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# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock
# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/#use-with-ide
.pdm.toml
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/
# PyCharm
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/

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{
"project_name": "project_name",
"repo_name": "{{ cookiecutter.project_name.lower().replace(' ', '_') }}",
"module_name": "{{ cookiecutter.repo_name }}",
"author_name": "Your name (or your organization/company/team)",
"description": "A short description of the project.",
"open_source_license": ["MIT", "BSD-3-Clause", "No license file"],
}

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GNU GENERAL PUBLIC LICENSE
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Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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option of following the terms and conditions either of that numbered
version or of any later version published by the Free Software
Foundation. If the Program does not specify a version number of the
GNU General Public License, you may choose any version ever published
by the Free Software Foundation.
If the Program specifies that a proxy can decide which future
versions of the GNU General Public License can be used, that proxy's
public statement of acceptance of a version permanently authorizes you
to choose that version for the Program.
Later license versions may give you additional or different
permissions. However, no additional obligations are imposed on any
author or copyright holder as a result of your choosing to follow a
later version.
15. Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
16. Limitation of Liability.
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.

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APP_NAME=ml_pipeline
PYTHON=.venv/bin/python3
INTERPRETER=/usr/bin/python3
.PHONY: help test
all: run
init: ## create a venv
$(INTERPRETER) -m venv .venv
run: ## run the pipeline (train)
$(PYTHON) -m $(APP_NAME) pipeline:train
data: ## download the mnist data
$(PYTHON) -m $(APP_NAME) data:download
# wget https://pjreddie.com/media/files/mnist_train.csv -O data/mnist_train.csv
# wget https://pjreddie.com/media/files/mnist_test.csv -O data/mnist_test.csv
test:
find . -iname "*.py" | entr -c pytest
serve:
$(PYTHON) -m $(APP_NAME) app:serve
install:
$(PYTHON) -m pip install -r requirements.txt
help: ## display this help message
@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-30s\033[0m %s\n", $$1, $$2}'

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# Mimimal Viable Deep Learning Infrastructure
Deep learning pipelines are hard to reason about and difficult to code consistently.
Instead of remembering where to put everything and making a different choice for each project, this repository is an attempt to standardize on good defaults.
Think of it like a mini-pytorch lightening, with all the fory internals exposed for extension and modification.
## Usage
### Install:
Install the conda requirements:
```bash
make install
```
Which is a proxy for calling:
```bash
conda env updates -n ml_pipeline --file environment.yml
```
### Run:
Run the code on MNIST with the following command:
```bash
make run
```

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book

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[book]
authors = ["publicmatt"]
language = "en"
multilingual = false
src = "src"
title = "ml_pipeline"

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# Summary
- [Overview](./index.md)
- [Chapter 1](./chapter_1.md)

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# Chapter 1

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# Overview

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[build-system]
requires = ["setuptools", "wheel"]
build-backend = "setuptools.build_meta"
[project]
name = "ml_pipeline"
version = "0.1.0"
authors = [
{name = "publicmatt", email = "git@publicmatt.com"},
]
description = "A minimal viable pytorch training pipeline."
readme = "README.md"
license = {file = "LICENSE"}
dependencies = [
"click==8.1.7",
"einops==0.7.0",
"matplotlib==3.8.4",
"numpy==1.26.4",
"pytest==8.1.1",
"pytest-cov==5.0.0",
"python-dotenv==1.0.1",
"requests==2.31.0",
"torch==2.2.2",
"torchvision=0.17.2",
"tqdm==4.66.2",
"wandb==0.16.6",
"python-configuration[toml]",
"pandas==2.2.1",
"notebook==7.1.2",
"fastapi==0.110.1",
"uvicorn==0.29.0",
]
[project.urls]
homepage = "https://example.com/my_project"
repository = "https://example.com/my_project/repo"
documentation = "https://example.com/my_project/docs"
[tool.setuptools]
packages = ["ml_pipeline"]
[tool.pytest.ini_options]
# Run tests in parallel using pytest-xdist
addopts = "--cov=ml_pipeline --cov-report=term"
# Specify the paths to look for tests
testpaths = [
"test",
]
# Set default Python classes, functions, and methods to consider as tests
python_files = [
"test_*.py",
"test*.py",
"*_test.py",
]
python_classes = [
"Test*",
"*Test",
"*Tests",
"*TestCase",
]
python_functions = [
"test_*",
"*_test",
]
# Configure markers (custom or otherwise)
markers = [
"slow: marks tests as slow (deselect with '-m \"not slow\"')",
"online: marks tests that require internet access",
]

