", "
") + # Remove any HTML comments from the Markdown. They are fine to keep in the + # notebooks, but are not really useful in the MDX. + mdx = re.sub("()", "", mdx, flags=re.DOTALL) + # "\" Escape braces to make the text MDX compatible. + mdx = re.sub("([^\\\\])([{}])", "\\g<1>\\\\\\g<2>", mdx) + + # -- KaTeX -- + # Wrap '\begin{}...\end{}' in $$ for KaTeX to work. + mdx = re.sub( + "(\\\\begin\\\\{(\\w*?)\\\\}(.|\n)*?end\\\\{\\2\\\\})", "$$\\g<1>$$", mdx + ) + # Make sure $$ symbols are not escaped and include line breaks. + mdx = re.sub( + "\\\\?\\$\\\\?\\$((?:.|\n)*?)\\\\?\\$\\\\?\\$", "\n$$\n\\g<1>\n$$\n", mdx + ) + # Escaping braces causes issues in math blocks, unescape them. + mdx = re.sub( + "\\$?\\$(.|\n)*?\\$\\$?", + lambda match: match[0].replace("\\{", "{").replace("\\}", "}"), + mdx, + ) + + return mdx + + +def handle_markdown_cell( + cell: NotebookNode, + new_img_dir: Path, + lib_dir: Path, +) -> str: + """ + Handle the given Jupyter Markdown cell and convert it to MDX. + + Args: + cell (NotebookNode): Jupyter Markdown cell object. + new_img_dir (Path): Path where images are copied to for display in the + Markdown cell. + lib_dir (Path): The location for the Bean Machine library. + + Returns: + str: Transformed Markdown object suitable for inclusion in MDX. + """ + markdown = cell["source"] + + # Update image paths in the Markdown and copy them to the Markdown tutorials folder. + # Skip - Our images are base64 encoded, so we don't need to copy them to the docs + # folder. + # markdown = handle_images_found_in_markdown(markdown, new_img_dir, lib_dir) + + markdown = sanitize_mdx(markdown) + mdx = mdformat.text(markdown, options={"wrap": 88}, extensions={"myst"}) + + # We will attempt to handle inline style attributes written in HTML by converting + # them to something React can consume. This has to be done after the + # mdformat.text() step since mdformat complains about the converted styles. + mdx = transform_style_attributes(mdx) + + return f"{mdx}\n" + + +def handle_cell_input(cell: NotebookNode, language: str) -> str: + """ + Create a Markdown cell block using the given cell source, and the language. + + The given language will determine cell input syntax styles. Docusaurus uses Prism as + the syntax highlighter, https://prismjs.com. See the Docusaurus documentation for + more information on code blocks + https://docusaurus.io/docs/markdown-features/code-blocks. + + Args: + cell (NotebookNode): A notebook cell. + language (str): Language specifier for syntax highlighting. + + Returns: + str: Code block formatted Markdown string. + """ + cell_source = cell.get("source", "") + return f"```{language}\n{cell_source}\n```\n\n" + + +def handle_image( + values: List[Dict[str, Union[int, str, NotebookNode]]], +) -> List[Tuple[int, str]]: + """ + Convert embedded images to string MDX can consume. + + Args: + values (List[Dict[str, Union[int, str, NotebookNode]]]): Bokeh tagged cell + outputs. + + Returns: + List[Tuple[int, str]]: A list of tuples, where the first entry in the tuple is + the index where the output occurred from the cell, and the second entry of + the tuple is the MDX formatted string. + """ + output = [] + for value in values: + index = value["index"] + mime_type = value["mime_type"] + img = value["data"] + output.append((index, f"\n\n")) + return output + + +def handle_markdown( + values: List[Dict[str, Union[int, str, NotebookNode]]], +) -> List[Tuple[int, str]]: + """ + Convert and format Markdown for MDX. + + Args: + values (List[Dict[str, Union[int, str, NotebookNode]]]): Bokeh tagged cell + outputs. + + Returns: + List[Tuple[int, str]]: A list of tuples, where the first entry in the tuple is + the index where the output occurred from the cell, and the second entry of + the tuple is the MDX formatted string. + """ + output = [] + for value in values: + index = int(value["index"]) + markdown = str(value["data"]) + markdown = mdformat.text(markdown, options={"wrap": 88}, extensions={"myst"}) + output.append((index, f"{markdown}\n\n")) + return output + + +def handle_pandas( + values: List[Dict[str, Union[int, str, NotebookNode]]], +) -> List[Tuple[int, str]]: + """ + Handle how to display pandas DataFrames. + + There is a scoped style tag in the DataFrame output that uses the class name + `dataframe` to style the output. We will use this token to determine if a pandas + DataFrame is being displayed. + + Args: + values (List[Dict[str, Union[int, str, NotebookNode]]]): Bokeh tagged cell + outputs. + + Returns: + List[Tuple[int, str]]: A list of tuples, where the first entry in the tuple is + the index where the output occurred from the cell, and the second entry of + the tuple is the MDX formatted string. + """ + output = [] + for value in values: + index = int(value["index"]) + data = str(value["data"]) + if 'class="dataframe"' not in data: + # pd.read_html() raises an error if there's no dataframe. + continue + df = pd.read_html(io.StringIO(data), flavor="lxml") + # NOTE: The return is a list of dataframes and we only care about the first + # one. + md_df = df[0] + for column in md_df.columns: + if column.startswith("Unnamed"): + md_df.rename(columns={column: ""}, inplace=True) + # Remove the index if it is just a range, and output to markdown. + mdx = "" + if isinstance(md_df.index, pd.RangeIndex): + mdx = md_df.to_markdown(index=False) + elif not isinstance(md_df.index, pd.RangeIndex): + mdx = md_df.to_markdown() + output.append((index, f"\n{sanitize_mdx(mdx)}\n\n")) + return output + + +def handle_plain( + values: List[Dict[str, Union[int, str, NotebookNode]]], +) -> List[Tuple[int, str]]: + """ + Handle how to plain cell output should be displayed in MDX. + + Args: + values (List[Dict[str, Union[int, str, NotebookNode]]]): Bokeh tagged cell + outputs. + + Returns: + List[Tuple[int, str]]: A list of tuples, where the first entry in the tuple is + the index where the output occurred from the cell, and the second entry of + the tuple is the MDX formatted string. + """ + output = [] + for value in values: + index = int(value["index"]) + data = str(value["data"]) + data = [line.strip() for line in data.splitlines() if line] + data = [datum for datum in data if datum] + if data: + data = "\n".join([line for line in str(value["data"]).splitlines() if line]) + # Remove backticks to make the text MDX compatible. + data = data.replace("`", "") + output.append( + (index, f"
-
-
-
- );
-}
-
-module.exports = Versions;
diff --git a/setup.py b/setup.py
index 1be63417b4a..99931affeb7 100644
--- a/setup.py
+++ b/setup.py
@@ -43,12 +43,15 @@
"pytest-cov",
"sphinx",
"sphinx-autodoc-typehints",
+ "sphinx_rtd_theme",
"torchvision>=0.5.0",
"nbconvert",
"jupyter-client==6.1.12",
# Replace with `tensorboard >= x.x` once tb cuts a release.
# https://github.com/tensorflow/tensorboard/issues/6869#issuecomment-2273718763
"numpy<2.0",
+ "lxml",
+ "mdformat-myst",
]
MYSQL_REQUIRES = ["SQLAlchemy==1.4.17"]
@@ -75,6 +78,7 @@
"pytorch-lightning", # For the early stopping tutorial.
"papermill", # For executing the tutorials.
"submitit", # Required for building the SubmitIt notebook.
+ "mdformat",
]
diff --git a/sphinx/Makefile b/sphinx/Makefile
index 52636c09d35..5c459aa146c 100644
--- a/sphinx/Makefile
+++ b/sphinx/Makefile
@@ -5,7 +5,7 @@
SPHINXOPTS =
SPHINXBUILD = sphinx-build
PAPER =
-BUILDDIR = build
+BUILDDIR = ../website/build
# User-friendly check for sphinx-build
ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)
@@ -49,17 +49,17 @@ help:
@echo " coverage to run coverage check of the documentation (if enabled)"
clean:
- rm -rf $(BUILDDIR)/*
+ rm -rf $(BUILDDIR)/api/*
html:
- $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
+ $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/api
@echo
- @echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
+ @echo "Build finished. The HTML pages are in $(BUILDDIR)/api."
dirhtml:
- $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
+ $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/api
@echo
- @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
+ @echo "Build finished. The HTML pages are in $(BUILDDIR)/api."
singlehtml:
$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
diff --git a/sphinx/source/_static/custom.css b/sphinx/source/_static/custom.css
new file mode 100644
index 00000000000..827ccb0baf8
--- /dev/null
+++ b/sphinx/source/_static/custom.css
@@ -0,0 +1,3 @@
+.wy-nav-content {
+ max-width: initial;
+}
diff --git a/sphinx/source/conf.py b/sphinx/source/conf.py
index 234ba679ea7..414a273a956 100644
--- a/sphinx/source/conf.py
+++ b/sphinx/source/conf.py
@@ -42,6 +42,7 @@
"sphinx.ext.todo",
"sphinx.ext.coverage",
"sphinx.ext.viewcode",
+ "sphinx_rtd_theme",
]
# Add any paths that contain templates here, relative to this directory.
@@ -121,7 +122,7 @@
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
-html_theme = "alabaster"
+html_theme = "sphinx_rtd_theme"
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
@@ -150,7 +151,9 @@
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = []
+html_static_path = ["_static"]
+
+html_css_files = ['custom.css']
# Add any extra paths that contain custom files (such as robots.txt or
# .htaccess) here, relative to this directory. These files are copied
diff --git a/tutorials/early_stopping/early_stopping.ipynb b/tutorials/early_stopping/early_stopping.ipynb
index 1444a2a0372..2c1541f8cda 100644
--- a/tutorials/early_stopping/early_stopping.ipynb
+++ b/tutorials/early_stopping/early_stopping.ipynb
@@ -15,6 +15,20 @@
"NOTE: Although the original NAS tutorial is for a multi-objective problem, this tutorial focuses on a single objective (validation accuracy) problem. Early stopping currently does not support \\\"true\\\" multi-objective stopping, although one can use [logical compositions of early stopping strategies](https://github.com/facebook/Ax/blob/main/ax/early_stopping/strategies/logical.py) to target multiple objectives separately. Early stopping for the multi-objective case is currently a work in progress."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "779ea790",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "import plotly.io as pio\n",
+ "if 'google.colab' in sys.modules:\n",
+ " pio.renderers.default = \"colab\"\n",
+ " %pip install ax-platform"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
diff --git a/tutorials/external_generation_node.ipynb b/tutorials/external_generation_node/external_generation_node.ipynb
similarity index 98%
rename from tutorials/external_generation_node.ipynb
rename to tutorials/external_generation_node/external_generation_node.ipynb
index 5817bfa5bf8..2e9b6690937 100644
--- a/tutorials/external_generation_node.ipynb
+++ b/tutorials/external_generation_node/external_generation_node.ipynb
@@ -20,6 +20,19 @@
"NOTE: This is for illustration purposes only. We do not recommend using this strategy as it typically does not perform well compared to Ax's default algorithms due to it's overly greedy behavior."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "import plotly.io as pio\n",
+ "if 'google.colab' in sys.modules:\n",
+ " pio.renderers.default = \"colab\"\n",
+ " %pip install ax-platform"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
diff --git a/tutorials/factorial.ipynb b/tutorials/factorial/factorial.ipynb
similarity index 98%
rename from tutorials/factorial.ipynb
rename to tutorials/factorial/factorial.ipynb
index 63c559635d6..29f54eff6bc 100644
--- a/tutorials/factorial.ipynb
+++ b/tutorials/factorial/factorial.ipynb
@@ -24,6 +24,18 @@
"In this example, we first run an exploratory batch to collect data on all possible combinations. Then we use empirical Bayes to model the data and shrink noisy estimates toward the mean. Next, we use Thompson Sampling to suggest a set of arms (combinations of factors and levels) on which to collect more data. We repeat the process until we have identified the best performing combination(s)."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "in_colab = 'google.colab' in sys.modules\n",
+ "if in_colab:\n",
+ " %pip install ax-platform"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -75,7 +87,10 @@
},
"outputs": [],
"source": [
- "init_notebook_plotting()"
+ "import plotly.io as pio\n",
+ "init_notebook_plotting()\n",
+ "if in_colab:\n",
+ " pio.renderers.default = \"colab\""
]
},
{
diff --git a/tutorials/generation_strategy.ipynb b/tutorials/generation_strategy/generation_strategy.ipynb
similarity index 98%
