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Updated all referenecs to old github repo #479 (#520)

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joshy authored and BenjaminBossan committed Sep 2, 2019
1 parent 8cf8f45 commit 066eb39fd4b9fb5946a092d858a210def1016c45
@@ -66,10 +66,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
- Helper functions to build command line interfaces with almost no
boilerplate, [example][1811191713] that shows usage

[1810251445]: https://colab.research.google.com/github/dnouri/skorch/blob/master/notebooks/Basic_Usage.ipynb
[1810261633]: https://colab.research.google.com/github/dnouri/skorch/blob/master/notebooks/Advanced_Usage.ipynb
[1811011230]: https://colab.research.google.com/github/dnouri/skorch/blob/master/notebooks/MNIST.ipynb
[1811191713]: https://github.com/dnouri/skorch/tree/master/examples/cli
[1810251445]: https://colab.research.google.com/github/skorch-dev/skorch/blob/master/notebooks/Basic_Usage.ipynb
[1810261633]: https://colab.research.google.com/github/skorch-dev/skorch/blob/master/notebooks/Advanced_Usage.ipynb
[1811011230]: https://colab.research.google.com/github/skorch-dev/skorch/blob/master/notebooks/MNIST.ipynb
[1811191713]: https://github.com/skorch-dev/skorch/tree/master/examples/cli

### Changed

@@ -106,7 +106,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
- `NeuralNetwork.load_params` now supports loading from `Checkpoint` instances
- Added documentation for [saving and loading][4]

[1]: https://nbviewer.jupyter.org/github/dnouri/skorch/blob/master/examples/nuclei_image_segmentation/Nuclei_Image_Segmentation.ipynb
[1]: https://nbviewer.jupyter.org/github/skorch-dev/skorch/blob/master/examples/nuclei_image_segmentation/Nuclei_Image_Segmentation.ipynb
[2]: https://skorch.readthedocs.io/en/latest/toy.html
[3]: https://skorch.readthedocs.io/en/latest/callbacks.html#skorch.callbacks.ParamMapper
[4]: https://skorch.readthedocs.io/en/latest/user/save_load.html
@@ -136,6 +136,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
the net was configured to use the CPU (#354, #358)


[Unreleased]: https://github.com/dnouri/skorch/compare/v0.5.0...HEAD
[0.4.0]: https://github.com/dnouri/skorch/compare/v0.3.0...v0.4.0
[0.5.0]: https://github.com/dnouri/skorch/compare/v0.4.0...v0.5.0
[Unreleased]: https://github.com/skorch-dev/skorch/compare/v0.5.0...HEAD
[0.4.0]: https://github.com/skorch-dev/skorch/compare/v0.3.0...v0.4.0
[0.5.0]: https://github.com/skorch-dev/skorch/compare/v0.4.0...v0.5.0
@@ -260,7 +260,7 @@ def project_linkcode_resolve(domain, info):
return _linkcode_resolve(domain, info,
package='skorch',
revision=_linkcode_git_revision,
url_fmt='https://github.com/dnouri/skorch/'
url_fmt='https://github.com/skorch-dev/skorch/'
'blob/{revision}/'
'{package}/{path}#L{lineno}')

@@ -4,7 +4,7 @@ REST Service

In this section we'll take the RNN sentiment classifer from the
example `Predicting sentiment on the IMDB dataset
<https://github.com/dnouri/skorch/blob/master/examples/rnn_classifer/RNN_sentiment_classification.ipynb>`_
<https://github.com/skorch-dev/skorch/blob/master/examples/rnn_classifer/RNN_sentiment_classification.ipynb>`_
and use it to demonstrate how to easily expose your PyTorch module on
the web using skorch and another library called `Palladium
<https://github.com/ottogroup/palladium>`_.
@@ -47,7 +47,7 @@ defines the dataset and model:
}
You can save this configuration as ``palladium-config.py``.

