Add dark theme to experiment pane

CONTRIBUTING.md

@@ -477,13 +477,6 @@ If contributing to MLflow's R APIs, install
 [R](https://cloud.r-project.org/) and make sure that you have satisfied
 all the [Environment Setup and Python configuration](#environment-setup-and-python-configuration).
 
-For changes to R documentation, also install
-[pandoc](https://pandoc.org/installing.html) 2.2.1 or above, verifying
-the version of your installation via `pandoc --version`. If using Mac
-OSX, note that the homebrew installation of pandoc may be out of date -
-you can find newer pandoc versions at
-<https://github.com/jgm/pandoc/releases>.
-
 The `mlflow/R/mlflow` directory contains R wrappers for the Projects,
 Tracking and Models components. These wrappers depend on the Python
 package, so first install the Python package in a conda environment:
@@ -836,6 +829,16 @@ python setup.py bdist_wheel
 
 First, install dependencies for building docs as described in [Environment Setup and Python configuration](#environment-setup-and-python-configuration).
 
+Building documentation requires [Pandoc](https://pandoc.org/index.html). It should have already been
+installed if you used the automated env setup script
+([dev-env-setup.sh](https://github.com/mlflow/mlflow/blob/master/dev/dev-env-setup.sh)),
+but if you are manually installing dependencies, please follow [the official instruction](https://pandoc.org/installing.html).
+
+Also, check the version of your installation via `pandoc --version` and ensure it is 2.2.1 or above.
+If you are using Mac OSX, be aware that the Homebrew installation of Pandoc may be outdated. If you are using Linux,
+you should use a deb installer or install from the source, instead of running `apt` / `apt-get` commands. Pandoc package available on official
+repositories is an older version and contains several bugs. You can find newer versions at <https://github.com/jgm/pandoc/releases>.
+
 To generate a live preview of Python & other rst documentation, run the
 following snippet. Note that R & Java API docs must be regenerated
 separately after each change and are not live-updated; see subsequent

dev/dev-env-setup.sh

@@ -105,6 +105,45 @@ minor_to_micro() {
   esac
 }
 
+# Check if brew is installed and install it if it isn't present
+# Note: if xcode isn't installed, this will fail.
+# $1: name of package that requires brew
+check_and_install_brew() {
+  if [ -z "$(command -v brew)" ]; then
+    echo "Homebrew is required to install $1 on MacOS. Installing in your home directory."
+    bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
+  fi
+  echo "Updating brew..."
+  brew update
+}
+
+# Compare two version numbers
+# Usage: version_gt version1 version2
+# Returns 0 (true) if version1 > version2, 1 (false) otherwise
+version_gt() {
+    IFS='.' read -ra VER1 <<< "$1"
+    IFS='.' read -ra VER2 <<< "$2"
+
+    # Compare each segment of the version numbers
+    for (( i=0; i<"${#VER1[@]}"; i++ )); do
+        # If VER2 is shorter and we haven't found a difference yet, VER1 is greater
+        if [[ -z ${VER2[i]} ]]; then
+            return 0
+        fi
+
+        # If some segments are not equal, return their comparison result
+        if (( ${VER1[i]} > ${VER2[i]} )); then
+            return 0
+        elif (( ${VER1[i]} < ${VER2[i]} )); then
+            return 1
+        fi
+    done
+
+    # If all common length segments are same, the one with more segments is greater
+    return $(( ${#VER1[@]} <= ${#VER2[@]} ))
+}
+
+
 # Check if pyenv is installed and offer to install it if not present
 pyenv_exist=$(command -v pyenv)
 
@@ -115,15 +154,9 @@ if [ -z "$pyenv_exist" ]; then
   fi
   if [[ $REPLY =~ ^[Yy]$ || -n "$GITHUB_ACTIONS" ]]; then
     if [[ "$machine" == mac ]]; then
-      # Check if brew is installed and install it if it isn't present
-      # Note: if xcode isn't installed, this will fail.
-      if [ -z "$(command -v brew)" ]; then
-        echo "Homebrew is required to install pyenv on MacOS. Installing in your home directory."
-        bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
-      fi
-      echo "Updating brew and installing pyenv..."
+      check_and_install_brew "pyenv"
+      echo "Installing pyenv..."
       echo "Note: this will probably take a considerable amount of time."
-      brew update
       brew install pyenv
       brew install openssl readline sqlite3 xz zlib
     elif [[ "$machine" == linux ]]; then
@@ -242,6 +275,34 @@ echo "$(pip freeze)$(tput sgr0)"
 
 command -v docker >/dev/null 2>&1 || echo "$(tput bold; tput setaf 1)A docker installation cannot be found. Please ensure that docker is installed to run all tests locally.$(tput sgr0)"
 
