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|
%global _empty_manifest_terminate_build 0
Name: python-thinc
Version: 8.1.9
Release: 1
Summary: A refreshing functional take on deep learning, compatible with your favorite libraries
License: MIT
URL: https://github.com/explosion/thinc
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/31/4d/7c07727e1d4f08e307c367c6574baf5f44fd5c4e1dfb06ea8606d047c02a/thinc-8.1.9.tar.gz
Requires: python3-blis
Requires: python3-murmurhash
Requires: python3-cymem
Requires: python3-preshed
Requires: python3-wasabi
Requires: python3-srsly
Requires: python3-catalogue
Requires: python3-confection
Requires: python3-setuptools
Requires: python3-numpy
Requires: python3-pydantic
Requires: python3-packaging
Requires: python3-dataclasses
Requires: python3-contextvars
Requires: python3-typing-extensions
Requires: python3-cupy
Requires: python3-cupy-wheel
Requires: python3-cupy-cuda100
Requires: python3-cupy-cuda101
Requires: python3-cupy-cuda102
Requires: python3-cupy-cuda110
Requires: python3-cupy-cuda111
Requires: python3-cupy-cuda112
Requires: python3-cupy-cuda113
Requires: python3-cupy-cuda114
Requires: python3-cupy-cuda115
Requires: python3-cupy-cuda116
Requires: python3-cupy-cuda117
Requires: python3-cupy-cuda11x
Requires: python3-cupy-cuda80
Requires: python3-cupy-cuda90
Requires: python3-cupy-cuda91
Requires: python3-cupy-cuda92
Requires: python3-ml-datasets
Requires: python3-mxnet
Requires: python3-tensorflow
Requires: python3-torch
%description
<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>
# Thinc: A refreshing functional take on deep learning, compatible with your favorite libraries
### From the makers of [spaCy](https://spacy.io) and [Prodigy](https://prodi.gy)
[Thinc](https://thinc.ai) is a **lightweight deep learning library** that offers an elegant,
type-checked, functional-programming API for **composing models**, with support
for layers defined in other frameworks such as **PyTorch, TensorFlow and MXNet**. You
can use Thinc as an interface layer, a standalone toolkit or a flexible way to
develop new models. Previous versions of Thinc have been running quietly in
production in thousands of companies, via both [spaCy](https://spacy.io) and
[Prodigy](https://prodi.gy). We wrote the new version to let users **compose,
configure and deploy custom models** built with their favorite framework.
[](https://dev.azure.com/explosion-ai/public/_build?definitionId=7)
[](https://github.com/explosion/thinc/releases)
[](https://pypi.python.org/pypi/thinc)
[](https://anaconda.org/conda-forge/thinc)
[](https://github.com/explosion/wheelwright/releases)
[](https://github.com/ambv/black)
[![Open demo in Colab][colab]][intro_to_thinc_colab]
## 🔥 Features
- **Type-check** your model definitions with custom types and [`mypy`](https://mypy.readthedocs.io/en/latest/) plugin.
- Wrap **PyTorch**, **TensorFlow** and **MXNet** models for use in your network.
- Concise **functional-programming** approach to model definition, using composition rather than inheritance.
- Optional custom infix notation via **operator overloading**.
- Integrated **config system** to describe trees of objects and hyperparameters.
- Choice of **extensible backends**.
- **[Read more →](https://thinc.ai/docs)**
## 🚀 Quickstart
Thinc is compatible with **Python 3.6+** and runs on **Linux**,
**macOS** and **Windows**. The latest releases with binary wheels are available from
[pip](https://pypi.python.org/pypi/thinc). Before you install Thinc and its
dependencies, make sure that your `pip`, `setuptools` and `wheel` are up to
date. For the most recent releases, pip 19.3 or newer is recommended.
```bash
pip install -U pip setuptools wheel
pip install thinc
```
See the [extended installation docs](https://thinc.ai/docs/install#extended) for details on optional dependencies for different backends and GPU. You might also want to [set up static type checking](https://thinc.ai/docs/install#type-checking) to take advantage of Thinc's type system.
> ⚠️ If you have installed PyTorch and you are using Python 3.7+, uninstall the
> package `dataclasses` with `pip uninstall dataclasses`, since it may have
> been installed by PyTorch and is incompatible with Python 3.7+.
