refactor files.

add a notebook module. add config package.
add licence.
2024-04-05 18:37:24 -07:00 · 2024-04-05 12:47:58 -07:00 · 2024-03-14 13:47:37 -07:00 · 2023-01-18 11:26:48 -08:00
34 changed files with 1364 additions and 394 deletions
--- a/.env.example
+++ b/.env.example
@@ -0,0 +1,2 @@
 MODEL__INPUT_FEATURES=300
 DATA__TRAIN_PATH=/path/to/data/mnist_train.csv
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,161 @@
-storage/
+outputs
 # 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/
--- a/674
+++ b/674
@@ -0,0 +1,674 @@
                    GNU GENERAL PUBLIC LICENSE
                       Version 3, 29 June 2007
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  Each version is given a distinguishing version number.  If the
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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
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  If the Program specifies that a proxy can decide which future
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 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
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 author or copyright holder as a result of your choosing to follow a
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  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
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 IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
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  16. Limitation of Liability.
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  17. Interpretation of Sections 15 and 16.
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                     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.
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    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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 Also add information on how to contact you by electronic and paper mail.
  If the program does terminal interaction, make it output a short
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    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
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  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.
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 <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
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 <https://www.gnu.org/licenses/why-not-lgpl.html>.
--- a/29
+++ b/29
@@ -1,15 +1,30 @@
-CONDA_ENV=ml_pipeline
+PYTHON=.venv/bin/python3
 .PHONY: help test
 all: run
-run:
+init:
-	python src/pipeline.py train
+	python3.9 -m virtualenv .venv
-data:
+run: ## run the pipeline (train)
-	python src/data.py
+	$(PYTHON) src/train.py \
 		debug=false
-batch:
+debug: ## run the pipeline (train) with debugging enabled
-	python src/batch.py
+	$(PYTHON) src/train.py \
 		debug=true
 data: ## download the mnist data
 	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
 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}'
 install:
 	conda env updates -n ${CONDA_ENV} --file environment.yml
--- a/environment.yml
+++ b/environment.yml
@@ -1,111 +0,0 @@
 name: ml
 channels:
  - pytorch
  - conda-forge
  - defaults
 dependencies:
  - _libgcc_mutex=0.1=conda_forge
  - _openmp_mutex=4.5=2_gnu
  - black=22.6.0=py310h06a4308_0
  - blas=1.0=mkl
  - brotli=1.0.9=h5eee18b_7
  - brotli-bin=1.0.9=h5eee18b_7
  - bzip2=1.0.8=h7f98852_4
  - ca-certificates=2022.10.11=h06a4308_0
  - click=8.0.3=pyhd3eb1b0_0
  - colorama=0.4.6=pyhd8ed1ab_0
  - cycler=0.11.0=pyhd3eb1b0_0
  - dbus=1.13.18=hb2f20db_0
  - einops=0.4.1=pyhd8ed1ab_0
  - expat=2.4.9=h6a678d5_0
  - fontconfig=2.13.1=h6c09931_0
  - fonttools=4.25.0=pyhd3eb1b0_0
  - freetype=2.12.1=h4a9f257_0
