From 6ff1debb9343bf763a8610744609f9f3c9b8eec3 Mon Sep 17 00:00:00 2001
From: jatkinson1000 <109271713+jatkinson1000@users.noreply.github.com>
Date: Sun, 19 Nov 2023 18:38:14 +0000
Subject: [PATCH 1/7] Add setup.py file to allow running on binder.
---
setup.py | 6 ++++++
1 file changed, 6 insertions(+)
create mode 100644 setup.py
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..93c246e
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,6 @@
+#!usr/bin/env python
+
+from setuptools import setup
+
+if __name__ == "__main__":
+ setup()
From 282a14639cf76424b7f8829aa09b091bb97902b3 Mon Sep 17 00:00:00 2001
From: jatkinson1000 <109271713+jatkinson1000@users.noreply.github.com>
Date: Sun, 19 Nov 2023 18:51:53 +0000
Subject: [PATCH 2/7] Add binder info to README.
---
README.md | 14 +++++++++++++-
1 file changed, 13 insertions(+), 1 deletion(-)
diff --git a/README.md b/README.md
index 0abb846..771d911 100644
--- a/README.md
+++ b/README.md
@@ -6,6 +6,7 @@
[![CC BY-NC-SA 4.0][cc-by-nc-sa-shield]][cc-by-nc-sa]
+[](https://mybinder.org/v2/gh/Cambridge-ICCS/ml-training-material/main)
This repository contains documentation, resources, and code for the Introduction to
Machine Learning with PyTorch session designed and delivered by [Jack Atkinson](https://jackatkinson.net/) ([**@jatkinson1000**](https://github.com/jatkinson1000))
@@ -136,10 +137,11 @@ us before a training session.
## Installation and setup
-There are two options for participating in this workshop for which instructions are provided below:
+There are three options for participating in this workshop for which instructions are provided below:
* via a [local install](#local-install)
* on [Google Colab](#google-colab)
+* on [binder](#binder)
We recommend the [local install](#local-install) approach, especially if you forked
the repository, as it is the easiest way to keep a copy of your work and push back to GitHub.
@@ -219,6 +221,7 @@ python -m ipykernel install --user --name=MLvenv
### Google Colab
+Running on Colab is useful as it allows you to access GPU resources.
To run the notebooks in Google Colab click the following links for each of the exercises:
* [Exercise 01](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/exercises/01_penguin_classification.ipynb)
@@ -231,6 +234,15 @@ _Notes:_
* _If you leave a Colab session your work will be lost, so be careful to save any work
you want to keep._
+### binder
+
+If you cannot operate using a local install, and do not wish to sign up for a Colab account,
+the exercises can be launched
+[on binder](https://mybinder.org/v2/gh/Cambridge-ICCS/ml-training-material/main).
+
+_Notes:_
+* _It is not possible to save work from a binder session, so we advise using one of the above options.
+ This is an easy way to run the worked solutions, however._
## License
From 9b379879933454d3da4cd89652ab6012489be5c7 Mon Sep 17 00:00:00 2001
From: jatkinson1000 <109271713+jatkinson1000@users.noreply.github.com>
Date: Sun, 19 Nov 2023 19:31:11 +0000
Subject: [PATCH 3/7] Update README moving colab solutions and adding binder
info.
---
README.md | 24 ++++++++++--------------
1 file changed, 10 insertions(+), 14 deletions(-)
diff --git a/README.md b/README.md
index 771d911..49ce479 100644
--- a/README.md
+++ b/README.md
@@ -71,13 +71,6 @@ These are for recapping after the course in case you missed anything, and contai
[linted](https://docs.pylint.org/intro.html), and conforming to the
[black](https://black.readthedocs.io/en/stable/) code style.
