Merge pull request #137 from rstudio/docker-prepare

New `prepare_docker` function

Author: isabelizimm

.github/workflows/tests.yml

@@ -35,7 +35,7 @@ jobs:
           python -m pip install -e .[dev,torch,statsmodels,xgboost]
       - name: Run Tests
         run: |
-          pytest -m 'not rsc_test' --cov --cov-report xml
+          pytest -m 'not rsc_test and not docker' --cov --cov-report xml
       - name: Upload coverage
         uses: codecov/codecov-action@v2
 
@@ -80,13 +80,16 @@ jobs:
           run: |
             python -m pip install --upgrade pip
             python -m pip install -e ".[dev]"
-        - name: run Docker
+        - name: Run Docker
           run: |
             python script/setup-docker/docker.py
             docker build -t mock .
             docker run -d -v $PWD/pinsboard:/vetiver/pinsboard -p 8080:8080 mock
             sleep 5
             curl -s --retry 10 --retry-connrefused http://0.0.0.0:8080
+        - name: Run tests
+          run: |
+            pytest vetiver -m 'docker'
 
   test-no-extras:
     name: "Test no exra ml frameworks"

Makefile

@@ -55,7 +55,7 @@ lint:
 	flake8 vetiver
 
 test: clean-test
-	pytest -m 'not rsc_test'
+	pytest -m 'not rsc_test and not docker'
 
 test-rsc: clean-test
 	pytest

pyproject.toml

@@ -12,6 +12,7 @@ addopts = "--doctest-modules"
 doctest_optionflags = "NORMALIZE_WHITESPACE"
 markers = [
     "rsc_test: tests for rstudio connect",
+    "docker: tests for docker deployments"
 ]
 
 [tool.setuptools_scm]

script/setup-docker/docker.py

@@ -1,5 +1,8 @@
 import vetiver
 import pins
+import numpy as np
+
+np.random.seed(500)
 
 X, y = vetiver.get_mock_data()
 model = vetiver.get_mock_model().fit(X, y)
@@ -9,6 +12,5 @@
 v = vetiver.VetiverModel(model, "mymodel", ptype_data=X)
 
 vetiver.vetiver_pin_write(board, v)
-vetiver.load_pkgs(v, path="vetiver_")
-vetiver.write_app(board, "mymodel")
-vetiver.write_docker()
+
+vetiver.prepare_docker(board, "mymodel")

vetiver/__init__.py

@@ -9,7 +9,7 @@
 from .pin_read_write import vetiver_pin_write  # noqa
 from .attach_pkgs import *  # noqa
 from .meta import *  # noqa
-from .write_docker import write_docker  # noqa
+from .write_docker import write_docker, prepare_docker  # noqa
 from .write_fastapi import write_app, vetiver_write_app  # noqa
 from .handlers.base import BaseHandler, create_handler, InvalidModelError  # noqa
 from .handlers.sklearn import SKLearnHandler  # noqa

vetiver/tests/test_prepare_docker.py

@@ -0,0 +1,19 @@
+import pytest
+import vetiver
+import pandas as pd
+import numpy as np
+
+DOCKER_URL = "http://0.0.0.0:8080/predict"
+
+pytestmark = pytest.mark.docker  # noqa
+
+
+def test_predict_sklearn_df_check_ptype():
+    np.random.seed(500)
+
+    X, y = vetiver.mock.get_mock_data()
+    response = vetiver.predict(endpoint=DOCKER_URL, data=X)
+
+    assert isinstance(response, pd.DataFrame), response
+    assert response.iloc[0, 0] == 44.47
+    assert len(response) == 100

vetiver/write_docker.py

@@ -1,5 +1,10 @@
 import sys
 import warnings
+from pathlib import Path
+
+from .write_fastapi import write_app
+from .attach_pkgs import load_pkgs
+from .vetiver_model import VetiverModel
 
 
 def vetiver_write_docker(
@@ -88,5 +93,43 @@ def write_docker(
 CMD ["uvicorn", "app.app:api", "--host", "{host}", "--port", "{port}"]
 """
 
-    f = open(f"{path}Dockerfile", "x")
+    f = open(Path(path, "Dockerfile"), "x")
     f.write(docker_script)
+
+
+def prepare_docker(
+    board,
+    pin_name: str,
+    path: str = "./",
+    version=None,
+    rspm_env: bool = False,
+    host: str = "0.0.0.0",
+    port: str = "8080",
+):
+    """Create all files needed for Docker
+
+    Parameters
+    ----------
+        board :
+            Pin board for model
+        pin_name : str
+            Name of pin
+        path :
+            Path to output
+        version :
+            Pin version to be used
+        rspm_env: bool
+            Whether or not RStudio Package Manager should be used
+        host: str
+            Host address to run VetiverAPI from Dockerfile
+        port: str
+            Port to run VetiverAPI from Dockerfile
+
+    """
+
+    v = VetiverModel.from_pin(board=board, name=pin_name, version=version)
+    write_app(
+        board=board, pin_name=pin_name, version=version, file=Path(path, "app.py")
+    )
+    load_pkgs(v, path=Path(path, "vetiver_"))
+    write_docker(path=path, rspm_env=rspm_env, host=host, port=port)