  - giflib=5.2.1=h7b6447c_0
  - glib=2.69.1=h4ff587b_1
  - gst-plugins-base=1.14.0=h8213a91_2
  - gstreamer=1.14.0=h28cd5cc_2
  - icu=58.2=he6710b0_3
  - intel-openmp=2021.4.0=h06a4308_3561
  - jpeg=9e=h7f8727e_0
  - kiwisolver=1.4.2=py310h295c915_0
  - krb5=1.19.2=hac12032_0
  - lcms2=2.12=h3be6417_0
  - ld_impl_linux-64=2.39=hc81fddc_0
  - lerc=3.0=h295c915_0
  - libbrotlicommon=1.0.9=h5eee18b_7
  - libbrotlidec=1.0.9=h5eee18b_7
  - libbrotlienc=1.0.9=h5eee18b_7
  - libclang=10.0.1=default_hb85057a_2
  - libdeflate=1.8=h7f8727e_5
  - libedit=3.1.20210910=h7f8727e_0
  - libevent=2.1.12=h8f2d780_0
  - libffi=3.3=he6710b0_2
  - libgcc-ng=12.2.0=h65d4601_19
  - libgfortran-ng=12.2.0=h69a702a_19
  - libgfortran5=12.2.0=h337968e_19
  - libgomp=12.2.0=h65d4601_19
  - libllvm10=10.0.1=hbcb73fb_5
  - libnsl=2.0.0=h7f98852_0
  - libopenblas=0.3.21=pthreads_h78a6416_3
  - libpng=1.6.37=hbc83047_0
  - libpq=12.9=h16c4e8d_3
  - libstdcxx-ng=12.2.0=h46fd767_19
  - libtiff=4.4.0=hecacb30_0
  - libuuid=1.0.3=h7f8727e_2
  - libwebp=1.2.4=h11a3e52_0
  - libwebp-base=1.2.4=h5eee18b_0
  - libxcb=1.15=h7f8727e_0
  - libxkbcommon=1.0.1=hfa300c1_0
  - libxml2=2.9.14=h74e7548_0
  - libxslt=1.1.35=h4e12654_0
  - lz4-c=1.9.3=h295c915_1
  - matplotlib=3.5.2=py310h06a4308_0
  - matplotlib-base=3.5.2=py310hf590b9c_0
  - mkl=2021.4.0=h06a4308_640
  - mkl-service=2.4.0=py310h7f8727e_0
  - mkl_fft=1.3.1=py310hd6ae3a3_0
  - mkl_random=1.2.2=py310h00e6091_0
  - munkres=1.1.4=py_0
  - mypy_extensions=0.4.3=py310h06a4308_0
  - ncurses=6.3=h27087fc_1
  - nspr=4.33=h295c915_0
  - nss=3.74=h0370c37_0
  - numpy=1.23.3=py310hd5efca6_0
  - numpy-base=1.23.3=py310h8e6c178_0
  - openssl=1.1.1q=h7f8727e_0
  - packaging=21.3=pyhd3eb1b0_0
  - pathspec=0.10.1=pyhd8ed1ab_0
  - pcre=8.45=h295c915_0
  - pillow=9.2.0=py310hace64e9_1
  - pip=22.3=pyhd8ed1ab_0
  - platformdirs=2.5.2=pyhd8ed1ab_1
  - ply=3.11=py310h06a4308_0
  - pyparsing=3.0.9=py310h06a4308_0
  - pyqt=5.15.7=py310h6a678d5_1
  - python=3.10.6=haa1d7c7_1
  - python-dateutil=2.8.2=pyhd3eb1b0_0
  - pytorch=1.13.0=py3.10_cpu_0
  - pytorch-mutex=1.0=cpu
  - qt-main=5.15.2=h327a75a_7
  - qt-webengine=5.15.9=hd2b0992_4
  - qtwebkit=5.212=h4eab89a_4
  - readline=8.1.2=h0f457ee_0
  - setuptools=65.5.0=pyhd8ed1ab_0
  - sip=6.6.2=py310h6a678d5_0
  - six=1.16.0=pyhd3eb1b0_1
  - sqlite=3.39.3=h5082296_0
  - tk=8.6.12=h1ccaba5_0
  - toml=0.10.2=pyhd3eb1b0_0
  - tomli=2.0.1=py310h06a4308_0
  - tornado=6.2=py310h5eee18b_0
  - tqdm=4.64.1=pyhd8ed1ab_0
  - typing_extensions=4.3.0=py310h06a4308_0
  - tzdata=2022e=h191b570_0
  - wheel=0.37.1=pyhd8ed1ab_0
  - xz=5.2.6=h166bdaf_0
  - zlib=1.2.13=h5eee18b_0
  - zstd=1.5.2=ha4553b6_0
  - pip:
    - pyqt5-sip==12.11.0
 prefix: /home/personal/Dev/conda/envs/ml
--- a/ml_pipeline/init.py
+++ b/ml_pipeline/init.py
@@ -0,0 +1,4 @@
 from .config import config
 config = config()
--- a/ml_pipeline/main.py
+++ b/ml_pipeline/main.py
@@ -0,0 +1,5 @@
 from .cli import cli
 if __name__ == "__main__":
    cli()
--- a/ml_pipeline/batch.py
+++ b/ml_pipeline/batch.py
@@ -2,16 +2,17 @@ import torch
 from torch import nn
 from torch import optim
 from torch.utils.data import DataLoader
-from data import FashionDataset
+from ml_pipeline.data import FashionDataset
 from tqdm import tqdm
-from utils import Stage
+from ml_pipeline.common import Stage
 class Batch:
    def __init__(
        self,
        stage: Stage,
-        model: nn.Module, device,
+        model: nn.Module,
        device,
        loader: DataLoader,
        optimizer: optim.Optimizer,
        criterion: nn.Module,
--- a/ml_pipeline/cli.py
+++ b/ml_pipeline/cli.py
@@ -0,0 +1,21 @@
 import click
@click.group()
@click.version_option()
 def cli():
    """
    ml_pipeline: a template for building, training and running pytorch models.