The ``dataset_loader_train`` and ``dataset_loader_test`` entries
define where the data comes from. They refer to a Python class
defined inside the ``model`` module. Let's create a file and call it
@@ -106,7 +106,7 @@ we'll create next:
from skorch import NeuralNetClassifier
import torch
def create_pipeline(
vocab_size=1000,
max_len=50,
@@ -132,14 +132,14 @@ You've noticed that this function's job is to create the model and
return it. Here, we're defining a pipeline that wraps skorch's
``NeuralNetClassifier``, which in turn is a wrapper around our PyTorch
module, as it's defined in the `predicting sentiment tutorial
<https://github.com/dnouri/skorch/blob/master/examples/rnn_classifer/RNN_sentiment_classification.ipynb>`_.
<https://github.com/skorch-dev/skorch/blob/master/examples/rnn_classifer/RNN_sentiment_classification.ipynb>`_.
We'll also add the RNNClassifier to ``model.py``:

.. code:: python
from torch import nn
F = nn.functional
class RNNClassifier(nn.Module):
def __init__(
@@ -20,7 +20,7 @@ The base class for each callback is :class:`.Callback`. If you would
like to write your own callbacks, you should inherit from this class.
A guide and practical example on how to write your own callbacks is
shown in this `notebook
<https://nbviewer.jupyter.org/github/dnouri/skorch/blob/master/notebooks/Advanced_Usage.ipynb#Writing-a-custom-callback>`_.
<https://nbviewer.jupyter.org/github/skorch-dev/skorch/blob/master/notebooks/Advanced_Usage.ipynb#Writing-a-custom-callback>`_.
In general, remember this:


@@ -153,7 +153,7 @@ In general, the scoring callbacks are useful when the default scores
determined by the :class:`.NeuralNet` are not enough. They allow you
to easily add new metrics to be logged during training. For an example
of how to add a new score to your model, look `at this notebook
<https://nbviewer.jupyter.org/github/dnouri/skorch/blob/master/notebooks/Basic_Usage.ipynb#Callbacks>`_.
<https://nbviewer.jupyter.org/github/skorch-dev/skorch/blob/master/notebooks/Basic_Usage.ipynb#Callbacks>`_.

The first argument to both callbacks is ``name`` and should be a
string. This determines the column name of the score shown by the
@@ -203,4 +203,4 @@ as usual).
.. _fire: https://github.com/google/python-fire
.. _numpydoc: https://github.com/numpy/numpydoc
.. _example: https://github.com/dnouri/skorch/tree/master/examples/cli
.. _example: https://github.com/skorch-dev/skorch/tree/master/examples/cli
@@ -33,7 +33,7 @@ If you just want to use skorch, use:

.. code:: bash
git clone https://github.com/dnouri/skorch.git
git clone https://github.com/skorch-dev/skorch.git
cd skorch
conda env create
source activate skorch
@@ -44,7 +44,7 @@ If you want to help developing, run:

.. code:: bash
git clone https://github.com/dnouri/skorch.git
git clone https://github.com/skorch-dev/skorch.git
cd skorch
conda env create
source activate skorch
@@ -62,7 +62,7 @@ If you just want to use skorch, use:

.. code:: bash
git clone https://github.com/dnouri/skorch.git
git clone https://github.com/skorch-dev/skorch.git
cd skorch
# create and activate a virtual environment
pip install -r requirements.txt
@@ -73,7 +73,7 @@ If you want to help developing, run:

.. code:: bash
git clone https://github.com/dnouri/skorch.git
git clone https://github.com/skorch-dev/skorch.git
cd skorch
# create and activate a virtual environment
pip install -r requirements.txt
@@ -5,20 +5,20 @@ Tutorials

The following are examples and notebooks on how to use skorch.

