add conda lock file.

add miniconda install script.
reorganize pipeline dir and location of files.
2023-01-27 21:51:43 -08:00 · 2023-01-27 13:52:47 -08:00 · 2023-01-26 11:49:28 -08:00 · 2023-01-26 11:00:24 -08:00 · 2023-01-26 07:25:07 -08:00
47 changed files with 3748 additions and 1336 deletions
--- a/.env.example
+++ b/.env.example
@@ -1,2 +0,0 @@
 MODEL__INPUT_FEATURES=300
 DATA__TRAIN_PATH=/path/to/data/mnist_train.csv
--- a/.gitignore
+++ b/.gitignore
@@ -1,161 +1,4 @@
-outputs
+storage/
 # Byte-compiled / optimized / DLL files
 __pycache__/
-*.py[cod]
+outputs/
 *$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
@@ -1,674 +0,0 @@
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 address new problems or concerns.
  Each version is given a distinguishing version number.  If the
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  If the Program specifies that a proxy can decide which future
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 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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  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.
    <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>.
--- a/28
+++ b/28
@@ -1,30 +1,28 @@
-PYTHON=.venv/bin/python3
+CONDA_ENV=ml_pipeline
-.PHONY: help test
+.PHONY: help
-all: run
+all: help
 init:
 	python3.9 -m virtualenv .venv
 run: ## run the pipeline (train)
-	$(PYTHON) src/train.py \
+	python src/train.py \
 		debug=false
 debug: ## run the pipeline (train) with debugging enabled
-	$(PYTHON) src/train.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:
+install: conda-lock.yml ## import any changes to env.yml into conda env
-	$(PYTHON) -m pip install -r requirements.txt
+	conda-lock install --name ${CONDA_ENV} $^
 lock: environment.yml ## lock the current conda env
 	conda-lock
 env_export: ## export the conda envirnoment without package or name
 	conda env export | head -n -1 | tail -n +2 > $@
 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/README.md
+++ b/README.md
@@ -6,10 +6,16 @@ Instead of remembering where to put everything and making a different choice for
 Think of it like a mini-pytorch lightening, with all the fory internals exposed for extension and modification.
 This project lives here: [https://github.com/publicmatt.com/ml_pipeline](https://github.com/publicmatt.com/ml_pipeline).
 ## Usage
-### Install:
+# Usage
 ```bash
 make help # lists available options.
 ```
 ## Install:
 Install the conda requirements:
@@ -17,13 +23,15 @@ Install the conda requirements:
 make install
 ```
-Which is a proxy for calling:
+## Data:
 Download mnist data from PJReadie's website:
 ```bash
-conda env updates -n ml_pipeline --file environment.yml
+make data
 ```
-### Run:
+## Run:
 Run the code on MNIST with the following command:
@@ -31,3 +39,123 @@ Run the code on MNIST with the following command:
 make run
 ```
 # Tutorial
 The motivation for building a template for deep learning pipelines is this: deep learning is hard enough without every code baase being a little different.
 Especially in a research lab, standardizing on a few components makes switching between projects easier.
 In this template, you'll see the following:
 ## directory structure
 ```
 .
 ├── README.md
 ├── environment.yml
 ├── launch.sh
 ├── Makefile
 ├── data
 │   ├── mnist_test.csv
 │   └── mnist_train.csv
 ├── docs
 │   └── 2023-01-26.md
 ├── src
 │   ├── config
 │   │   └── main.yaml
 │   ├── data
 │   │   ├── __init__.py
 │   │   ├── README.md
 │   │   ├── collate.py
 │   │   └── dataset.py
 │   ├── eval.py
 │   ├── __init__.py
 │   ├── model
 │   │   ├── __init__.py
 │   │   ├── README.md
 │   │   ├── cnn.py
 │   │   └── linear.py
 │   ├── pipeline
 │   │   ├── __init__.py
 │   │   ├── README.md
 │   │   ├── logger.py
 │   │   ├── runner.py
 │   │   └── utils.py
 │   ├── sample.py
 │   └── train.py
 └── test
    ├── __init__.py
    └── test_pipeline.py
 8 directories, 25 files
 ```
 ## what and why?
 - `environment.yml`
    - hutch research has standardized on conda
    - here's a good tutorial on getting that setup: [seth email](emailto:bassetis@wwu.edu)
 - `launch.sh` or `Makefile`
    - to install and run stuff.
    - houses common operations and scripts.
    - `launch.sh` to dispatch training.
 - `README.md`
    - explain the project and how to run it.