rename from tutorials/generation_strategy.ipynb
rename to tutorials/generation_strategy/generation_strategy.ipynb
index 5d2ebe2810c..4f585cc9a54 100644
--- a/tutorials/generation_strategy.ipynb
+++ b/tutorials/generation_strategy/generation_strategy.ipynb
@@ -1,5 +1,18 @@
{
"cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "import plotly.io as pio\n",
+ "if 'google.colab' in sys.modules:\n",
+ " pio.renderers.default = \"colab\"\n",
+ " %pip install ax-platform"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
diff --git a/tutorials/gpei_hartmann_developer.ipynb b/tutorials/gpei_hartmann_developer/gpei_hartmann_developer.ipynb
similarity index 98%
rename from tutorials/gpei_hartmann_developer.ipynb
rename to tutorials/gpei_hartmann_developer/gpei_hartmann_developer.ipynb
index 4b216818bac..a0adc099c5f 100644
--- a/tutorials/gpei_hartmann_developer.ipynb
+++ b/tutorials/gpei_hartmann_developer/gpei_hartmann_developer.ipynb
@@ -14,6 +14,18 @@
"The Developer API is suitable when the user wants maximal customization of the optimization loop. This tutorial demonstrates optimization of a Hartmann6 function using the `Experiment` construct. In this example, trials will be evaluated synchronously."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "in_colab = 'google.colab' in sys.modules\n",
+ "if in_colab:\n",
+ " %pip install ax-platform"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -44,8 +56,11 @@
")\n",
"from ax.modelbridge.registry import Models\n",
"from ax.utils.notebook.plotting import init_notebook_plotting, render\n",
+ "import plotly.io as pio\n",
"\n",
- "init_notebook_plotting()"
+ "init_notebook_plotting()\n",
+ "if in_colab:\n",
+ " pio.renderers.default = \"colab\""
]
},
{
diff --git a/tutorials/gpei_hartmann_loop.ipynb b/tutorials/gpei_hartmann_loop/gpei_hartmann_loop.ipynb
similarity index 95%
rename from tutorials/gpei_hartmann_loop.ipynb
rename to tutorials/gpei_hartmann_loop/gpei_hartmann_loop.ipynb
index 119970e1788..320df994853 100644
--- a/tutorials/gpei_hartmann_loop.ipynb
+++ b/tutorials/gpei_hartmann_loop/gpei_hartmann_loop.ipynb
@@ -11,6 +11,18 @@
"For more customizability of the optimization procedure, consider the Service or Developer API."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "in_colab = 'google.colab' in sys.modules\n",
+ "if in_colab:\n",
+ " %pip install ax-platform"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -25,8 +37,11 @@
"from ax.service.managed_loop import optimize\n",
"from ax.utils.measurement.synthetic_functions import hartmann6\n",
"from ax.utils.notebook.plotting import init_notebook_plotting, render\n",
+ "import plotly.io as pio\n",
"\n",
- "init_notebook_plotting()"
+ "init_notebook_plotting()\n",
+ "if in_colab:\n",
+ " pio.renderers.default = \"colab\""
]
},
{
diff --git a/tutorials/gpei_hartmann_service.ipynb b/tutorials/gpei_hartmann_service/gpei_hartmann_service.ipynb
similarity index 98%
rename from tutorials/gpei_hartmann_service.ipynb
rename to tutorials/gpei_hartmann_service/gpei_hartmann_service.ipynb
index 177f7a182d4..c56aa2e5542 100644
--- a/tutorials/gpei_hartmann_service.ipynb
+++ b/tutorials/gpei_hartmann_service/gpei_hartmann_service.ipynb
@@ -15,6 +15,18 @@
"- Repeat"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "in_colab = 'google.colab' in sys.modules\n",
+ "if in_colab:\n",
+ " %pip install ax-platform"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -24,8 +36,11 @@
"from ax.service.ax_client import AxClient, ObjectiveProperties\n",
"from ax.utils.measurement.synthetic_functions import hartmann6\n",
"from ax.utils.notebook.plotting import init_notebook_plotting, render\n",
+ "import plotly.io as pio\n",
"\n",
- "init_notebook_plotting()"
+ "init_notebook_plotting()\n",
+ "if in_colab:\n",
+ " pio.renderers.default = \"colab\""
]
},
{
diff --git a/tutorials/gss.ipynb b/tutorials/gss/gss.ipynb
similarity index 97%
rename from tutorials/gss.ipynb
rename to tutorials/gss/gss.ipynb
index 6e6ff3d8aed..3fcc7260d31 100644
--- a/tutorials/gss.ipynb
+++ b/tutorials/gss/gss.ipynb
@@ -13,6 +13,18 @@
"Global Stopping stops an optimization loop when some data-based criteria are met which suggest that future trials will not be very helpful. For example, we might stop when there has been very little improvement in the last five trials. This is as opposed to trial-level early stopping, which monitors the results of expensive evaluations and terminates those that are unlikely to produce promising results, freeing resources to explore more promising configurations. For more on trial-level early stopping, see the tutorial: https://ax.dev/tutorials/early_stopping/early_stopping.html"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "in_colab = 'google.colab' in sys.modules\n",
+ "if in_colab:\n",
+ " %pip install ax-platform"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
@@ -30,8 +42,11 @@
"from ax.service.ax_client import AxClient, ObjectiveProperties\n",
"from ax.utils.measurement.synthetic_functions import Branin, branin\n",
"from ax.utils.notebook.plotting import init_notebook_plotting, render\n",
+ "import plotly.io as pio\n",
"\n",
- "init_notebook_plotting()"
+ "init_notebook_plotting()\n",
+ "if in_colab:\n",
+ " pio.renderers.default = \"colab\""
]
},
{
diff --git a/tutorials/human_in_the_loop/human_in_the_loop.ipynb b/tutorials/human_in_the_loop/human_in_the_loop.ipynb
index 93af094fb7a..1f0d274c1ec 100644
--- a/tutorials/human_in_the_loop/human_in_the_loop.ipynb
+++ b/tutorials/human_in_the_loop/human_in_the_loop.ipynb
@@ -39,6 +39,18 @@
"For this tutorial, we will assume our experiment has already been created."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "in_colab = 'google.colab' in sys.modules\n",
+ "if in_colab:\n",
+ " %pip install ax-platform"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -63,8 +75,11 @@
"from ax.utils.notebook.plotting import render, init_notebook_plotting\n",
"\n",
"import pandas as pd\n",
+ "import plotly.io as pio\n",
"\n",
- "init_notebook_plotting()"
+ "init_notebook_plotting()\n",
+ "if in_colab:\n",
+ " pio.renderers.default = \"colab\""
]
},
{
diff --git a/tutorials/modular_botax.ipynb b/tutorials/modular_botax/modular_botax.ipynb
similarity index 82%
rename from tutorials/modular_botax.ipynb
rename to tutorials/modular_botax/modular_botax.ipynb
index dc8f72147ec..444c47fb1f7 100644
--- a/tutorials/modular_botax.ipynb
+++ b/tutorials/modular_botax/modular_botax.ipynb
@@ -1,44 +1,54 @@
{
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5,
"cells": [
{
"cell_type": "code",
+ "execution_count": null,
+ "id": "dc0b0d48",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "import plotly.io as pio\n",
+ "if 'google.colab' in sys.modules:\n",
+ " pio.renderers.default = \"colab\"\n",
+ " %pip install ax-platform"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
"metadata": {
+ "collapsed": false,
+ "customOutput": null,
+ "executionStartTime": 1730916291451,
+ "executionStopTime": 1730916298337,
+ "id": "about-preview",
+ "isAgentGenerated": false,
+ "language": "python",
"metadata": {
"originalKey": "cca773d8-5e94-4b5a-ae54-22295be8936a"
},
- "id": "about-preview",
"originalKey": "f4e8ae18-2aa3-4943-a15a-29851889445c",
"outputsInitialized": true,
- "isAgentGenerated": false,
- "language": "python",
- "executionStartTime": 1730916291451,
- "executionStopTime": 1730916298337,
- "serverExecutionDuration": 4531.2523420434,
- "collapsed": false,
"requestMsgId": "f4e8ae18-2aa3-4943-a15a-29851889445c",
- "customOutput": null
+ "serverExecutionDuration": 4531.2523420434
},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "I1106 100452.333 _utils_internal.py:321] NCCL_DEBUG env var is set to None\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "I1106 100452.334 _utils_internal.py:339] NCCL_DEBUG is forced to WARN from None\n"
+ ]
+ }
+ ],
"source": [
"from typing import Any, Dict, Optional, Tuple, Type\n",
"\n",
@@ -71,37 +81,20 @@
"# BoTorch components\n",
"from botorch.models.model import Model\n",
"from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood"
- ],
- "execution_count": 1,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "I1106 100452.333 _utils_internal.py:321] NCCL_DEBUG env var is set to None\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "I1106 100452.334 _utils_internal.py:339] NCCL_DEBUG is forced to WARN from None\n"
- ]
- }
]
},
{
"cell_type": "markdown",
"metadata": {
+ "id": "northern-affairs",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "58ea5ebf-ff3a-40b4-8be3-1b85c99d1c4a"
},
- "id": "northern-affairs",
"originalKey": "c9a665ca-497e-4d7c-bbb5-1b9f8d1d311c",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"# Setup and Usage of BoTorch Models in Ax\n",
@@ -127,15 +120,15 @@
{
"cell_type": "markdown",
"metadata": {
+ "id": "pending-support",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "c06d1b5c-067d-4618-977e-c8269a98bd0a"
},
- "id": "pending-support",
"originalKey": "4706d02e-6b3f-4161-9e08-f5a31328b1d1",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"## 1. Quick-start example\n",
@@ -145,54 +138,64 @@
},
{
"cell_type": "code",
+ "execution_count": 2,
"metadata": {
+ "collapsed": false,
+ "customOutput": null,
+ "executionStartTime": 1730916294801,
+ "executionStopTime": 1730916298389,
+ "id": "parental-sending",
+ "isAgentGenerated": false,
+ "language": "python",
"metadata": {
"originalKey": "72934cf2-4ecf-483a-93bd-4df88b19a7b8"
},
- "id": "parental-sending",
"originalKey": "20f25ded-5aae-47ee-955e-a2d5a2a1fe09",
"outputsInitialized": true,
- "isAgentGenerated": false,
- "language": "python",
- "executionStartTime": 1730916294801,
- "executionStopTime": 1730916298389,
- "serverExecutionDuration": 22.605526028201,
- "collapsed": false,
"requestMsgId": "20f25ded-5aae-47ee-955e-a2d5a2a1fe09",
- "customOutput": null
+ "serverExecutionDuration": 22.605526028201
},
- "source": [
- "experiment = get_branin_experiment(with_trial=True)\n",
- "data = get_branin_data(trials=[experiment.trials[0]])"
- ],
- "execution_count": 2,
"outputs": [
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
"[INFO 11-06 10:04:56] ax.core.experiment: The is_test flag has been set to True. This flag is meant purely for development and integration testing purposes. If you are running a live experiment, please set this flag to False\n"
]
}
+ ],
+ "source": [
+ "experiment = get_branin_experiment(with_trial=True)\n",
+ "data = get_branin_data(trials=[experiment.trials[0]])"
]
},
{
"cell_type": "code",
+ "execution_count": 3,
"metadata": {
+ "collapsed": false,
+ "executionStartTime": 1730916295849,
+ "executionStopTime": 1730916299900,
+ "id": "rough-somerset",
+ "isAgentGenerated": false,
+ "language": "python",
"metadata": {
"originalKey": "e571212c-7872-4ebc-b646-8dad8d4266fd"
},
- "id": "rough-somerset",
"originalKey": "c0806cce-a1d3-41b8-96fc-678aa3c9dd92",
"outputsInitialized": true,
- "isAgentGenerated": false,
- "language": "python",
- "executionStartTime": 1730916295849,
- "executionStopTime": 1730916299900,
- "serverExecutionDuration": 852.73489891551,
- "collapsed": false,
- "requestMsgId": "c0806cce-a1d3-41b8-96fc-678aa3c9dd92"
+ "requestMsgId": "c0806cce-a1d3-41b8-96fc-678aa3c9dd92",
+ "serverExecutionDuration": 852.73489891551
},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "[INFO 11-06 10:04:57] ax.modelbridge.transforms.standardize_y: Outcome branin is constant, within tolerance.\n"
+ ]
+ }
+ ],
"source": [
"# `Models` automatically selects a model + model bridge combination.\n",
"# For `BOTORCH_MODULAR`, it will select `BoTorchModel` and `TorchModelBridge`.\n",
@@ -204,30 +207,20 @@
" ), # Optional, will use default if unspecified\n",
" botorch_acqf_class=qLogNoisyExpectedImprovement, # Optional, will use default if unspecified\n",
")"
- ],
- "execution_count": 3,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "[INFO 11-06 10:04:57] ax.modelbridge.transforms.standardize_y: Outcome branin is constant, within tolerance.\n"
- ]
- }
]
},
{
"cell_type": "markdown",
"metadata": {
+ "id": "hairy-wiring",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "fba91372-7aa6-456d-a22b-78ab30c26cd8"
},
- "id": "hairy-wiring",
"originalKey": "46f5c2c7-400d-4d8d-b0b9-a241657b173f",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"Now we can use this model to generate candidates (`gen`), predict outcome at a point (`predict`), or evaluate acquisition function value at a given point (`evaluate_acquisition_function`)."
