adding dict to ptype

Author: isabelizimm

examples/coffeeratings.py

@@ -39,3 +39,8 @@
 
 # write Dockerfile
 vetiver.vetiver_write_docker(path=path, host="0.0.0.0", port="80")
+
+## to run Dockerfile, in CLI
+# cd ./coffeeratings
+# docker build -t myimage .
+# docker run -d --name mycontainer -p 80:80 myimage

examples/coffeeratings/Dockerfile

@@ -1,18 +1,18 @@
-# #
+# # Generated by the vetiver package; edit with care
+#
 FROM python:3.8
 
-# #
+#
 WORKDIR /code
 
-# #
+#
 COPY vetiver_requirements.txt /code/requirements.txt
 
-# #
+#
 RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
 
-# #
+# edited to copy all information into Dockerfile
 COPY . /code/app
 
-# #
+#
 CMD ["uvicorn", "app.app:api", "--host", "0.0.0.0", "--port", "80"]
-    

examples/coffeeratings/app.py

@@ -1,9 +1,9 @@
 import vetiver
 import pins
 
-
-b = pins.board_folder('./examples/coffeeratings/')
-v = vetiver.vetiver_pin_read(b, 'v', version = '20220414T132046Z-c5e63')
+# edited to reflect Docker container path, and allow_pickle_read=True
+b = pins.board_folder('/code/app', allow_pickle_read=True)
+v = vetiver.vetiver_pin_read(b, 'v', version = '20220415T174503Z-06d9b')
 
 vetiver_api = vetiver.VetiverAPI(v)
-app = vetiver_api.app
+api = vetiver_api.app

examples/coffeeratings/v/20220414T132046Z-c5e63/data.txt

@@ -0,0 +1,15 @@
+api_version: 1
+created: 20220415T174503Z
+description: Scikit-learn model of type <class 'sklearn.linear_model._base.LinearRegression'>
+file: v.joblib
+file_size: 1035
+pin_hash: 06d9b5c677c7dcd0
+title: 'v: a pinned LinearRegression object'
+type: joblib
+user:
+  ptype: '{"aroma": 7.5, "flavor": 7.67, "sweetness": 10.0, "acidity": 7.67, "body":
+    7.58, "uniformity": 10.0, "balance": 7.42}'
+  required_pkgs:
+  - vetiver
+  - scikit-learn
+  save_ptype: true

examples/coffeeratings/v/20220415T174503Z-06d9b/data.txt

@@ -2,7 +2,7 @@
 # This file is autogenerated by pip-compile with python 3.8
 # To update, run:
 #
-#    pip-compile --output-file=./examples/coffeeratings/vetiver_requirements.txt /var/folders/5w/dhznpltj14n3nxr4fybjj8_w0000gn/T/tmpkg4uih6q.in
+#    pip-compile --output-file=./examples/coffeeratings/vetiver_requirements.txt /var/folders/5w/dhznpltj14n3nxr4fybjj8_w0000gn/T/tmpwe9ooski.in
 #
 anyio==3.5.0
     # via starlette
@@ -15,9 +15,7 @@ charset-normalizer==2.0.12
 click==8.1.2
     # via uvicorn
 fastapi==0.75.1
-    # via
-    #   -r /var/folders/5w/dhznpltj14n3nxr4fybjj8_w0000gn/T/tmpkg4uih6q.in
-    #   vetiver
+    # via vetiver
 h11==0.13.0
     # via uvicorn
 idna==3.3
@@ -37,9 +35,7 @@ numpy==1.22.3
     #   scipy
     #   vetiver
 pandas==1.4.2
-    # via
-    #   pins
-    #   vetiver
+    # via vetiver
 pydantic==1.9.0
     # via
     #   fastapi
@@ -48,15 +44,11 @@ python-dateutil==2.8.2
     # via pandas
 pytz==2022.1
     # via pandas
-pyyaml==6.0
-    # via pins
 requests==2.27.1
-    # via
-    #   pins
-    #   vetiver
+    # via vetiver
 scikit-learn==1.0.2
     # via
-    #   -r /var/folders/5w/dhznpltj14n3nxr4fybjj8_w0000gn/T/tmpkg4uih6q.in
+    #   -r /var/folders/5w/dhznpltj14n3nxr4fybjj8_w0000gn/T/tmpwe9ooski.in
     #   vetiver
 scipy==1.8.0
     # via scikit-learn
@@ -78,5 +70,6 @@ urllib3==1.26.9
     # via requests
 uvicorn==0.17.6
     # via vetiver
-git+https://github.com/isabelizimm/vetiver-python@write-docker-file
-git+https://github.com/machow/pins-python
+
+# manually edited to reflect appropriate versions of packages currently being developed
+git+https://github.com/isabelizimm/vetiver-python.git@37dff15a63238b1e6c44788af70473f19c29d83e

…atings/v/20220414T132046Z-c5e63/v.joblib …atings/v/20220415T174503Z-06d9b/v.joblibexamples/coffeeratings/v/20220414T132046Z-c5e63/v.joblib renamed to examples/coffeeratings/v/20220415T174503Z-06d9b/v.joblib examples/coffeeratings/v/20220414T132046Z-c5e63/v.joblib renamed to examples/coffeeratings/v/20220415T174503Z-06d9b/v.joblib

@@ -1,22 +1,21 @@
-import numpy as np
 import tempfile
 import os
 from vetiver import VetiverModel
 
 def load_pkgs(model: VetiverModel = None, packages: list = None, path=""):
-    """_summary_
+    """Load packages necessary for predictions
 
     Args
     ----
-        model (VetiverModel, optional):
-            VetiverModel to extract packages from. Defaults to None.
-        packages (list, optional): 
-            List of extra packages to include. Defaults to None.
-        path (str, optional): 
-            Where to save output file. Defaults to ".".
+        model: VetiverModel
+            VetiverModel to extract packages from
+        packages: list
+            List of extra packages to include
+        path: str
+            Where to save output file
     """
 
-    required_pkgs = ["fastapi", "vetiver", "pins"]
+    required_pkgs = ["vetiver"]
     if packages:
         required_pkgs = list(set(required_pkgs + packages))
     if model.metadata.get("required_pkgs"):

examples/coffeeratings/vetiver_requirements.txt

@@ -2,19 +2,6 @@
 from torch import nn
 import sklearn
 
-class NoAvailableHandlerError(Exception):
-    """
-    Throw an error if we don't find a method
-    available to create a handler for `model`
-    """
-
-    def __init__(
-        self,
-        message="Unsupported model type. Supported models are: sklearn, torch.",
-    ):
-        self.message = message
-        super().__init__(self.message)
-
 def create_translator(model, ptype_data, save_ptype):
     """check for model type to handle prediction
 
@@ -36,4 +23,4 @@ def create_translator(model, ptype_data, save_ptype):
         return sklearn_vt.SKLearnHandler(model, ptype_data, save_ptype)
 
     else:
-        NoAvailableHandlerError
+        raise NotImplementedError

vetiver/attach_pkgs.py

@@ -1,6 +1,18 @@
-def vetiver_meta(user:list = None, version = None,
-            url = None, required_pkgs = None):
+def vetiver_meta(user: dict = None, version: str = None,
+            url: str = None, required_pkgs: list = None):
+    """Populate relevant metadata for VetiverModel
 
+    Args
+    ----
+        user: dict
+           Extra user-defined information
+        version: str
+            Model version, generally populated from pins
+        url: str
+            Discoverable URL for API
+        required_pkgs: list
+            Packages necessary to make predictions
+    """
     meta = {
         'user': user,
         'version': version,