### 📓 Selected examples and notebooks
Also see the [`/examples`](examples) directory and [usage documentation](https://thinc.ai/docs) for more examples. Most examples are Jupyter notebooks – to launch them on [Google Colab](https://colab.research.google.com) (with GPU support!) click on the button next to the notebook name.
| Notebook | Description |
| --------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [`intro_to_thinc`][intro_to_thinc]<br />[![Open in Colab][colab]][intro_to_thinc_colab] | Everything you need to know to get started. Composing and training a model on the MNIST data, using config files, registering custom functions and wrapping PyTorch, TensorFlow and MXNet models. |
| [`transformers_tagger_bert`][transformers_tagger_bert]<br />[![Open in Colab][colab]][transformers_tagger_bert_colab] | How to use Thinc, `transformers` and PyTorch to train a part-of-speech tagger. From model definition and config to the training loop. |
| [`pos_tagger_basic_cnn`][pos_tagger_basic_cnn]<br />[![Open in Colab][colab]][pos_tagger_basic_cnn_colab] | Implementing and training a basic CNN for part-of-speech tagging model without external dependencies and using different levels of Thinc's config system. |
| [`parallel_training_ray`][parallel_training_ray]<br />[![Open in Colab][colab]][parallel_training_ray_colab] | How to set up synchronous and asynchronous parameter server training with Thinc and [Ray](https://ray.readthedocs.io/en/latest/). |
**[View more →](examples)**
[colab]: https://gistcdn.githack.com/ines/dcf354aa71a7665ae19871d7fd14a4e0/raw/461fc1f61a7bc5860f943cd4b6bcfabb8c8906e7/colab-badge.svg
[intro_to_thinc]: examples/00_intro_to_thinc.ipynb
[intro_to_thinc_colab]: https://colab.research.google.com/github/explosion/thinc/blob/master/examples/00_intro_to_thinc.ipynb
[transformers_tagger_bert]: examples/02_transformers_tagger_bert.ipynb
[transformers_tagger_bert_colab]: https://colab.research.google.com/github/explosion/thinc/blob/master/examples/02_transformers_tagger_bert.ipynb
[pos_tagger_basic_cnn]: examples/03_pos_tagger_basic_cnn.ipynb
[pos_tagger_basic_cnn_colab]: https://colab.research.google.com/github/explosion/thinc/blob/master/examples/03_pos_tagger_basic_cnn.ipynb
[parallel_training_ray]: examples/04_parallel_training_ray.ipynb
[parallel_training_ray_colab]: https://colab.research.google.com/github/explosion/thinc/blob/master/examples/04_parallel_training_ray.ipynb
### 📖 Documentation & usage guides
| Documentation | Description |
| --------------------------------------------------------------------------------- | ----------------------------------------------------- |
| [Introduction](https://thinc.ai/docs) | Everything you need to know. |
| [Concept & Design](https://thinc.ai/docs/concept) | Thinc's conceptual model and how it works. |
| [Defining and using models](https://thinc.ai/docs/usage-models) | How to compose models and update state. |
| [Configuration system](https://thinc.ai/docs/usage-config) | Thinc's config system and function registry. |
| [Integrating PyTorch, TensorFlow & MXNet](https://thinc.ai/docs/usage-frameworks) | Interoperability with machine learning frameworks |
| [Layers API](https://thinc.ai/docs/api-layers) | Weights layers, transforms, combinators and wrappers. |
| [Type Checking](https://thinc.ai/docs/usage-type-checking) | Type-check your model definitions and more. |
## 🗺 What's where
| Module | Description |
| ----------------------------------------- | --------------------------------------------------------------------------------- |
| [`thinc.api`](thinc/api.py) | **User-facing API.** All classes and functions should be imported from here. |
| [`thinc.types`](thinc/types.py) | Custom [types and dataclasses](https://thinc.ai/docs/api-types). |
| [`thinc.model`](thinc/model.py) | The `Model` class. All Thinc models are an instance (not a subclass) of `Model`. |
| [`thinc.layers`](thinc/layers) | The layers. Each layer is implemented in its own module. |
| [`thinc.shims`](thinc/shims) | Interface for external models implemented in PyTorch, TensorFlow etc. |
| [`thinc.loss`](thinc/loss.py) | Functions to calculate losses. |
| [`thinc.optimizers`](thinc/optimizers.py) | Functions to create optimizers. Currently supports "vanilla" SGD, Adam and RAdam. |
| [`thinc.schedules`](thinc/schedules.py) | Generators for different rates, schedules, decays or series. |
| [`thinc.backends`](thinc/backends) | Backends for `numpy` and `cupy`. |
| [`thinc.config`](thinc/config.py) | Config parsing and validation and function registry system. |
| [`thinc.util`](thinc/util.py) | Utilities and helper functions. |
## 🐍 Development notes
Thinc uses [`black`](https://github.com/psf/black) for auto-formatting, [`flake8`](http://flake8.pycqa.org/en/latest/) for linting and [`mypy`](https://mypy.readthedocs.io/en/latest/) for type checking. All code is written compatible with **Python 3.6+**, with type hints wherever possible. See the [type reference](https://thinc.ai/docs/api-types) for more details on Thinc's custom types.