@@ -235,49 +228,51 @@
},
{
"cell_type": "code",
+ "execution_count": 4,
"metadata": {
+ "collapsed": false,
+ "executionStartTime": 1730916299852,
+ "executionStopTime": 1730916300305,
+ "id": "consecutive-summary",
+ "isAgentGenerated": false,
+ "language": "python",
"metadata": {
"originalKey": "59582fc6-8089-4320-864e-d98ee271d4f7"
},
- "id": "consecutive-summary",
"originalKey": "f64e9d2e-bfd4-47da-8292-dbe7e70cbe1f",
"outputsInitialized": true,
- "isAgentGenerated": false,
- "language": "python",
- "executionStartTime": 1730916299852,
- "executionStopTime": 1730916300305,
- "serverExecutionDuration": 233.20194100961,
- "collapsed": false,
- "requestMsgId": "f64e9d2e-bfd4-47da-8292-dbe7e70cbe1f"
+ "requestMsgId": "f64e9d2e-bfd4-47da-8292-dbe7e70cbe1f",
+ "serverExecutionDuration": 233.20194100961
},
- "source": [
- "generator_run = model_bridge_with_GPEI.gen(n=1)\n",
- "generator_run.arms[0]"
- ],
- "execution_count": 4,
"outputs": [
{
- "output_type": "execute_result",
"data": {
- "text/plain": "Arm(parameters={'x1': 10.0, 'x2': 15.0})"
+ "text/plain": [
+ "Arm(parameters={'x1': 10.0, 'x2': 15.0})"
+ ]
},
+ "execution_count": 4,
"metadata": {},
- "execution_count": 4
+ "output_type": "execute_result"
}
+ ],
+ "source": [
+ "generator_run = model_bridge_with_GPEI.gen(n=1)\n",
+ "generator_run.arms[0]"
]
},
{
"cell_type": "markdown",
"metadata": {
+ "id": "diverse-richards",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "8cfe0fa9-8cce-4718-ba43-e8a63744d626"
},
- "id": "diverse-richards",
"originalKey": "804bac30-db07-4444-98a2-7a5f05007495",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"-----\n",
@@ -290,15 +285,15 @@
{
"cell_type": "markdown",
"metadata": {
+ "id": "grand-committee",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "7037fd14-bcfe-44f9-b915-c23915d2bda9"
},
- "id": "grand-committee",
"originalKey": "31b54ce5-2590-4617-b10c-d24ed3cce51d",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"## 2. BoTorchModel = Surrogate + Acquisition\n",
@@ -309,15 +304,15 @@
{
"cell_type": "markdown",
"metadata": {
+ "id": "thousand-blanket",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "08b12c6c-14da-4342-95bd-f607a131ce9d"
},
- "id": "thousand-blanket",
"originalKey": "4a4e006e-07fa-4d63-8b9a-31b67075e40e",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"### 2A. Example that uses defaults and requires no options\n",
@@ -327,21 +322,23 @@
},
{
"cell_type": "code",
+ "execution_count": 5,
"metadata": {
+ "collapsed": false,
+ "executionStartTime": 1730916302730,
+ "executionStopTime": 1730916304031,
+ "id": "changing-xerox",
+ "isAgentGenerated": false,
+ "language": "python",
"metadata": {
"originalKey": "b1bca702-07b2-4818-b2b9-2107268c383c"
},
- "id": "changing-xerox",
"originalKey": "fa86552a-0b80-4040-a0c4-61a0de37bdc1",
"outputsInitialized": true,
- "isAgentGenerated": false,
- "language": "python",
- "executionStartTime": 1730916302730,
- "executionStopTime": 1730916304031,
- "serverExecutionDuration": 1.7747740494087,
- "collapsed": false,
- "requestMsgId": "fa86552a-0b80-4040-a0c4-61a0de37bdc1"
+ "requestMsgId": "fa86552a-0b80-4040-a0c4-61a0de37bdc1",
+ "serverExecutionDuration": 1.7747740494087
},
+ "outputs": [],
"source": [
"# The surrogate is not specified, so it will be auto-selected\n",
"# during `model.fit`.\n",
@@ -357,22 +354,20 @@
"\n",
"# Both the surrogate and acquisition class will be auto-selected.\n",
"GPEI_model = BoTorchModel()"
- ],
- "execution_count": 5,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
"metadata": {
+ "id": "lovely-mechanics",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "5cec0f06-ae2c-47d3-bd95-441c45762e38"
},
- "id": "lovely-mechanics",
"originalKey": "7b9fae38-fe5d-4e5b-8b5f-2953c1ef09d2",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"### 2B. Example with all the options\n",
@@ -381,21 +376,23 @@
},
{
"cell_type": "code",
+ "execution_count": 6,
"metadata": {
+ "collapsed": false,
+ "executionStartTime": 1730916305930,
+ "executionStopTime": 1730916306168,
+ "id": "twenty-greek",
+ "isAgentGenerated": false,
+ "language": "python",
"metadata": {
"originalKey": "25b13c48-edb0-4b3f-ba34-4f4a4176162a"
},
- "id": "twenty-greek",
"originalKey": "8d824e37-b087-4bab-9b16-4354e9509df7",
"outputsInitialized": true,
- "isAgentGenerated": false,
- "language": "python",
- "executionStartTime": 1730916305930,
- "executionStopTime": 1730916306168,
- "serverExecutionDuration": 2.6916969800368,
- "collapsed": false,
- "requestMsgId": "8d824e37-b087-4bab-9b16-4354e9509df7"
+ "requestMsgId": "8d824e37-b087-4bab-9b16-4354e9509df7",
+ "serverExecutionDuration": 2.6916969800368
},
+ "outputs": [],
"source": [
"model = BoTorchModel(\n",
" # Optional `Surrogate` specification to use instead of default\n",
@@ -425,22 +422,20 @@
" refit_on_cv=False,\n",
" warm_start_refit=True,\n",
")"
- ],
- "execution_count": 6,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
"metadata": {
+ "id": "fourth-material",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "db0feafe-8af9-40a3-9f67-72c7d1fd808e"
},
- "id": "fourth-material",
"originalKey": "7140bb19-09b4-4abe-951d-53902ae07833",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"## 2C. `Surrogate` and `Acquisition` Q&A\n",
@@ -455,15 +450,15 @@
{
"cell_type": "markdown",
"metadata": {
+ "id": "violent-course",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "86018ee5-f7b8-41ae-8e2d-460fe5f0c15b"
},
- "id": "violent-course",
"originalKey": "71f92895-874d-4fc7-ae87-a5519b18d1a0",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"## 3. I know which Botorch `Model` and `AcquisitionFunction` I'd like to combine in Ax. How do set this up?"
@@ -472,18 +467,18 @@
{
"cell_type": "markdown",
"metadata": {
+ "id": "unlike-football",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"code_folding": [],
"hidden_ranges": [],
"originalKey": "b29a846d-d7bc-4143-8318-10170c9b4298",
"showInput": false
},
- "id": "unlike-football",
"originalKey": "4af8afa2-5056-46be-b7b9-428127e668cc",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"### 3a. Making a `Surrogate` from BoTorch `Model`:\n",
@@ -496,23 +491,25 @@
},
{
"cell_type": "code",
+ "execution_count": 7,
"metadata": {
+ "collapsed": false,
+ "executionStartTime": 1730916308518,
+ "executionStopTime": 1730916308769,
+ "id": "dynamic-university",
+ "isAgentGenerated": false,
+ "language": "python",
"metadata": {
"code_folding": [],
"hidden_ranges": [],
"originalKey": "6c2ea955-c7a4-42ff-a4d7-f787113d4d53"
},
- "id": "dynamic-university",
"originalKey": "746fc2a3-0e0e-4ab4-84d9-32434eb1fc34",
"outputsInitialized": true,
- "isAgentGenerated": false,
- "language": "python",
- "executionStartTime": 1730916308518,
- "executionStopTime": 1730916308769,
- "serverExecutionDuration": 2.4644429795444,
- "collapsed": false,
- "requestMsgId": "746fc2a3-0e0e-4ab4-84d9-32434eb1fc34"
+ "requestMsgId": "746fc2a3-0e0e-4ab4-84d9-32434eb1fc34",
+ "serverExecutionDuration": 2.4644429795444
},
+ "outputs": [],
"source": [
"from botorch.models.model import Model\n",
"from botorch.utils.datasets import SupervisedDataset\n",
@@ -545,22 +542,20 @@
" )\n",
" ]\n",
")"
- ],
- "execution_count": 7,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
"metadata": {
+ "id": "otherwise-context",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "b9072296-956d-4add-b1f6-e7e0415ba65c"
},
- "id": "otherwise-context",
"originalKey": "5a27fd2c-4c4c-41fe-a634-f6d0ec4f1666",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"NOTE: if you run into a case where base `Surrogate` does not work with your BoTorch `Model`, please let us know in this Github issue: https://github.com/facebook/Ax/issues/363, so we can find the right solution and augment this tutorial."
@@ -569,15 +564,15 @@
{
"cell_type": "markdown",
"metadata": {
+ "id": "northern-invite",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "335cabdf-2bf6-48e8-ba0c-1404a8ef47f9"
},
- "id": "northern-invite",
"originalKey": "df06d02b-95cb-4d34-aac6-773231f1a129",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"### 3B. Using an arbitrary BoTorch `AcquisitionFunction` in Ax"
@@ -586,18 +581,18 @@
{
"cell_type": "markdown",
"metadata": {
+ "id": "surrounded-denial",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"code_folding": [],
"hidden_ranges": [],
"originalKey": "e3f0c788-2131-4116-9518-4ae7daeb991f",
"showInput": false
},
- "id": "surrounded-denial",
"originalKey": "d4861847-b757-4fcd-9f35-ba258080812c",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"Steps to set up any `AcquisitionFunction` in Ax are:\n",
@@ -609,24 +604,37 @@
},
{
"cell_type": "code",
+ "execution_count": 8,
"metadata": {
+ "collapsed": false,
+ "customOutput": null,
+ "executionStartTime": 1730916310518,
+ "executionStopTime": 1730916310772,
+ "id": "interested-search",
+ "isAgentGenerated": false,
+ "language": "python",
"metadata": {
"code_folding": [],
"hidden_ranges": [],
"originalKey": "6967ce3e-929b-4d9a-8cd1-72bf94f0be3a"
},
- "id": "interested-search",
"originalKey": "f188f40b-64ba-4b0c-b216-f3dea8c7465e",
"outputsInitialized": true,
- "isAgentGenerated": false,
- "language": "python",
- "executionStartTime": 1730916310518,
- "executionStopTime": 1730916310772,
- "serverExecutionDuration": 4.9752569757402,
- "collapsed": false,
"requestMsgId": "f188f40b-64ba-4b0c-b216-f3dea8c7465e",
- "customOutput": null
+ "serverExecutionDuration": 4.9752569757402
},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "BoTorchModel"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"from ax.models.torch.botorch_modular.optimizer_argparse import optimizer_argparse\n",
"from botorch.acquisition.acquisition import AcquisitionFunction\n",
@@ -663,31 +671,20 @@
" \"optimizer_options\": {\"sequential\": False},\n",
" },\n",
")"
- ],
- "execution_count": 8,
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": "BoTorchModel"
- },
- "metadata": {},
- "execution_count": 8
- }
]
},
{
"cell_type": "markdown",
"metadata": {
+ "id": "metallic-imaging",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "29256ab1-f214-4604-a423-4c7b4b36baa0"
},
- "id": "metallic-imaging",
"originalKey": "b057722d-b8ca-47dd-b2c8-1ff4a71c4863",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"See section 2A for combining the resulting `Surrogate` instance and `Acquisition` type into a `BoTorchModel`. You can also leverage `Models.BOTORCH_MODULAR` for ease of use; more on it in section 4 below or in section 1 quick-start example."
@@ -696,15 +693,15 @@
{
"cell_type": "markdown",
"metadata": {
+ "id": "descending-australian",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "1d15082f-1df7-4cdb-958b-300483eb7808"
},
- "id": "descending-australian",
"originalKey": "a7406f13-1468-487d-ac5e-7d2a45394850",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"## 4. Using `Models.BOTORCH_MODULAR` \n",
@@ -716,123 +713,129 @@
},
{
"cell_type": "code",
+ "execution_count": 9,
"metadata": {
+ "collapsed": false,
+ "executionStartTime": 1730916311983,
+ "executionStopTime": 1730916312395,
+ "id": "attached-border",
+ "isAgentGenerated": false,
+ "language": "python",
"metadata": {
"originalKey": "385b2f30-fd86-4d88-8784-f238ea8a6abb"
},
- "id": "attached-border",
"originalKey": "052cf2e4-8de0-4ec3-a3f9-478194b10928",
"outputsInitialized": true,
- "isAgentGenerated": false,
- "language": "python",
- "executionStartTime": 1730916311983,
- "executionStopTime": 1730916312395,
- "serverExecutionDuration": 202.78578903526,
- "collapsed": false,
- "requestMsgId": "052cf2e4-8de0-4ec3-a3f9-478194b10928"
+ "requestMsgId": "052cf2e4-8de0-4ec3-a3f9-478194b10928",
+ "serverExecutionDuration": 202.78578903526
},
- "source": [
- "model_bridge_with_GPEI = Models.BOTORCH_MODULAR(\n",
- " experiment=experiment,\n",
- " data=data,\n",
- ")\n",
- "model_bridge_with_GPEI.gen(1)"
- ],
- "execution_count": 9,
"outputs": [
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
"[INFO 11-06 10:05:12] ax.modelbridge.transforms.standardize_y: Outcome branin is constant, within tolerance.\n"
]
},
{
- "output_type": "execute_result",
"data": {
- "text/plain": "GeneratorRun(1 arms, total weight 1.0)"
+ "text/plain": [
+ "GeneratorRun(1 arms, total weight 1.0)"
+ ]