    - list authors.
    - list resources that new collaborators might need.
    - root level dir.
    - can exist inside any dir.
    - reads nicely on github.com.
 - `docs/`
    - switching projects is easier with these in place.
    - organize them by meeting, or weekly agenda.
    - generally collection of markdown files.
 - `test/`
    - TODO
    - pytest: unit testing.
    - good for data shape. not sure what else.
 - `data/`
    - raw data
    - do not commit these to repo generally.
        - `echo "*.csv" >> data/.gitignore`
 - `__init__.py`
    - creates modules out of dir.
    - `import module` works b/c of these.
 - `src/model/`
    - if you have a large project, you might have multiple architectures/models.
    - small projects might just have `model/VGG.py` or `model/3d_unet.py`.
 - `src/config`
    - based on hydra python package.
    - quickly change run variables and hyperparameters.
 - `src/pipeline`
    - where the magic happens.
    - `train.py` creates all the objects, hands them off to runner for batching, monitors each epoch.
 ## testing
 - `if __name__ == "__main__"`.
    - good way to test things
 - enables lots breakpoints.
 ## config
 - Hydra config.
    - quickly experiment with hyperparameters
    - good way to define env. variables
        - lr, workers, batch_size
        - debug
 ## data
 - collate functions!
 - datasets.
 - dataloader.
 ## formatting python
 - python type hints.
 - automatic linting with the `black` package.
 ## running
 - tqdm to track progress.
 - wandb for logging.
 ## architecture
 - dataloader, optimizer, criterion, device, state are constructed in main, but passed to an object that runs batches.
--- a/bin/install_conda.sh
+++ b/bin/install_conda.sh
@@ -0,0 +1,73 @@
 PYTHON_VERSION=3.10
 ENV_NAME=ml_pipeline
 INSTALL_DIR=$HOME/Dev
 # for wwu research:
 # INSTALL_DIR=/research/hutchinson/workspace/$USERNAME
 ####################
 #
 # download miniconda
 #
 ####################
 wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O $HOME/Downloads/Miniconda3-latest-Linux-x86_64.sh
 ####################
 #
 # run install script
 # headless
 #
 ####################
 rm -rf $INSTALL_DIR/miniconda3
 bash $HOME/Downloads/Miniconda3-latest-Linux-x86_64.sh -b -p $INSTALL_DIR/miniconda3
 ####################
 #
 # create first conda environment
 #
 ####################
 conda create --name $ENV_NAME python=$PYTHON_VERSION -y
 ################
 #
 # place the following in $HOME/.bashrc
 #
 # then use `hutchconda` to activate base env
 #
 ################
 # WORKSPACE_DIR=/research/hutchinson/workspace/$USERNAME
 # hutchconda() {
 #     __conda_setup="$('$WORKSPACE_DIR/miniconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)"
 #     if [ $? -eq 0 ]; then
 #     eval "$__conda_setup"
 #     else
 #     if [ -f "$WORKSPACE_DIR/miniconda3/etc/profile.d/conda.sh" ]; then
 #     . "$WORKSPACE_DIR/miniconda3/etc/profile.d/conda.sh"
 #     else
 #     export PATH="$WORKSPACE_DIR/miniconda3/bin:$PATH"
 #     fi
 #     fi
 #     unset __conda_setup
 # }
 ####################
 #
 # activate conda environment
 #
 ####################
 conda activate $ENV_NAME
 ####################
 #
 # install pytorch
 #
 ####################
 conda install -c pytorch pytorch -y
 ####################
 #
 # or install from envirnoment.yml
 #
 ####################
 conda env update -n $ENV_NAME --file environment.yml
--- a/conda-lock.yml
+++ b/conda-lock.yml
--- a/data/.gitignore
+++ b/data/.gitignore
@@ -0,0 +1 @@
 *.csv
--- a/ml_pipeline/common.py
+++ b/ml_pipeline/common.py
--- a/environment.yml
+++ b/environment.yml
@@ -0,0 +1,23 @@
 channels:
  - pytorch
  - conda-forge
  - defaults
 dependencies:
  - conda-lock
  - black
  - click
  - einops
  - hydra-core
  - matplotlib
  - numpy
  - pip
  - wandb
  - pytest
  - python=3.10
  - python-dotenv
  - pytorch=1.13
  - requests
  - sqlite
  - tqdm
 platforms:
  - linux-64
--- a/launch.sh
+++ b/launch.sh
@@ -0,0 +1,2 @@
 python src/train.py \
    debug=false
--- a/ml_pipeline/init.py
+++ b/ml_pipeline/init.py
@@ -1,4 +0,0 @@
 from .config import config
 config = config()
--- a/ml_pipeline/main.py
+++ b/ml_pipeline/main.py
@@ -1,5 +0,0 @@
 from .cli import cli
 if __name__ == "__main__":
    cli()
--- a/ml_pipeline/cli.py
+++ b/ml_pipeline/cli.py
@@ -1,21 +0,0 @@
 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/config/init.py
+++ b/ml_pipeline/config/init.py
@@ -1,14 +0,0 @@
 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/data.toml
+++ b/ml_pipeline/config/data.toml
