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https://travis-ci.org/pytorch/ignite.svg?branch=masterhttps://pepy.tech/badge/pytorch-ignitehttps://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fpytorch-ignite%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v

Ignite is a high-level library to help with training neural networks in PyTorch.

  • ignite helps you write compact but full-featured training loops in a few lines of code
  • you get a training loop with metrics, early-stopping, model checkpointing and other features without the boilerplate

Below we show a side-by-side comparison of using pure pytorch and using ignite to create a training loop to train and validate your model with occasional checkpointing:

assets/ignite_vs_bare_pytorch.png

As you can see, the code is more concise and readable with ignite. Furthermore, adding additional metrics, or things like early stopping is a breeze in ignite, but can start to rapidly increase the complexity of your code when "rolling your own" training loop.

Installation

From pip:

pip install pytorch-ignite

From conda:

conda install ignite -c pytorch

From source:

pip install git+https://github.com/pytorch/ignite

Nightly releases

From pip:

pip install --pre pytorch-ignite

From conda (this suggests to install pytorch nightly release instead of stable version as dependency):

conda install ignite -c pytorch-nightly

Why Ignite?

Ignite's high level of abstraction assumes less about the type of network (or networks) that you are training, and we require the user to define the closure to be run in the training and validation loop. This level of abstraction allows for a great deal more of flexibility, such as co-training multiple models (i.e. GANs) and computing/tracking multiple losses and metrics in your training loop.

Ignite also allows for multiple handlers to be attached to events, and a finer granularity of events in the engine loop.

Documentation

API documentation and an overview of the library can be found here.

Structure

  • ignite: Core of the library, contains an engine for training and evaluating, all of the classic machine learning metrics and a variety of handlers to ease the pain of training and validation of neural networks!
  • ignite.contrib: The Contrib directory contains additional modules contributed by Ignite users. Modules vary from TBPTT engine, various optimisation parameter schedulers, logging handlers and a metrics module containing many regression metrics (ignite.contrib.metrics.regression)!

The code in ignite.contrib is not as fully maintained as the core part of the library. It may change or be removed at any time without notice.

Examples

Please check out the examples to see how to use ignite to train various types of networks, as well as how to use visdom or tensorboardX for training visualizations.

Contributing

We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.

Please see the contribution guidelines for more information.

As always, PRs are welcome :)