### 👷♀️ Building Thinc from source
Building Thinc from source requires the full dependencies listed in
[`requirements.txt`](requirements.txt) to be installed. You'll also need a
compiler to build the C extensions.
```bash
git clone https://github.com/explosion/thinc
cd thinc
python -m venv .env
source .env/bin/activate
pip install -U pip setuptools wheel
pip install -r requirements.txt
pip install --no-build-isolation .
```
Alternatively, install in editable mode:
```bash
pip install -r requirements.txt
pip install --no-build-isolation --editable .
```
Or by setting `PYTHONPATH`:
```bash
export PYTHONPATH=`pwd`
pip install -r requirements.txt
python setup.py build_ext --inplace
```
### 🚦 Running tests
Thinc comes with an [extensive test suite](thinc/tests). The following should all pass and not report any warnings or errors:
```bash
python -m pytest thinc # test suite
python -m mypy thinc # type checks
python -m flake8 thinc # linting
```
To view test coverage, you can run `python -m pytest thinc --cov=thinc`. We aim for a 100% test coverage. This doesn't mean that we meticulously write tests for every single line – we ignore blocks that are not relevant or difficult to test and make sure that the tests execute all code paths.
%package -n python3-thinc
Summary: A refreshing functional take on deep learning, compatible with your favorite libraries
Provides: python-thinc
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
BuildRequires: python3-cffi
BuildRequires: gcc
BuildRequires: gdb
%description -n python3-thinc
<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>
# Thinc: A refreshing functional take on deep learning, compatible with your favorite libraries
### From the makers of [spaCy](https://spacy.io) and [Prodigy](https://prodi.gy)
[Thinc](https://thinc.ai) is a **lightweight deep learning library** that offers an elegant,
type-checked, functional-programming API for **composing models**, with support
for layers defined in other frameworks such as **PyTorch, TensorFlow and MXNet**. You
can use Thinc as an interface layer, a standalone toolkit or a flexible way to
develop new models. Previous versions of Thinc have been running quietly in
production in thousands of companies, via both [spaCy](https://spacy.io) and
[Prodigy](https://prodi.gy). We wrote the new version to let users **compose,
configure and deploy custom models** built with their favorite framework.
[](https://dev.azure.com/explosion-ai/public/_build?definitionId=7)
[](https://github.com/explosion/thinc/releases)
[](https://pypi.python.org/pypi/thinc)
[](https://anaconda.org/conda-forge/thinc)
[](https://github.com/explosion/wheelwright/releases)
[](https://github.com/ambv/black)
[![Open demo in Colab][colab]][intro_to_thinc_colab]
## 🔥 Features
- **Type-check** your model definitions with custom types and [`mypy`](https://mypy.readthedocs.io/en/latest/) plugin.
- Wrap **PyTorch**, **TensorFlow** and **MXNet** models for use in your network.
- Concise **functional-programming** approach to model definition, using composition rather than inheritance.
- Optional custom infix notation via **operator overloading**.
- Integrated **config system** to describe trees of objects and hyperparameters.
- Choice of **extensible backends**.