-If you were working on Colab you can open the worked solutions using the following links:
-
-* [Exercise 01](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/worked-solutions/01_penguin_classification_solutions.ipynb)
-* [Exercise 02](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/worked-solutions/02_penguin_regression_solutions.ipynb)
-* [Exercise 03](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/worked-solutions/03_mnist_classification_solutions.ipynb)
-* [Exercise 04](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/worked-solutions/04_ellipse_regression_solutions.ipynb)
-
## Preparation and prerequisites
@@ -148,7 +141,7 @@ the repository, as it is the easiest way to keep a copy of your work and push ba
However, if you experience issues with the installation process or are unfamiliar with
the terminal/installation process there is the option to run the notebooks in
-[Google Colab](#google-colab).
+[Google Colab](#google-colab) or on [binder](#binder).
### Local Install
@@ -224,10 +217,10 @@ python -m ipykernel install --user --name=MLvenv
Running on Colab is useful as it allows you to access GPU resources.
To run the notebooks in Google Colab click the following links for each of the exercises:
-* [Exercise 01](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/exercises/01_penguin_classification.ipynb)
-* [Exercise 02](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/exercises/02_penguin_regression.ipynb)
-* [Exercise 03](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/exercises/03_mnist_classification.ipynb)
-* [Exercise 04](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/exercises/04_ellipse_regression.ipynb)
+* [Exercise 01](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/exercises/01_penguin_classification.ipynb) - [Worked Solution 01](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/worked-solutions/01_penguin_classification_solutions.ipynb)
+* [Exercise 02](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/exercises/02_penguin_regression.ipynb) - [Worked Solution 02](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/worked-solutions/02_penguin_regression_solutions.ipynb)
+* [Exercise 03](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/exercises/03_mnist_classification.ipynb) - [Worked Solution 03](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/worked-solutions/03_mnist_classification_solutions.ipynb)
+* [Exercise 04](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/exercises/04_ellipse_regression.ipynb) - [Worked Solution 04](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/worked-solutions/04_ellipse_regression_solutions.ipynb)
_Notes:_
* _Running in Google Colab requires you to have a Google account._
@@ -241,8 +234,11 @@ the exercises can be launched
[on binder](https://mybinder.org/v2/gh/Cambridge-ICCS/ml-training-material/main).
_Notes:_
-* _It is not possible to save work from a binder session, so we advise using one of the above options.
- This is an easy way to run the worked solutions, however._
+* _If you leave a binder session your work will be lost, so be careful to save any work
+ you want to keep_
+* _Due to the limited resources provided by binder you will struggle to run training in
+ exercises 3 and 4._
+
## License
From 1f819ada4184cda4b82377bc01ddd998835e8238 Mon Sep 17 00:00:00 2001
From: jatkinson1000 <109271713+jatkinson1000@users.noreply.github.com>
Date: Sun, 19 Nov 2023 20:20:25 +0000
Subject: [PATCH 4/7] Update githubb pages site with binder and small readme
phrasing.
---
README.md | 6 +++---
slides/index.html | 18 ++++++++++++++++--
2 files changed, 19 insertions(+), 5 deletions(-)
diff --git a/README.md b/README.md
index 49ce479..e12c340 100644
--- a/README.md
+++ b/README.md
@@ -215,7 +215,7 @@ python -m ipykernel install --user --name=MLvenv
### Google Colab
Running on Colab is useful as it allows you to access GPU resources.
-To run the notebooks in Google Colab click the following links for each of the exercises:
+To launch the notebooks in Google Colab click the following links for each of the exercises:
* [Exercise 01](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/exercises/01_penguin_classification.ipynb) - [Worked Solution 01](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/worked-solutions/01_penguin_classification_solutions.ipynb)
* [Exercise 02](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/exercises/02_penguin_regression.ipynb) - [Worked Solution 02](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/worked-solutions/02_penguin_regression_solutions.ipynb)
@@ -229,8 +229,8 @@ _Notes:_
### binder
-If you cannot operate using a local install, and do not wish to sign up for a Colab account,
-the exercises can be launched
+If you cannot operate using a local install, and do not wish to sign up for a Google account,
+the repository can be launched
[on binder](https://mybinder.org/v2/gh/Cambridge-ICCS/ml-training-material/main).
