diff --git a/README.md b/README.md
new file mode 100644
index 00000000000..58ddf59253d
--- /dev/null
+++ b/README.md
@@ -0,0 +1,258 @@
+
+
+![Ignite Logo](assets/ignite_logo.svg)
+
+
+[![image](https://travis-ci.org/pytorch/ignite.svg?branch=master)](https://travis-ci.org/pytorch/ignite)
+[![image](https://github.com/pytorch/ignite/workflows/.github/workflows/unittests.yml/badge.svg?branch=master)](.github/workflows/unittests.yml)
+[![image](https://codecov.io/gh/pytorch/ignite/branch/master/graph/badge.svg)](https://codecov.io/gh/pytorch/ignite)
+[![image](https://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)](https://pytorch.org/ignite/index.html)
+
+
+[![image](https://anaconda.org/pytorch/ignite/badges/version.svg)](https://anaconda.org/pytorch/ignite)
+[![image](https://anaconda.org/pytorch/ignite/badges/downloads.svg)](https://anaconda.org/pytorch/ignite)
+[![image](https://img.shields.io/badge/dynamic/json.svg?label=PyPI&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fpytorch-ignite%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v)](https://pypi.org/project/pytorch-ignite/)
+[![image](https://pepy.tech/badge/pytorch-ignite)](https://pepy.tech/project/pytorch-ignite)
+
+[![image](https://img.shields.io/badge/Optuna-integrated-blue)](https://optuna.org)
+[![image](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+
+
+
+## TL;DR
+
+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:
+
+[![image](assets/ignite_vs_bare_pytorch.png)](https://raw.githubusercontent.com/pytorch/ignite/master/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.
+
+# Table of Contents
+- [Installation](#installation)
+ * [Nightly releases](#nightly-releases)
+- [Why Ignite?](#why-ignite)
+- [Documentation](#documentation)
+ * [Additional Materials](#additional-materials)
+- [Structure](#structure)
+- [Examples](#examples)
+ * [MNIST Example](#mnist-example)
+ * [Tutorials](#tutorials)
+ * [Distributed CIFAR10 Example](#distributed-cifar10-example)
+ * [Other Examples](#other-examples)
+ * [Reproducible Training Examples](#reproducible-training-examples)
+- [Contributing](#contributing)
+- [Projects using Ignite](#projects-using-ignite)
+- [User feedback](#user-feedback)
+
+
+# Installation
+
+From [pip](https://pypi.org/project/pytorch-ignite/):
+
+``` {.sourceCode .bash}
+pip install pytorch-ignite
+```
+
+From [conda](https://anaconda.org/pytorch/ignite):
+
+``` {.sourceCode .bash}
+conda install ignite -c pytorch
+```
+
+From source:
+
+``` {.sourceCode .bash}
+pip install git+https://github.com/pytorch/ignite
+```
+
+## Nightly releases
+
+From pip:
+
+``` {.sourceCode .bash}
+pip install --pre pytorch-ignite
+```
+
+From conda (this suggests to install [pytorch nightly
+release](https://anaconda.org/pytorch-nightly/pytorch) instead of stable
+version as dependency):
+
+``` {.sourceCode .bash}
+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](https://pytorch.org/ignite/index.html).