},
+ "execution_count": 9,
"metadata": {},
- "execution_count": 9
+ "output_type": "execute_result"
}
+ ],
+ "source": [
+ "model_bridge_with_GPEI = Models.BOTORCH_MODULAR(\n",
+ " experiment=experiment,\n",
+ " data=data,\n",
+ ")\n",
+ "model_bridge_with_GPEI.gen(1)"
]
},
{
"cell_type": "code",
+ "execution_count": 10,
"metadata": {
+ "collapsed": false,
+ "executionStartTime": 1730916312432,
+ "executionStopTime": 1730916312657,
+ "id": "powerful-gamma",
+ "isAgentGenerated": false,
+ "language": "python",
"metadata": {
"originalKey": "89930a31-e058-434b-b587-181931e247b6"
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- "serverExecutionDuration": 3.1334219966084,
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+ "requestMsgId": "b7f924fe-f3d9-4211-b402-421f4c90afe5",
+ "serverExecutionDuration": 3.1334219966084
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- "source": [
- "model_bridge_with_GPEI.model.botorch_acqf_class"
- ],
- "execution_count": 10,
"outputs": [
{
- "output_type": "execute_result",
"data": {
- "text/plain": "botorch.acquisition.logei.qLogNoisyExpectedImprovement"
+ "text/plain": [
+ "botorch.acquisition.logei.qLogNoisyExpectedImprovement"
+ ]
},
+ "execution_count": 10,
"metadata": {},
- "execution_count": 10
+ "output_type": "execute_result"
}
+ ],
+ "source": [
+ "model_bridge_with_GPEI.model.botorch_acqf_class"
]
},
{
"cell_type": "code",
+ "execution_count": 11,
"metadata": {
+ "collapsed": false,
+ "executionStartTime": 1730916312847,
+ "executionStopTime": 1730916313093,
+ "id": "improved-replication",
+ "isAgentGenerated": false,
+ "language": "python",
"metadata": {
"originalKey": "f9a9cb14-20c3-4e1d-93a3-6a35c281ae01"
},
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- "language": "python",
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- "serverExecutionDuration": 3.410067060031,
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- "requestMsgId": "942f1817-8d40-48f8-8725-90c25a079e4c"
+ "requestMsgId": "942f1817-8d40-48f8-8725-90c25a079e4c",
+ "serverExecutionDuration": 3.410067060031
},
- "source": [
- "model_bridge_with_GPEI.model.surrogate.model.__class__"
- ],
- "execution_count": 11,
"outputs": [
{
- "output_type": "execute_result",
"data": {
- "text/plain": "botorch.models.gp_regression.SingleTaskGP"
+ "text/plain": [
+ "botorch.models.gp_regression.SingleTaskGP"
+ ]
},
+ "execution_count": 11,
"metadata": {},
- "execution_count": 11
+ "output_type": "execute_result"
}
+ ],
+ "source": [
+ "model_bridge_with_GPEI.model.surrogate.model.__class__"
]
},
{
"cell_type": "markdown",
"metadata": {
+ "id": "connected-sheet",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "8b6a9ddc-d2d2-4cd5-a6a8-820113f78262"
},
- "id": "connected-sheet",
"originalKey": "f5c0adbd-00a6-428d-810f-1e7ed0954b08",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"We can use the same `Models.BOTORCH_MODULAR` to set up a model for multi-objective optimization:"
@@ -840,139 +843,145 @@
},
{
"cell_type": "code",
+ "execution_count": 12,
"metadata": {
+ "collapsed": false,
+ "executionStartTime": 1730916314009,
+ "executionStopTime": 1730916314736,
+ "id": "documentary-jurisdiction",
+ "isAgentGenerated": false,
+ "language": "python",
"metadata": {
"originalKey": "8001de33-d9d9-4888-a5d1-7a59ebeccfd5"
},
- "id": "documentary-jurisdiction",
"originalKey": "9c64c497-f663-42a6-aa48-1f1f2ae2b80b",
"outputsInitialized": true,
- "isAgentGenerated": false,
- "language": "python",
- "executionStartTime": 1730916314009,
- "executionStopTime": 1730916314736,
- "serverExecutionDuration": 518.53136904538,
- "collapsed": false,
- "requestMsgId": "9c64c497-f663-42a6-aa48-1f1f2ae2b80b"
+ "requestMsgId": "9c64c497-f663-42a6-aa48-1f1f2ae2b80b",
+ "serverExecutionDuration": 518.53136904538
},
- "source": [
- "model_bridge_with_EHVI = Models.BOTORCH_MODULAR(\n",
- " experiment=get_branin_experiment_with_multi_objective(\n",
- " has_objective_thresholds=True, with_batch=True\n",
- " ),\n",
- " data=get_branin_data_multi_objective(),\n",
- ")\n",
- "model_bridge_with_EHVI.gen(1)"
- ],
- "execution_count": 12,
"outputs": [
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
"[INFO 11-06 10:05:14] ax.core.experiment: The is_test flag has been set to True. This flag is meant purely for development and integration testing purposes. If you are running a live experiment, please set this flag to False\n"
]
},
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
"[INFO 11-06 10:05:14] ax.modelbridge.transforms.standardize_y: Outcome branin_a is constant, within tolerance.\n"
]
},
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
"[INFO 11-06 10:05:14] ax.modelbridge.transforms.standardize_y: Outcome branin_b is constant, within tolerance.\n"
]
},
{
- "output_type": "execute_result",
"data": {
- "text/plain": "GeneratorRun(1 arms, total weight 1.0)"
+ "text/plain": [
+ "GeneratorRun(1 arms, total weight 1.0)"
+ ]
},
+ "execution_count": 12,
"metadata": {},
- "execution_count": 12
+ "output_type": "execute_result"
}
+ ],
+ "source": [
+ "model_bridge_with_EHVI = Models.BOTORCH_MODULAR(\n",
+ " experiment=get_branin_experiment_with_multi_objective(\n",
+ " has_objective_thresholds=True, with_batch=True\n",
+ " ),\n",
+ " data=get_branin_data_multi_objective(),\n",
+ ")\n",
+ "model_bridge_with_EHVI.gen(1)"
]
},
{
"cell_type": "code",
+ "execution_count": 13,
"metadata": {
+ "collapsed": false,
+ "executionStartTime": 1730916314586,
+ "executionStopTime": 1730916314842,
+ "id": "changed-maintenance",
+ "isAgentGenerated": false,
+ "language": "python",
"metadata": {
"originalKey": "dcfdbecc-4a9a-49ac-ad55-0bc04b2ec566"
},
- "id": "changed-maintenance",
"originalKey": "ab6e84ac-2a55-4f48-9ab7-06b8d9b58d1f",
"outputsInitialized": true,
- "isAgentGenerated": false,
- "language": "python",
- "executionStartTime": 1730916314586,
- "executionStopTime": 1730916314842,
- "serverExecutionDuration": 3.3097150735557,
- "collapsed": false,
- "requestMsgId": "ab6e84ac-2a55-4f48-9ab7-06b8d9b58d1f"
+ "requestMsgId": "ab6e84ac-2a55-4f48-9ab7-06b8d9b58d1f",
+ "serverExecutionDuration": 3.3097150735557
},
- "source": [
- "model_bridge_with_EHVI.model.botorch_acqf_class"
- ],
- "execution_count": 13,
"outputs": [
{
- "output_type": "execute_result",
"data": {
- "text/plain": "botorch.acquisition.multi_objective.logei.qLogNoisyExpectedHypervolumeImprovement"
+ "text/plain": [
+ "botorch.acquisition.multi_objective.logei.qLogNoisyExpectedHypervolumeImprovement"
+ ]
},
+ "execution_count": 13,
"metadata": {},
- "execution_count": 13
+ "output_type": "execute_result"
}
+ ],
+ "source": [
+ "model_bridge_with_EHVI.model.botorch_acqf_class"
]
},
{
"cell_type": "code",
+ "execution_count": 14,
"metadata": {
+ "collapsed": false,
+ "executionStartTime": 1730916315097,
+ "executionStopTime": 1730916315308,
+ "id": "operating-shelf",
+ "isAgentGenerated": false,
+ "language": "python",
"metadata": {
"originalKey": "16727a51-337d-4715-bf51-9cb6637a950f"
},
- "id": "operating-shelf",
"originalKey": "1e980e3c-09f6-44c1-a79f-f59867de0c3e",
"outputsInitialized": true,
- "isAgentGenerated": false,
- "language": "python",
- "executionStartTime": 1730916315097,
- "executionStopTime": 1730916315308,
- "serverExecutionDuration": 3.4662369871512,
- "collapsed": false,
- "requestMsgId": "1e980e3c-09f6-44c1-a79f-f59867de0c3e"
+ "requestMsgId": "1e980e3c-09f6-44c1-a79f-f59867de0c3e",
+ "serverExecutionDuration": 3.4662369871512
},
- "source": [
- "model_bridge_with_EHVI.model.surrogate.model.__class__"
- ],
- "execution_count": 14,
"outputs": [
{
- "output_type": "execute_result",
"data": {
- "text/plain": "botorch.models.gp_regression.SingleTaskGP"
+ "text/plain": [
+ "botorch.models.gp_regression.SingleTaskGP"
+ ]
},
+ "execution_count": 14,
"metadata": {},
- "execution_count": 14
+ "output_type": "execute_result"
}
+ ],
+ "source": [
+ "model_bridge_with_EHVI.model.surrogate.model.__class__"
]
},
{
"cell_type": "markdown",
"metadata": {
+ "id": "fatal-butterfly",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "5c64eecc-5ce5-4907-bbcc-5b3cbf4358ae"
},
- "id": "fatal-butterfly",
"originalKey": "3ad7c4a7-fe19-44ad-938d-1be4f8b09bfb",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"Furthermore, the quick-start example at the top of this tutorial shows how to specify surrogate and acquisition subcomponents to `Models.BOTORCH_MODULAR`. "
@@ -981,15 +990,15 @@
{
"cell_type": "markdown",
"metadata": {
+ "id": "hearing-interface",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "a0163432-f0ca-4582-ad84-16c77c99f20b"
},
- "id": "hearing-interface",
"originalKey": "44adf1ce-6d3e-455d-b53c-32d3c42a843f",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"## 5. Utilizing `BoTorchModel` in generation strategies\n",
@@ -1001,21 +1010,23 @@
},
{
"cell_type": "code",
+ "execution_count": 15,
"metadata": {
+ "collapsed": false,
+ "executionStartTime": 1730916316730,
+ "executionStopTime": 1730916316968,
+ "id": "received-registration",
+ "isAgentGenerated": false,
+ "language": "python",
"metadata": {
"originalKey": "f7eabbcf-607c-4bed-9a0e-6ac6e8b04350"
},
- "id": "received-registration",
"originalKey": "4ee172c8-0648-418b-9968-647e8e916507",
"outputsInitialized": true,
- "isAgentGenerated": false,
- "language": "python",
- "executionStartTime": 1730916316730,
- "executionStopTime": 1730916316968,
- "serverExecutionDuration": 2.2927720565349,
- "collapsed": false,
- "requestMsgId": "4ee172c8-0648-418b-9968-647e8e916507"
+ "requestMsgId": "4ee172c8-0648-418b-9968-647e8e916507",
+ "serverExecutionDuration": 2.2927720565349
},
+ "outputs": [],
"source": [
"from ax.modelbridge.generation_strategy import GenerationStep, GenerationStrategy\n",
"from ax.modelbridge.modelbridge_utils import get_pending_observation_features\n",
@@ -1045,22 +1056,20 @@
" ),\n",
" ]\n",
")"
- ],
- "execution_count": 15,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
"metadata": {
+ "id": "logical-windsor",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "212c4543-220e-4605-8f72-5f86cf52f722"
},
- "id": "logical-windsor",
"originalKey": "ba3783ee-3d88-4e44-ad07-77de3c50f84d",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"Set up an experiment and generate 10 trials in it, adding synthetic data to experiment after each one:"
@@ -1068,58 +1077,60 @@
},
{
"cell_type": "code",
+ "execution_count": 16,
"metadata": {
+ "collapsed": false,
+ "executionStartTime": 1730916317751,
+ "executionStopTime": 1730916318153,
+ "id": "viral-cheese",
+ "isAgentGenerated": false,
+ "language": "python",
"metadata": {
"originalKey": "30cfcdd7-721d-4f89-b851-7a94140dfad6"
},
- "id": "viral-cheese",
"originalKey": "1b7d0cfc-f7cf-477d-b109-d34db9604938",
"outputsInitialized": true,
- "isAgentGenerated": false,
- "language": "python",
- "executionStartTime": 1730916317751,
- "executionStopTime": 1730916318153,
- "serverExecutionDuration": 3.9581339806318,
- "collapsed": false,
- "requestMsgId": "1b7d0cfc-f7cf-477d-b109-d34db9604938"
+ "requestMsgId": "1b7d0cfc-f7cf-477d-b109-d34db9604938",
+ "serverExecutionDuration": 3.9581339806318
},
- "source": [
- "experiment = get_branin_experiment(minimize=True)\n",
- "\n",
- "assert len(experiment.trials) == 0\n",
- "experiment.search_space"
- ],
- "execution_count": 16,
"outputs": [
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
"[INFO 11-06 10:05:18] ax.core.experiment: The is_test flag has been set to True. This flag is meant purely for development and integration testing purposes. If you are running a live experiment, please set this flag to False\n"
]
},
{
- "output_type": "execute_result",
"data": {
- "text/plain": "SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-5.0, 10.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 15.0])], parameter_constraints=[])"
+ "text/plain": [
+ "SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-5.0, 10.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 15.0])], parameter_constraints=[])"
+ ]
},
+ "execution_count": 16,
"metadata": {},
- "execution_count": 16
+ "output_type": "execute_result"
}
+ ],
+ "source": [
+ "experiment = get_branin_experiment(minimize=True)\n",
+ "\n",
+ "assert len(experiment.trials) == 0\n",
+ "experiment.search_space"