_Notes:_
diff --git a/slides/index.html b/slides/index.html
index f516c17..80e998f 100644
--- a/slides/index.html
+++ b/slides/index.html
@@ -86,6 +86,7 @@ Contents
Prerequisites
@@ -135,6 +136,7 @@ Setup Instructions
We recommend the local install approach, especially if you forked the repository, as it is the easiest way to keep a copy of your work and push back to github.
@@ -183,7 +185,8 @@ Optional) Keep virtual environment persistent in Jupyter Notebooks
python -m ipykernel install --user --name=MLvenv
Google Colab
- To run the notebooks in Google Colab click the following links for each of the exercises:
+ Running on Colab is useful as it allows you to access GPU resources.
+ To launch the notebooks in Google Colab click the following links for each of the exercises:
- Exercise 01
- Exercise 02
@@ -196,6 +199,17 @@ Google Colab
If you leave a Colab session your work will be lost, so be careful to save any work you want to keep.
+ binder
+ To run the notebooks in binder click the following link:
+
+
+ Notes:
+ If you leave a binder session your work will be lost, so be careful to save any work you want to keep.
+ Due to the limited resources provided by binder you will struggle to run training in exercises 3 and 4.
+
+
Solutions
Worked solutions for all of the exercises can be found in the worked-solutions/ directory.
@@ -246,7 +260,7 @@
Python
git and GitHub
- Unless participating via Colab you will be expected to know how to:
+ Unless participating via Colab or binder you will be expected to know how to:
- clone and/or fork a repository,
- commit, and
From 8566ac5673a3d0ec77d99f783ba1a417be78a311 Mon Sep 17 00:00:00 2001
From: Dominic Orchard
Date: Fri, 22 Dec 2023 10:25:33 +0000
Subject: [PATCH 5/7] Small updates for NCAS summer school improving background
info (#24)
* some comments on notebooks to help students
* make worked examples consistent with the additional commentary added in the exercises
* typo fix for consistency
---
exercises/01_penguin_classification.ipynb | 12 +++++++++---
src/ml_workshop/_penguins.py | 1 +
.../01_penguin_classification_solutions.ipynb | 11 +++++++----
3 files changed, 17 insertions(+), 7 deletions(-)
diff --git a/exercises/01_penguin_classification.ipynb b/exercises/01_penguin_classification.ipynb
index c807dab..4d8b931 100644
--- a/exercises/01_penguin_classification.ipynb
+++ b/exercises/01_penguin_classification.ipynb
@@ -105,7 +105,9 @@
" train=True,\n",
")\n",
"\n",
+ "\n",
"for features, target in data_set:\n",
+ " # print the features and targets here\n",
" pass"
]
},
@@ -124,7 +126,7 @@
"source": [
"### Task 4: Applying transforms to the data\n",
"\n",
- "A common way of transforming inputs to neural networks is to apply a series of transforms using ``torchvision.transforms.Compose``. The ``Compose`` object takes a list of callable objects and applies them to the incoming data.\n",
+ "A common way of transforming inputs to neural networks is to apply a series of transforms using ``torchvision.transforms.Compose``. The [``Compose``](https://pytorch.org/vision/stable/generated/torchvision.transforms.Compose.html) object takes a list of callable objects (i.e., functions) and applies them to the incoming data.\n",
"\n",
"These transforms can be very useful for mapping between file paths and tensors of images, etc.\n",
"\n",
@@ -141,8 +143,12 @@
"outputs": [],
"source": [
"from torchvision.transforms import Compose\n",
+ "# import some useful functions here, see https://pytorch.org/docs/stable/torch.html\n",
+ "# where `tensor` and `eye` are used for constructing tensors,\n",
+ "# and using a lower-precision float32 is advised for performance\n",
+ "from torch import tensor, eye, float32 \n",
"\n",
- "# Apply the transforms we need to the PenguinDataset to get out inputs\n",
+ "# Apply the transforms we need to the PenguinDataset to get out input\n",
"# targets as Tensors."