About

High-level library to help with training neural networks in PyTorch

pytorch.org/ignite

Resources

Readme
BSD-3-Clause license

Contributing

Contributing
Activity

Stars

0 stars

Watchers

0 watching

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Ignite
https://travis-ci.org/pytorch/ignite.svg?branch=masterhttps://pepy.tech/badge/pytorch-ignitehttps://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fpytorch-ignite%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v
Ignite is a high-level library to help with training neural networks in PyTorch.
  • ignite helps you write compact but full-featured training loops in a few lines of code
  • you get a training loop with metrics, early-stopping, model checkpointing and other features without the boilerplate
Below we show a side-by-side comparison of using pure pytorch and using ignite to create a training loop
to train and validate your model with occasional checkpointing:
assets/ignite_vs_bare_pytorch.png
As you can see, the code is more concise and readable with ignite. Furthermore, adding additional metrics, or
things like early stopping is a breeze in ignite, but can start to rapidly increase the complexity of
your code when "rolling your own" training loop.
Installation
From pip:
pip install pytorch-ignite
From conda:
conda install ignite -c pytorch
From source:
pip install git+https://github.com/pytorch/ignite
Nightly releases
From pip:
pip install --pre pytorch-ignite
From conda (this suggests to install pytorch nightly release instead
of stable version as dependency):
conda install ignite -c pytorch-nightly
Why Ignite?
Ignite's high level of abstraction assumes less about the type of network (or networks) that you are training, and we require the user to define the closure to be run in the training and validation loop. This level of abstraction allows for a great deal more of flexibility, such as co-training multiple models (i.e. GANs) and computing/tracking multiple losses and metrics in your training loop.
Ignite also allows for multiple handlers to be attached to events, and a finer granularity of events in the engine loop.
Documentation
API documentation and an overview of the library can be found here.
Structure
  • ignite: Core of the library, contains an engine for training and evaluating, all of the classic machine learning metrics and a variety of handlers to ease the pain of training and validation of neural networks!
  • ignite.contrib: The Contrib directory contains additional modules contributed by Ignite users. Modules vary from TBPTT engine, various optimisation parameter schedulers, logging handlers and a metrics module containing many regression metrics (ignite.contrib.metrics.regression)!
The code in ignite.contrib is not as fully maintained as the core part of the library. It may change or be removed at any time without notice.
Examples
Please check out the examples to see how to use ignite to train various types of networks, as well as how to use visdom or tensorboardX for training visualizations.
Contributing
We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.
Please see the contribution guidelines for more information.
As always, PRs are welcome :)
About
High-level library to help with training neural networks in PyTorch
pytorch.org/ignite
Resources
Readme
BSD-3-Clause license
Contributing
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Ignite
https://travis-ci.org/pytorch/ignite.svg?branch=masterhttps://pepy.tech/badge/pytorch-ignitehttps://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fpytorch-ignite%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v
Ignite is a high-level library to help with training neural networks in PyTorch.
  • ignite helps you write compact but full-featured training loops in a few lines of code
  • you get a training loop with metrics, early-stopping, model checkpointing and other features without the boilerplate
Below we show a side-by-side comparison of using pure pytorch and using ignite to create a training loop
to train and validate your model with occasional checkpointing:
assets/ignite_vs_bare_pytorch.png
As you can see, the code is more concise and readable with ignite. Furthermore, adding additional metrics, or
things like early stopping is a breeze in ignite, but can start to rapidly increase the complexity of
your code when "rolling your own" training loop.
Installation
From pip:
pip install pytorch-ignite
From conda:
conda install ignite -c pytorch
From source:
pip install git+https://github.com/pytorch/ignite
Nightly releases
From pip:
pip install --pre pytorch-ignite
From conda (this suggests to install pytorch nightly release instead
of stable version as dependency):
conda install ignite -c pytorch-nightly
Why Ignite?
Ignite's high level of abstraction assumes less about the type of network (or networks) that you are training, and we require the user to define the closure to be run in the training and validation loop. This level of abstraction allows for a great deal more of flexibility, such as co-training multiple models (i.e. GANs) and computing/tracking multiple losses and metrics in your training loop.
Ignite also allows for multiple handlers to be attached to events, and a finer granularity of events in the engine loop.
Documentation
API documentation and an overview of the library can be found here.
Structure
  • ignite: Core of the library, contains an engine for training and evaluating, all of the classic machine learning metrics and a variety of handlers to ease the pain of training and validation of neural networks!