* `Basic Usage <https://nbviewer.jupyter.org/github/dnouri/skorch/blob/master/notebooks/Basic_Usage.ipynb>`_ - Explores the basics of the skorch API. `Run in Google Colab 💻 <https://colab.research.google.com/github/dnouri/skorch/blob/master/notebooks/Basic_Usage.ipynb>`_
* `Basic Usage <https://nbviewer.jupyter.org/github/skorch-dev/skorch/blob/master/notebooks/Basic_Usage.ipynb>`_ - Explores the basics of the skorch API. `Run in Google Colab 💻 <https://colab.research.google.com/github/skorch-dev/skorch/blob/master/notebooks/Basic_Usage.ipynb>`_

* `MNIST with scikit-learn and skorch <https://github.com/dnouri/skorch/blob/master/notebooks/MNIST.ipynb>`_ - Define and train a simple neural network with PyTorch and use it with skorch. `Run in Google Colab 💻 <https://colab.research.google.com/github/dnouri/skorch/blob/master/notebooks/MNIST.ipynb>`_
* `MNIST with scikit-learn and skorch <https://github.com/skorch-dev/skorch/blob/master/notebooks/MNIST.ipynb>`_ - Define and train a simple neural network with PyTorch and use it with skorch. `Run in Google Colab 💻 <https://colab.research.google.com/github/skorch-dev/skorch/blob/master/notebooks/MNIST.ipynb>`_

* `Benchmarks skorch vs pure PyTorch <https://github.com/dnouri/skorch/blob/master/examples/benchmarks/mnist.py>`_ - Compares the performance of skorch and using pure PyTorch on MNIST.
* `Benchmarks skorch vs pure PyTorch <https://github.com/skorch-dev/skorch/blob/master/examples/benchmarks/mnist.py>`_ - Compares the performance of skorch and using pure PyTorch on MNIST.

* `Transfer Learning with skorch <https://github.com/dnouri/skorch/blob/master/notebooks/Transfer_Learning.ipynb>`_ - Train a neutral network using transfer learning with skorch. `Run in Google Colab 💻 <https://colab.research.google.com/github/dnouri/skorch/blob/master/notebooks/Transfer_Learning.ipynb>`_
* `Transfer Learning with skorch <https://github.com/skorch-dev/skorch/blob/master/notebooks/Transfer_Learning.ipynb>`_ - Train a neutral network using transfer learning with skorch. `Run in Google Colab 💻 <https://colab.research.google.com/github/skorch-dev/skorch/blob/master/notebooks/Transfer_Learning.ipynb>`_

* `Image Segmentation with UNets <https://github.com/dnouri/skorch/blob/master/examples/nuclei_image_segmentation>`_ - Use transfer learning to train a UNet model for image segmentation.
* `Image Segmentation with UNets <https://github.com/skorch-dev/skorch/blob/master/examples/nuclei_image_segmentation>`_ - Use transfer learning to train a UNet model for image segmentation.

* `Using skorch with Dask <https://github.com/dnouri/skorch/tree/master/examples/rnn_classifer>`_ - Using Dask to parallelize grid search across GPUs.
* `Using skorch with Dask <https://github.com/skorch-dev/skorch/tree/master/examples/rnn_classifer>`_ - Using Dask to parallelize grid search across GPUs.

* `World level language modeling RNN <https://github.com/dnouri/skorch/tree/master/examples/word_language_model>`_ - Uses skorch to train a language model.
* `World level language modeling RNN <https://github.com/skorch-dev/skorch/tree/master/examples/word_language_model>`_ - Uses skorch to train a language model.

* `Seq2Seq Translation using skorch <https://github.com/dnouri/skorch/tree/master/examples/translation>`_ - Translation with a seqeuence to sequence network.
* `Seq2Seq Translation using skorch <https://github.com/skorch-dev/skorch/tree/master/examples/translation>`_ - Translation with a seqeuence to sequence network.