]
},
@@ -154,7 +160,7 @@
"\n",
"- Once we have created a ``Dataset`` object, we wrap it in a ``DataLoader``.\n",
" - The ``DataLoader`` object allows us to put our inputs and targets in mini-batches, which makes for more efficient training.\n",
- " - Note: rather than supplying one input-target pair to the model at a time, we supply \"mini-batches\" of these data at once.\n",
+ " - Note: rather than supplying one input-target pair to the model at a time, we supply \"mini-batches\" of these data at once (typically a small power of 2, like 16 or 32).\n",
" - The number of items we supply at once is called the batch size.\n",
" - The ``DataLoader`` can also randomly shuffle the data each epoch (when training).\n",
" - It allows us to load different mini-batches in parallel, which can be very useful for larger datasets and images that can't all fit in memory at once.\n",
diff --git a/src/ml_workshop/_penguins.py b/src/ml_workshop/_penguins.py
index a3a5d79..fc304ec 100644
--- a/src/ml_workshop/_penguins.py
+++ b/src/ml_workshop/_penguins.py
@@ -109,6 +109,7 @@ def _load_penguin_data() -> DataFrame:
.sort_values(by=sorted(data.keys()))
.reset_index(drop=True)
)
+ # Transform the sex field into a float, with male represented by 1.0, female by 0.0
data.sex = (data.sex == "male").astype(float)
return data
diff --git a/worked-solutions/01_penguin_classification_solutions.ipynb b/worked-solutions/01_penguin_classification_solutions.ipynb
index ef9a308..c8666eb 100644
--- a/worked-solutions/01_penguin_classification_solutions.ipynb
+++ b/worked-solutions/01_penguin_classification_solutions.ipynb
@@ -214,7 +214,7 @@
"source": [
"### Task 4: Applying transforms to the data\n",
"\n",
- "A common way of transforming inputs to neural networks is to apply a series of transforms using ``torchvision.transforms.Compose``. The ``Compose`` object takes a list of callable objects and applies them to the incoming data.\n",
+ "A common way of transforming inputs to neural networks is to apply a series of transforms using ``torchvision.transforms.Compose``. The [``Compose``](https://pytorch.org/vision/stable/generated/torchvision.transforms.Compose.html) object takes a list of callable objects and applies them to the incoming data.\n",
"\n",
"These transforms can be very useful for mapping between file paths and tensors of images, etc.\n",
"\n",
@@ -242,11 +242,14 @@
}
],
"source": [
- "from torch import tensor, float32, eye\n",
"from torchvision.transforms import Compose\n",
+ "# import some useful functions here, see https://pytorch.org/docs/stable/torch.html\n",
+ "# where `tensor` and `eye` are used for constructing tensors,\n",
+ "# and using a lower-precision float32 is advised for performance\n",
+ "from torch import tensor, float32, eye\n",
"\n",
"\n",
- "# Apply the transforms we need to the PenguinDataset to get out inputs\n",
+ "# Apply the transforms we need to the PenguinDataset to get out input\n",
"# targets as Tensors.\n",
"\n",
"\n",
@@ -321,7 +324,7 @@
"\n",
"- Once we have created a ``Dataset`` object, we wrap it in a ``DataLoader``.\n",
" - The ``DataLoader`` object allows us to put our inputs and targets in mini-batches, which makes for more efficient training.\n",
- " - Note: rather than supplying one input-target pair to the model at a time, we supply \"mini-batches\" of these data at once.\n",
+ " - Note: rather than supplying one input-target pair to the model at a time, we supply \"mini-batches\" of these data at once (typically a small power of 2, like 16 or 32).\n",
" - The number of items we supply at once is called the batch size.\n",
" - The ``DataLoader`` can also randomly shuffle the data each epoch (when training).\n",
" - It allows us to load different mini-batches in parallel, which can be very useful for larger datasets and images that can't all fit in memory at once.\n",
From e7dccf5437df459740322977badd2483133cb357 Mon Sep 17 00:00:00 2001
From: Jim Denholm <44713405+jdenholm@users.noreply.github.com>
Date: Tue, 26 Mar 2024 18:39:58 +0000
Subject: [PATCH 6/7] :memo: Type-hint and docstring fix (#44)
---
src/ml_workshop/_penguins.py | 7 ++++---
1 file changed, 4 insertions(+), 3 deletions(-)
diff --git a/src/ml_workshop/_penguins.py b/src/ml_workshop/_penguins.py
index fc304ec..d0fbd0f 100644
--- a/src/ml_workshop/_penguins.py
+++ b/src/ml_workshop/_penguins.py
@@ -1,4 +1,5 @@
"""Penguins dataset."""