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black==24.3.0
click==8.1.7
einops==0.7.0
matplotlib==3.8.4
numpy==1.26.4
pytest==8.1.1
requests==2.31.0
torch==2.2.2
tqdm==4.66.2
wandb==0.16.6
pandas==2.2.1
notebook==7.1.2
fastapi==0.110.1
uvicorn==0.29.0
python-configuration[toml]

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TRAIN_PATH=${HOME}/Dev/ml/data/mnist_train.csv
INPUT_FEATURES=40

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from ml_pipeline import config
from ml_pipeline.model.cnn import VGG11
def test_in_channels():
assert config.model.name == 'vgg11'

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from ml_pipeline.data.dataset import MnistDataset
from ml_pipeline import config
from pathlib import Path
import pytest
@pytest.mark.skip()
def test_init():
pass
def test_getitem():
train_set = MnistDataset(config.data.train_path)
assert train_set[0][1].item() == 5
repeated = 8
length = 28
channels = 1
assert train_set[0][0].shape == (channels, length * repeated, length * repeated)
@pytest.mark.skip()
def test_loader():
from torch.utils.data import DataLoader
train_set = MnistDataset(config.data.train_path)
# train_loader = DataLoader(train_set, batch_size=config.training.batch_size, shuffle=True)
# for sample, target in train_loader:
# assert len(sample) == config.training.batch_size
# len(sample)
# len(target)

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from config import ConfigurationSet, config_from_env, config_from_dotenv, config_from_toml
from pathlib import Path
pwd = Path(__file__).parent
config_path = pwd / 'config'
root_path = pwd.parent
config = ConfigurationSet(
config_from_env(prefix="ML_PIPELINE", separator="__", lowercase_keys=True),
config_from_dotenv(root_path / ".env", read_from_file=True, lowercase_keys=True, interpolate=True, interpolate_type=1),
config_from_toml(config_path / "training.toml", read_from_file=True),
config_from_toml(config_path / "data.toml", read_from_file=True),
config_from_toml(config_path / "model.toml", read_from_file=True),
config_from_toml(config_path / "app.toml", read_from_file=True),
config_from_toml(config_path / "paths.toml", read_from_file=True),
)
import logging
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

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from ml_pipeline.cli import cli
if __name__ == "__main__":
cli()

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from ml_pipeline import config
from fastapi import FastAPI, Response
import logging
import uvicorn
app = FastAPI()
logger = logging.getLogger(__name__)
def run():
uvicorn.run("ml_pipeline.app:app", host=config.app.host, port=config.app.port, proxy_headers=True)

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import torch
from torch import nn
from torch import optim
from torch.utils.data import DataLoader
from ml_pipeline.data import FashionDataset
from tqdm import tqdm
from ml_pipeline.common import Stage
class Batch:
def __init__(
self,
stage: Stage,
model: nn.Module,
device,
loader: DataLoader,
optimizer: optim.Optimizer,
criterion: nn.Module,
):
"""todo"""
self.stage = stage
self.device = device
self.model = model.to(device)
self.loader = loader
self.criterion = criterion
self.optimizer = optimizer
self.loss = 0
def run(self, desc):
self.model.train()
epoch = 0
for epoch, (x, y) in enumerate(tqdm(self.loader, desc=desc)):
self.optimizer.zero_grad()
loss = self._run_batch((x, y))
loss.backward() # Send loss backwards to accumulate gradients
self.optimizer.step() # Perform a gradient update on the weights of the mode
self.loss += loss.item()
def _run_batch(self, sample):
true_x, true_y = sample
true_x, true_y = true_x.to(self.device), true_y.to(self.device)
pred_y = self.model(true_x)
loss = self.criterion(pred_y, true_y)
return loss
def main():
model = nn.Conv2d(1, 64, 3)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=2e-4)
path = "fashion-mnist_train.csv"
dataset = FashionDataset(path)
batch_size = 16
num_workers = 1
loader = torch.utils.data.DataLoader(
dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers
)
batch = Batch(
Stage.TRAIN,
device=torch.device("cpu"),
model=model,
criterion=criterion,
optimizer=optimizer,
loader=loader,
)
batch.run("test")
if __name__ == "__main__":
main()