    """
@cli.command("train")
 def train():
    """run the training pipeline with train data"""
    from ml_pipeline.training.pipeline import run
    run()
@cli.command("evaluate")
 def evaluate():
    """run the training pipeline with test data"""
    from ml_pipeline.training.pipeline import run
    run(evaluate=True)
--- a/ml_pipeline/common.py
+++ b/ml_pipeline/common.py
--- a/ml_pipeline/config/init.py
+++ b/ml_pipeline/config/init.py
@@ -0,0 +1,14 @@
 from config import ConfigurationSet, config_from_env, config_from_dotenv, config_from_toml
 from pathlib import Path
 def config():
    config = Path(__file__).parent
    root = config.parent.parent
    return ConfigurationSet(
        config_from_env(prefix="ML_PIPELINE", separator="__", lowercase_keys=True),
        config_from_dotenv(root / ".env", read_from_file=True, lowercase_keys=True, interpolate=True, interpolate_type=1),
        config_from_toml(config / "training.toml", read_from_file=True),
        config_from_toml(config / "data.toml", read_from_file=True),
        config_from_toml(config / "model.toml", read_from_file=True),
    )
--- a/ml_pipeline/config/config.toml
+++ b/ml_pipeline/config/config.toml
--- a/ml_pipeline/config/data.toml
+++ b/ml_pipeline/config/data.toml
@@ -0,0 +1,4 @@
 [data]
 train_path = "/path/to/data/mnist_train.csv"
 in_channels = 1
 num_classes = 10
--- a/ml_pipeline/config/model.toml
+++ b/ml_pipeline/config/model.toml
@@ -0,0 +1,3 @@
 [model]
 hidden_size = 8
 name = 'vgg11'
--- a/ml_pipeline/config/training.toml
+++ b/ml_pipeline/config/training.toml
@@ -0,0 +1,8 @@
 [training]
 batch_size = 16
 epochs = 10
 learning_rate = 0.01
 device = 'cpu'
 # examples = 50
 examples = -1
--- a/ml_pipeline/data/dataset.py
+++ b/ml_pipeline/data/dataset.py
@@ -0,0 +1,68 @@
 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
 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 main():
    path = Path("storage/mnist_train.csv")
    dataset = MnistDataset(path=path)
    print(f"len: {len(dataset)}")
    print(f"first shape: {dataset[0][0].shape}")
    mean = einops.reduce(dataset[:10][0], "n w h -> w h", "mean")
    print(f"mean shape: {mean.shape}")
    print(f"mean image: {mean}")
 if __name__ == "__main__":
    main()
--- a/ml_pipeline/data/spark.py
+++ b/ml_pipeline/data/spark.py
@@ -0,0 +1,21 @@
 #!/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())
--- a/ml_pipeline/model/init.py
+++ b/ml_pipeline/model/init.py
--- a/ml_pipeline/model/cnn.py
+++ b/ml_pipeline/model/cnn.py
@@ -37,10 +37,10 @@ class VGG11(nn.Module):
        self.linear_layers = nn.Sequential(
            nn.Linear(in_features=512 * 7 * 7, out_features=4096),
            nn.ReLU(),
-            nn.Dropout2d(0.5),
+            nn.Dropout(0.5),
            nn.Linear(in_features=4096, out_features=4096),
            nn.ReLU(),
-            nn.Dropout2d(0.5),
+            nn.Dropout(0.5),
            nn.Linear(in_features=4096, out_features=self.num_classes),
        )
--- a/ml_pipeline/model/linear.py
+++ b/ml_pipeline/model/linear.py
@@ -0,0 +1,19 @@
 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)
--- a/ml_pipeline/notebooks/features.ipynb
+++ b/ml_pipeline/notebooks/features.ipynb
@@ -0,0 +1,23 @@
 {
 "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
 }
--- a/ml_pipeline/notebooks/main.ipynb
+++ b/ml_pipeline/notebooks/main.ipynb
@@ -0,0 +1,85 @@
 {
 "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
 }
--- a/ml_pipeline/training/init.py
+++ b/ml_pipeline/training/init.py
@@ -1,3 +1,7 @@
 """
 main class for building a DL pipeline.
