fix project_slug error.

replace ml_pipeline with project_name or module_name from config

cookiecutter.json

@@ -4,5 +4,8 @@
     "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"]
+    "open_source_license": ["MIT", "BSD-3-Clause", "No license file"],
+    "_copy_without_render": [
+        "docs/book"
+    ]
 }

{{cookiecutter.project_name}}/.env.example

@@ -0,0 +1,7 @@
+MODEL__IN_CHANNELS=1
+MODEL__NUM_CLASSES=10
+DATA__TRAIN_PATH=/path/to/{{cookiecutter.project_name}}/data/mnist_train.csv
+DATA__TEST_PATH=/path/to/{{cookiecutter.project_name}}/data/mnist_test.csv
+PATHS__APP=/path/to/{{cookiecutter.project_name}}/{{cookiecutter.module_name}}/
+PATHS__ROOT=/path/to/{{cookiecutter.project_name}}/
+PATHS__DATA=/path/to/{{cookiecutter.project_name}}/data/

{{cookiecutter.project_name}}/Makefile

@@ -1,30 +1,36 @@
-APP_NAME=ml_pipeline
+APP_NAME={{cookiecutter.module_name}}
 PYTHON=.venv/bin/python3
 INTERPRETER=/usr/bin/python3
 .PHONY: help test
 
 
-all: run
+all: help
 
 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
+	cp .env.example .env
 
 install:
 	$(PYTHON) -m pip install -r requirements.txt
 
+data: ## download the mnist data
+	$(PYTHON) -m $(APP_NAME) data:download 
+
+run: ## run the pipeline (train)
+	$(PYTHON) -m $(APP_NAME) pipeline:train 
+
+serve: ## start fastapi uvicorn server
+	$(PYTHON) -m $(APP_NAME) app:serve
+
+docs: ## serve the mdbook docs directory
+	mdbook serve docs
+
+test: ## run pytest tests
+	$(PYTHON) -m pytest
+
+test-watch: ## run pytest on .py changes
+	find . -iname "*.py" | entr -c $(PYTHON) -m pytest
+
 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, $$2}'
+	@echo "available commands:"
+	@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36mmake %-30s\033[0m %s\n", $$1, $$2}'

{{cookiecutter.project_name}}/docs/book.toml

@@ -3,4 +3,4 @@ authors = ["publicmatt"]
 language = "en"
 multilingual = false
 src = "src"
-title = "ml_pipeline"
+title = "{{cookiecutter.project_name}}"

{{cookiecutter.project_name}}/pyproject.toml

@@ -3,7 +3,7 @@ requires = ["setuptools", "wheel"]
 build-backend = "setuptools.build_meta"
 
 [project]
-name = "ml_pipeline"
+name = "{{cookiecutter.project_name}}"
 version = "0.1.0"
 authors = [
     {name = "publicmatt", email = "git@publicmatt.com"},
@@ -18,7 +18,6 @@ dependencies = [
     "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",
@@ -37,11 +36,11 @@ repository = "https://example.com/my_project/repo"
 documentation = "https://example.com/my_project/docs"
 
 [tool.setuptools]
-packages = ["ml_pipeline"]
+packages = ["{{cookiecutter.module_name}}"]
 
 [tool.pytest.ini_options]
 # Run tests in parallel using pytest-xdist
-addopts = "--cov=ml_pipeline --cov-report=term"
+addopts = "--cov={{cookiecutter.module_name}} --cov-report=term"
 # Specify the paths to look for tests
 testpaths = [
     "test",

{{cookiecutter.project_name}}/test/test_cnn.py

@@ -0,0 +1,6 @@
+from {{cookiecutter.module_name}} import config
+from {{cookiecutter.module_name}}.model.cnn import VGG11
+
+def test_in_channels():
+    assert config.model.name == 'vgg11' 
+

{{cookiecutter.project_name}}/test/test_inputs.py

@@ -1,5 +1,5 @@
-from ml_pipeline.data.dataset import MnistDataset
-from ml_pipeline import config
+from {{cookiecutter.module_name}}.data.dataset import MnistDataset
+from {{cookiecutter.module_name}} import config
 from pathlib import Path
 import pytest
 

{{cookiecutter.project_name}}/{{cookiecutter.module_name}}/__init__.py

@@ -5,7 +5,7 @@ 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_env(prefix="{{cookiecutter.module_name.upper()}}", 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),

{{cookiecutter.project_name}}/{{cookiecutter.module_name}}/__main__.py

@@ -0,0 +1,5 @@
+from {{cookiecutter.module_name}}.cli import cli
+
+if __name__ == "__main__":
+    cli()
+