]
},
{
"cell_type": "markdown",
"metadata": {
+ "id": "incident-newspaper",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "2807d7ce-8a6b-423c-b5f5-32edba09c78e"
},
- "id": "incident-newspaper",
"originalKey": "df2e90f5-4132-4d87-989b-e6d47c748ddc",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"## 5a. Specifying `pending_observations`\n",
@@ -1130,98 +1141,102 @@
},
{
"cell_type": "code",
+ "execution_count": 17,
"metadata": {
+ "collapsed": false,
+ "executionStartTime": 1730916318830,
+ "executionStopTime": 1730916321328,
+ "id": "casual-spread",
+ "isAgentGenerated": false,
+ "language": "python",
"metadata": {
"originalKey": "58aafd65-a366-4b66-a1b1-31b207037a2e"
},
- "id": "casual-spread",
"originalKey": "fe7437c5-8834-46cc-94b2-91782d91ee96",
"outputsInitialized": true,
- "isAgentGenerated": false,
- "language": "python",
- "executionStartTime": 1730916318830,
- "executionStopTime": 1730916321328,
- "serverExecutionDuration": 2274.8276960338,
- "collapsed": false,
- "requestMsgId": "fe7437c5-8834-46cc-94b2-91782d91ee96"
+ "requestMsgId": "fe7437c5-8834-46cc-94b2-91782d91ee96",
+ "serverExecutionDuration": 2274.8276960338
},
- "source": [
- "for _ in range(10):\n",
- " # Produce a new generator run and attach it to experiment as a trial\n",
- " generator_run = gs.gen(\n",
- " experiment=experiment,\n",
- " n=1,\n",
- " pending_observations=get_pending_observation_features(experiment=experiment),\n",
- " )\n",
- " trial = experiment.new_trial(generator_run)\n",
- "\n",
- " # Mark the trial as 'RUNNING' so we can mark it 'COMPLETED' later\n",
- " trial.mark_running(no_runner_required=True)\n",
- "\n",
- " # Attach data for the new trial and mark it 'COMPLETED'\n",
- " experiment.attach_data(get_branin_data(trials=[trial]))\n",
- " trial.mark_completed()\n",
- "\n",
- " print(f\"Completed trial #{trial.index}, suggested by {generator_run._model_key}.\")"
- ],
- "execution_count": 17,
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
- "Completed trial #0, suggested by Sobol.\nCompleted trial #1, suggested by Sobol.\nCompleted trial #2, suggested by Sobol.\nCompleted trial #3, suggested by Sobol.\nCompleted trial #4, suggested by Sobol.\n"
+ "Completed trial #0, suggested by Sobol.\n",
+ "Completed trial #1, suggested by Sobol.\n",
+ "Completed trial #2, suggested by Sobol.\n",
+ "Completed trial #3, suggested by Sobol.\n",
+ "Completed trial #4, suggested by Sobol.\n"
]
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Completed trial #5, suggested by BoTorch.\n"
]
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Completed trial #6, suggested by BoTorch.\n"
]
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Completed trial #7, suggested by BoTorch.\n"
]
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Completed trial #8, suggested by BoTorch.\n"
]
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Completed trial #9, suggested by BoTorch.\n"
]
}
+ ],
+ "source": [
+ "for _ in range(10):\n",
+ " # Produce a new generator run and attach it to experiment as a trial\n",
+ " generator_run = gs.gen(\n",
+ " experiment=experiment,\n",
+ " n=1,\n",
+ " pending_observations=get_pending_observation_features(experiment=experiment),\n",
+ " )\n",
+ " trial = experiment.new_trial(generator_run)\n",
+ "\n",
+ " # Mark the trial as 'RUNNING' so we can mark it 'COMPLETED' later\n",
+ " trial.mark_running(no_runner_required=True)\n",
+ "\n",
+ " # Attach data for the new trial and mark it 'COMPLETED'\n",
+ " experiment.attach_data(get_branin_data(trials=[trial]))\n",
+ " trial.mark_completed()\n",
+ "\n",
+ " print(f\"Completed trial #{trial.index}, suggested by {generator_run._model_key}.\")"
]
},
{
"cell_type": "markdown",
"metadata": {
+ "id": "circular-vermont",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "9d3b86bf-b691-4315-8b8f-60504b37818c"
},
- "id": "circular-vermont",
"originalKey": "6a78ef13-fbaa-4cae-934b-d57f5807fe25",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"Now we examine the experiment and observe the trials that were added to it and produced by the generation strategy:"
@@ -1229,200 +1244,346 @@
},
{
"cell_type": "code",
+ "execution_count": 18,
"metadata": {
+ "collapsed": false,
+ "executionStartTime": 1730916319576,
+ "executionStopTime": 1730916321368,
+ "id": "significant-particular",
+ "isAgentGenerated": false,
+ "language": "python",
"metadata": {
"originalKey": "ca12913d-e3fd-4617-a247-e3432665bac1"
},
- "id": "significant-particular",
"originalKey": "b3160bc0-d5d1-45fa-bf62-4b9dd5778cac",
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+ "1 1 1_0 COMPLETED ... 74.072517 3.535081 10.528676\n",
+ "2 2 2_0 COMPLETED ... 5.610080 8.741262 3.706691\n",
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+ "9 9 9_0 COMPLETED ... 132.986209 1.451895 15.000000\n",
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+ "[10 rows x 7 columns]"
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},
+ "execution_count": 18,
"metadata": {},
- "execution_count": 18
+ "output_type": "execute_result"
}
+ ],
+ "source": [
+ "exp_to_df(experiment)"
]
},
{
"cell_type": "markdown",
"metadata": {
+ "id": "obvious-transparency",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "c25da720-6d3d-4f16-b878-24f2d2755783"
},
- "id": "obvious-transparency",
"originalKey": "633c66af-a89f-4f03-a88b-866767d0a52f",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"## 6. Customizing a `Surrogate` or `Acquisition`\n",
@@ -1434,21 +1595,23 @@
},
{
"cell_type": "code",
+ "execution_count": 19,
"metadata": {
+ "collapsed": false,
+ "executionStartTime": 1730916320585,
+ "executionStopTime": 1730916321384,
+ "id": "organizational-balance",
+ "isAgentGenerated": false,
+ "language": "python",
"metadata": {
"originalKey": "e7f8e413-f01e-4f9d-82c1-4912097637af"
},
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"originalKey": "2949718a-8a4e-41e5-91ac-5b020eface47",
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- "serverExecutionDuration": 2.2059100447223,
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- "requestMsgId": "2949718a-8a4e-41e5-91ac-5b020eface47"
+ "requestMsgId": "2949718a-8a4e-41e5-91ac-5b020eface47",
+ "serverExecutionDuration": 2.2059100447223
},
+ "outputs": [],
"source": [
"from botorch.acquisition.objective import MCAcquisitionObjective, PosteriorTransform\n",
"from botorch.acquisition.risk_measures import RiskMeasureMCObjective\n",
@@ -1466,22 +1629,20 @@
" risk_measure: Optional[RiskMeasureMCObjective] = None,\n",
" ) -> Tuple[Optional[MCAcquisitionObjective], Optional[PosteriorTransform]]:\n",
" ... # Produce the desired `MCAcquisitionObjective` and `PosteriorTransform` instead of the default"
- ],
- "execution_count": 19,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
"metadata": {
+ "id": "theoretical-horizon",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "7299f0fc-e19e-4383-99de-ef7a9a987fe9"
},
- "id": "theoretical-horizon",
"originalKey": "0ec8606d-9d5b-4bcb-ad7e-f54839ad6f9b",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"Then to use the new subclass in `BoTorchModel`, just specify `acquisition_class` argument along with `botorch_acqf_class` (to `BoTorchModel` directly or to `Models.BOTORCH_MODULAR`, which just passes the relevant arguments to `BoTorchModel` under the hood, as discussed in section 4):"
@@ -1489,60 +1650,62 @@
},
{
"cell_type": "code",
+ "execution_count": 20,
"metadata": {
+ "collapsed": false,
+ "executionStartTime": 1730916321675,
+ "executionStopTime": 1730916321901,
+ "id": "approximate-rolling",
+ "isAgentGenerated": false,
+ "language": "python",
"metadata": {
"originalKey": "07fe169a-78de-437e-9857-7c99cc48eedc"
},
- "id": "approximate-rolling",
"originalKey": "e231ea1e-c70d-48dc-b6c6-1611c5ea1b26",
"outputsInitialized": true,
- "isAgentGenerated": false,
- "language": "python",
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- "serverExecutionDuration": 12.351316981949,
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- "requestMsgId": "e231ea1e-c70d-48dc-b6c6-1611c5ea1b26"
+ "requestMsgId": "e231ea1e-c70d-48dc-b6c6-1611c5ea1b26",
+ "serverExecutionDuration": 12.351316981949
},
- "source": [
- "Models.BOTORCH_MODULAR(\n",
- " experiment=experiment,\n",
- " data=data,\n",
- " acquisition_class=CustomObjectiveAcquisition,\n",
- " botorch_acqf_class=MyAcquisitionFunctionClass,\n",
- ")"
- ],
- "execution_count": 20,
"outputs": [
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
"[INFO 11-06 10:05:21] ax.modelbridge.transforms.standardize_y: Outcome branin is constant, within tolerance.\n"
]
},
{
- "output_type": "execute_result",
"data": {
- "text/plain": "TorchModelBridge(model=BoTorchModel)"
+ "text/plain": [
+ "TorchModelBridge(model=BoTorchModel)"
+ ]
},
+ "execution_count": 20,
"metadata": {},
- "execution_count": 20
+ "output_type": "execute_result"
}
+ ],
+ "source": [
+ "Models.BOTORCH_MODULAR(\n",
+ " experiment=experiment,\n",
+ " data=data,\n",
+ " acquisition_class=CustomObjectiveAcquisition,\n",
+ " botorch_acqf_class=MyAcquisitionFunctionClass,\n",
+ ")"
]
},
{
"cell_type": "markdown",
"metadata": {
+ "id": "representative-implement",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "608d5f0d-4528-4aa6-869d-db38fcbfb256"
},
- "id": "representative-implement",
"originalKey": "cdcfb2bc-3016-4681-9fff-407f28321c3f",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"To use a custom `Surrogate` subclass, pass the `surrogate` argument of that type:\n",
@@ -1558,15 +1721,15 @@
{
"cell_type": "markdown",
"metadata": {
+ "id": "framed-intermediate",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "64f1289e-73c7-4cc5-96ee-5091286a8361"
},
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- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"------"
@@ -1575,15 +1738,15 @@
{
"cell_type": "markdown",
"metadata": {
+ "id": "metropolitan-feedback",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "d1e37569-dd0d-4561-b890-2f0097a345e0"
},
- "id": "metropolitan-feedback",
"originalKey": "f71fcfa1-fc59-4bfb-84d6-b94ea5298bfa",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"## Appendix 1: Methods available on `BoTorchModel`\n",
@@ -1599,22 +1762,21 @@
"* `update` updates surrogate model with training data and optionally reoptimizes model parameters via `Surrogate.update`,\n",
"* `cross_validate` re-fits the surrogate model to subset of training data and makes predictions for test data,\n",
"* `evaluate_acquisition_function` instantiates an acquisition function and evaluates it for a given point.\n",
- "------\n",
- ""
+ "------\n"
]
},
{
"cell_type": "markdown",
"metadata": {
+ "id": "possible-transsexual",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "b02f928c-57d9-4b2a-b4fe-c6d28d368b12"
},
- "id": "possible-transsexual",
"originalKey": "91cedde4-8911-441f-af05-eb124581cbbc",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"## Appendix 2: Default surrogate models and acquisition functions\n",
@@ -1633,15 +1795,15 @@
{
"cell_type": "markdown",
"metadata": {
+ "id": "continuous-strain",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "76ae9852-9d21-43d6-bf75-bb087a474dd6"
},
- "id": "continuous-strain",
"originalKey": "c8b0f933-8df6-479b-aa61-db75ca877624",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"## Appendix 3: Handling storage errors that arise from objects that don't have serialization logic in A\n",
@@ -1652,15 +1814,15 @@
{
"cell_type": "markdown",
"metadata": {
+ "id": "broadband-voice",
+ "isAgentGenerated": false,
+ "language": "markdown",
"metadata": {
"originalKey": "6487b68e-b808-4372-b6ba-ab02ce4826bc"
},
- "id": "broadband-voice",
"originalKey": "4d82f49a-3a8b-42f0-a4f5-5c079b793344",
- "showInput": false,
"outputsInitialized": false,
- "isAgentGenerated": false,
- "language": "markdown"
+ "showInput": false
},
"source": [
"The two options for handling this error are:\n",
@@ -1668,5 +1830,26 @@
"2. specifying serialization logic for a given object that needs to occur among the `Model` or `AcquisitionFunction` options. Tutorial for this is in the works, but in the meantime you can [post an issue on the Ax GitHub](https://github.com/facebook/Ax/issues) to get help with this."