- **[Read more →](https://thinc.ai/docs)**
## 🚀 Quickstart
Thinc is compatible with **Python 3.6+** and runs on **Linux**,
**macOS** and **Windows**. The latest releases with binary wheels are available from
[pip](https://pypi.python.org/pypi/thinc). Before you install Thinc and its
dependencies, make sure that your `pip`, `setuptools` and `wheel` are up to
date. For the most recent releases, pip 19.3 or newer is recommended.
```bash
pip install -U pip setuptools wheel
pip install thinc
```
See the [extended installation docs](https://thinc.ai/docs/install#extended) for details on optional dependencies for different backends and GPU. You might also want to [set up static type checking](https://thinc.ai/docs/install#type-checking) to take advantage of Thinc's type system.
> ⚠️ If you have installed PyTorch and you are using Python 3.7+, uninstall the
> package `dataclasses` with `pip uninstall dataclasses`, since it may have
> been installed by PyTorch and is incompatible with Python 3.7+.
### 📓 Selected examples and notebooks
Also see the [`/examples`](examples) directory and [usage documentation](https://thinc.ai/docs) for more examples. Most examples are Jupyter notebooks – to launch them on [Google Colab](https://colab.research.google.com) (with GPU support!) click on the button next to the notebook name.
| Notebook | Description |
| --------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [`intro_to_thinc`][intro_to_thinc]<br />[![Open in Colab][colab]][intro_to_thinc_colab] | Everything you need to know to get started. Composing and training a model on the MNIST data, using config files, registering custom functions and wrapping PyTorch, TensorFlow and MXNet models. |
| [`transformers_tagger_bert`][transformers_tagger_bert]<br />[![Open in Colab][colab]][transformers_tagger_bert_colab] | How to use Thinc, `transformers` and PyTorch to train a part-of-speech tagger. From model definition and config to the training loop. |
| [`pos_tagger_basic_cnn`][pos_tagger_basic_cnn]<br />[![Open in Colab][colab]][pos_tagger_basic_cnn_colab] | Implementing and training a basic CNN for part-of-speech tagging model without external dependencies and using different levels of Thinc's config system. |
| [`parallel_training_ray`][parallel_training_ray]<br />[![Open in Colab][colab]][parallel_training_ray_colab] | How to set up synchronous and asynchronous parameter server training with Thinc and [Ray](https://ray.readthedocs.io/en/latest/). |
**[View more →](examples)**
[colab]: https://gistcdn.githack.com/ines/dcf354aa71a7665ae19871d7fd14a4e0/raw/461fc1f61a7bc5860f943cd4b6bcfabb8c8906e7/colab-badge.svg
[intro_to_thinc]: examples/00_intro_to_thinc.ipynb
[intro_to_thinc_colab]: https://colab.research.google.com/github/explosion/thinc/blob/master/examples/00_intro_to_thinc.ipynb
[transformers_tagger_bert]: examples/02_transformers_tagger_bert.ipynb
[transformers_tagger_bert_colab]: https://colab.research.google.com/github/explosion/thinc/blob/master/examples/02_transformers_tagger_bert.ipynb
[pos_tagger_basic_cnn]: examples/03_pos_tagger_basic_cnn.ipynb
[pos_tagger_basic_cnn_colab]: https://colab.research.google.com/github/explosion/thinc/blob/master/examples/03_pos_tagger_basic_cnn.ipynb
[parallel_training_ray]: examples/04_parallel_training_ray.ipynb
[parallel_training_ray_colab]: https://colab.research.google.com/github/explosion/thinc/blob/master/examples/04_parallel_training_ray.ipynb
### 📖 Documentation & usage guides
| Documentation | Description |
| --------------------------------------------------------------------------------- | ----------------------------------------------------- |
| [Introduction](https://thinc.ai/docs) | Everything you need to know. |
| [Concept & Design](https://thinc.ai/docs/concept) | Thinc's conceptual model and how it works. |
| [Defining and using models](https://thinc.ai/docs/usage-models) | How to compose models and update state. |
| [Configuration system](https://thinc.ai/docs/usage-config) | Thinc's config system and function registry. |
| [Integrating PyTorch, TensorFlow & MXNet](https://thinc.ai/docs/usage-frameworks) | Interoperability with machine learning frameworks |