 """
 from enum import Enum, auto
@@ -5,3 +9,4 @@ class Stage(Enum):
    TRAIN = auto()
    DEV = auto()
    TEST = auto()
--- a/ml_pipeline/training/pipeline.py
+++ b/ml_pipeline/training/pipeline.py
@@ -0,0 +1,59 @@
 from torch.utils.data import DataLoader
 from torch.optim import AdamW
 from ml_pipeline.training.runner import Runner
 from ml_pipeline import config
 def run():
    # Initialize the training set and a dataloader to iterate over the dataset
    # train_set = GenericDataset()
    train_set = get_dataset()
    train_loader = DataLoader(train_set, batch_size=config.training.batch_size, shuffle=True)
    model = get_model(name=config.model.name)
    # Get the size of the input and output vectors from the training set
    # in_features, out_features = train_set.get_in_out_size()
    optimizer = AdamW(model.parameters(), lr=config.training.learning_rate)
    # Create a runner that will handle
    runner = Runner(
        train_set=train_set,
        train_loader=train_loader,
        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():
            print(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(source='mnist', split='train'):
    # Usage
    from ml_pipeline.data.dataset import MnistDataset
    from torchvision import transforms
    csv_file_path = config.data.train_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
--- a/ml_pipeline/training/runner.py
+++ b/ml_pipeline/training/runner.py
@@ -0,0 +1,46 @@
 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, train_set: Dataset, train_loader: DataLoader, model: nn.Module, optimizer: Optimizer):
        # Initialize class attributes
        self.train_set = train_set
        # Prepare opt, model, and train_loader (helps accelerator auto-cast to devices)
        self.optimizer, self.model, self.train_loader = (
            optimizer, model, train_loader
        )
        # 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.train_loader:
            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
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -0,0 +1,68 @@
 [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",
 ]
 [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",
 ]
--- a/requirements.txt
+++ b/requirements.txt
@@ -0,0 +1,11 @@
 black==24.3.0
 click==8.1.7
 einops==0.7.0
 matplotlib==3.8.4
 numpy==1.26.4
 pytest==8.1.1
 python-dotenv==1.0.1
 requests==2.31.0
 torch==2.2.2
 tqdm==4.66.2
 wandb==0.16.6
--- a/src/data.py
+++ b/src/data.py
@@ -1,54 +0,0 @@
 from torch.utils.data import Dataset
 import numpy as np
 import einops
 import csv
 import torch
 class FashionDataset(Dataset):
    def __init__(self, path: str):
        self.path = path
        self.x, self.y = self.load()
    def __getitem__(self, idx):
        return (self.x[idx], self.y[idx])
    def __len__(self):
        return len(self.x)
    def load(self):
        # opening the CSV file
        with open(self.path, mode="r") as file:
            images = list()
            classes = list()
            # reading the CSV file
            csvFile = csv.reader(file)
            # displaying the contents of the CSV file
            header = next(csvFile)
            limit = 1000
            for line in csvFile:
                if limit < 1:
                    break
                classes.append(int(line[:1][0]))
                images.append([int(x) for x in line[1:]])
                limit -= 1
            classes = torch.tensor(classes, dtype=torch.long)
            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, classes)
 def main():
    path = "fashion-mnist_train.csv"
    dataset = FashionDataset(path=path)
    print(f"len: {len(dataset)}")
    print(f"first shape: {dataset[0][0].shape}")
    mean = einops.reduce(dataset[:10], "n w h -> w h", "mean")
    print(f"mean shape: {mean.shape}")
 if __name__ == "__main__":
    main()