]
}
- ]
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/tutorials/multi_task.ipynb b/tutorials/multi_task/multi_task.ipynb
similarity index 95%
rename from tutorials/multi_task.ipynb
rename to tutorials/multi_task/multi_task.ipynb
index 4e1559430f6..c543120a8c1 100644
--- a/tutorials/multi_task.ipynb
+++ b/tutorials/multi_task/multi_task.ipynb
@@ -22,16 +22,25 @@
"Throughout the optimization we can make nosiy observations directly of the objective (an online observation), and we can make noisy observations of a biased version of the objective (offline observations). Bias is simulated by passing the function values through a piecewise linear function. Offline observations are much less time-consuming than online observations, so we wish to use them to improve our ability to optimize the online objective."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "in_colab = 'google.colab' in sys.modules\n",
+ "if in_colab:\n",
+ " %pip install ax-platform"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"code_folding": [],
"hidden_ranges": [],
- "originalKey": "3ce827be-d20b-48d3-a6ff-291bd442c748",
- "vscode": {
- "languageId": "python"
- }
+ "originalKey": "3ce827be-d20b-48d3-a6ff-291bd442c748"
},
"outputs": [],
"source": [
@@ -68,8 +77,11 @@
"from ax.runners.synthetic import SyntheticRunner\n",
"from ax.utils.notebook.plotting import init_notebook_plotting, render\n",
"from pyre_extensions import assert_is_instance\n",
+ "import plotly.io as pio\n",
"\n",
"init_notebook_plotting()\n",
+ "if in_colab:\n",
+ " pio.renderers.default = \"colab\"\n",
"\n",
"# Transforms for pre-processing the data from a multi-type experiment to \n",
"# construct a multi-task GP model.\n",
@@ -85,11 +97,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "vscode": {
- "languageId": "python"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
"SMOKE_TEST = os.environ.get(\"SMOKE_TEST\")"
@@ -114,10 +122,7 @@
"metadata": {
"code_folding": [],
"hidden_ranges": [],
- "originalKey": "2315ca64-74e5-4084-829e-e8a482c653e5",
- "vscode": {
- "languageId": "python"
- }
+ "originalKey": "2315ca64-74e5-4084-829e-e8a482c653e5"
},
"outputs": [],
"source": [
@@ -161,10 +166,7 @@
"metadata": {
"code_folding": [],
"hidden_ranges": [],
- "originalKey": "39504f84-793e-4dae-ae55-068f1b762706",
- "vscode": {
- "languageId": "python"
- }
+ "originalKey": "39504f84-793e-4dae-ae55-068f1b762706"
},
"outputs": [],
"source": [
@@ -234,10 +236,7 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "originalKey": "8260b668-91ef-404e-aa8c-4bf43f6a5660",
- "vscode": {
- "languageId": "python"
- }
+ "originalKey": "8260b668-91ef-404e-aa8c-4bf43f6a5660"
},
"outputs": [],
"source": [
@@ -272,10 +271,7 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "originalKey": "3d124563-8a1f-411e-9822-972568ce1970",
- "vscode": {
- "languageId": "python"
- }
+ "originalKey": "3d124563-8a1f-411e-9822-972568ce1970"
},
"outputs": [],
"source": [
@@ -313,10 +309,7 @@
"metadata": {
"code_folding": [],
"hidden_ranges": [],
- "originalKey": "040354c2-4313-46db-b40d-8adc8da6fafb",
- "vscode": {
- "languageId": "python"
- }
+ "originalKey": "040354c2-4313-46db-b40d-8adc8da6fafb"
},
"outputs": [],
"source": [
@@ -368,11 +361,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "vscode": {
- "languageId": "python"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
"def get_MTGP(\n",
@@ -444,10 +433,7 @@
"metadata": {
"code_folding": [],
"hidden_ranges": [],
- "originalKey": "37735b0e-e488-4927-a3da-a7d32d9f1ae0",
- "vscode": {
- "languageId": "python"
- }
+ "originalKey": "37735b0e-e488-4927-a3da-a7d32d9f1ae0"
},
"outputs": [],
"source": [
@@ -550,10 +536,7 @@
"metadata": {
"code_folding": [],
"hidden_ranges": [],
- "originalKey": "f94a7537-61a6-4200-8e56-01de41aff6c9",
- "vscode": {
- "languageId": "python"
- }
+ "originalKey": "f94a7537-61a6-4200-8e56-01de41aff6c9"
},
"outputs": [],
"source": [
diff --git a/tutorials/multiobjective_optimization.ipynb b/tutorials/multiobjective_optimization/multiobjective_optimization.ipynb
similarity index 99%
rename from tutorials/multiobjective_optimization.ipynb
rename to tutorials/multiobjective_optimization/multiobjective_optimization.ipynb
index c52e040f982..6bff2384850 100644
--- a/tutorials/multiobjective_optimization.ipynb
+++ b/tutorials/multiobjective_optimization/multiobjective_optimization.ipynb
@@ -18,6 +18,18 @@
"To learn more about how to choose a threshold, see [Set Objective Thresholds to focus candidate generation in a region of interest](#Set-Objective-Thresholds-to-focus-candidate-generation-in-a-region-of-interest). See the [Service API Tutorial](/tutorials/gpei_hartmann_service.html) for more infomation on running experiments with the Service API."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "in_colab = 'google.colab' in sys.modules\n",
+ "if in_colab:\n",
+ " %pip install ax-platform"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -39,8 +51,11 @@
"# Plotting imports and initialization\n",
"from ax.utils.notebook.plotting import init_notebook_plotting, render\n",
"from botorch.test_functions.multi_objective import BraninCurrin\n",
+ "import plotly.io as pio\n",
"\n",
- "init_notebook_plotting()"
+ "init_notebook_plotting()\n",
+ "if in_colab:\n",
+ " pio.renderers.default = \"colab\""
]
},
{
diff --git a/tutorials/raytune_pytorch_cnn.ipynb b/tutorials/raytune_pytorch_cnn/raytune_pytorch_cnn.ipynb
similarity index 96%
rename from tutorials/raytune_pytorch_cnn.ipynb
rename to tutorials/raytune_pytorch_cnn/raytune_pytorch_cnn.ipynb
index 805e7b6fdd8..c71b9537c9b 100644
--- a/tutorials/raytune_pytorch_cnn.ipynb
+++ b/tutorials/raytune_pytorch_cnn/raytune_pytorch_cnn.ipynb
@@ -16,6 +16,18 @@
"Ray 'Actors' are a simple and clean abstraction for replicating your Python classes across multiple workers and nodes. Each hyperparameter evaluation is asynchronously executed on a separate Ray actor and reports intermediate training progress back to RayTune. Upon reporting, RayTune then uses this information to performs actions such as early termination, re-prioritization, or checkpointing."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "in_colab = 'google.colab' in sys.modules\n",
+ "if in_colab:\n",
+ " %pip install ax-platform"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -51,8 +63,11 @@
"from ax.service.ax_client import AxClient\n",
"from ax.utils.notebook.plotting import init_notebook_plotting, render\n",
"from ax.utils.tutorials.cnn_utils import CNN, evaluate, load_mnist, train\n",
+ "import plotly.io as pio\n",
"\n",
- "init_notebook_plotting()"
+ "init_notebook_plotting()\n",
+ "if in_colab:\n",
+ " pio.renderers.default = \"colab\""
]
},
{
diff --git a/tutorials/saasbo.ipynb b/tutorials/saasbo/saasbo.ipynb
similarity index 97%
rename from tutorials/saasbo.ipynb
rename to tutorials/saasbo/saasbo.ipynb
index 875415cde0e..0973d869817 100644
--- a/tutorials/saasbo.ipynb
+++ b/tutorials/saasbo/saasbo.ipynb
@@ -15,6 +15,19 @@
"[1] D. Eriksson, M. Jankowiak. High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces. Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "import plotly.io as pio\n",
+ "if 'google.colab' in sys.modules:\n",
+ " pio.renderers.default = \"colab\"\n",
+ " %pip install ax-platform"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
diff --git a/tutorials/saasbo_nehvi.ipynb b/tutorials/saasbo_nehvi/saasbo_nehvi.ipynb
similarity index 98%
rename from tutorials/saasbo_nehvi.ipynb
rename to tutorials/saasbo_nehvi/saasbo_nehvi.ipynb
index d49edb5f7b3..6de7d7ae942 100644
--- a/tutorials/saasbo_nehvi.ipynb
+++ b/tutorials/saasbo_nehvi/saasbo_nehvi.ipynb
@@ -117,6 +117,18 @@
"- To learn about multi-objective optimization in Ax Service API: https://ax.dev/tutorials/multiobjective_optimization.html#Using-the-Service-API."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "in_colab = 'google.colab' in sys.modules\n",
+ "if in_colab:\n",
+ " %pip install ax-platform"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -178,7 +190,10 @@
"metadata": {},
"outputs": [],
"source": [
- "init_notebook_plotting()"
+ "import plotly.io as pio\n",
+ "init_notebook_plotting()\n",
+ "if in_colab:\n",
+ " pio.renderers.default = \"colab\""
]
},
{
diff --git a/tutorials/scheduler.ipynb b/tutorials/scheduler/scheduler.ipynb
similarity index 99%
rename from tutorials/scheduler.ipynb
rename to tutorials/scheduler/scheduler.ipynb
index 6ca9dc5cae1..650bd3ca127 100644
--- a/tutorials/scheduler.ipynb
+++ b/tutorials/scheduler/scheduler.ipynb
@@ -76,6 +76,18 @@
"An example of an 'external system' running trial evaluations could be a remote server executing scheduled jobs, a subprocess conducting ML training runs, an engine running physics simulations, etc. For the sake of example here, let us assume a dummy external system with the following client:"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "in_colab = 'google.colab' in sys.modules\n",
+ "if in_colab:\n",
+ " %pip install ax-platform"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -438,8 +450,11 @@
"import numpy as np\n",
"from ax.plot.trace import optimization_trace_single_method\n",
"from ax.utils.notebook.plotting import render, init_notebook_plotting\n",
+ "import plotly.io as pio\n",
"\n",
"init_notebook_plotting()\n",
+ "if in_colab:\n",
+ " pio.renderers.default = \"colab\"\n",
"\n",
"\n",
"def get_plot():\n",
diff --git a/tutorials/sebo.ipynb b/tutorials/sebo/sebo.ipynb
similarity index 98%
rename from tutorials/sebo.ipynb
rename to tutorials/sebo/sebo.ipynb
index 730ead6c532..d6abeba2d4d 100644
--- a/tutorials/sebo.ipynb
+++ b/tutorials/sebo/sebo.ipynb
@@ -24,6 +24,19 @@
"By following this tutorial, you will learn how to leverage the SEBO method through the Ax API, empowering you to effectively balance objectives and sparsity in your optimization tasks. Let's get started!"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "import plotly.io as pio\n",
+ "if 'google.colab' in sys.modules:\n",
+ " pio.renderers.default = \"colab\"\n",
+ " %pip install ax-platform"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
diff --git a/tutorials/submitit.ipynb b/tutorials/submitit/submitit.ipynb
similarity index 99%
rename from tutorials/submitit.ipynb
rename to tutorials/submitit/submitit.ipynb
index 155f1c57cd5..d671326c11c 100644
--- a/tutorials/submitit.ipynb
+++ b/tutorials/submitit/submitit.ipynb
@@ -16,6 +16,19 @@
"Let's start by importing the necessary libraries."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "import plotly.io as pio\n",
+ "if 'google.colab' in sys.modules:\n",
+ " pio.renderers.default = \"colab\"\n",
+ " %pip install ax-platform"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
@@ -7589,21 +7602,21 @@
"metadata": {
"fileHeader": "",
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
},
"language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.5"
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.5"
}
},
"nbformat": 4,
diff --git a/tutorials/tune_cnn_service.ipynb b/tutorials/tune_cnn_service/tune_cnn_service.ipynb
similarity index 98%
rename from tutorials/tune_cnn_service.ipynb
rename to tutorials/tune_cnn_service/tune_cnn_service.ipynb
index f2bcc0d90da..eecbf4e09c0 100644
--- a/tutorials/tune_cnn_service.ipynb
+++ b/tutorials/tune_cnn_service/tune_cnn_service.ipynb
@@ -14,6 +14,18 @@
"This tutorial walks through using Ax to tune two hyperparameters (learning rate and momentum) for a PyTorch CNN on the MNIST dataset trained using SGD with momentum."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "in_colab = 'google.colab' in sys.modules\n",
+ "if in_colab:\n",
+ " %pip install ax-platform"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -39,8 +51,11 @@
"from ax.utils.tutorials.cnn_utils import evaluate, load_mnist, train\n",
"from torch._tensor import Tensor\n",
"from torch.utils.data import DataLoader\n",
+ "import plotly.io as pio\n",
"\n",
- "init_notebook_plotting()"
+ "init_notebook_plotting()\n",
+ "if in_colab:\n",
+ " pio.renderers.default = \"colab\""
]
},
{
diff --git a/tutorials/visualizations.ipynb b/tutorials/visualizations/visualizations.ipynb
similarity index 96%
rename from tutorials/visualizations.ipynb
rename to tutorials/visualizations/visualizations.ipynb
index 4603bcbb30e..80d23f712ef 100644
--- a/tutorials/visualizations.ipynb
+++ b/tutorials/visualizations/visualizations.ipynb
@@ -11,6 +11,18 @@
"This tutorial illustrates the core visualization utilities available in Ax."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "in_colab = 'google.colab' in sys.modules\n",
+ "if in_colab:\n",
+ " %pip install ax-platform"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -34,8 +46,11 @@
"from ax.service.ax_client import AxClient, ObjectiveProperties\n",
"from ax.utils.measurement.synthetic_functions import hartmann6\n",
"from ax.utils.notebook.plotting import init_notebook_plotting, render\n",
+ "import plotly.io as pio\n",
"\n",
- "init_notebook_plotting()"
+ "init_notebook_plotting()\n",
+ "if in_colab:\n",
+ " pio.renderers.default = \"colab\""
]
},
{
@@ -386,8 +401,8 @@
},
"indentAmount": 2,
"kernelspec": {
- "name": "python3",
- "display_name": "python3"
+ "display_name": "python3",
+ "name": "python3"
}
},
"nbformat": 4,
diff --git a/website/README.md b/website/README.md
new file mode 100644
index 00000000000..2961e06b2cd
--- /dev/null
+++ b/website/README.md
@@ -0,0 +1,52 @@
+The Ax website is built using [Docusaurus](https://docusaurus.io/), a modern static website generator.
+
+## Building
+
+### Notebooks
+We convert tutorial notebooks to MDX for embedding as docs. This needs to be done before serving the website and can be done by running this script from the project root:
+
+```bash
+python3 scripts/convert_ipynb_to_mdx.py --clean
+```
+
+If the script fails ensure you have the necessary dependencies (ideally to your virtual env):
+
+```bash
+pip install -e ".[tutorial]"
+```
+
+### Docusaurus
+You need [Node](https://nodejs.org/en/) >= 18.x and
+[Yarn](https://yarnpkg.com/en/) in order to build the Ax website.
+
+Switch to the `website` dir from the project root and start the server:
+```bash
+cd website
+yarn
+yarn start
+```
+
+Open http://localhost:3000 (if doesn't automatically open).
+
+Anytime you change the contents of the page, the page should auto-update.
+
+Note that you may need to switch to the "Next" version of the website documentation to see your latest changes.
+
+### All-in-one
+
+For convenience we provide a single shell script to convert the tutorials and build the website in one command:
+```bash
+./scripts/make_docs.sh
+```
+
+To generate a static build of the website in the `website/build` directory, run
+```bash
+./scripts/make_docs.sh -b
+```
+
+## Publishing
+
+The site is hosted on GitHub pages, via the `gh-pages` branch of the Ax
+[GitHub repo](https://github.com/facebook/Ax/tree/gh-pages).
+The website is automatically built and published from GitHub Actions - see the
+[config file](https://github.com/facebook/Ax/blob/main/.github/workflows/publish_website.yml) for details.