@@ -1,4 +0,0 @@
 [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
@@ -1,3 +0,0 @@
 [model]
 hidden_size = 8
 name = 'vgg11'
--- a/ml_pipeline/config/training.toml
+++ b/ml_pipeline/config/training.toml
@@ -1,8 +0,0 @@
 [training]
 batch_size = 16
 epochs = 10
 learning_rate = 0.01
 device = 'cpu'
 # examples = 50
 examples = -1
--- a/ml_pipeline/data/spark.py
+++ b/ml_pipeline/data/spark.py
@@ -1,21 +0,0 @@
 #!/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/linear.py
+++ b/ml_pipeline/model/linear.py
@@ -1,19 +0,0 @@
 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
@@ -1,23 +0,0 @@
 {
 "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
@@ -1,85 +0,0 @@
 {
 "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/pipeline.py
+++ b/ml_pipeline/training/pipeline.py
@@ -1,59 +0,0 @@
 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
@@ -1,46 +0,0 @@
 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
@@ -1,68 +0,0 @@
 [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
@@ -1,11 +0,0 @@
 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/ml_pipeline/model/init.py
+++ b/ml_pipeline/model/init.py
--- a/src/config/main.yaml
+++ b/src/config/main.yaml
@@ -0,0 +1,8 @@
 app_dir: ${hydra:runtime.cwd}
 debug: true
 lr: 2e-4
 batch_size: 16
 num_workers: 0
 device: "cpu"
 epochs: 4
 dev_after: 20
--- a/ml_pipeline/config/config.toml
+++ b/ml_pipeline/config/config.toml
--- a/src/data/init.py
+++ b/src/data/init.py
--- a/src/data/collate.py
+++ b/src/data/collate.py
@@ -0,0 +1,6 @@
 from einops import rearrange
 def channel_to_batch(batch):
    """TODO"""
    return batch
--- a/ml_pipeline/data/dataset.py
+++ b/ml_pipeline/data/dataset.py
@@ -5,7 +5,6 @@ import csv
 import torch
 from pathlib import Path
 from typing import Tuple
 from ml_pipeline import config
 class MnistDataset(Dataset):
@@ -22,10 +21,8 @@ class MnistDataset(Dataset):
        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
-        self.path = Path(path)
+        self.features, self.labels = self.load()
        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])
@@ -33,19 +30,25 @@ class MnistDataset(Dataset):
    def __len__(self):
        return len(self.features)
-    def _load(self) -> Tuple[torch.Tensor, torch.Tensor]:
+    def load(self) -> Tuple[torch.Tensor, torch.Tensor]:
        # opening the CSV file
        with open(self.path, mode="r") as file:
-            images, labels = [], []
+            images = list()
            labels = list()
            # reading the CSV file
            csvFile = csv.reader(file)
-            examples = config.training.examples
+            # displaying the contents of the CSV file
-            for line, content in enumerate(csvFile):
+            # header = next(csvFile)
-                if line == examples:
+            limit = 1000
            for line in csvFile:
                if limit < 1:
                    break
-                labels.append(int(content[0]))
+                label = int(line[0])
-                image = [int(x) for x in content[1:]]
+                labels.append(label)
                image = [int(x) for x in line[1:]]
                images.append(image)
-            labels = torch.tensor(labels, dtype=torch.int64)
+                limit -= 1
            labels = torch.tensor(labels, 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(
@@ -55,7 +58,8 @@ class MnistDataset(Dataset):
 def main():
-    path = Path("storage/mnist_train.csv")
+
    path = "storage/mnist_train.csv"
    dataset = MnistDataset(path=path)
    print(f"len: {len(dataset)}")
    print(f"first shape: {dataset[0][0].shape}")
--- a/src/eval.py
+++ b/src/eval.py
--- a/src/model/init.py
+++ b/src/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.Dropout(0.5),
+            nn.Dropout2d(0.5),
            nn.Linear(in_features=4096, out_features=4096),
            nn.ReLU(),
-            nn.Dropout(0.5),
+            nn.Dropout2d(0.5),
            nn.Linear(in_features=4096, out_features=self.num_classes),
        )
--- a/src/model/linear.py
+++ b/src/model/linear.py
@@ -0,0 +1,10 @@
 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/pipeline/README.md
+++ b/src/pipeline/README.md
--- a/src/pipeline/logger.py
+++ b/src/pipeline/logger.py
@@ -0,0 +1,111 @@
 from tkinter import W
 import torch
 import wandb
 import numpy as np
 from PIL import Image
 from einops import rearrange
 from typing import Protocol, Tuple, Optional
 class Logger(Protocol):
    def metrics(self, metrics: dict, epoch: int):
        """loss etc."""