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import click
@click.group()
@click.version_option()
def cli():
"""
ml_pipeline: a template for building, training and running pytorch models.
"""
@cli.command("pipeline:train")
def pipeline_train():
"""run the training pipeline with train data"""
from ml_pipeline.training import pipeline
pipeline.run(evaluate=False)
@cli.command("pipeline:evaluate")
def pipeline_evaluate():
"""run the training pipeline with test data"""
from ml_pipeline.training import pipeline
pipeline.run(evaluate=True)
@cli.command("app:serve")
def app_serve():
"""run the api server pipeline with pretrained model"""
from ml_pipeline import app
app.run()
@cli.command("data:download")
def data_download():
"""download the train and test data"""
from ml_pipeline import data
from ml_pipeline import config
from pathlib import Path
data.download(Path(config.paths.data))
@cli.command("data:debug")
def data_debug():
"""debug the dataset class"""
from ml_pipeline.data import dataset
dataset.debug()

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[app]
host = "127.0.0.1"
port = 8001

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[data]
train_path = "/path/to/data/mnist_train.csv"
in_channels = 1
num_classes = 10

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[model]
hidden_size = 8
name = 'vgg11'

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[paths]
repo = "/path/to/root"
app = "/path/to/root/ml_pipeline"
data = "/path/to/root/data"

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[training]
batch_size = 16
epochs = 10
learning_rate = 0.01
device = 'cpu'
# examples = 50
examples = -1

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from pathlib import Path
import requests
import logging
from ml_pipeline import config
logger = logging.getLogger(__name__)
def download(data_path: Path, force=False):
urls = {
'train' : 'https://pjreddie.com/media/files/mnist_train.csv',
'test' : 'https://pjreddie.com/media/files/mnist_test.csv'
}
for dataset, url in urls.items():
filename = data_path / url.split('/')[-1]
if filename.exists() and not force:
logger.info(f'file exists {filename} (set force to overwrite)')
continue
logger.info(f'downloading {dataset} {url}')
response = requests.get(url)
if response.status_code == 200:
with open(filename, 'wb') as file:
file.write(response.content)
logger.info(f'file downloaded {filename}')
else:
logger.info(f'failed to download file {filename}')

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from torch.utils.data import Dataset
import numpy as np
import einops
import csv
import torch
from pathlib import Path
from typing import Tuple
from ml_pipeline import config, logger
class MnistDataset(Dataset):
"""
The MNIST database of handwritten digits.
Training set is 60k labeled examples, test is 10k examples.
The b/w images normalized to 20x20, preserving aspect ratio.
It's the defacto standard image training set to learn about classification in DL
"""
def __init__(self, path: Path):
"""
give a path to a dir that contains the following csv files:
https://pjreddie.com/projects/mnist-in-csv/
"""
assert path, "dataset path required"
self.path = Path(path)
assert self.path.exists(), f"could not find dataset path: {path}"
self.features, self.labels = self._load()
def __getitem__(self, idx):
return (self.features[idx], self.labels[idx])
def __len__(self):
return len(self.features)
def _load(self) -> Tuple[torch.Tensor, torch.Tensor]:
# opening the CSV file
with open(self.path, mode="r") as file:
images, labels = [], []
csvFile = csv.reader(file)
examples = config.training.examples
for line, content in enumerate(csvFile):
if line == examples:
break
labels.append(int(content[0]))
image = [int(x) for x in content[1:]]
images.append(image)
labels = torch.tensor(labels, dtype=torch.int64)
images = torch.tensor(images, dtype=torch.float32)
images = einops.rearrange(images, "n (w h) -> n w h", w=28, h=28)
images = einops.repeat(
images, "n w h -> n c (w r_w) (h r_h)", c=1, r_w=8, r_h=8
)
return (images, labels)
def debug():
path = Path(config.paths.data) / "mnist_train.csv"
dataset = MnistDataset(path=path)
logger.info(f"len: {len(dataset)}")
logger.info(f"first shape: {dataset[0][0].shape}")
mean = einops.reduce(dataset[:10][0], "n w h -> w h", "mean")
logger.info(f"mean shape: {mean.shape}")
logger.info(f"mean image: {mean}")