+
+## Additional Materials
+
+- [8 Creators and Core Contributors Talk About Their Model Training Libraries From PyTorch Ecosystem](https://neptune.ai/blog/model-training-libraries-pytorch-ecosystem?utm_source=reddit&utm_medium=post&utm_campaign=blog-model-training-libraries-pytorch-ecosystem)
+- Ignite Posters from Pytorch Developer Conferences:
+ - [2019](https://drive.google.com/open?id=1bqIl-EM6GCCCoSixFZxhIbuF25F2qTZg)
+ - [2018](https://drive.google.com/open?id=1_2vzBJ0KeCjGv1srojMHiJRvceSVbVR5)
+
+
+
+# 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](https://github.com/pytorch/ignite/tree/master/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
+
+We provide several examples ported from
+[pytorch/examples](https://github.com/pytorch/examples) using `ignite` to display how it helps to write compact and
+full-featured training loops in a few lines of code:
+
+## MNIST Example
+
+Basic neural network training on MNIST dataset with/without `ignite.contrib` module:
+
+- [MNIST with ignite.contrib TQDM/Tensorboard/Visdom
+ loggers](https://github.com/pytorch/ignite/tree/master/examples/contrib/mnist)
+- [MNIST with native TQDM/Tensorboard/Visdom
+ logging](https://github.com/pytorch/ignite/tree/master/examples/mnist)
+
+## Tutorials
+
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/TextCNN.ipynb) [Text Classification using Convolutional Neural
+ Networks](https://github.com/pytorch/ignite/blob/master/examples/notebooks/TextCNN.ipynb)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/VAE.ipynb) [Variational Auto
+ Encoders](https://github.com/pytorch/ignite/blob/master/examples/notebooks/VAE.ipynb)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/FashionMNIST.ipynb) [Convolutional Neural Networks for Classifying Fashion-MNIST
+ Dataset](https://github.com/pytorch/ignite/blob/master/examples/notebooks/FashionMNIST.ipynb)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/CycleGAN.ipynb) [Training Cycle-GAN on Horses to
+ Zebras](https://github.com/pytorch/ignite/blob/master/examples/notebooks/CycleGAN.ipynb)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/EfficientNet_Cifar100_finetuning.ipynb) [Finetuning EfficientNet-B0 on
+ CIFAR100](https://github.com/pytorch/ignite/blob/master/examples/notebooks/EfficientNet_Cifar100_finetuning.ipynb)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/Cifar10_Ax_hyperparam_tuning.ipynb) [Hyperparameters tuning with
+ Ax](https://github.com/pytorch/ignite/blob/master/examples/notebooks/Cifar10_Ax_hyperparam_tuning.ipynb)
+
+## Distributed CIFAR10 Example
+
+Training a small variant of ResNet on CIFAR10 in various configurations:
+1\) single gpu, 2) single node multiple gpus, 3) multiple nodes and
+multilple gpus.
+
+- [CIFAR10](https://github.com/pytorch/ignite/tree/master/examples/contrib/cifar10)
+
+## Other Examples
+
+- [DCGAN](https://github.com/pytorch/ignite/tree/master/examples/gan)
+- [Reinforcement
+ Learning](https://github.com/pytorch/ignite/tree/master/examples/reinforcement_learning)
+- [Fast Neural
+ Style](https://github.com/pytorch/ignite/tree/master/examples/fast_neural_style)
+
+## Reproducible Training Examples
+
+Inspired by
+[torchvision/references](https://github.com/pytorch/vision/tree/master/references),
+we provide several reproducible baselines for vision tasks:
+
+- [ImageNet](examples/references/classification/imagenet)
+- [Pascal VOC2012](examples/references/segmentation/pascal_voc2012)
+
+Features:
+
+- Distributed training with mixed precision by
+ [nvidia/apex](https://github.com/NVIDIA/apex/)
+- Experiments tracking with [MLflow](https://mlflow.org/) or
+ [Polyaxon](https://polyaxon.com/)
+
+# 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](https://github.com/pytorch/ignite/blob/master/CONTRIBUTING.md)
+for more information.