  • ignite.contrib: The Contrib directory contains additional modules contributed by Ignite users. Modules vary from TBPTT engine, various optimisation parameter schedulers, logging handlers and a metrics module containing many regression metrics (ignite.contrib.metrics.regression)!
The code in ignite.contrib is not as fully maintained as the core part of the library. It may change or be removed at any time without notice.
Examples
Please check out the examples to see how to use ignite to train various types of networks, as well as how to use visdom or tensorboardX for training visualizations.
Contributing
We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.
Please see the contribution guidelines for more information.
As always, PRs are welcome :)
About
High-level library to help with training neural networks in PyTorch
pytorch.org/ignite
Resources
Readme
BSD-3-Clause license
Contributing
Contributing
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Ignite
https://travis-ci.org/pytorch/ignite.svg?branch=masterhttps://pepy.tech/badge/pytorch-ignitehttps://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fpytorch-ignite%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v
Ignite is a high-level library to help with training neural networks in PyTorch.
  • ignite helps you write compact but full-featured training loops in a few lines of code
  • you get a training loop with metrics, early-stopping, model checkpointing and other features without the boilerplate
Below we show a side-by-side comparison of using pure pytorch and using ignite to create a training loop
to train and validate your model with occasional checkpointing:
assets/ignite_vs_bare_pytorch.png
As you can see, the code is more concise and readable with ignite. Furthermore, adding additional metrics, or
things like early stopping is a breeze in ignite, but can start to rapidly increase the complexity of
your code when "rolling your own" training loop.
Installation
From pip:
pip install pytorch-ignite
From conda:
conda install ignite -c pytorch
From source:
pip install git+https://github.com/pytorch/ignite
Nightly releases
From pip:
pip install --pre pytorch-ignite
From conda (this suggests to install pytorch nightly release instead
of stable version as dependency):
conda install ignite -c pytorch-nightly
Why Ignite?
Ignite's high level of abstraction assumes less about the type of network (or networks) that you are training, and we require the user to define the closure to be run in the training and validation loop. This level of abstraction allows for a great deal more of flexibility, such as co-training multiple models (i.e. GANs) and computing/tracking multiple losses and metrics in your training loop.
Ignite also allows for multiple handlers to be attached to events, and a finer granularity of events in the engine loop.
Documentation
API documentation and an overview of the library can be found here.
Structure
  • ignite: Core of the library, contains an engine for training and evaluating, all of the classic machine learning metrics and a variety of handlers to ease the pain of training and validation of neural networks!
  • ignite.contrib: The Contrib directory contains additional modules contributed by Ignite users. Modules vary from TBPTT engine, various optimisation parameter schedulers, logging handlers and a metrics module containing many regression metrics (ignite.contrib.metrics.regression)!
The code in ignite.contrib is not as fully maintained as the core part of the library. It may change or be removed at any time without notice.
Examples
Please check out the examples to see how to use ignite to train various types of networks, as well as how to use visdom or tensorboardX for training visualizations.
Contributing
We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.
Please see the contribution guidelines for more information.
As always, PRs are welcome :)
About
High-level library to help with training neural networks in PyTorch
pytorch.org/ignite
Resources
Readme
BSD-3-Clause license
Contributing
Contributing
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Stars
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Ignite
https://travis-ci.org/pytorch/ignite.svg?branch=masterhttps://pepy.tech/badge/pytorch-ignitehttps://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fpytorch-ignite%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v
Ignite is a high-level library to help with training neural networks in PyTorch.
  • ignite helps you write compact but full-featured training loops in a few lines of code
  • you get a training loop with metrics, early-stopping, model checkpointing and other features without the boilerplate
Below we show a side-by-side comparison of using pure pytorch and using ignite to create a training loop
to train and validate your model with occasional checkpointing:
assets/ignite_vs_bare_pytorch.png
As you can see, the code is more concise and readable with ignite. Furthermore, adding additional metrics, or
things like early stopping is a breeze in ignite, but can start to rapidly increase the complexity of
your code when "rolling your own" training loop.
Installation
From pip:
pip install pytorch-ignite
From conda:
conda install ignite -c pytorch
From source:
pip install git+https://github.com/pytorch/ignite
Nightly releases
From pip:
pip install --pre pytorch-ignite
From conda (this suggests to install pytorch nightly release instead
of stable version as dependency):
conda install ignite -c pytorch-nightly
Why Ignite?