+
+# check if pandoc with required version is installed and offer to install it if not present
+pandoc_version=$(pandoc --version | grep "pandoc" | awk '{print $2}')
+if [[ -z "$pandoc_version" ]] || ! version_gt "$pandoc_version" "2.2.1"; then
+  if [ -z "$GITHUB_ACTIONS" ]; then
+    read -p "Pandoc version 2.2.1 or above is required to generate documentation. Would you like to install it? $(tput bold)(y/n)$(tput sgr0): " -n 1 -r
+    echo
+  fi
+
+  if [[ $REPLY =~ ^[Yy]$ || -n "$GITHUB_ACTIONS" ]]; then
+    echo "Installing Pandoc..."
+    if [[ "$machine" == mac ]]; then
+      check_and_install_brew "pandoc"
+      brew install pandoc
+    elif [[ "$machine" == linux ]]; then
+      # install pandoc via deb package as `apt-get` gives too old version
+      TEMP_DEB=$(mktemp) && \
+        wget --directory-prefix $TEMP_DEB https://github.com/jgm/pandoc/releases/download/2.16.2/pandoc-2.16.2-1-amd64.deb && \
+        sudo dpkg --install $(find $TEMP_DEB -name '*.deb') && \
+        rm -rf $TEMP_DEB
+    else
+      echo "Unknown operating system environment: $machine exiting."
+      exit 1
+    fi
+  fi
+fi
+
+
 # Setup git environment configuration for proper signing of commits
 git_user=$(git config user.name)
 git_email=$(git config user.email)
@@ -265,4 +326,4 @@ fi
 # setup pre-commit hooks
 pre-commit install -t pre-commit -t prepare-commit-msg
 
-echo "$(tput setaf 2)Your MLflow development environment can be activated by running: $(tput bold)source $VENV_DIR$(tput sgr0)"
+echo "$(tput setaf 2)Your MLflow development environment can be activated by running: $(tput bold)source $VENV_DIR$(tput sgr0)"

docs/source/deep-learning/index.rst

@@ -47,27 +47,27 @@ The officially supported integrations for deep learning libraries in MLflow enco
         <div class="logo-grid">
             <a href="../models.html#pytorch-pytorch">
                 <div class="logo-card">
-                    <img src="../_static/images/logos/pytorch-logo.svg" alt="pytorch Logo">
+                    <img src="../_static/images/logos/pytorch-logo.svg" alt="pytorch Logo"/>
                 </div>
             </a>
             <a href="../models.html#keras-keras">
                 <div class="logo-card">
-                    <img src="../_static/images/logos/keras-logo.svg" alt="keras Logo">
+                    <img src="../_static/images/logos/keras-logo.svg" alt="keras Logo"/>
                 </div>
             </a>
             <a href="../models.html#tensorflow-tensorflow">
                 <div class="logo-card">
-                    <img src="../_static/images/logos/TensorFlow-logo.svg" alt="TensorFlow Logo">
+                    <img src="../_static/images/logos/TensorFlow-logo.svg" alt="TensorFlow Logo"/>
                 </div>
             </a>
             <a href="../models.html#spacy-spacy">
                 <div class="logo-card">
-                    <img src="../_static/images/logos/spacy-logo.svg" alt="spaCy Logo">
+                    <img src="../_static/images/logos/spacy-logo.svg" alt="spaCy Logo"/>
                 </div>
             </a>
             <a href="../models.html#fastai-fastai">
                 <div class="logo-card">
-                    <img src="../_static/images/logos/fastai-logo.png" alt="fast.ai Logo">
+                    <img src="../_static/images/logos/fastai-logo.png" alt="fast.ai Logo"/>
                 </div>
             </a>
         </div>

docs/source/deployment/deploy-model-to-kubernetes/index.rst

@@ -126,6 +126,7 @@ Next, use the MLflow UI to compare the models that you have produced. In the sam
 as the one that contains the ``mlruns`` run:
 