    def hyperparameters(self, hyperparameters: dict):
        """model states"""
    def predictions(self, predictions: dict):
        """inference time stuff"""
    def images(self, images: np.ndarray):
        """log images"""
 class WandbLogger:
    def __init__(self, project: str, entity: str, name: Optional[str], notes: str):
        self.project = project
        self.entity = entity
        self.notes = notes
        self.experiment = wandb.init(project=project, entity=entity, notes=notes)
        self.experiment.name = name
        self.data_dict = {}
    def metrics(self, metrics: dict):
        """loss etc."""
        self.data_dict.update(metrics)
    def hyperparameters(self, hyperparameters: dict):
        """model states"""
        self.experiment.config.update(hyperparameters, allow_val_change=True)
    def predictions(self, predictions: dict):
        """inference time stuff"""
    def image(self, image: dict):
        """log images to wandb"""
        self.data_dict.update({'Generate Image' : image})
    def video(self, images: str, title: str):
        """log images to wandb"""
        images = np.uint8(rearrange(images, 't b c h w -> b t c h w'))
        self.data_dict.update({f"{title}": wandb.Video(images, fps=20)})
    def flush(self):
        self.experiment.log(self.data_dict)
        self.data_dict = {}
 class DebugLogger:
    def __init__(self, project: str, entity: str, name: str, notes: str):
        self.project = project
        self.entity = entity
        self.name = name
        self.notes = notes
    def metrics(self, metrics: dict, epoch: int = None):
        """
        loss etc.
        """
        print(f"metrics: {metrics}")
    def hyperparameters(self, hyperparameters: dict):
        """
        model states
        """
        print(f"hyperparameters: {hyperparameters}")
    def predictions(self, predictions: dict):
        """
        inference time stuff
        """
 class Checkpoint:
    def __init__(self, checkpoint_path):
        self.checkpoint_path = checkpoint_path
    def load(self) -> Tuple:
        checkpoint = torch.load(self.checkpoint_path)
        model = checkpoint["model"]
        optimizer = checkpoint["optimizer"]
        epoch = checkpoint["epoch"]
        loss = checkpoint["loss"]
        return (model, optimizer, epoch, loss)
    def save(self, model: torch.nn.Module, optimizer, epoch, loss):
        checkpoint = {
            "model": model,
            "optimizer": optimizer,
            "epoch": epoch,
            "loss": loss,
        }
        import random
        import string
        name = "".join(random.choices(string.ascii_letters, k=10)) + ".tar"
        torch.save(checkpoint, f"{name}")
--- a/src/pipeline/runner.py
+++ b/src/pipeline/runner.py
@@ -1,13 +1,16 @@
 """
 runner for training and valdating
 """
 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
+from pipeline.utils import Stage
 from omegaconf import DictConfig
-class Batch:
+class Runner:
    def __init__(
        self,
        stage: Stage,
@@ -16,8 +19,9 @@ class Batch:
        loader: DataLoader,
        optimizer: optim.Optimizer,
        criterion: nn.Module,
        config: DictConfig = None,
    ):
-        """todo"""
+        self.config = config
        self.stage = stage
        self.device = device
        self.model = model.to(device)
@@ -27,14 +31,18 @@ class Batch:
        self.loss = 0
    def run(self, desc):
        # set the model to train model
        if self.stage == Stage.TRAIN:
            self.model.train()
-        epoch = 0
+        if self.config.debug:
-        for epoch, (x, y) in enumerate(tqdm(self.loader, desc=desc)):
+            breakpoint()
        for batch, (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()
        return self.loss
    def _run_batch(self, sample):
        true_x, true_y = sample
@@ -42,29 +50,3 @@ class Batch:
        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()
--- a/ml_pipeline/training/init.py
+++ b/ml_pipeline/training/init.py
@@ -1,7 +1,3 @@
 """
 main class for building a DL pipeline.