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#!/usr/bin/env python3
from sys import stdout
import csv
# 'pip install pyspark' for these
from pyspark import SparkFiles
from pyspark.sql import SparkSession
# make a spark "session". this creates a local hadoop cluster by default (!)
spark = SparkSession.builder.getOrCreate()
# put the input file in the cluster's filesystem:
spark.sparkContext.addFile("https://csvbase.com/meripaterson/stock-exchanges.csv")
# the following is much like for pandas
df = (
spark.read.csv(f"file://{SparkFiles.get('stock-exchanges.csv')}", header=True)
.select("MIC")
.na.drop()
.sort("MIC")
)
# pyspark has no easy way to write csv to stdout - use python's csv lib
csv.writer(stdout).writerows(df.collect())

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from torch import nn
# the VGG11 architecture
class VGG11(nn.Module):
def __init__(self, in_channels, num_classes=1000):
super(VGG11, self).__init__()
self.in_channels = in_channels
self.num_classes = num_classes
# convolutional layers
self.conv_layers = nn.Sequential(
nn.Conv2d(self.in_channels, 64, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(64, 128, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(128, 256, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(256, 512, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
)
# fully connected linear layers
self.linear_layers = nn.Sequential(
nn.Linear(in_features=512 * 7 * 7, out_features=4096),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(in_features=4096, out_features=4096),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(in_features=4096, out_features=self.num_classes),
)
def forward(self, x):
x = self.conv_layers(x)
# flatten to prepare for the fully connected layers
x = x.view(x.size(0), -1)
x = self.linear_layers(x)
return x
class VGG16(nn.Module):
def __init__(self, num_classes=10):
super(VGG16, self).__init__()
self.layer1 = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
)
self.layer2 = nn.Sequential(
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.layer3 = nn.Sequential(
nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
)
self.layer4 = nn.Sequential(
nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.layer5 = nn.Sequential(
nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
)
self.layer6 = nn.Sequential(
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
)
self.layer7 = nn.Sequential(
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.layer8 = nn.Sequential(
nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(),
)
self.layer9 = nn.Sequential(
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(),
)
self.layer10 = nn.Sequential(
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.layer11 = nn.Sequential(
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(),
)
self.layer12 = nn.Sequential(
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(),
)
self.layer13 = nn.Sequential(
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.fc = nn.Sequential(
nn.Dropout(0.5), nn.Linear(7 * 7 * 512, 4096), nn.ReLU()
)
self.fc1 = nn.Sequential(nn.Dropout(0.5), nn.Linear(4096, 4096), nn.ReLU())
self.fc2 = nn.Sequential(nn.Linear(4096, num_classes))
def forward(self, x):
out = self.layer1(x)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = self.layer5(out)
out = self.layer6(out)
out = self.layer7(out)
out = self.layer8(out)
out = self.layer9(out)
out = self.layer10(out)
out = self.layer11(out)
out = self.layer12(out)
out = self.layer13(out)
out = out.reshape(out.size(0), -1)
out = self.fc(out)
out = self.fc1(out)
out = self.fc2(out)
return out

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{{cookiecutter.project_slug}}/{{cookiecutter.module_name}}/model/linear.py Normal file
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from torch import nn
class DNN(nn.Module):
def __init__(self, in_size, hidden_size, out_size):
super().__init__()
# Define the activation function and the linear functions
self.act = nn.ReLU()
self.in_linear = nn.Linear(in_size, hidden_size)
self.out_linear = nn.Linear(hidden_size, out_size)
def forward(self, x):
# Send x through first linear layer and activation function
x = self.act(self.in_linear(x))
# Return x through the out linear function
return self.out_linear(x)

23
{{cookiecutter.project_slug}}/{{cookiecutter.module_name}}/notebooks/features.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "634a9940-7cda-4fe3-bd68-cd69c7db199d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "",
"name": ""
},
"language_info": {
"name": ""
}
},
"nbformat": 4,
"nbformat_minor": 5
}