diff --git a/website/core/Footer.js b/website/core/Footer.js
deleted file mode 100644
index f1c1792b09f..00000000000
--- a/website/core/Footer.js
+++ /dev/null
@@ -1,67 +0,0 @@
-/**
- * Copyright (c) Meta Platforms, Inc. and affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */
-
-const React = require('react');
-
-class Footer extends React.Component {
-
- docUrl(doc, language) {
- const baseUrl = this.props.config.baseUrl;
- const docsUrl = this.props.config.docsUrl;
- const docsPart = `${docsUrl ? `${docsUrl}/` : ''}`;
- const langPart = `${language ? `${language}/` : ''}`;
- return `${baseUrl}${docsPart}${langPart}${doc}`;
- }
-
- pageUrl(doc, language) {
- const baseUrl = this.props.config.baseUrl;
- return baseUrl + (language ? `${language}/` : '') + doc;
- }
-
- render() {
- const currentYear = new Date().getFullYear();
-
- return (
-
- );
- }
-}
-
-module.exports = Footer;
diff --git a/website/core/Tutorial.js b/website/core/Tutorial.js
deleted file mode 100644
index a844f68a232..00000000000
--- a/website/core/Tutorial.js
+++ /dev/null
@@ -1,133 +0,0 @@
-/**
- * Copyright (c) Meta Platforms, Inc. and affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- *
- * @format
- */
-
-const React = require('react');
-
-const fs = require('fs-extra');
-const path = require('path');
-const CWD = process.cwd();
-
-const CompLibrary = require(`${CWD}/node_modules/docusaurus/lib/core/CompLibrary.js`);
-const Container = CompLibrary.Container;
-
-const TutorialSidebar = require(`${CWD}/core/TutorialSidebar.js`);
-
-function timeToMinAndSec(time) {
- const mins = Math.floor(time / 60);
- const total_secs = time - mins * 60;
- const secs = Math.round(total_secs * 100) / 100;
- return {mins, secs};
-}
-
-function renderDownloadIcon() {
- return (
-
- );
-}
-
-class Tutorial extends React.Component {
- render() {
- const {baseUrl, tutorialDir, tutorialID, totalExecTime} = this.props;
-
- let htmlFile = null;
- let pyFile = null;
- let ipynbFile = null;
- let directoryDownloadButton = null;
- let totalExecTimeText = null;
-
- if (tutorialDir != null && tutorialDir !== '') {
- htmlFile = `${CWD}/_tutorials/${tutorialDir}/${tutorialID}.html`;
- ipynbFile = `${baseUrl}files/${tutorialDir}/${tutorialID}.ipynb`;
- pyFile = `${baseUrl}files/${tutorialDir}/${tutorialID}.py`;
- directoryDownloadButton = (
-
- );
- } else {
- htmlFile = `${CWD}/_tutorials/${tutorialID}.html`;
- ipynbFile = `${baseUrl}files/${tutorialID}.ipynb`;
- pyFile = `${baseUrl}files/${tutorialDir}/${tutorialID}.py`;
- }
- const normalizedHtmlFile = path.normalize(htmlFile);
-
- if (totalExecTime != null) {
- const minsAndSecs = timeToMinAndSec(totalExecTime);
- const timeText =
- 'Total runtime of script: ' +
- (minsAndSecs['mins'] === 0
- ? `${minsAndSecs['secs']} seconds.`
- : `${minsAndSecs['mins']} minutes, ${minsAndSecs['secs']} seconds.`);
-
- totalExecTimeText = (
- | \n", + " | trial_index | \n", + "arm_name | \n", + "trial_status | \n", + "generation_method | \n", + "branin | \n", + "x1 | \n", + "x2 | \n", + "
|---|---|---|---|---|---|---|---|
| 0 | \n", + "0 | \n", + "0_0 | \n", + "COMPLETED | \n", + "Sobol | \n", + "26.922506 | \n", + "-2.244023 | \n", + "5.435609 | \n", + "
| 1 | \n", + "1 | \n", + "1_0 | \n", + "COMPLETED | \n", + "Sobol | \n", + "74.072517 | \n", + "3.535081 | \n", + "10.528676 | \n", + "
| 2 | \n", + "2 | \n", + "2_0 | \n", + "COMPLETED | \n", + "Sobol | \n", + "5.610080 | \n", + "8.741262 | \n", + "3.706691 | \n", + "
| 3 | \n", + "3 | \n", + "3_0 | \n", + "COMPLETED | \n", + "Sobol | \n", + "56.657623 | \n", + "-0.069164 | \n", + "12.199905 | \n", + "
| 4 | \n", + "4 | \n", + "4_0 | \n", + "COMPLETED | \n", + "Sobol | \n", + "27.932704 | \n", + "0.862014 | \n", + "1.306074 | \n", + "
| 5 | \n", + "5 | \n", + "5_0 | \n", + "COMPLETED | \n", + "BoTorch | \n", + "5.423062 | \n", + "10.000000 | \n", + "4.868411 | \n", + "
| 6 | \n", + "6 | \n", + "6_0 | \n", + "COMPLETED | \n", + "BoTorch | \n", + "9.250452 | \n", + "10.000000 | \n", + "0.299753 | \n", + "
| 7 | \n", + "7 | \n", + "7_0 | \n", + "COMPLETED | \n", + "BoTorch | \n", + "308.129096 | \n", + "-5.000000 | \n", + "0.000000 | \n", + "
| 8 | \n", + "8 | \n", + "8_0 | \n", + "COMPLETED | \n", + "BoTorch | \n", + "17.607633 | \n", + "0.778687 | \n", + "5.717932 | \n", + "
| 9 | \n", + "9 | \n", + "9_0 | \n", + "COMPLETED | \n", + "BoTorch | \n", + "132.986209 | \n", + "1.451895 | \n", + "15.000000 | \n", + "
-
- );
- }
-
- return (
- {timeText}
-
-
-
- {directoryDownloadButton}
-
-
- {totalExecTimeText}
-
-
- );
- }
-}
-
-module.exports = Tutorial;
diff --git a/website/core/TutorialSidebar.js b/website/core/TutorialSidebar.js
deleted file mode 100644
index 3ab142f22f7..00000000000
--- a/website/core/TutorialSidebar.js
+++ /dev/null
@@ -1,96 +0,0 @@
-/**
- * Copyright (c) Meta Platforms, Inc. and affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- *
- * @format
- */
-
-const React = require('react');
-const fs = require('fs-extra');
-const path = require('path');
-const join = path.join;
-const CWD = process.cwd();
-
-const CompLibrary = require(join(
- CWD,
- '/node_modules/docusaurus/lib/core/CompLibrary.js',
-));
-const SideNav = require(join(
- CWD,
- '/node_modules/docusaurus/lib/core/nav/SideNav.js',
-));
-
-const Container = CompLibrary.Container;
-
-const OVERVIEW_ID = 'tutorial_overview';
-
-class TutorialSidebar extends React.Component {
- render() {
- const {currentTutorialID} = this.props;
- const current = {
- id: currentTutorialID || OVERVIEW_ID,
- };
-
- const toc = [
- {
- type: 'CATEGORY',
- title: 'Tutorials',
- children: [
- {
- type: 'LINK',
- item: {
- permalink: 'tutorials/',
- id: OVERVIEW_ID,
- title: 'Overview',
- },
- },
- ],
- },
- ];
-
- const jsonFile = join(CWD, 'tutorials.json');
- const normJsonFile = path.normalize(jsonFile);
- const json = JSON.parse(fs.readFileSync(normJsonFile, {encoding: 'utf8'}));
-
- Object.keys(json).forEach(category => {
- const categoryItems = json[category];
- const items = [];
- categoryItems.map(item => {
- let permalink = `tutorials/${item.id}.html`;
- if ('dir' in item) {
- permalink = `tutorials/${item.dir}/${item.id}.html`;
- }
- items.push({
- type: 'LINK',
- item: {
- permalink: permalink,
- id: item.id,
- title: item.title,
- },
- });
- });
-
- toc.push({
- type: 'CATEGORY',
- title: category,
- children: items,
- });
- });
-
- return (
- ', + }, + { + type: 'html', + className: 'margin-horiz--sm text--bold', + value: 'Archived versions', + }, + { + href: 'https://archive.ax.dev/versions.html', + label: '0.x.x', + }, + ], + }, + { + "href": "https://github.com/facebook/Ax", + "className": "header-github-link", + "aria-label": "GitHub", + "position": "right" + }, + { + to: 'blog', + label: 'Blog', + position: 'left' + } + ] + }, + "docs": { + "sidebar": { + autoCollapseCategories: true, + hideable: true, + }, + }, + "image": "img/ax.svg", + "footer": { + style: 'dark', + "logo": { + alt: "Ax", + "src": "img/meta_opensource_logo_negative.svg", + }, + links: [ + { + title: 'Docs', + items: [ + { + label: 'Introduction', + to: 'docs/why-ax', + }, + ], + }, + { + title: 'Social', + items: [ + { + label: 'GitHub', + href: 'https://github.com/facebook/ax', + } + ], + }, + { + title: 'Legal', + // Please do not remove the privacy and terms, it's a legal requirement. + items: [ + { + label: 'Privacy', + href: 'https://opensource.facebook.com/legal/privacy/', + target: '_blank', + rel: 'noreferrer noopener', + }, + { + label: 'Terms', + href: 'https://opensource.facebook.com/legal/terms/', + target: '_blank', + rel: 'noreferrer noopener', + }, + ], + }, + ], + copyright: `Copyright © ${new Date().getFullYear()} Meta Platforms, Inc.`, + }, + algolia: { + appId: 'O2Q3QH4SYH', + apiKey: '330b76ae9b20640dacf7ef3e1256f584', + indexName: 'ax', + }, + } +} diff --git a/website/package.json b/website/package.json index 7d66debef68..a145a621fb9 100644 --- a/website/package.json +++ b/website/package.json @@ -1,14 +1,33 @@ { "scripts": { "examples": "docusaurus-examples", - "start": "docusaurus-start", - "build": "docusaurus-build", + "start": "DOCUSAURUS_IGNORE_SSG_WARNINGS=true docusaurus start", + "build": "DOCUSAURUS_IGNORE_SSG_WARNINGS=true docusaurus build", "publish-gh-pages": "docusaurus-publish", "write-translations": "docusaurus-write-translations", "version": "docusaurus-version", - "rename-version": "docusaurus-rename-version" + "rename-version": "docusaurus-rename-version", + "swizzle": "docusaurus swizzle", + "deploy": "docusaurus deploy", + "serve": "docusaurus serve", + "clear": "docusaurus clear", + "docusaurus": "docusaurus" }, - "devDependencies": { - "docusaurus": "^1.7.2" + "devDependencies": {}, + "dependencies": { + "@docusaurus/core": "3.6.3", + "@docusaurus/faster": "3.6.3", + "@docusaurus/plugin-client-redirects": "3.6.3", + "@docusaurus/preset-classic": "3.6.3", + "clsx": "^1.1.1", + "plotly.js": "^2.8.1", + "react": "^18.3.1", + "react-dom": "^18.3.1", + "react-plotly.js": "^2.5.1", + "rehype-katex": "7", + "remark-math": "6" + }, + "engines": { + "node": ">=18.0" } } diff --git a/website/pages/en/index.js b/website/pages/en/index.js deleted file mode 100644 index 723f6045f6d..00000000000 --- a/website/pages/en/index.js +++ /dev/null @@ -1,235 +0,0 @@ -/** - * Copyright (c) Meta Platforms, Inc. and affiliates. - * - * This source code is licensed under the MIT license found in the - * LICENSE file in the root directory of this source tree. - * - * @format - */ - -const React = require('react'); - -const CompLibrary = require('../../core/CompLibrary.js'); - -const MarkdownBlock = CompLibrary.MarkdownBlock; -const Container = CompLibrary.Container; -const GridBlock = CompLibrary.GridBlock; - -const bash = (...args) => `~~~bash\n${String.raw(...args)}\n~~~`; - -class HomeSplash extends React.Component { - render() { - const {siteConfig, language = ''} = this.props; - const {baseUrl, docsUrl} = siteConfig; - const docsPart = `${docsUrl ? `${docsUrl}/` : ''}`; - const langPart = `${language ? `${language}/` : ''}`; - const docUrl = doc => `${baseUrl}${docsPart}${langPart}${doc}`; - - const SplashContainer = props => ( -
-
- );
-
- const Logo = props => (
-
-
- {props.children}
-
- 
-
- );
-
- const ProjectTitle = () => (
- - {siteConfig.tagline} -
- ); - - const PromoSection = props => ( -
-
- );
-
- const Button = props => (
-
- );
-
- return (
-
-
- {props.children}
-
-
-
-
-
-
-
-
-
-
-
- );
-
- const Features = () => (
- Get Started
--
-
-
- Install Ax:
-
{bash`conda install pytorch torchvision -c pytorch # OSX only`} -{bash`pip3 install ax-platform # all systems`} -
- -
- Run an optimization:
-
{codeExample} -
-
-
- {[
- {
- content:
- 'Easy to plug in new algorithms and use the library across ' +
- 'different domains.',
- image: `${baseUrl}img/th-large-solid.svg`,
- imageAlign: 'top',
- title: 'Modular',
- },
- {
- content:
- 'Field experiments require a range of considerations ' +
- 'beyond standard optimization problems.',
- image: `${baseUrl}img/dice-solid.svg`,
- imageAlign: 'top',
- title: 'Supports A/B Tests',
- },
- {
- content:
- 'Support for industry-grade experimentation ' +
- 'and optimization management, including MySQL storage.',
- image: `${baseUrl}img/database-solid.svg`,
- imageAlign: 'top',
- title: 'Production-Ready',
- },
- ]}
-
-
- );
-
- const Showcase = () => {
- if ((siteConfig.users || []).length === 0) {
- return null;
- }
-
- const showcase = siteConfig.users
- .filter(user => user.pinned)
- .map(user => (
-
- Key Features
-
-
- );
- };
-
- return (
- Who is Using This?
-This project is used by all these people
-{showcase}
-
-
-
- );
- }
-}
-
-module.exports = Index;
diff --git a/website/pages/tutorials/index.js b/website/pages/tutorials/index.js
deleted file mode 100644
index 263a30a972c..00000000000
--- a/website/pages/tutorials/index.js
+++ /dev/null
@@ -1,186 +0,0 @@
-/**
- * Copyright (c) Meta Platforms, Inc. and affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- *
- * @format
- */
-
-const React = require('react');
-
-const CWD = process.cwd();
-
-const CompLibrary = require(
- `${CWD}/node_modules/docusaurus/lib/core/CompLibrary.js`,
-);
-const Container = CompLibrary.Container;
-
-const TutorialSidebar = require(`${CWD}/core/TutorialSidebar.js`);
-
-class TutorialHome extends React.Component {
- render() {
- return (
-
-
-
-
-
-
- );
- }
-}
-
-module.exports = TutorialHome;
diff --git a/website/sidebars.js b/website/sidebars.js
new file mode 100644
index 00000000000..59391b7c014
--- /dev/null
+++ b/website/sidebars.js
@@ -0,0 +1,52 @@
+/**
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ *
+ * @format
+ */
+
+const tutorials = () => {
+ const allTutorialMetadata = require('./tutorials.json');
+ const tutorialsSidebar = [{
+ type: 'category',
+ label: 'Tutorials',
+ collapsed: false,
+ items: [
+ {
+ type: 'doc',
+ id: 'tutorials/index',
+ label: 'Overview',
+ },
+ ],
+ },];
+ for (var category in allTutorialMetadata) {
+ const categoryItems = allTutorialMetadata[category];
+ const items = [];
+ categoryItems.map(item => {
+ items.push({
+ type: 'doc',
+ label: item.title,
+ id: `tutorials/${item.id}/index`,
+ });
+ });
+
+ tutorialsSidebar.push({
+ type: 'category',
+ label: category,
+ items: items,
+ });
+ }
+ return tutorialsSidebar;
+};
+
+export default {
+ docs: {
+ "Introduction": ["why-ax"],
+ "Getting Started": ["installation", "api", "glossary"],
+ "Algorithms": ["bayesopt", "banditopt"],
+ "Components": ["core", "trial-evaluation", "data", "models", "storage"],
+ },
+ tutorials: tutorials(),
+};
diff --git a/website/sidebars.json b/website/sidebars.json
deleted file mode 100644
index d83373ff632..00000000000
--- a/website/sidebars.json
+++ /dev/null
@@ -1,8 +0,0 @@
-{
- "docs": {
- "Introduction": ["why-ax"],
- "Getting Started": ["installation", "api", "glossary"],
- "Algorithms": ["bayesopt", "banditopt"],
- "Components": ["core", "trial-evaluation", "data", "models", "storage"]
- }
-}
diff --git a/website/siteConfig.js b/website/siteConfig.js
deleted file mode 100644
index ddc63117cc8..00000000000
--- a/website/siteConfig.js
+++ /dev/null
@@ -1,103 +0,0 @@
-/**
- * Copyright (c) Meta Platforms, Inc. and affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- *
- * @format
- */
-
-// See https://docusaurus.io/docs/site-config for all the possible
-// site configuration options.
