]) +edge = a["beta"] ^ a["alpha"] +``` + +## Edge reordering. +To assert that your result's axes are in the correct order, you can reorder a node at any time during computation. +```python +a = tn.Node(np.zeros((1, 2, 3))) +e1 = a[0] +e2 = a[1] +e3 = a[2] +a.reorder_edges([e3, e1, e2]) +# If you already know the axis values, you can equivalently do +# a.reorder_axes([2, 0, 1]) +print(a.tensor.shape) # Should print (3, 1, 2) +``` + +## NCON interface. +For a more compact specification of a tensor network and its contraction, there is `ncon()`. For example: +```python +from tensornetwork import ncon +a = np.ones((2, 2)) +b = np.ones((2, 2)) +c = ncon([a, b], [(-1, 1), (1, -2)]) +print(c) +``` + +## Different backend support. +Currently, we support JAX, TensorFlow, PyTorch and NumPy as TensorNetwork backends. +We also support tensors with Abelian symmetries via a `symmetric` backend, see the [reference +documentation](https://tensornetwork.readthedocs.io/en/latest/block_sparse_tutorial.html) for more details. + +To change the default global backend, you can do: +```python +tn.set_default_backend("jax") # tensorflow, pytorch, numpy, symmetric +``` +Or, if you only want to change the backend for a single `Node`, you can do: +```python +tn.Node(tensor, backend="jax") +``` + +If you want to run your contractions on a GPU, we highly recommend using JAX, as it has the closet API to NumPy. + +## Disclaimer +This library is in *alpha* and will be going through a lot of breaking changes. While releases will be stable enough for research, we do not recommend using this in any production environment yet. + +TensorNetwork is not an official Google product. Copyright 2019 The TensorNetwork Developers. + +## Citation +If you are using TensorNetwork for your research please cite this work using the following bibtex entry: + +``` +@misc{roberts2019tensornetwork, + title={TensorNetwork: A Library for Physics and Machine Learning}, + author={Chase Roberts and Ashley Milsted and Martin Ganahl and Adam Zalcman and Bruce Fontaine and Yijian Zou and Jack Hidary and Guifre Vidal and Stefan Leichenauer}, + year={2019}, + eprint={1905.01330}, + archivePrefix={arXiv}, + primaryClass={physics.comp-ph} +} +``` + + + + + +%prep +%autosetup -n tensornetwork-0.4.6 + +%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-tensornetwork -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.6-1 +- Package Spec generated @@ -0,0 +1 @@ +185b6051b17d7f90cfee33a1da1ba4ec tensornetwork-0.4.6.tar.gz |