85
{{cookiecutter.project_slug}}/{{cookiecutter.module_name}}/notebooks/main.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "9f86d9e7-ca94-4dce-b86d-7ddb261f4e25",
"metadata": {},
"outputs": [],
"source": [
"# Now you can import your package\n",
"import ml_pipeline"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6ba8b629-82db-487f-acbf-2ca20feee7e2",
"metadata": {},
"outputs": [],
"source": [
"from ml_pipeline.data.dataset import MnistDataset"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8fb6c881-46ba-40e5-b837-c507c5bfae21",
"metadata": {},
"outputs": [],
"source": [
"from ml_pipeline import config"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c8ce7920-c056-44ac-93df-b25bae870592",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<ConfigurationSet: 0x7fcf70fc1a50>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"config"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "83293ef7-37b3-452f-8de5-13bee633d099",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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{{cookiecutter.project_slug}}/{{cookiecutter.module_name}}/training/__init__.py Normal file
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"""
main class for building a DL pipeline.
"""
from enum import Enum, auto
class Stage(Enum):
TRAIN = auto()
DEV = auto()
TEST = auto()

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from torch.utils.data import DataLoader
from torch.optim import AdamW
from ml_pipeline.training.runner import Runner
from ml_pipeline import config, logger
def run(evaluate=False):
# Initialize the training set and a dataloader to iterate over the dataset
# train_set = GenericDataset()
dataset = get_dataset(evaluate)
dataloader = DataLoader(dataset, batch_size=config.training.batch_size, shuffle=True)
model = get_model(name=config.model.name)
optimizer = AdamW(model.parameters(), lr=config.training.learning_rate)
# Create a runner that will handle
runner = Runner(
dataset=dataset,
dataloader=dataloader,
model=model,
optimizer=optimizer,
)
# Train the model
for _ in range(config.training.epochs):
# Run one loop of training and record the average loss
for step in runner.step():
logger.info(f"{step}")
def get_model(name='vgg11'):
from ml_pipeline.model.linear import DNN
from ml_pipeline.model.cnn import VGG11
if name == 'vgg11':
return VGG11(config.data.in_channels, config.data.num_classes)
else:
# Create the model and optimizer and cast model to the appropriate GPU
in_features, out_features = dataset.in_out_features()
model = DNN(in_features, config.model.hidden_size, out_features)
return model.to(config.training.device)
def get_dataset(evaluate=False):
# Usage
from ml_pipeline.data.dataset import MnistDataset
from torchvision import transforms
csv_file_path = config.data.train_path if not evaluate else config.data.test_path
transform = transforms.Compose([
transforms.ToTensor(), # Converts a PIL Image or numpy.ndarray to a FloatTensor and scales the image's pixel intensity values to the [0., 1.] range
transforms.Normalize((0.1307,), (0.3081,)) # Normalize using the mean and std specific to MNIST
])
dataset = MnistDataset(csv_file_path)
return dataset

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{{cookiecutter.project_slug}}/{{cookiecutter.module_name}}/training/runner.py Normal file
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from torch import nn
from torch.utils.data import Dataset, DataLoader
from torch.optim import Optimizer
class Runner:
"""Runner class that is in charge of implementing routine training functions such as running epochs or doing inference time"""
def __init__(self, dataset: Dataset, dataloader: DataLoader, model: nn.Module, optimizer: Optimizer):
# Initialize class attributes
self.dataset = dataset
# Prepare opt, model, and dataloader (helps accelerator auto-cast to devices)
self.optimizer, self.model, self.dataloader = (
optimizer, model, dataloader
)
# Since data is for targets, use Mean Squared Error Loss
# self.criterion = nn.MSELoss()
self.criterion = nn.CrossEntropyLoss()
def step(self):
"""Runs an epoch of training.
Includes updating model weights and tracking training loss
Returns:
float: The loss averaged over the entire epoch
"""
# turn the model to training mode (affects batchnorm and dropout)
self.model.train()
total_loss, total_samples = 0.0, 0.0
for sample, target in self.dataloader:
self.optimizer.zero_grad() # reset gradients to 0
prediction = self.model(sample) # forward pass through model
loss = self.criterion(prediction, target) # error calculation
# increment gradients within model by sending loss backwards
loss.backward()
self.optimizer.step() # update model weights
total_loss += loss # increment running loss
total_samples += len(sample)
yield total_loss / total_samples # take the average of the loss over each sample