have stable and latest docs

.github/workflows/docs.yml

@@ -10,7 +10,7 @@ on:
 
 jobs:
   tests:
-    name: "Tests"
+    name: "Docs"
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v2
@@ -20,20 +20,34 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          python -m pip install -e .[dev,torch]
+          python -m pip install -e .[dev]
       - name: build docs
         run: |
-          cd docs
-          make html
+          make docs
       - name: save docs
         uses: actions/upload-artifact@v3
         with:
           name: docs-html
           path: docs/_build/html/
-      - name: publish docs
+      - name: publish dev docs
         if: github.ref_name == 'main' && github.ref_type == 'branch'
         uses: peaceiris/actions-gh-pages@v3
         with:
           github_token: ${{ secrets.GITHUB_TOKEN }}
           publish_dir: docs/_build/*
           publish_branch: gh-pages
+          destination_dir: main
+      - name: publish stable docs
+        if: github.ref_type == 'tag' && startswith(github.ref, 'refs/tags/v')
+        uses: peaceiris/actions-gh-pages@v3
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          publish_dir: docs/_build/*
+          publish_branch: gh-pages
+          destination_dir: github.ref_name
+      - name: create symlink stable to new version
+        if: github.ref_type == 'tag' && startswith(github.ref, 'refs/tags/v')
+        run: |
+          rm stable
+          ln -s "${{ github.ref_name }}" stable
+          ls -la

.github/workflows/weekly.yml

@@ -10,7 +10,7 @@ on:
   workflow_dispatch:
   schedule:
     # At 00:00 on Monday
-    - cron: 0 0 * * MON
+    - cron: "0 0 * * MON"
 
 jobs:
   vetiver_main_pins_main:

docs/source/advancedusage/custom_handler.md

@@ -8,7 +8,7 @@ This example shows a custom handler of `newmodeltype` type.
 from vetiver.handlers.base import BaseHandler
 
 class CustomHandler(BaseHandler):
-    def __init__(model, ptype_data):
+    def __init__(self, model, ptype_data):
         super().__init__(model, ptype_data)
 
     model_type = staticmethod(lambda: newmodeltype)

docs/source/index.rst

@@ -10,15 +10,14 @@ You can install the released version of vetiver from `PyPI <https://pypi.org/pro
 
 .. code-block:: bash
 
-   pip install vetiver
+   python -m pip install vetiver
 
 And the development version from `GitHub <https://github.com/rstudio/vetiver-python>`_ with:
 
 .. code-block:: bash
 
    python -m pip install git+https://github.com/rstudio/vetiver-python
 
-
 This website documents the public API of Vetiver (for Python). See `vetiver.rstudio.com <https://vetiver.rstudio.com>`_ for
 more on how to get started.
 
@@ -36,7 +35,8 @@ Version
    :caption: Version
 
    ~VetiverModel
-   ~pin_read_write.vetiver_pin_write
+   ~vetiver_pin_write
+   ~vetiver_create_ptype
 
 Deploy
 ==================
@@ -52,6 +52,7 @@ Deploy
    ~predict
    ~write_app
    ~write_docker
+   ~deploy_rsconnect
 
 Monitor
 ==================

setup.cfg

@@ -1,6 +1,5 @@
 [metadata]
 name = vetiver
-version = 0.1.5
 description = Deploy models into REST endpoints
 long_description = file: README.md
 long_description_content_type = text/markdown
@@ -44,7 +43,7 @@ dev =
     pytest-snapshot
     sphinx
     sphinx-autodoc-typehints
-    sphinx-book-theme
+    sphinx-book-theme==0.3.3
     myst-parser
 
 torch =

vetiver/__init__.py

@@ -10,7 +10,7 @@
 from .attach_pkgs import *  # noqa
 from .meta import *  # noqa
 from .write_docker import write_docker  # noqa
-from .write_fastapi import write_app  # 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
 from .handlers.torch import TorchHandler  # noqa

vetiver/monitor.py

@@ -32,13 +32,17 @@ def compute_metrics(
 
     Example
     -------
-    from sklearn import metrics
-    rng = pd.date_range("1/1/2012", periods=10, freq="S")
-    new = dict(x=range(len(rng)), y = range(len(rng)))
-    df = pd.DataFrame(new, index = rng).reset_index(inplace=True)
-    td = timedelta(seconds = 2)
-    metric_set = [sklearn.metrics.mean_squared_error, sklearn.metrics.mean_absolute_error]
-    compute_metrics(df, "index", td, metric_set=metric_set, truth="x", estimate="y")
+    >>> from sklearn.metrics import mean_squared_error, mean_absolute_error
+    >>> df = pd.DataFrame(
+    ...   {
+    ...        "index": ["2021-01-01", "2021-01-02", "2021-01-03"],
+    ...        "truth": [200, 201, 199],
+    ...        "pred": [198, 200, 199],
+    ...   }
+    ... )
+    >>> td = timedelta(days = 1)
+    >>> metric_set = [mean_squared_error, mean_absolute_error]
+    >>> metrics = compute_metrics(df, "index", td, metric_set, "truth", "pred")
 
     """