--- a/src/model/linear.py
+++ b/src/model/linear.py
@@ -1,10 +0,0 @@
 from torch import nn
 class DNN(nn.Module):
    def __init__(self, in_dim, out_dim):
        super(DNN, self).__init__()
        self.layer1 = nn.Linear(in_dim, out_dim)
    def forward(self, x):
        return self.layer1(x)
--- a/src/mpv.py
+++ b/src/mpv.py
@@ -1,158 +0,0 @@
 # pytorch mlp for multiclass classification
 from numpy import vstack
 from numpy import argmax
 from pandas import read_csv
 from sklearn.preprocessing import LabelEncoder
 from sklearn.metrics import accuracy_score
 from torch import Tensor
 from torch.utils.data import Dataset
 from torch.utils.data import DataLoader
 from torch.utils.data import random_split
 from torch.nn import Linear
 from torch.nn import ReLU
 from torch.nn import Softmax
 from torch.nn import Module
 from torch.optim import SGD
 from torch.nn import CrossEntropyLoss
 from torch.nn.init import kaiming_uniform_
 from torch.nn.init import xavier_uniform_
 # dataset definition
 class CSVDataset(Dataset):
    # load the dataset
    def __init__(self, path):
        # load the csv file as a dataframe
        df = read_csv(path, header=None)
        # store the inputs and outputs
        self.X = df.values[:, :-1]
        self.y = df.values[:, -1]
        # ensure input data is floats
        self.X = self.X.astype('float32')
        # label encode target and ensure the values are floats
        self.y = LabelEncoder().fit_transform(self.y)
    # number of rows in the dataset
    def __len__(self):
        return len(self.X)
    # get a row at an index
    def __getitem__(self, idx):
        return [self.X[idx], self.y[idx]]
    # get indexes for train and test rows
    def get_splits(self, n_test=0.33):
        # determine sizes
        test_size = round(n_test * len(self.X))
        train_size = len(self.X) - test_size
        # calculate the split
        return random_split(self, [train_size, test_size])
 # model definition
 class MLP(Module):
    # define model elements
    def __init__(self, n_inputs):
        super(MLP, self).__init__()
        # input to first hidden layer
        self.hidden1 = Linear(n_inputs, 10)
        kaiming_uniform_(self.hidden1.weight, nonlinearity='relu')
        self.act1 = ReLU()
        # second hidden layer
        self.hidden2 = Linear(10, 8)
        kaiming_uniform_(self.hidden2.weight, nonlinearity='relu')
        self.act2 = ReLU()
        # third hidden layer and output
        self.hidden3 = Linear(8, 3)
        xavier_uniform_(self.hidden3.weight)
        self.act3 = Softmax(dim=1)
    # forward propagate input
    def forward(self, X):
        # input to first hidden layer
        X = self.hidden1(X)
        X = self.act1(X)
        # second hidden layer
        X = self.hidden2(X)
        X = self.act2(X)
        # output layer
        X = self.hidden3(X)
        X = self.act3(X)
        return X
 # prepare the dataset
 def prepare_data(path):
    # load the dataset
    dataset = CSVDataset(path)
    # calculate split
    train, test = dataset.get_splits()
    # prepare data loaders
    train_dl = DataLoader(train, batch_size=32, shuffle=True)
    test_dl = DataLoader(test, batch_size=1024, shuffle=False)
    return train_dl, test_dl
 # train the model
 def train_model(train_dl, model):
    # define the optimization
    criterion = CrossEntropyLoss()
    optimizer = SGD(model.parameters(), lr=0.01, momentum=0.9)
    # enumerate epochs
    for epoch in range(500):
        # enumerate mini batches
        for i, (inputs, targets) in enumerate(train_dl):
            # clear the gradients
            optimizer.zero_grad()
            # compute the model output
            yhat = model(inputs)
            # calculate loss
            loss = criterion(yhat, targets)
            # credit assignment
            loss.backward()
            # update model weights