+
+As always, PRs are welcome :)
+
+# Projects using Ignite
+
+- [State-of-the-Art Conversational AI with Transfer
+ Learning](https://github.com/huggingface/transfer-learning-conv-ai)
+- [Tutorial on Transfer Learning in NLP held at NAACL
+ 2019](https://github.com/huggingface/naacl_transfer_learning_tutorial)
+- [Implementation of \"Attention is All You Need\"
+ paper](https://github.com/akurniawan/pytorch-transformer)
+- [Implementation of DropBlock: A regularization method for
+ convolutional networks in
+ PyTorch](https://github.com/miguelvr/dropblock)
+- [Deep-Reinforcement-Learning-Hands-On-Second-Edition, published by
+ Packt](https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On-Second-Edition)
+- [Kaggle Kuzushiji Recognition: 2nd place
+ solution](https://github.com/lopuhin/kaggle-kuzushiji-2019)
+- [Unsupervised Data Augmentation experiments in
+ PyTorch](https://github.com/vfdev-5/UDA-pytorch)
+- [Hyperparameters tuning with
+ Optuna](https://github.com/pfnet/optuna/blob/master/examples/pytorch_ignite_simple.py)
+- [Project MONAI -
+ AI Toolkit for Healthcare Imaging
+ ](https://github.com/Project-MONAI/MONAI)
+
+See other projects at [\"Used
+by\"](https://github.com/pytorch/ignite/network/dependents?package_id=UGFja2FnZS02NzI5ODEwNA%3D%3D)
+
+If your project implements a paper, represents other use-cases not
+covered in our official tutorials, Kaggle competition\'s code or just
+your code presents interesting results and uses Ignite. We would like to
+add your project in this list, so please send a PR with brief
+description of the project.
+
+# User feedback
+
+We have created a form for [\"user
+feedback\"](https://github.com/pytorch/ignite/issues/new/choose). We
+appreciate any type of feedback and this is how we would like to see our
+community:
+
+- If you like the project and want to say thanks, this the right
+ place.
+- If you do not like something, please, share it with us and we can
+ see how to improve it.
+
+Thank you !
diff --git a/README.rst b/README.rst
deleted file mode 100644
index 41dee47dd5c..00000000000
--- a/README.rst
+++ /dev/null
@@ -1,219 +0,0 @@
-Ignite
-======
-
-
-.. image:: https://travis-ci.org/pytorch/ignite.svg?branch=master
- :target: https://travis-ci.org/pytorch/ignite
-
-
-.. image:: https://github.com/pytorch/ignite/workflows/.github/workflows/unittests.yml/badge.svg?branch=master
- :target: .github/workflows/unittests.yml
-
-
-.. image:: https://codecov.io/gh/pytorch/ignite/branch/master/graph/badge.svg
- :target: https://codecov.io/gh/pytorch/ignite
-
-
-.. image:: https://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
- :target: https://pytorch.org/ignite/index.html
-
-
-.. image:: https://anaconda.org/pytorch/ignite/badges/version.svg
- :target: https://anaconda.org/pytorch/ignite
-
-
-.. image:: https://anaconda.org/pytorch/ignite/badges/downloads.svg
- :target: https://anaconda.org/pytorch/ignite
-
-
-.. image:: https://img.shields.io/badge/dynamic/json.svg?label=PyPI&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fpytorch-ignite%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v
- :target: https://pypi.org/project/pytorch-ignite/
-
-
-.. image:: https://pepy.tech/badge/pytorch-ignite
- :target: https://pepy.tech/project/pytorch-ignite
-
-
-.. image:: https://img.shields.io/badge/Optuna-integrated-blue
- :target: https://optuna.org
-
-
-.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
- :target: https://github.com/psf/black
-
-
-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:
-
-.. image:: assets/ignite_vs_bare_pytorch.png
- :target: https://raw.githubusercontent.com/pytorch/ignite/master/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 `_:
-
-.. code:: bash
-
- pip install pytorch-ignite
-
-
-From `conda `_:
-
-.. code:: bash
-
- conda install ignite -c pytorch
-
-
-From source:
-
-.. code:: bash
-
- pip install git+https://github.com/pytorch/ignite
-
-
-
-Nightly releases
-----------------
-
-From pip:
-
-.. code:: bash
-
- pip install --pre pytorch-ignite
-
-
-From conda (this suggests to install `pytorch nightly release `_ instead
-of stable version as dependency):
-
-.. code:: bash
-
- 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
-========
-
-We provide several examples ported from `pytorch/examples `_ using `ignite`
-to display how it helps to write compact and full-featured training loops in a few lines of code:
-
-MNIST example
---------------
-
-Basic neural network training on MNIST dataset with/without `ignite.contrib` module:
-
-- `MNIST with ignite.contrib TQDM/Tensorboard/Visdom loggers `_
-- `MNIST with native TQDM/Tensorboard/Visdom logging `_
-
-Distributed CIFAR10 example
----------------------------
-
-Training a small variant of ResNet on CIFAR10 in various configurations: 1) single gpu, 2) single node multiple gpus, 3) multiple nodes and multilple gpus.