+
from typing import Optional, List, Dict, Tuple, Any
from torch.utils.data import Dataset
@@ -17,9 +18,9 @@ class PenguinDataset(Dataset):
Parameters
----------
- input_keys : Sequence[str]
+ input_keys : List[str]
The column titles to use in the input feature vectors.
- target_keys : Sequnce[str]
+ target_keys : List[str]
The column titles to use in the target feature vectors.
train : bool
If ``True``, this object will serve as the training set, and if
@@ -39,7 +40,7 @@ class PenguinDataset(Dataset):
def __init__(
self,
input_keys: List[str],
- target_keys: str,
+ target_keys: List[str],
train: bool,
x_tfms: Optional[Compose] = None,
y_tfms: Optional[Compose] = None,
From f31c074e2a62afcfe0a04a4586dc7e53d91b7337 Mon Sep 17 00:00:00 2001
From: jatkinson1000 <109271713+jatkinson1000@users.noreply.github.com>
Date: Thu, 9 May 2024 17:15:51 +0000
Subject: [PATCH 7/7] Add contribution and support guidelines to the README.
(#48)
* Add contribution and support guidelines to the README.
* Add contribution and support guidelines to the webpage.
---
README.md | 22 ++++++++++++++++++++++
slides/index.html | 7 +++++++
2 files changed, 29 insertions(+)
diff --git a/README.md b/README.md
index e12c340..52e590b 100644
--- a/README.md
+++ b/README.md
@@ -24,6 +24,7 @@ A website for this workshop can be found at [https://cambridge-iccs.github.io/ml
- [Preparation and prerequisites](#preparation-and-prerequisites)
- [Installation and setup](#installation-and-setup)
- [License information](#license)
+- [Contribution Guidelines and Support](#contribution-guidelines-and-support)
## Learning Objectives
@@ -252,3 +253,24 @@ The teaching materials are licensed under a
[cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg
[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]
+
+## Contribution Guidelines and Support
+
+If you spot an issue with the materials please let us know by
+[opening an issue](https://github.com/Cambridge-ICCS/ml-training-material/issues/new/choose)
+here on GitHub clearly describing the problem.
+
+If you are able to fix an issue that you spot, or an
+[existing open issue](https://github.com/Cambridge-ICCS/ml-training-material/issues/new/choose)
+please get in touch by commenting on the issue thread.
+
+Contributions from the community are welcome.
+To contribute back to the repository please first
+[fork it](https://github.com/Cambridge-ICCS/ml-training-material/fork),
+make the neccessary changes to fix the problem, and then open a pull request back to
+this repository clerly describing the changes you have made.
+We will then preform a review and merge once ready.
+
+If you would like support using these materials, adapting them to your needs, or
+delivering them please get in touch either via GitHub or via
+[ICCS](https://github.com/Cambridge-ICCS).
diff --git a/slides/index.html b/slides/index.html
index 80e998f..e4f46fb 100644
--- a/slides/index.html
+++ b/slides/index.html
@@ -91,6 +91,7 @@ Contents
Prerequisites
License
+ Contribution Guidelines and Support
@@ -290,6 +291,12 @@ License
The teaching materials are licensed under CC BY-NC-SA 4.0.
+ Contribution Guidelines and Support
+ If you spot an issue with the materials please let us know by opening an issue on GitHub clearly describing the problem.
+ If you are able to fix an issue that you spot, or an existing open issue please get in touch by commenting on the issue thread.
+ Contributions from the community are welcome. To contribute back to the repository please first fork it, make the neccessary changes to fix the problem, and then open a pull request back to this repository clerly describing the changes you have made. We will then preform a review and merge once ready.
+ If you would like support using these materials, adapting them to your needs, or delivering them please get in touch either via GitHub or via ICCS.
+