| [Layers API](https://thinc.ai/docs/api-layers) | Weights layers, transforms, combinators and wrappers. |
| [Type Checking](https://thinc.ai/docs/usage-type-checking) | Type-check your model definitions and more. |
## 🗺 What's where
| Module | Description |
| ----------------------------------------- | --------------------------------------------------------------------------------- |
| [`thinc.api`](thinc/api.py) | **User-facing API.** All classes and functions should be imported from here. |
| [`thinc.types`](thinc/types.py) | Custom [types and dataclasses](https://thinc.ai/docs/api-types). |
| [`thinc.model`](thinc/model.py) | The `Model` class. All Thinc models are an instance (not a subclass) of `Model`. |
| [`thinc.layers`](thinc/layers) | The layers. Each layer is implemented in its own module. |
| [`thinc.shims`](thinc/shims) | Interface for external models implemented in PyTorch, TensorFlow etc. |
| [`thinc.loss`](thinc/loss.py) | Functions to calculate losses. |
| [`thinc.optimizers`](thinc/optimizers.py) | Functions to create optimizers. Currently supports "vanilla" SGD, Adam and RAdam. |
| [`thinc.schedules`](thinc/schedules.py) | Generators for different rates, schedules, decays or series. |
| [`thinc.backends`](thinc/backends) | Backends for `numpy` and `cupy`. |
| [`thinc.config`](thinc/config.py) | Config parsing and validation and function registry system. |
| [`thinc.util`](thinc/util.py) | Utilities and helper functions. |
## 🐍 Development notes
Thinc uses [`black`](https://github.com/psf/black) for auto-formatting, [`flake8`](http://flake8.pycqa.org/en/latest/) for linting and [`mypy`](https://mypy.readthedocs.io/en/latest/) for type checking. All code is written compatible with **Python 3.6+**, with type hints wherever possible. See the [type reference](https://thinc.ai/docs/api-types) for more details on Thinc's custom types.
### 👷♀️ Building Thinc from source
Building Thinc from source requires the full dependencies listed in
[`requirements.txt`](requirements.txt) to be installed. You'll also need a
compiler to build the C extensions.
```bash
git clone https://github.com/explosion/thinc
cd thinc
python -m venv .env
source .env/bin/activate
pip install -U pip setuptools wheel
pip install -r requirements.txt
pip install --no-build-isolation .
```
Alternatively, install in editable mode:
```bash
pip install -r requirements.txt
pip install --no-build-isolation --editable .
```
Or by setting `PYTHONPATH`:
```bash
export PYTHONPATH=`pwd`
pip install -r requirements.txt
python setup.py build_ext --inplace
```
### 🚦 Running tests
Thinc comes with an [extensive test suite](thinc/tests). The following should all pass and not report any warnings or errors:
```bash
python -m pytest thinc # test suite
python -m mypy thinc # type checks
python -m flake8 thinc # linting
```
To view test coverage, you can run `python -m pytest thinc --cov=thinc`. We aim for a 100% test coverage. This doesn't mean that we meticulously write tests for every single line – we ignore blocks that are not relevant or difficult to test and make sure that the tests execute all code paths.
%package help
Summary: Development documents and examples for thinc
Provides: python3-thinc-doc
%description help
<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>
# Thinc: A refreshing functional take on deep learning, compatible with your favorite libraries
### From the makers of [spaCy](https://spacy.io) and [Prodigy](https://prodi.gy)
[Thinc](https://thinc.ai) is a **lightweight deep learning library** that offers an elegant,
type-checked, functional-programming API for **composing models**, with support
for layers defined in other frameworks such as **PyTorch, TensorFlow and MXNet**. You
can use Thinc as an interface layer, a standalone toolkit or a flexible way to
develop new models. Previous versions of Thinc have been running quietly in
production in thousands of companies, via both [spaCy](https://spacy.io) and
[Prodigy](https://prodi.gy). We wrote the new version to let users **compose,
configure and deploy custom models** built with their favorite framework.