-
-- `CIFAR10 `_
-
-
-Other examples
---------------
-
-- `DCGAN `_
-- `Reinforcement Learning `_
-- `Fast Neural Style `_
-
-
-Notebooks
----------
-
-- `Text Classification using Convolutional Neural Networks `_
-- `Variational Auto Encoders `_
-- `Training Cycle-GAN on Horses to Zebras `_
-- `Finetuning EfficientNet-B0 on CIFAR100 `_
-- `Convolutional Neural Networks for Classifying Fashion-MNIST Dataset `_
-- `Hyperparameters tuning with Ax `_
-
-
-`Reproducible trainings `_
------------------------------------------------
-
-Inspired by `torchvision/references `_, we provide several
-reproducible baselines for vision tasks:
-
-- `ImageNet `_
-- `Pascal VOC2012 `_
-
-Features:
-
-- Distributed training with mixed precision by `nvidia/apex `_
-- Experiments tracking with `MLflow `_ or `Polyaxon `_
-
-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 :)
-
-
-They use Ignite
-===============
-
-- `State-of-the-Art Conversational AI with Transfer Learning `_
-- `Tutorial on Transfer Learning in NLP held at NAACL 2019 `_
-- `Implementation of "Attention is All You Need" paper `_
-- `Implementation of DropBlock: A regularization method for convolutional networks in PyTorch `_
-- `Deep-Reinforcement-Learning-Hands-On-Second-Edition, published by Packt `_
-- `Kaggle Kuzushiji Recognition: 2nd place solution `_
-- `Unsupervised Data Augmentation experiments in PyTorch `_
-- `Hyperparameters tuning with Optuna `_
-
-See other projects at `"Used by" `_
-
-If your project implements a paper, represents other use-cases not covered in our official tutorials,
-Kaggle competition's code or just your code presents interesting results and uses Ignite. We would like to add your project
-in this list, so please send a PR with brief description of the project.
-
-
-User feedback
-=============
-
-We have created a form for `"user feedback" `_.
-We appreciate any type of feedback and this is how we would like to see our community:
-
-- If you like the project and want to say thanks, this the right place.
-
-- If you do not like something, please, share it with us and we can see how to improve it.
-
-Thank you !
-
diff --git a/assets/ignite_logo.svg b/assets/ignite_logo.svg
new file mode 100644
index 00000000000..cd4719ffe67
--- /dev/null
+++ b/assets/ignite_logo.svg
@@ -0,0 +1,18 @@
+
+
+
diff --git a/examples/notebooks/Cifar10_Ax_hyperparam_tuning.ipynb b/examples/notebooks/Cifar10_Ax_hyperparam_tuning.ipynb
index 835bb4dc96c..85a751fca80 100644
--- a/examples/notebooks/Cifar10_Ax_hyperparam_tuning.ipynb
+++ b/examples/notebooks/Cifar10_Ax_hyperparam_tuning.ipynb
@@ -35,6 +35,15 @@
"- tensorboard\n"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install pytorch-ignite tensorboardX "
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
@@ -8031,4 +8040,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/CycleGAN.ipynb b/examples/notebooks/CycleGAN.ipynb
index 41051b119b7..ea100e2a269 100644
--- a/examples/notebooks/CycleGAN.ipynb
+++ b/examples/notebooks/CycleGAN.ipynb
@@ -69,6 +69,39 @@
"```"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install pytorch-ignite tensorboardX"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Apex:\n",
+ "# ref: https://stackoverflow.com/a/59320819\n",
