From 0581348aab5c8e09599d7392c0f9087d8034453d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Morales?= Date: Fri, 10 Nov 2023 13:27:10 -0600 Subject: [PATCH] redirect to mintlify docs (#816) --- .github/workflows/build-docs.yaml | 22 ++++++++++++++++++---- .github/workflows/deploy.yaml | 9 --------- .gitmodules | 4 ---- docs-scripts | 1 - nbs/core.ipynb | 2 +- nbs/mint.json | 1 - nbs/models.ipynb | 2 +- 7 files changed, 20 insertions(+), 21 deletions(-) delete mode 100644 .github/workflows/deploy.yaml delete mode 160000 docs-scripts diff --git a/.github/workflows/build-docs.yaml b/.github/workflows/build-docs.yaml index e132568f7..54e07e8d2 100644 --- a/.github/workflows/build-docs.yaml +++ b/.github/workflows/build-docs.yaml @@ -14,9 +14,14 @@ jobs: build-docs: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - name: Clone repo + uses: actions/checkout@v3 + - name: Clone docs repo + uses: actions/checkout@v3 with: - submodules: 'recursive' + repository: Nixtla/docs + ref: scripts + path: docs-scripts - uses: actions/setup-python@v4 with: cache: "pip" @@ -45,7 +50,16 @@ jobs: github_token: ${{ secrets.GITHUB_TOKEN }} publish_branch: docs publish_dir: ./_docs - # The following lines assign commit authorship to the official GH-Actions bot for deploys to `docs` branch. - # You can swap them out with your own user credentials. + user_name: github-actions[bot] + user_email: 41898282+github-actions[bot]@users.noreply.github.com + - name: Configure redirects for gh-pages + run: python docs-scripts/configure-redirects.py neuralforecast + - name: Deploy to Github Pages + if: github.event_name == 'push' + uses: peaceiris/actions-gh-pages@v3 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_branch: gh-pages + publish_dir: ./gh-pages user_name: github-actions[bot] user_email: 41898282+github-actions[bot]@users.noreply.github.com diff --git a/.github/workflows/deploy.yaml b/.github/workflows/deploy.yaml deleted file mode 100644 index 8d077a768..000000000 --- a/.github/workflows/deploy.yaml +++ /dev/null @@ -1,9 +0,0 @@ -name: Deploy to GitHub Pages -on: - push: - branches: ["main"] - workflow_dispatch: -jobs: - deploy: - runs-on: ubuntu-latest - steps: [uses: fastai/workflows/quarto-ghp@master] diff --git a/.gitmodules b/.gitmodules index d785203a9..e69de29bb 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,4 +0,0 @@ -[submodule "docs-scripts"] - path = docs-scripts - url = https://github.com/Nixtla/docs.git - branch = scripts diff --git a/docs-scripts b/docs-scripts deleted file mode 160000 index d63d02696..000000000 --- a/docs-scripts +++ /dev/null @@ -1 +0,0 @@ -Subproject commit d63d02696ad23a3104636207152d2ac393291315 diff --git a/nbs/core.ipynb b/nbs/core.ipynb index 7f43b13bc..2760c9e4a 100644 --- a/nbs/core.ipynb +++ b/nbs/core.ipynb @@ -28,7 +28,7 @@ "id": "12fa25a4", "metadata": {}, "source": [ - "# Core \n", + "# Core\n", "> NeuralForecast contains two main components, PyTorch implementations deep learning predictive models, as well as parallelization and distributed computation utilities. The first component comprises low-level PyTorch model estimator classes like `models.NBEATS` and `models.RNN`. The second component is a high-level `core.NeuralForecast` wrapper class that operates with sets of time series data stored in pandas DataFrames." ] }, diff --git a/nbs/mint.json b/nbs/mint.json index 95609775e..93df520ba 100644 --- a/nbs/mint.json +++ b/nbs/mint.json @@ -70,7 +70,6 @@ "pages": [ "core.html", "models.html", - "models.html", { "group": "Models' Documentation", "pages": [ diff --git a/nbs/models.ipynb b/nbs/models.ipynb index 42dce4aa6..2093624c1 100644 --- a/nbs/models.ipynb +++ b/nbs/models.ipynb @@ -119,7 +119,7 @@ "id": "7ae65ca7", "metadata": {}, "source": [ - "# Models \n", + "# Models\n", "\n", "> NeuralForecast contains user-friendly implementations of neural forecasting models that allow for easy transition of computing capabilities (GPU/CPU), computation parallelization, and hyperparameter tuning.

All the NeuralForecast models are \"global\" because we train them with all the series from the input pd.DataFrame data `Y_df`, yet the optimization objective is, momentarily, \"univariate\" as it does not consider the interaction between the output predictions across time series. Like the StatsForecast library, `core.NeuralForecast` allows you to explore collections of models efficiently and contains functions for convenient wrangling of input and output pd.DataFrames predictions." ]