[](https://dev.azure.com/explosion-ai/public/_build?definitionId=7)
[](https://github.com/explosion/thinc/releases)
[](https://pypi.python.org/pypi/thinc)
[](https://anaconda.org/conda-forge/thinc)
[](https://github.com/explosion/wheelwright/releases)
[](https://github.com/ambv/black)
[![Open demo in Colab][colab]][intro_to_thinc_colab]
## 🔥 Features
- **Type-check** your model definitions with custom types and [`mypy`](https://mypy.readthedocs.io/en/latest/) plugin.
- Wrap **PyTorch**, **TensorFlow** and **MXNet** models for use in your network.
- Concise **functional-programming** approach to model definition, using composition rather than inheritance.
- Optional custom infix notation via **operator overloading**.
- Integrated **config system** to describe trees of objects and hyperparameters.
- Choice of **extensible backends**.
- **[Read more →](https://thinc.ai/docs)**
## 🚀 Quickstart
Thinc is compatible with **Python 3.6+** and runs on **Linux**,
**macOS** and **Windows**. The latest releases with binary wheels are available from
[pip](https://pypi.python.org/pypi/thinc). Before you install Thinc and its
dependencies, make sure that your `pip`, `setuptools` and `wheel` are up to
date. For the most recent releases, pip 19.3 or newer is recommended.
```bash
pip install -U pip setuptools wheel
pip install thinc
```
See the [extended installation docs](https://thinc.ai/docs/install#extended) for details on optional dependencies for different backends and GPU. You might also want to [set up static type checking](https://thinc.ai/docs/install#type-checking) to take advantage of Thinc's type system.
> ⚠️ If you have installed PyTorch and you are using Python 3.7+, uninstall the
> package `dataclasses` with `pip uninstall dataclasses`, since it may have
> been installed by PyTorch and is incompatible with Python 3.7+.
### 📓 Selected examples and notebooks
Also see the [`/examples`](examples) directory and [usage documentation](https://thinc.ai/docs) for more examples. Most examples are Jupyter notebooks – to launch them on [Google Colab](https://colab.research.google.com) (with GPU support!) click on the button next to the notebook name.
| Notebook | Description |
| --------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [`intro_to_thinc`][intro_to_thinc]<br />[![Open in Colab][colab]][intro_to_thinc_colab] | Everything you need to know to get started. Composing and training a model on the MNIST data, using config files, registering custom functions and wrapping PyTorch, TensorFlow and MXNet models. |
| [`transformers_tagger_bert`][transformers_tagger_bert]<br />[![Open in Colab][colab]][transformers_tagger_bert_colab] | How to use Thinc, `transformers` and PyTorch to train a part-of-speech tagger. From model definition and config to the training loop. |
| [`pos_tagger_basic_cnn`][pos_tagger_basic_cnn]<br />[![Open in Colab][colab]][pos_tagger_basic_cnn_colab] | Implementing and training a basic CNN for part-of-speech tagging model without external dependencies and using different levels of Thinc's config system. |
| [`parallel_training_ray`][parallel_training_ray]<br />[![Open in Colab][colab]][parallel_training_ray_colab] | How to set up synchronous and asynchronous parameter server training with Thinc and [Ray](https://ray.readthedocs.io/en/latest/). |
**[View more →](examples)**
[colab]: https://gistcdn.githack.com/ines/dcf354aa71a7665ae19871d7fd14a4e0/raw/461fc1f61a7bc5860f943cd4b6bcfabb8c8906e7/colab-badge.svg
[intro_to_thinc]: examples/00_intro_to_thinc.ipynb
[intro_to_thinc_colab]: https://colab.research.google.com/github/explosion/thinc/blob/master/examples/00_intro_to_thinc.ipynb
[transformers_tagger_bert]: examples/02_transformers_tagger_bert.ipynb
[transformers_tagger_bert_colab]: https://colab.research.google.com/github/explosion/thinc/blob/master/examples/02_transformers_tagger_bert.ipynb
[pos_tagger_basic_cnn]: examples/03_pos_tagger_basic_cnn.ipynb
[pos_tagger_basic_cnn_colab]: https://colab.research.google.com/github/explosion/thinc/blob/master/examples/03_pos_tagger_basic_cnn.ipynb
[parallel_training_ray]: examples/04_parallel_training_ray.ipynb