 .. code-section::
+
     .. code-block:: shell
 
         mlflow ui

docs/source/getting-started/index.rst

@@ -30,7 +30,7 @@ If you would like to get started immediately by interactively running the notebo
 .. raw:: html
 
     <a href="https://raw.githubusercontent.com/mlflow/mlflow/master/docs/source/getting-started/intro-quickstart/notebooks/tracking_quickstart.ipynb" class="notebook-download-btn">
-        <i class="fas fa-download"></i>Download the Notebook</a><br>
+        <i class="fas fa-download"></i>Download the Notebook</a><br/>
 
 Quickstart elements
 ^^^^^^^^^^^^^^^^^^^
@@ -76,7 +76,7 @@ If you would like to get started immediately by interactively running the notebo
 .. raw:: html
 
     <a href="https://raw.githubusercontent.com/mlflow/mlflow/master/docs/source/getting-started/logging-first-model/notebooks/logging-first-model.ipynb" class="notebook-download-btn">
-        <i class="fas fa-download"></i>Download the Notebook</a><br>
+        <i class="fas fa-download"></i>Download the Notebook</a><br/>
 
 Guide sections
 ^^^^^^^^^^^^^^

docs/source/getting-started/intro-quickstart/index.rst

@@ -41,6 +41,7 @@ Step 1 - Get MLflow
 MLflow is available on PyPI. If you don't already have it installed on your system, you can install it with:
 
 .. code-section::
+
     .. code-block:: bash
         :name: download-mlflow
 
@@ -53,6 +54,7 @@ We're going to start a local MLflow Tracking Server, which we will connect to fo
 From a terminal, run:
 
 .. code-section::
+
     .. code-block:: bash
         :name: tracking-server-start
 
@@ -72,6 +74,7 @@ In this section, we're going to log a model with MLflow. A quick overview of the
 
 
 .. code-section::
+
     .. code-block:: python
         :name: train-model
 
@@ -132,6 +135,7 @@ The steps that we will take are:
     to ensure that the loggable content (parameters, metrics, artifacts, and the model) are fully materialized prior to logging. 
 
 .. code-section::
+
     .. code-block:: python
         :name: log-model
 
@@ -177,6 +181,7 @@ After logging the model, we can perform inference by:
     below.
 
 .. code-section::
+
     .. code-block:: python
         :name: load-model 
 

docs/source/getting-started/logging-first-model/step1-tracking-server.rst

@@ -15,6 +15,7 @@ Step 1: Install MLflow from PyPI
 MLflow is conveniently available on PyPI. Installing it is as simple as running a pip command.
 
 .. code-section::
+
     .. code-block:: bash
         :name: download-mlflow
 
@@ -27,6 +28,7 @@ To begin, you'll need to initiate the MLflow Tracking Server. Remember to keep t
 running during the tutorial, as closing it will shut down the server.
 
 .. code-section::
+
     .. code-block:: bash
         :name: tracking-server-start
 

docs/source/getting-started/logging-first-model/step2-mlflow-client.rst

@@ -18,6 +18,7 @@ Importing Dependencies
 In order to use the MLflowClient API, the initial step involves importing the necessary modules.
 
 .. code-section::
+
     .. code-block:: python
         :name: imports
         :emphasize-lines: 1
@@ -43,6 +44,7 @@ assigned the server when we started it. The two components that we submitted as
 ``host`` and the ``port``. Combined, these form the ``tracking_uri`` argument that we will specify to start an instance of the client.
 
 .. code-section::
+
     .. code-block:: python
         :name: client
 
@@ -70,6 +72,7 @@ The first thing that we're going to do is to view the metadata associated with t
 use of the :py:func:`mlflow.client.MlflowClient.search_experiments` API. Let's issue a search query to see what the results are.
 
 .. code-section::
+
     .. code-block:: python
 
         all_experiments = client.search_experiments()
@@ -91,6 +94,7 @@ To get familiar with accessing elements from returned collections from MLflow AP
 query and extract these attributes into a dict.
 