+ "%%writefile setup.sh\n",
+ "\n",
+ "export CUDA_HOME=/usr/local/cuda-10.1\n",
+ "git clone https://github.com/NVIDIA/apex\n",
+ "pip install -v --no-cache-dir --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext\" ./apex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!sh setup.sh"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 2,
@@ -4590,9 +4623,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.3"
+ "version": "3.8.1-final"
}
},
"nbformat": 4,
"nbformat_minor": 2
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/EfficientNet_Cifar100_finetuning.ipynb b/examples/notebooks/EfficientNet_Cifar100_finetuning.ipynb
index 96789106fb6..ae59e7712cc 100644
--- a/examples/notebooks/EfficientNet_Cifar100_finetuning.ipynb
+++ b/examples/notebooks/EfficientNet_Cifar100_finetuning.ipynb
@@ -75,6 +75,39 @@
"```"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install pytorch-ignite tensorboardX"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Apex:\n",
+ "# ref: https://stackoverflow.com/a/59320819\n",
+ "%%writefile setup.sh\n",
+ "\n",
+ "export CUDA_HOME=/usr/local/cuda-10.1\n",
+ "git clone https://github.com/NVIDIA/apex\n",
+ "pip install -v --no-cache-dir --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext\" ./apex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!sh setup.sh"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
@@ -1807,4 +1840,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/FashionMNIST.ipynb b/examples/notebooks/FashionMNIST.ipynb
index 34c37e6dbaa..418b2908666 100644
--- a/examples/notebooks/FashionMNIST.ipynb
+++ b/examples/notebooks/FashionMNIST.ipynb
@@ -29,6 +29,15 @@
"### Importing libraries"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# !pip install pytorch-ignite"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {
@@ -41,12 +50,8 @@
},
{
"cell_type": "code",
- "execution_count": 0,
- "metadata": {
- "colab": {},
- "colab_type": "code",
- "id": "HQhJpdeXabx1"
- },
+ "execution_count": 2,
+ "metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
@@ -70,12 +75,8 @@
},
{
"cell_type": "code",
- "execution_count": 0,
- "metadata": {
- "colab": {},
- "colab_type": "code",
- "id": "DqR2DIQIabx6"
- },
+ "execution_count": 3,
+ "metadata": {},
"outputs": [],
"source": [
"import torch\n",
@@ -106,12 +107,8 @@
},
{
"cell_type": "code",
- "execution_count": 0,
- "metadata": {
- "colab": {},
- "colab_type": "code",
- "id": "3zyDA_Wiabx8"
- },
+ "execution_count": 4,
+ "metadata": {},
"outputs": [],
"source": [
"from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator\n",
@@ -138,12 +135,8 @@
},
{
"cell_type": "code",
- "execution_count": 0,
- "metadata": {
- "colab": {},
- "colab_type": "code",
- "id": "ggAEyjyuabx_"
- },
+ "execution_count": 5,
+ "metadata": {},
"outputs": [],
"source": [
"# transform to normalize the data\n",
@@ -186,12 +179,8 @@
},
{
"cell_type": "code",
- "execution_count": 0,
- "metadata": {
- "colab": {},
- "colab_type": "code",
- "id": "ohVVYyJpabyC"
- },
+ "execution_count": 6,
+ "metadata": {},
"outputs": [],
"source": [
"class CNN(nn.Module):\n",
@@ -252,12 +241,8 @@
},
{
"cell_type": "code",
- "execution_count": 0,
- "metadata": {
- "colab": {},
- "colab_type": "code",