[parallel_training_ray_colab]: https://colab.research.google.com/github/explosion/thinc/blob/master/examples/04_parallel_training_ray.ipynb
### 📖 Documentation & usage guides
| Documentation | Description |
| --------------------------------------------------------------------------------- | ----------------------------------------------------- |
| [Introduction](https://thinc.ai/docs) | Everything you need to know. |
| [Concept & Design](https://thinc.ai/docs/concept) | Thinc's conceptual model and how it works. |
| [Defining and using models](https://thinc.ai/docs/usage-models) | How to compose models and update state. |
| [Configuration system](https://thinc.ai/docs/usage-config) | Thinc's config system and function registry. |
| [Integrating PyTorch, TensorFlow & MXNet](https://thinc.ai/docs/usage-frameworks) | Interoperability with machine learning frameworks |
| [Layers API](https://thinc.ai/docs/api-layers) | Weights layers, transforms, combinators and wrappers. |
| [Type Checking](https://thinc.ai/docs/usage-type-checking) | Type-check your model definitions and more. |
## 🗺 What's where
| Module | Description |
| ----------------------------------------- | --------------------------------------------------------------------------------- |
| [`thinc.api`](thinc/api.py) | **User-facing API.** All classes and functions should be imported from here. |
| [`thinc.types`](thinc/types.py) | Custom [types and dataclasses](https://thinc.ai/docs/api-types). |
| [`thinc.model`](thinc/model.py) | The `Model` class. All Thinc models are an instance (not a subclass) of `Model`. |
| [`thinc.layers`](thinc/layers) | The layers. Each layer is implemented in its own module. |
| [`thinc.shims`](thinc/shims) | Interface for external models implemented in PyTorch, TensorFlow etc. |
| [`thinc.loss`](thinc/loss.py) | Functions to calculate losses. |
| [`thinc.optimizers`](thinc/optimizers.py) | Functions to create optimizers. Currently supports "vanilla" SGD, Adam and RAdam. |
| [`thinc.schedules`](thinc/schedules.py) | Generators for different rates, schedules, decays or series. |
| [`thinc.backends`](thinc/backends) | Backends for `numpy` and `cupy`. |
| [`thinc.config`](thinc/config.py) | Config parsing and validation and function registry system. |
| [`thinc.util`](thinc/util.py) | Utilities and helper functions. |
## 🐍 Development notes
Thinc uses [`black`](https://github.com/psf/black) for auto-formatting, [`flake8`](http://flake8.pycqa.org/en/latest/) for linting and [`mypy`](https://mypy.readthedocs.io/en/latest/) for type checking. All code is written compatible with **Python 3.6+**, with type hints wherever possible. See the [type reference](https://thinc.ai/docs/api-types) for more details on Thinc's custom types.
### 👷♀️ Building Thinc from source
Building Thinc from source requires the full dependencies listed in
[`requirements.txt`](requirements.txt) to be installed. You'll also need a
compiler to build the C extensions.
```bash
git clone https://github.com/explosion/thinc
cd thinc
python -m venv .env
source .env/bin/activate
pip install -U pip setuptools wheel
pip install -r requirements.txt
pip install --no-build-isolation .
```
Alternatively, install in editable mode:
```bash
pip install -r requirements.txt
pip install --no-build-isolation --editable .
```
Or by setting `PYTHONPATH`:
```bash
export PYTHONPATH=`pwd`
pip install -r requirements.txt
python setup.py build_ext --inplace
```
### 🚦 Running tests
Thinc comes with an [extensive test suite](thinc/tests). The following should all pass and not report any warnings or errors:
```bash
python -m pytest thinc # test suite
python -m mypy thinc # type checks
python -m flake8 thinc # linting
```
To view test coverage, you can run `python -m pytest thinc --cov=thinc`. We aim for a 100% test coverage. This doesn't mean that we meticulously write tests for every single line – we ignore blocks that are not relevant or difficult to test and make sure that the tests execute all code paths.
%prep
%autosetup -n thinc-8.1.9
%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-thinc -f filelist.lst
%dir %{python3_sitearch}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri Apr 21 2023 Python_Bot <Python_Bot@openeuler.org> - 8.1.9-1
- Package Spec generated
|