{{cookiecutter.project_name}}/{{cookiecutter.module_name}}/app/__init__.py

@@ -0,0 +1,11 @@
+from {{cookiecutter.module_name}} import config
+from fastapi import FastAPI, Response
+import logging
+import uvicorn
+
+app = FastAPI()
+
+logger = logging.getLogger(__name__)
+
+def run():
+    uvicorn.run("{{cookiecutter.module_name}}.app:app", host=config.app.host, port=config.app.port, proxy_headers=True)

{{cookiecutter.project_name}}/{{cookiecutter.module_name}}/cli.py

@@ -4,38 +4,38 @@ import click
 @click.version_option()
 def cli():
     """
-    ml_pipeline: a template for building, training and running pytorch models.
+    build, train and run machine learning models.
     """
 
 
 @cli.command("pipeline:train")
 def pipeline_train():
     """run the training pipeline with train data"""
-    from ml_pipeline.training import pipeline
+    from {{cookiecutter.module_name}}.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
+    from {{cookiecutter.module_name}}.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
+    from {{cookiecutter.module_name}} 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 {{cookiecutter.module_name}} import data
+    from {{cookiecutter.module_name}} 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
+    from {{cookiecutter.module_name}}.data import dataset
     dataset.debug()

{{cookiecutter.project_name}}/{{cookiecutter.module_name}}/config/paths.toml

@@ -1,4 +1,4 @@
 [paths]
 repo = "/path/to/root"
-app = "/path/to/root/ml_pipeline"
+app = "/path/to/root/{{cookiecutter.module_name}}"
 data = "/path/to/root/data"

{{cookiecutter.project_name}}/{{cookiecutter.module_name}}/data/__init__.py

@@ -1,7 +1,7 @@
 from pathlib import Path
 import requests
 import logging
-from ml_pipeline import config
+from {{cookiecutter.module_name}} import config
 
 
 logger = logging.getLogger(__name__)

{{cookiecutter.project_name}}/{{cookiecutter.module_name}}/data/dataset.py

@@ -5,7 +5,7 @@ import csv
 import torch
 from pathlib import Path
 from typing import Tuple
-from ml_pipeline import config, logger
+from {{cookiecutter.module_name}} import config, logger
 
 
 class MnistDataset(Dataset):

{{cookiecutter.project_name}}/{{cookiecutter.module_name}}/notebooks/main.ipynb

@@ -8,7 +8,7 @@
    "outputs": [],
    "source": [
     "# Now you can import your package\n",
-    "import ml_pipeline"
+    "import {{cookiecutter.module_name}}"
    ]
   },
   {
@@ -18,7 +18,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from ml_pipeline.data.dataset import MnistDataset"
+    "from {{cookiecutter.module_name}}.data.dataset import MnistDataset"
    ]
   },
   {
@@ -28,38 +28,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from ml_pipeline import config"
+    "from {{cookiecutter.module_name}} 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": {

{{cookiecutter.project_name}}/{{cookiecutter.module_name}}/training/pipeline.py

@@ -1,8 +1,8 @@
 
 from torch.utils.data import DataLoader
 from torch.optim import AdamW
-from ml_pipeline.training.runner import Runner
-from ml_pipeline import config, logger
+from {{cookiecutter.module_name}}.training.runner import Runner
+from {{cookiecutter.module_name}} import config, logger
 
 
 def run(evaluate=False):
@@ -30,8 +30,8 @@ def run(evaluate=False):
             logger.info(f"{step}")
 
 def get_model(name='vgg11'):
-    from ml_pipeline.model.linear import DNN
-    from ml_pipeline.model.cnn import VGG11
+    from {{cookiecutter.module_name}}.model.linear import DNN
+    from {{cookiecutter.module_name}}.model.cnn import VGG11
     if name == 'vgg11':
         return VGG11(config.data.in_channels, config.data.num_classes)
     else:
@@ -43,7 +43,7 @@ def get_model(name='vgg11'):
 
 def get_dataset(evaluate=False):
     # Usage
-    from ml_pipeline.data.dataset import MnistDataset
+    from {{cookiecutter.module_name}}.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([

{{cookiecutter.project_slug}}/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' 
-

{{cookiecutter.project_slug}}/{{cookiecutter.module_name}}/__main__.py

@@ -1,5 +0,0 @@
-from ml_pipeline.cli import cli
-
-if __name__ == "__main__":
-    cli()
-

{{cookiecutter.project_slug}}/{{cookiecutter.module_name}}/app/__init__.py

@@ -1,11 +0,0 @@
-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)