 """
 from enum import Enum, auto
@@ -9,4 +5,3 @@ class Stage(Enum):
    TRAIN = auto()
    DEV = auto()
    TEST = auto()
--- a/src/sample.py
+++ b/src/sample.py
--- a/src/train.py
+++ b/src/train.py
@@ -0,0 +1,88 @@
 """
 main class for building a DL pipeline.
 """
 """
 the main entry point for training a model
 coordinates:
 - datasets
 - dataloaders
 - runner
 """
 from pipeline.runner import Runner
 from model.linear import DNN
 from model.cnn import VGG16, VGG11
 from data.dataset import MnistDataset
 from pipeline.utils import Stage
 import torch
 from pathlib import Path
 from data.collate import channel_to_batch
 import hydra
 from omegaconf import DictConfig
@hydra.main(config_path="config", config_name="main")
 def train(config: DictConfig):
    if config.debug:
        breakpoint()
    lr = config.lr
    batch_size = config.batch_size
    num_workers = config.num_workers
    device = config.device
    epochs = config.epochs
    train_path = Path(config.app_dir) / "data/mnist_train.csv"
    trainset = MnistDataset(path=train_path)
    dev_path = Path(config.app_dir) / "data/mnist_test.csv"
    devset = MnistDataset(path=dev_path)
    trainloader = torch.utils.data.DataLoader(
        trainset,
        batch_size=batch_size,
        shuffle=True,
        num_workers=num_workers,
        # collate_fn=channel_to_batch,
    )
    devloader = torch.utils.data.DataLoader(
        devset,
        batch_size=batch_size,
        shuffle=False,
        num_workers=num_workers,
        # collate_fn=channel_to_batch,
    )
    model = VGG11(in_channels=1, num_classes=10)
    criterion = torch.nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=lr)
    train_runner = Runner(
        stage=Stage.TRAIN,
        model=model,
        device=torch.device(device),
        loader=trainloader,
        criterion=criterion,
        optimizer=optimizer,
        config=config,
    )
    dev_runner = Runner(
        stage=Stage.DEV,
        model=model,
        device=torch.device(device),
        loader=devloader,
        criterion=criterion,
        optimizer=optimizer,
        config=config,
    )
    for epoch in range(epochs):
        if epoch % config.dev_after == 0:
            dev_log = dev_runner.run("dev epoch")
        else:
            train_log = train_runner.run("train epoch")
 if __name__ == "__main__":
    train()
--- a/test/.env.test
+++ b/test/.env.test
@@ -1,2 +0,0 @@
 TRAIN_PATH=${HOME}/Dev/ml/data/mnist_train.csv
 INPUT_FEATURES=40
--- a/test/init.py
+++ b/test/init.py
--- a/test/test_cnn.py
+++ b/test/test_cnn.py
@@ -1,6 +0,0 @@
 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
@@ -1,28 +0,0 @@
 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)
--- a/test/test_pipeline.py
+++ b/test/test_pipeline.py
@@ -0,0 +1,10 @@
 from src.model.linear import DNN
 from src.data import GenericDataset
 import os
 def test_size_of_dataset():
    features = 40
    os.environ["INPUT_FEATURES"] = str(features)
    dataset = GenericDataset()
    assert len(dataset[0][0]) == features
Author	SHA1	Message	Date
Matt	a09926e9ca	add conda lock file.	2023-01-27 21:51:43 -08:00
Matt	996f4bc97c	add miniconda install script.	2023-01-27 13:52:47 -08:00
Matt	ecc8939517	reorganize pipeline dir and location of files. add readmes to all dir.	2023-01-26 11:49:28 -08:00
Matt	0f12b26e40	rename batch to runner. fill out makefile. add dev pipeline. gitignore data dir. add logger.py. fill out readme.md. export env.yml.	2023-01-26 11:00:24 -08:00
Matt	1f13224c4f	add hydra config. remove click. add launch script. add test dir. switch from fashion mnist to generic.	2023-01-26 07:25:07 -08:00
		`@@ -1,2 +0,0 @@`
			`MODEL__INPUT_FEATURES=300`
			`DATA__TRAIN_PATH=/path/to/data/mnist_train.csv`
		`@@ -1,4 +0,0 @@`
			`from .config import config`

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