- "id": "KSkcIOIlabyF"
- },
+ "execution_count": 7,
+ "metadata": {},
"outputs": [],
"source": [
"# creating model,and defining optimizer and loss\n",
@@ -299,12 +284,8 @@
},
{
"cell_type": "code",
- "execution_count": 0,
- "metadata": {
- "colab": {},
- "colab_type": "code",
- "id": "qMbolxIEabyJ"
- },
+ "execution_count": 8,
+ "metadata": {},
"outputs": [],
"source": [
"# defining the number of epochs\n",
@@ -336,12 +317,8 @@
},
{
"cell_type": "code",
- "execution_count": 0,
- "metadata": {
- "colab": {},
- "colab_type": "code",
- "id": "jokHCasNabyM"
- },
+ "execution_count": 9,
+ "metadata": {},
"outputs": [],
"source": [
"RunningAverage(output_transform=lambda x: x).attach(trainer, 'loss')"
@@ -361,13 +338,18 @@
},
{
"cell_type": "code",
- "execution_count": 0,
- "metadata": {
- "colab": {},
- "colab_type": "code",
- "id": "hV2yv-NAabyP"
- },
- "outputs": [],
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": ""
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"def score_function(engine):\n",
" val_loss = engine.state.metrics['nll']\n",
@@ -397,13 +379,18 @@
},
{
"cell_type": "code",
- "execution_count": 0,
- "metadata": {
- "colab": {},
- "colab_type": "code",
- "id": "_CSobXR1abyR"
- },
- "outputs": [],
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": ""
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"@trainer.on(Events.EPOCH_COMPLETED)\n",
"def log_training_results(trainer):\n",
@@ -454,12 +441,8 @@
},
{
"cell_type": "code",
- "execution_count": 0,
- "metadata": {
- "colab": {},
- "colab_type": "code",
- "id": "4ckHVNT6abyU"
- },
+ "execution_count": 12,
+ "metadata": {},
"outputs": [],
"source": [
"@trainer.on(Events.COMPLETED)\n",
@@ -497,15 +480,20 @@
},
{
"cell_type": "code",
- "execution_count": 0,
- "metadata": {
- "colab": {},
- "colab_type": "code",
- "id": "eU4ZeJakabyW"
- },
- "outputs": [],
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": ""
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "checkpointer = ModelCheckpoint('./models', 'fashionMNIST', n_saved=2, create_dir=True, save_as_state_dict=True,require_empty=False)\n",
+ "checkpointer = ModelCheckpoint('./saved_models', 'fashionMNIST', n_saved=2, create_dir=True, save_as_state_dict=True, require_empty=False)\n",
"trainer.add_event_handler(Events.EPOCH_COMPLETED, checkpointer, {'fashionMNIST': model})"
]
},
@@ -523,68 +511,30 @@
},
{
"cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 1000
- },
- "colab_type": "code",
- "id": "60nOj98kabyZ",
- "outputId": "7be20f83-52e2-4a15-de4c-dfc2d59e9e3f"
- },
+ "execution_count": 14,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
- "text": [
- "Training Results - Epoch: 1 Avg accuracy: 89.76 Avg loss: 0.29\n",
- "Validation Results - Epoch: 1 Avg accuracy: 88.49 Avg loss: 0.32\n",
- "Training Results - Epoch: 2 Avg accuracy: 91.58 Avg loss: 0.24\n",
- "Validation Results - Epoch: 2 Avg accuracy: 89.89 Avg loss: 0.29\n",
- "Training Results - Epoch: 3 Avg accuracy: 93.10 Avg loss: 0.19\n",
- "Validation Results - Epoch: 3 Avg accuracy: 90.76 Avg loss: 0.25\n",
- "Training Results - Epoch: 4 Avg accuracy: 92.99 Avg loss: 0.19\n",
- "Validation Results - Epoch: 4 Avg accuracy: 91.03 Avg loss: 0.26\n",