 .. code-section::
+
     .. code-block:: python
 
         default_experiment = [

docs/source/getting-started/logging-first-model/step3-create-experiment.rst

@@ -105,6 +105,7 @@ Creating the Apples Experiment with Meaningful tags
 ---------------------------------------------------
 
 .. code-section::
+
     .. code-block:: python
 
         # Provide an Experiment description that will appear in the UI

docs/source/getting-started/logging-first-model/step4-experiment-search.rst

@@ -51,6 +51,7 @@ tags, note the particular syntax used. The custom tag names are wrapped with bac
 condition is wrapped in single quotes.
 
 .. code-section::
+
     .. code-block:: python
 
         # Use search_experiments() to search on the project_name tag key

docs/source/getting-started/logging-first-model/step5-synthetic-data.rst

@@ -21,6 +21,7 @@ We can introduce this correlation by crafting a relationship between our feature
 The random elements of some of the factors will handle the unexplained variance portion.
 
 .. code-section::
+
     .. code-block:: python
 
         import pandas as pd

docs/source/getting-started/logging-first-model/step6-logging-a-run.rst

@@ -76,6 +76,7 @@ using MLflow to tracking a training iteration.
 To start with, we will need to import our required modules.
 
 .. code-section::
+
     .. code-block:: python
 
         import mlflow
@@ -94,6 +95,7 @@ In order to use the ``fluent`` API, we'll need to set the global reference to th
 address. We do this via the following command:
 
 .. code-section::
+
     .. code-block:: python
 
         mlflow.set_tracking_uri("http://127.0.0.1:8080")
@@ -104,6 +106,7 @@ to log runs to. The parent-child relationship of Experiments to Runs and its uti
 clear once we start iterating over some ideas and need to compare the results of our tests.
 
 .. code-section::
+
     .. code-block:: python
 
         # Sets the current active experiment to the "Apple_Models" experiment and
@@ -123,6 +126,7 @@ Firstly, let's look at what we're going to be running. Following the code displa
 an annotated version of the code.
 
 .. code-section::
+
     .. code-block:: python
 
         # Split the data into features and target and drop irrelevant date field and target field

docs/source/getting-started/quickstart-1/index.rst

@@ -86,6 +86,7 @@ In addition, or if you are using a library for which ``autolog`` is not yet supp
 This example demonstrates the use of these functions:
 
 .. code-section::
+
     .. code-block:: python
 
         import os

docs/source/getting-started/quickstart-2/index.rst

@@ -195,7 +195,7 @@ Choose **Chart view**. Choose the **Parallel coordinates** graph and configure i
     class="align-center"
     id="chart-view"
     alt="Screenshot of MLflow tracking UI parallel coordinates graph showing runs"
-  >
+  />
 
 The red graphs on this graph are runs that fared poorly. The lowest one is a baseline run with both **lr** and **momentum** set to 0.0. That baseline run has an RMSE of ~0.89. The other red lines show that high **momentum** can also lead to poor results with this problem and architecture. 
 

docs/source/llms/custom-pyfunc-for-llms/index.rst

@@ -27,7 +27,7 @@ Explore the Tutorial
 
 .. raw:: html
 
-    <a href="notebooks/index.html" class="download-btn">View the Custom Pyfunc for LLMs Tutorial</a><br>
+    <a href="notebooks/index.html" class="download-btn">View the Custom Pyfunc for LLMs Tutorial</a><br/>
 
 .. toctree::
     :maxdepth: 1

docs/source/llms/custom-pyfunc-for-llms/notebooks/index.rst

@@ -64,7 +64,7 @@ If you'd like to run a copy of the notebooks locally in your environment, you ca
 
 .. raw:: html
 
-    <a href="https://raw.githubusercontent.com/mlflow/mlflow/master/docs/source/llms/custom-pyfunc-for-llms/notebooks/custom-pyfunc-advanced-llm.ipynb" class="notebook-download-btn">Download the LLM Custom Pyfunc notebook</a><br>
+    <a href="https://raw.githubusercontent.com/mlflow/mlflow/master/docs/source/llms/custom-pyfunc-for-llms/notebooks/custom-pyfunc-advanced-llm.ipynb" class="notebook-download-btn">Download the LLM Custom Pyfunc notebook</a><br/>
 
 .. note::
     To execute the notebooks, ensure you either have a local MLflow Tracking Server running or adjust the ``mlflow.set_tracking_uri()`` to point to an active MLflow Tracking Server instance.