* `Advanced Usage <https://nbviewer.jupyter.org/github/dnouri/skorch/blob/master/notebooks/Advanced_Usage.ipynb>`_ - Dives deep into the inner works of skorch. `Run in Google Colab 💻 <https://colab.research.google.com/github/dnouri/skorch/blob/master/notebooks/Advanced_Usage.ipynb>`_
* `Advanced Usage <https://nbviewer.jupyter.org/github/skorch-dev/skorch/blob/master/notebooks/Advanced_Usage.ipynb>`_ - Dives deep into the inner works of skorch. `Run in Google Colab 💻 <https://colab.research.google.com/github/skorch-dev/skorch/blob/master/notebooks/Advanced_Usage.ipynb>`_
@@ -1,6 +1,6 @@
# Nuclei Image Segmentation With UNets

This tutorial can be viewed at [nbviewer](https://nbviewer.jupyter.org/github/dnouri/skorch/blob/master/examples/nuclei_image_segmentation/Nuclei_Image_Segmentation.ipynb).
This tutorial can be viewed at [nbviewer](https://nbviewer.jupyter.org/github/skorch-dev/skorch/blob/master/examples/nuclei_image_segmentation/Nuclei_Image_Segmentation.ipynb).

## Setup

@@ -14,10 +14,10 @@
"This notebook shows some more advanced features of `skorch`. More examples will be added with time.\n",
"\n",
"<table align=\"left\"><td>\n",
"<a target=\"_blank\" href=\"https://colab.research.google.com/github/dnouri/skorch/blob/master/notebooks/Advanced_Usage.ipynb\">\n",
"<a target=\"_blank\" href=\"https://colab.research.google.com/github/skorch-dev/skorch/blob/master/notebooks/Advanced_Usage.ipynb\">\n",
" <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a> \n",
"</td><td>\n",
"<a target=\"_blank\" href=\"https://github.com/dnouri/skorch/blob/master/notebooks/Advanced_Usage.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a></td></table>"
"<a target=\"_blank\" href=\"https://github.com/skorch-dev/skorch/blob/master/notebooks/Advanced_Usage.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a></td></table>"
]
},
{
@@ -14,10 +14,10 @@
"*`skorch`* is designed to maximize interoperability between `sklearn` and `pytorch`. The aim is to keep 99% of the flexibility of `pytorch` while being able to leverage most features of `sklearn`. Below, we show the basic usage of `skorch` and how it can be combined with `sklearn`.\n",
"\n",
"<table align=\"left\"><td>\n",
"<a target=\"_blank\" href=\"https://colab.research.google.com/github/dnouri/skorch/blob/master/notebooks/Basic_Usage.ipynb\">\n",
"<a target=\"_blank\" href=\"https://colab.research.google.com/github/skorch-dev/skorch/blob/master/notebooks/Basic_Usage.ipynb\">\n",
" <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a> \n",
"</td><td>\n",
"<a target=\"_blank\" href=\"https://github.com/dnouri/skorch/blob/master/notebooks/Basic_Usage.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a></td></table>"
"<a target=\"_blank\" href=\"https://github.com/skorch-dev/skorch/blob/master/notebooks/Basic_Usage.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a></td></table>"
]
},
{
@@ -977,7 +977,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"For information on how to write custom callbacks, have a look at the [Advanced_Usage](https://nbviewer.jupyter.org/github/dnouri/skorch/blob/master/notebooks/Advanced_Usage.ipynb) notebook."
"For information on how to write custom callbacks, have a look at the [Advanced_Usage](https://nbviewer.jupyter.org/github/skorch-dev/skorch/blob/master/notebooks/Advanced_Usage.ipynb) notebook."
]
},
{
@@ -9,17 +9,17 @@
"This notebooks shows how to define and train a simple Neural-Network with PyTorch and use it via skorch with the help of torchvision.\n",
"\n",
"<table align=\"left\"><td>\n",
"<a target=\"_blank\" href=\"https://colab.research.google.com/github/dnouri/skorch/blob/master/notebooks/MNIST.ipynb\">\n",
"<a target=\"_blank\" href=\"https://colab.research.google.com/github/skorch-dev/skorch/blob/master/notebooks/MNIST.ipynb\">\n",
" <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a> \n",
"</td><td>\n",
"<a target=\"_blank\" href=\"https://github.com/dnouri/skorch/blob/master/notebooks/MNIST.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a></td></table>"
"<a target=\"_blank\" href=\"https://github.com/skorch-dev/skorch/blob/master/notebooks/MNIST.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a></td></table>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note**: If you are running this in [a colab notebook](https://colab.research.google.com/github/dnouri/skorch/blob/master/notebooks/MNIST.ipynb), we recommend you enable a free GPU by going:\n",
"**Note**: If you are running this in [a colab notebook](https://colab.research.google.com/github/skorch-dev/skorch/blob/master/notebooks/MNIST.ipynb), we recommend you enable a free GPU by going:\n",
"\n",
"> **Runtime**   →   **Change runtime type**   →   **Hardware Accelerator: GPU**\n",
"\n",
@@ -9,17 +9,17 @@
"This notebooks shows how to define and train a simple Neural-Network with PyTorch and use it via skorch with SciKit-Learn.\n",
"\n",
"<table align=\"left\"><td>\n",
"<a target=\"_blank\" href=\"https://colab.research.google.com/github/dnouri/skorch/blob/master/notebooks/MNIST.ipynb\">\n",
"<a target=\"_blank\" href=\"https://colab.research.google.com/github/skorch-dev/skorch/blob/master/notebooks/MNIST.ipynb\">\n",
" <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a> \n",
"</td><td>\n",
"<a target=\"_blank\" href=\"https://github.com/dnouri/skorch/blob/master/notebooks/MNIST.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a></td></table>"
"<a target=\"_blank\" href=\"https://github.com/skorch-dev/skorch/blob/master/notebooks/MNIST.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a></td></table>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note**: If you are running this in [a colab notebook](https://colab.research.google.com/github/dnouri/skorch/blob/master/notebooks/MNIST.ipynb), we recommend you enable a free GPU by going:\n",
"**Note**: If you are running this in [a colab notebook](https://colab.research.google.com/github/skorch-dev/skorch/blob/master/notebooks/MNIST.ipynb), we recommend you enable a free GPU by going:\n",
"\n",
"> **Runtime**   →   **Change runtime type**   →   **Hardware Accelerator: GPU**\n",
"\n",
@@ -1,7 +1,7 @@
# Notebooks