-
-const baseUrl = '/';
-
-// If true, include Algolia search bar when building site
-// Note: this setting is toggled to false by publish_site.sh script, so
-// it should not be renamed without modifying that script.
-const includeAlgolia = true;
-
-// List of projects/orgs using your project for the users page.
-const users = [];
-
-const siteConfig = {
- title: 'Ax',
- tagline: 'Adaptive Experimentation Platform',
- url: 'https://ax.dev/',
- baseUrl: baseUrl,
-
- // Used for publishing and more
- projectName: 'Ax',
- organizationName: 'facebook',
-
- // Google analytics
- gaTrackingId: 'UA-139570076-1',
-
- // For no header links in the top nav bar -> headerLinks: [],
- headerLinks: [
- {doc: 'why-ax', label: 'Docs'},
- {href: `${baseUrl}tutorials/`, label: 'Tutorials'},
- {href: `${baseUrl}api/`, label: 'API'},
- // Search can be enabled when site is online
- // {search: true},
- {href: 'https://github.com/facebook/Ax', label: 'GitHub'},
- ],
-
- // If you have users set above, you add it here:
- users,
-
- /* path to images for header/footer */
- headerIcon: 'img/ax_lockup_white.svg',
- footerIcon: 'img/ax.svg',
- favicon: 'img/favicon.png',
-
- /* Colors for website */
- colors: {
- primaryColor: '#1F2833',
- secondaryColor: '#C5C6C7',
- },
-
- highlight: {
- // Highlight.js theme to use for syntax highlighting in code blocks.
- theme: 'default',
- },
-
- // Custom scripts that are placed in of each page
- scripts: [
- 'https://cdn.plot.ly/plotly-latest.min.js',
- `${baseUrl}js/plotUtils.js`,
- 'https://buttons.github.io/buttons.js',
- `${baseUrl}js/mathjax.js`,
- 'https://cdn.jsdelivr.net/npm/mathjax@2/MathJax.js?config=TeX-AMS_SVG',
- ],
-
- // On page navigation for the current documentation page.
- onPageNav: 'separate',
- // Use .html extensions for paths.
- cleanUrl: false,
-
- // Open Graph and Twitter card images.
- ogImage: 'img/ax.svg',
- twitterImage: 'img/ax.svg',
-
- // Show documentation's last contributor's name.
- // enableUpdateBy: true,
-
- // Show documentation's last update time.
- // enableUpdateTime: true,
-
- // enable scroll to top button a the bottom of the site
- scrollToTop: true,
-
- wrapPagesHTML: true,
-};
-
-if (includeAlgolia == true) {
- siteConfig['algolia'] = {
- apiKey: '467d4f1f6cace3ecb36ab551cb44905b',
- indexName: 'ax',
- algoliaOptions: {}, // Optional, if provided by Algolia
- };
-}
-
-module.exports = siteConfig;
diff --git a/website/src/components/CellOutput.jsx b/website/src/components/CellOutput.jsx
new file mode 100644
index 00000000000..434139c67ec
--- /dev/null
+++ b/website/src/components/CellOutput.jsx
@@ -0,0 +1,37 @@
+/**
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+import React from 'react';
+import {v4 as uuidv4} from 'uuid';
+
+const CellOutput = (props) => {
+ return (
+
-
-
-
- Welcome to Ax Tutorials
-- Here you can learn about the structure and applications of Ax from - examples. -
-- Our 3 API tutorials: - Loop, - Service, and - Developer — are a - good place to start. Each tutorial showcases optimization on a - constrained Hartmann6 problem, with the Loop API being the - simplest to use and the Developer API being the most customizable. -
-- - NOTE: We recommend the - Service API for the - vast majority of use cases. - - This API provides an ideal balance of flexibility and simplicity - for most users, and we are in the process of consolidating Ax - usage around it more formally. -
-- - Further, we explore the different components available in Ax in - more detail. - {' '} - The components explored below serve to set up an experiment, - visualize its results, configure an optimization algorithm, run an - entire experiment in a managed closed loop, and combine BoTorch - components in Ax in a modular way. -
--
-
- - Visualizations - illustrates the different plots available to view and understand - your results. - -
-
-
-
- GenerationStrategy
- steps through setting up a way to specify the optimization
- algorithm (or multiple). A
GenerationStrategy- is an important component of Service API and the -Scheduler. -
-
-
-
-
- Scheduler demonstrates an
- example of a managed and configurable closed-loop optimization,
- conducted in an asyncronous fashion.
Scheduleris a - manager abstraction in Ax that deploys trials, polls them, and - uses their results to produce more trials. -
-
-
-
-
-
- Modular
BoTorchModel- - walks though a new beta-feature — an improved - interface between Ax and{' '} - BoTorch — which allows - for combining arbitrary BoTorch components like -AcquisitionFunction,Model, -AcquisitionObjectiveetc. into a single{' '} -Modelin Ax. -
-
- Our other Bayesian Optimization tutorials include: -
--
-
- - - Hyperparameter Optimization for PyTorch - - provides an example of hyperparameter optimization with - Ax and integration with an external ML library. - -
-
-
- - - Hyperparameter Optimization on SLURM via SubmitIt - - shows how to use the AxClient to schedule jobs and tune - hyperparameters on a Slurm cluster. - -
-
-
- - Multi-Task Modeling - illustrates multi-task Bayesian Optimization on a - constrained synthetic Hartmann6 problem. - -
-
-
- - - Multi-Objective Optimization - - demonstrates Multi-Objective Bayesian Optimization on a - synthetic Branin-Currin test function. - -
-
-
- - - Trial-Level Early Stopping - - shows how to use trial-level early stopping on an ML - training job to save resources and iterate faster. - -
-
-
- - Benchmarking Suite - demonstrates how to use the Ax benchmarking suite to - compare Bayesian Optimization algorithm performances and - generate a comparative report with visualizations. - -
- For experiments done in a real-life setting, refer to our field - experiments tutorials: -
--
-
- - Bandit Optimization - shows how Thompson Sampling can be used to intelligently - reallocate resources to well-performing configurations in - real-time. - -
-
-
- - - Human-in-the-Loop Optimization - - walks through manually influencing the course of - optimization in real-time. - -
+ Out:
+
+ );
+};
+
+export default CellOutput;
diff --git a/website/src/components/LinkButtons.jsx b/website/src/components/LinkButtons.jsx
new file mode 100644
index 00000000000..d55f50593b2
--- /dev/null
+++ b/website/src/components/LinkButtons.jsx
@@ -0,0 +1,27 @@
+/**
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+import React from 'react';
+import Link from '@docusaurus/Link';
+import IconExternalLink from '@theme/Icon/ExternalLink'
+
+const LinkButtons = ({githubUrl, colabUrl}) => {
+ return (
+
+ {props.children.split('\n').map((line) => {
+ return (
+
+ {line}
+
+ );
+ })}
+
+
+
+ Open in GitHub
+
+ );
+};
+
+export default LinkButtons;
diff --git a/website/src/components/Plotting.jsx b/website/src/components/Plotting.jsx
new file mode 100644
index 00000000000..ef49acbe4b8
--- /dev/null
+++ b/website/src/components/Plotting.jsx
@@ -0,0 +1,49 @@
+/**
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+import React from 'react';
+import Loadable from 'react-loadable';
+import BrowserOnly from '@docusaurus/BrowserOnly';
+import '../css/bokeh.css';
+
+export const BokehFigure = React.memo(({data}) => {
+ const targetId = data['target_id'];
+ return (
+
+ loading...
}>
+ {() => {
+ {
+ window.Bokeh.embed.embed_item(data, targetId);
+ }
+ }}
+
+
+ );
+});
+
+const Plotly = Loadable({
+ loader: () => import(`react-plotly.js`),
+ loading: ({timedOut}) =>
+ timedOut ? (
+ Error: Loading Plotly timed out.+ ) : ( +
loading...
+ ),
+ timeout: 10000,
+});
+
+export const PlotlyFigure = React.memo(({data}) => {
+ return (
+
+
+ );
+});
diff --git a/website/src/css/bokeh.css b/website/src/css/bokeh.css
new file mode 100644
index 00000000000..5d74d372d37
--- /dev/null
+++ b/website/src/css/bokeh.css
@@ -0,0 +1,33 @@
+/**
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+ .bk.bm-tool-loading {
+ overflow: hidden;
+ }
+ .bk.bm-tool-loading:before {
+ position: absolute;
+ height: 100%;
+ width: 100%;
+ content: '';
+ z-index: 1000;
+ background-color: rgb(255, 255, 255, 0.75);
+ border-color: lightgray;
+ background-repeat: no-repeat;
+ background-position: center;
+ background-size: auto 50%;
+ border-width: 1px;
+ cursor: wait;
+ }
+ .bk.bm-tool-loading.arcs:hover:before {
+ content: 'Please select a Query from the Select menu above.';
+ font: x-large Arial, sans-serif;
+ color: black;
+ cursor: progress;
+ display: flex;
+ justify-content: center;
+ align-items: center;
+ }
diff --git a/website/src/css/customTheme.css b/website/src/css/customTheme.css
new file mode 100644
index 00000000000..44610ee4f7e
--- /dev/null
+++ b/website/src/css/customTheme.css
@@ -0,0 +1,37 @@
+:root{
+ /* --ifm-color-primary-lightest: #283442;
+ --ifm-color-primary-lighter: #242E3B;
+ --ifm-color-primary-light: #222C38; */
+ --ifm-color-primary: #4D5CD4;
+ /* --ifm-color-primary-dark: #1C242E;
+ --ifm-color-primary-darker: #1A222B;
+ --ifm-color-primary-darkest: #161C24; */
+}
+
+.hero {
+ background: linear-gradient(
+ 0deg,
+ rgba(2, 0, 36, 1),
+ rgba(91, 24, 97, 1) 0%,
+ rgba(72, 114, 249, 1) 100%
+ );
+}
+
+.hero .button.button--secondary.button--outline:not(.button--active):not(:hover) {
+ color: var(--ifm-color-secondary);
+}
+
+.header-github-link::before {
+ content: '';
+ width: 24px;
+ height: 24px;
+ display: flex;
+ background-color: var(--ifm-navbar-link-color);
+ mask-image: url("data:image/svg+xml,%3Csvg viewBox='0 0 24 24' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61C4.422 18.07 3.633 17.7 3.633 17.7c-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 22.092 24 17.592 24 12.297c0-6.627-5.373-12-12-12'/%3E%3C/svg%3E");
+ transition: background-color var(--ifm-transition-fast)
+ var(--ifm-transition-timing-default);
+}
+
+.header-github-link:hover::before {
+ background-color: var(--ifm-navbar-link-hover-color);
+}
diff --git a/website/src/pages/index.js b/website/src/pages/index.js
new file mode 100644
index 00000000000..6052e3b17b2
--- /dev/null
+++ b/website/src/pages/index.js
@@ -0,0 +1,169 @@
+/**
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ *
+ * @format
+ */
+
+import React from 'react';
+import Link from '@docusaurus/Link';
+import useDocusaurusContext from '@docusaurus/useDocusaurusContext';
+import useBaseUrl from '@docusaurus/useBaseUrl';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import CodeBlock from '@theme/CodeBlock';
+import Layout from "@theme/Layout";
+
+const features = [
+ {
+ content:
+ 'Easy to plug in new algorithms and use the library across ' +
+ 'different domains.',
+ image: 'img/th-large-solid.svg',
+ title: 'Modular',
+ },
+ {
+ content:
+ 'Field experiments require a range of considerations ' +
+ 'beyond standard optimization problems.',
+ image: 'img/dice-solid.svg',
+ title: 'Supports A/B Tests',
+ },
+ {
+ content:
+ 'Support for industry-grade experimentation ' +
+ 'and optimization management, including MySQL storage.',
+ image: 'img/database-solid.svg',
+ title: 'Production-Ready',
+ },
+];
+
+const Feature = ({imageUrl, title, content, image}) => {
+ const imgUrl = useBaseUrl(imageUrl);
+ return (
+
+ {imgUrl && (
+
+ );
+}
+
+const codeExample = `>>> from ax import optimize
+>>> best_parameters, best_values, experiment, model = optimize(
+ parameters=[
+ {
+ "name": "x1",
+ "type": "range",
+ "bounds": [-10.0, 10.0],
+ },
+ {
+ "name": "x2",
+ "type": "range",
+ "bounds": [-10.0, 10.0],
+ },
+ ],
+ # Booth function
+ evaluation_function=lambda p: (p["x1"] + 2*p["x2"] - 7)**2 + (2*p["x1"] + p["x2"] - 5)**2,
+ minimize=True,
+ )
+
+>>> best_parameters
+{'x1': 1.02, 'x2': 2.97} # true min is (1, 3)`;
+
+const QuickStart = () => (
+
+ 
+
+ )}
+ {image && (
+
+
+ 
+
+ )}
+ {title}
+{content}
+
+
+);
+
+const MyPage = () => {
+ const {siteConfig} = useDocusaurusContext();
+ return (
+ Get Started
+-
+
-
+ Install Ax:
+
+ ++ +{`pip3 install ax-platform`} ++ +{`conda install pytorch torchvision -c pytorch +pip3 install ax-platform`} +
+ -
+ Run an optimization:
+
+{codeExample} +
+
+
+
+
+
+ })
+
+
+ {siteConfig.tagline}
+
+
+ Why Ax?
+
+
+ Get started
+
+
+ Tutorials
+
+
+
+
+ Key Features
+ {features && features.length > 0 && ( +
+ {features.map(({title, imageUrl, content, image}) => (
+
+ )}
+