- "Training Results - Epoch: 5 Avg accuracy: 93.77 Avg loss: 0.17\n",
- "Validation Results - Epoch: 5 Avg accuracy: 91.00 Avg loss: 0.26\n",
- "Training Results - Epoch: 6 Avg accuracy: 94.42 Avg loss: 0.16\n",
- "Validation Results - Epoch: 6 Avg accuracy: 90.76 Avg loss: 0.27\n",
- "Training Results - Epoch: 7 Avg accuracy: 94.82 Avg loss: 0.14\n",
- "Validation Results - Epoch: 7 Avg accuracy: 91.21 Avg loss: 0.27\n",
- "Training Results - Epoch: 8 Avg accuracy: 95.62 Avg loss: 0.12\n",
- "Validation Results - Epoch: 8 Avg accuracy: 91.28 Avg loss: 0.26\n",
- "Training Results - Epoch: 9 Avg accuracy: 94.22 Avg loss: 0.16\n",
- "Validation Results - Epoch: 9 Avg accuracy: 90.57 Avg loss: 0.32\n",
- "Training Results - Epoch: 10 Avg accuracy: 95.28 Avg loss: 0.13\n",
- "Validation Results - Epoch: 10 Avg accuracy: 91.07 Avg loss: 0.30\n",
- "Training Results - Epoch: 11 Avg accuracy: 96.23 Avg loss: 0.10\n",
- "Validation Results - Epoch: 11 Avg accuracy: 91.10 Avg loss: 0.29\n",
- "Training Results - Epoch: 12 Avg accuracy: 96.75 Avg loss: 0.09\n",
- "Validation Results - Epoch: 12 Avg accuracy: 91.26 Avg loss: 0.29\n"
- ]
+ "text": "Training Results - Epoch: 1 Avg accuracy: 89.26 Avg loss: 0.29\nValidation Results - Epoch: 1 Avg accuracy: 88.04 Avg loss: 0.33\nTraining Results - Epoch: 2 Avg accuracy: 90.81 Avg loss: 0.26\nValidation Results - Epoch: 2 Avg accuracy: 89.37 Avg loss: 0.30\nTraining Results - Epoch: 3 Avg accuracy: 91.63 Avg loss: 0.23\nValidation Results - Epoch: 3 Avg accuracy: 89.31 Avg loss: 0.29\nTraining Results - Epoch: 4 Avg accuracy: 91.21 Avg loss: 0.23\nValidation Results - Epoch: 4 Avg accuracy: 88.86 Avg loss: 0.31\nTraining Results - Epoch: 5 Avg accuracy: 92.90 Avg loss: 0.19\nValidation Results - Epoch: 5 Avg accuracy: 90.39 Avg loss: 0.27\nTraining Results - Epoch: 6 Avg accuracy: 94.75 Avg loss: 0.15\nValidation Results - Epoch: 6 Avg accuracy: 91.85 Avg loss: 0.24\nTraining Results - Epoch: 7 Avg accuracy: 94.75 Avg loss: 0.15\nValidation Results - Epoch: 7 Avg accuracy: 91.60 Avg loss: 0.25\nTraining Results - Epoch: 8 Avg accuracy: 94.87 Avg loss: 0.14\nValidation Results - Epoch: 8 Avg accuracy: 91.69 Avg loss: 0.26\nTraining Results - Epoch: 9 Avg accuracy: 95.17 Avg loss: 0.13\nValidation Results - Epoch: 9 Avg accuracy: 91.29 Avg loss: 0.26\nTraining Results - Epoch: 10 Avg accuracy: 95.66 Avg loss: 0.12\nValidation Results - Epoch: 10 Avg accuracy: 91.38 Avg loss: 0.26\nTraining Results - Epoch: 11 Avg accuracy: 95.84 Avg loss: 0.11\nValidation Results - Epoch: 11 Avg accuracy: 91.18 Avg loss: 0.27\nTraining Results - Epoch: 12 Avg accuracy: 96.82 Avg loss: 0.09\nValidation Results - Epoch: 12 Avg accuracy: 91.91 Avg loss: 0.26\n"
},
{
"data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 13,
- "metadata": {
- "tags": []
+ "text/plain": "State:\n\titeration: 11256\n\tepoch: 12\n\tepoch_length: 938\n\tmax_epochs: 12\n\toutput: 0.09215123951435089\n\tbatch: \n\tmetrics: \n\tdataloader: \n\tseed: 12"
},
+ "execution_count": 14,
+ "metadata": {},
"output_type": "execute_result"
},
{
"data": {
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q/eUdWalSRSpVOopZs+ZSokRxJk4azuWXdafLJR3YsX0HL730Vm5EBWD33uRc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