            optimizer.step()
 # evaluate the model
 def evaluate_model(test_dl, model):
    predictions, actuals = list(), list()
    for i, (inputs, targets) in enumerate(test_dl):
        # evaluate the model on the test set
        yhat = model(inputs)
        # retrieve numpy array
        yhat = yhat.detach().numpy()
        actual = targets.numpy()
        # convert to class labels
        yhat = argmax(yhat, axis=1)
        # reshape for stacking
        actual = actual.reshape((len(actual), 1))
        yhat = yhat.reshape((len(yhat), 1))
        # store
        predictions.append(yhat)
        actuals.append(actual)
    predictions, actuals = vstack(predictions), vstack(actuals)
    # calculate accuracy
    acc = accuracy_score(actuals, predictions)
    return acc
 # make a class prediction for one row of data
 def predict(row, model):
    # convert row to data
    row = Tensor([row])
    # make prediction
    yhat = model(row)
    # retrieve numpy array
    yhat = yhat.detach().numpy()
    return yhat
 # prepare the data
 path = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv'
 train_dl, test_dl = prepare_data(path)
 print(len(train_dl.dataset), len(test_dl.dataset))
 # define the network
 model = MLP(4)
 # train the model
 train_model(train_dl, model)
 # evaluate the model
 acc = evaluate_model(test_dl, model)
 print('Accuracy: %.3f' % acc)
 # make a single prediction
 row = [5.1,3.5,1.4,0.2]
 yhat = predict(row, model)
 print('Predicted: %s (class=%d)' % (yhat, argmax(yhat)))
--- a/src/pipeline.py
+++ b/src/pipeline.py
@@ -1,48 +0,0 @@
 """
 main class for building a DL pipeline.
 """
 import click
 from batch import Batch
 from model.linear import DNN
 from model.cnn import VGG16, VGG11
 from data import FashionDataset
 from utils import Stage
 import torch
@click.group()
 def cli():
    pass
@cli.command()
 def train():
    batch_size = 16
    num_workers = 8
    path = "fashion-mnist_train.csv"
    trainset = FashionDataset(path=path)
    trainloader = torch.utils.data.DataLoader(
        trainset, batch_size=batch_size, shuffle=False, num_workers=num_workers
    )
    model = VGG11(in_channels=1, num_classes=10)
    criterion = torch.nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=2e-4)
    batch = Batch(
        stage=Stage.TRAIN,
        model=model,
        device=torch.device("cpu"),
        loader=trainloader,
        criterion=criterion,
        optimizer=optimizer,
    )
    batch.run(
        "Run run run run. Run run run away. Oh Oh oH OHHHHHHH yayayayayayayayaya! - David Byrne"
    )
 if __name__ == "__main__":
    cli()
--- a/test/.env.test
+++ b/test/.env.test
@@ -0,0 +1,2 @@
 TRAIN_PATH=${HOME}/Dev/ml/data/mnist_train.csv
 INPUT_FEATURES=40
--- a/test/test_cnn.py
+++ b/test/test_cnn.py
@@ -0,0 +1,6 @@
 from ml_pipeline import config
 from ml_pipeline.model.cnn import VGG11
 def test_in_channels():
    assert config.model.name == 'vgg11' 
--- a/test/test_inputs.py
+++ b/test/test_inputs.py
@@ -0,0 +1,28 @@
 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)
Author	SHA1	Message	Date
publicmatt	bae586612e	refactor files. add a notebook module. add config package.	2024-04-05 18:37:24 -07:00
publicmatt	e508efefee	add licence. add pyproject for installing as module. add cli and main method. move from src to ml_pipeline module dir.	2024-04-05 12:47:58 -07:00
publicmatt	44250fc618	remove conda. add requirements.txt. add tests	2024-03-14 13:47:37 -07:00
Matt	355e83843f	add accelerate package. add generic dataset with random data.	2023-01-18 11:26:48 -08:00
		`@@ -0,0 +1,2 @@`
							`MODEL__INPUT_FEATURES=300`
							`DATA__TRAIN_PATH=/path/to/data/mnist_train.csv`
		`@@ -0,0 +1,4 @@`
							`from .config import config`

							`config = config()`
		`@@ -0,0 +1,2 @@`
							`TRAIN_PATH=${HOME}/Dev/ml/data/mnist_train.csv`
							`INPUT_FEATURES=40`