* [Basic usage](https://nbviewer.jupyter.org/github/dnouri/skorch/blob/master/notebooks/Basic_Usage.ipynb)
* [Advanced usage](https://nbviewer.jupyter.org/github/dnouri/skorch/blob/master/notebooks/Advanced_Usage.ipynb)
* [MNIST](https://nbviewer.jupyter.org/github/dnouri/skorch/blob/master/notebooks/MNIST.ipynb)
* [MNIST using torchvision](https://nbviewer.jupyter.org/github/dnouri/skorch/blob/master/notebooks/MNIST-torchvision.ipynb)
* [Transfer Learning](https://nbviewer.jupyter.org/github/dnouri/skorch/blob/master/notebooks/Transfer_Learning.ipynb)
* [Basic usage](https://nbviewer.jupyter.org/github/skorch-dev/skorch/blob/master/notebooks/Basic_Usage.ipynb)
* [Advanced usage](https://nbviewer.jupyter.org/github/skorch-dev/skorch/blob/master/notebooks/Advanced_Usage.ipynb)
* [MNIST](https://nbviewer.jupyter.org/github/skorch-dev/skorch/blob/master/notebooks/MNIST.ipynb)
* [MNIST using torchvision](https://nbviewer.jupyter.org/github/skorch-dev/skorch/blob/master/notebooks/MNIST-torchvision.ipynb)
* [Transfer Learning](https://nbviewer.jupyter.org/github/skorch-dev/skorch/blob/master/notebooks/Transfer_Learning.ipynb)

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