Ignite's high level of abstraction assumes less about the type of network (or networks) that you are training, and we require the user to define the closure to be run in the training and validation loop. This level of abstraction allows for a great deal more of flexibility, such as co-training multiple models (i.e. GANs) and computing/tracking multiple losses and metrics in your training loop.
Ignite also allows for multiple handlers to be attached to events, and a finer granularity of events in the engine loop.
Documentation
API documentation and an overview of the library can be found here.
Structure
  • ignite: Core of the library, contains an engine for training and evaluating, all of the classic machine learning metrics and a variety of handlers to ease the pain of training and validation of neural networks!
  • ignite.contrib: The Contrib directory contains additional modules contributed by Ignite users. Modules vary from TBPTT engine, various optimisation parameter schedulers, logging handlers and a metrics module containing many regression metrics (ignite.contrib.metrics.regression)!
The code in ignite.contrib is not as fully maintained as the core part of the library. It may change or be removed at any time without notice.
Examples
Please check out the examples to see how to use ignite to train various types of networks, as well as how to use visdom or tensorboardX for training visualizations.
Contributing
We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.
Please see the contribution guidelines for more information.
As always, PRs are welcome :)
About
High-level library to help with training neural networks in PyTorch
pytorch.org/ignite
Resources
Readme
BSD-3-Clause license
Contributing
Contributing
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Ignite
https://travis-ci.org/pytorch/ignite.svg?branch=masterhttps://pepy.tech/badge/pytorch-ignitehttps://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fpytorch-ignite%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v
Ignite is a high-level library to help with training neural networks in PyTorch.
  • ignite helps you write compact but full-featured training loops in a few lines of code
  • you get a training loop with metrics, early-stopping, model checkpointing and other features without the boilerplate
Below we show a side-by-side comparison of using pure pytorch and using ignite to create a training loop
to train and validate your model with occasional checkpointing:
assets/ignite_vs_bare_pytorch.png
As you can see, the code is more concise and readable with ignite. Furthermore, adding additional metrics, or
things like early stopping is a breeze in ignite, but can start to rapidly increase the complexity of
your code when "rolling your own" training loop.
Installation
From pip:
pip install pytorch-ignite
From conda:
conda install ignite -c pytorch
From source:
pip install git+https://github.com/pytorch/ignite
Nightly releases
From pip:
pip install --pre pytorch-ignite
From conda (this suggests to install pytorch nightly release instead
of stable version as dependency):
conda install ignite -c pytorch-nightly
Why Ignite?
Ignite's high level of abstraction assumes less about the type of network (or networks) that you are training, and we require the user to define the closure to be run in the training and validation loop. This level of abstraction allows for a great deal more of flexibility, such as co-training multiple models (i.e. GANs) and computing/tracking multiple losses and metrics in your training loop.
Ignite also allows for multiple handlers to be attached to events, and a finer granularity of events in the engine loop.
Documentation
API documentation and an overview of the library can be found here.
Structure
  • ignite: Core of the library, contains an engine for training and evaluating, all of the classic machine learning metrics and a variety of handlers to ease the pain of training and validation of neural networks!
  • ignite.contrib: The Contrib directory contains additional modules contributed by Ignite users. Modules vary from TBPTT engine, various optimisation parameter schedulers, logging handlers and a metrics module containing many regression metrics (ignite.contrib.metrics.regression)!
The code in ignite.contrib is not as fully maintained as the core part of the library. It may change or be removed at any time without notice.
Examples
Please check out the examples to see how to use ignite to train various types of networks, as well as how to use visdom or tensorboardX for training visualizations.
Contributing
We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.
Please see the contribution guidelines for more information.
As always, PRs are welcome :)
About
High-level library to help with training neural networks in PyTorch
pytorch.org/ignite
Resources
Readme
BSD-3-Clause license
Contributing
Contributing
Activity
Stars
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Ignite
https://travis-ci.org/pytorch/ignite.svg?branch=masterhttps://pepy.tech/badge/pytorch-ignitehttps://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fpytorch-ignite%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v
Ignite is a high-level library to help with training neural networks in PyTorch.
  • ignite helps you write compact but full-featured training loops in a few lines of code
  • you get a training loop with metrics, early-stopping, model checkpointing and other features without the boilerplate
Below we show a side-by-side comparison of using pure pytorch and using ignite to create a training loop
to train and validate your model with occasional checkpointing:
assets/ignite_vs_bare_pytorch.png
As you can see, the code is more concise and readable with ignite. Furthermore, adding additional metrics, or
things like early stopping is a breeze in ignite, but can start to rapidly increase the complexity of
your code when "rolling your own" training loop.
Installation
From pip:
pip install pytorch-ignite
From conda:
conda install ignite -c pytorch
From source:
pip install git+https://github.com/pytorch/ignite
Nightly releases
From pip:
pip install --pre pytorch-ignite
From conda (this suggests to install pytorch nightly release instead
of stable version as dependency):
conda install ignite -c pytorch-nightly
Why Ignite?
Ignite's high level of abstraction assumes less about the type of network (or networks) that you are training, and we require the user to define the closure to be run in the training and validation loop. This level of abstraction allows for a great deal more of flexibility, such as co-training multiple models (i.e. GANs) and computing/tracking multiple losses and metrics in your training loop.
Ignite also allows for multiple handlers to be attached to events, and a finer granularity of events in the engine loop.
Documentation
API documentation and an overview of the library can be found here.
Structure
  • ignite: Core of the library, contains an engine for training and evaluating, all of the classic machine learning metrics and a variety of handlers to ease the pain of training and validation of neural networks!
  • ignite.contrib: The Contrib directory contains additional modules contributed by Ignite users. Modules vary from TBPTT engine, various optimisation parameter schedulers, logging handlers and a metrics module containing many regression metrics (ignite.contrib.metrics.regression)!
The code in ignite.contrib is not as fully maintained as the core part of the library. It may change or be removed at any time without notice.
Examples
Please check out the examples to see how to use ignite to train various types of networks, as well as how to use visdom or tensorboardX for training visualizations.
Contributing
We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.
Please see the contribution guidelines for more information.
As always, PRs are welcome :)
About
High-level library to help with training neural networks in PyTorch
pytorch.org/ignite
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Readme
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Ignite
https://travis-ci.org/pytorch/ignite.svg?branch=masterhttps://pepy.tech/badge/pytorch-ignitehttps://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fpytorch-ignite%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v
Ignite is a high-level library to help with training neural networks in PyTorch.
  • ignite helps you write compact but full-featured training loops in a few lines of code
  • you get a training loop with metrics, early-stopping, model checkpointing and other features without the boilerplate
Below we show a side-by-side comparison of using pure pytorch and using ignite to create a training loop
to train and validate your model with occasional checkpointing:
assets/ignite_vs_bare_pytorch.png
As you can see, the code is more concise and readable with ignite. Furthermore, adding additional metrics, or
things like early stopping is a breeze in ignite, but can start to rapidly increase the complexity of
your code when "rolling your own" training loop.
Installation
From pip:
pip install pytorch-ignite
From conda:
conda install ignite -c pytorch
From source:
pip install git+https://github.com/pytorch/ignite
Nightly releases
From pip:
pip install --pre pytorch-ignite
From conda (this suggests to install pytorch nightly release instead
of stable version as dependency):
conda install ignite -c pytorch-nightly
Why Ignite?
Ignite's high level of abstraction assumes less about the type of network (or networks) that you are training, and we require the user to define the closure to be run in the training and validation loop. This level of abstraction allows for a great deal more of flexibility, such as co-training multiple models (i.e. GANs) and computing/tracking multiple losses and metrics in your training loop.
Ignite also allows for multiple handlers to be attached to events, and a finer granularity of events in the engine loop.
Documentation
API documentation and an overview of the library can be found here.
Structure
  • ignite: Core of the library, contains an engine for training and evaluating, all of the classic machine learning metrics and a variety of handlers to ease the pain of training and validation of neural networks!
  • ignite.contrib: The Contrib directory contains additional modules contributed by Ignite users. Modules vary from TBPTT engine, various optimisation parameter schedulers, logging handlers and a metrics module containing many regression metrics (ignite.contrib.metrics.regression)!
The code in ignite.contrib is not as fully maintained as the core part of the library. It may change or be removed at any time without notice.
Examples
Please check out the examples to see how to use ignite to train various types of networks, as well as how to use visdom or tensorboardX for training visualizations.
Contributing
We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.
Please see the contribution guidelines for more information.
As always, PRs are welcome :)
About
High-level library to help with training neural networks in PyTorch
pytorch.org/ignite
Resources
Readme
BSD-3-Clause license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages