Update website (#349)

* Update announcement bar for release 0.3.0

* Add Google Analytics

* Update metric tracking sample

* Update run/install instructions

website/docusaurus.config.js

@@ -37,12 +37,16 @@ const config = {
                 theme: {
                     customCss: [require.resolve('./src/css/theming.css'), require.resolve('./src/css/announcement-bar.css'), require.resolve('./src/css/global.css')],
                 },
+                gtag: {
+                    trackingID: 'G-2YZLJEB3PY',
+                    anonymizeIP: true,
+                },
             }),
         ],
     ],
 
     themeConfig:
-    /** @type {import('@docusaurus/preset-classic').ThemeConfig} */
+        /** @type {import('@docusaurus/preset-classic').ThemeConfig} */
         ({
             image: 'images/project-social-preview.png', // project's social card
             navbar: {
@@ -130,8 +134,8 @@ const config = {
             },
             announcementBar: {
                 // https://docusaurus.io/docs/api/themes/configuration#announcement-bar
-                id: 'announcement-bar--1', // increment on change
-                content: `⚠️ FastTrackML is still a work in progress 🚧 and subject to change.`,
+                id: 'announcement-bar',
+                content: `FastTrackML 0.3.0 has been <a href="https://github.com/G-Research/fasttrackml/releases/tag/v0.3.0">released</a>!`,
                 isCloseable: true,
             },
             colorMode: {

website/src/components/Index/QuickStartSection/quickstart-section.md

@@ -1,14 +1,61 @@
 ## Run FastTrackML
 
+<details open>
+<summary>Using pip</summary>
+
+> <small><strong>Note</strong>: This step requires <a href="https://www.python.org/downloads/">Python 3</a> to be
+> installed.</small>
+
+Install FastTrackML:
+
+```bash
+pip install fasttrackml
+```
+
+Run the server:
+
+```bash
+fml server
+```
+</details>
+
+<details>
+<summary>Using a script</summary>
+
+Install on Linux and macOS:
+
+```bash
+curl -fsSL https://fasttrackml.io/install.sh | sh
+```
+
+Install on Windows:
+
+```bash
+iwr -useb https://fasttrackml.io/install.ps1 | iex
+```
+
+Run the server:
+
+```bash
+fml server
+```
+
+</details>
+
+<details>
+<summary>Using Docker</summary>
+
 > <small><strong>Note</strong>: This step requires <a href="https://docs.docker.com/get-docker/">Docker</a> to be
 > running.</small>
 
-FastTrackML can be run using the following command:
+Run the server within a container:
 
 ```bash
 docker run --rm -p 5000:5000 -ti gresearch/fasttrackml
 ```
 
+</details>
+
 Verify that you can see the UI by navigating to [http://localhost:5000/](http://localhost:5000/).
 
 ![FastTrackML UI](../../../../../docs/images/main_ui.png)
@@ -21,7 +68,7 @@ Verify that you can see the UI by navigating to [http://localhost:5000/](http://
 Install the MLflow Python package:
 
 ```bash
-pip install mlflow
+pip install mlflow-skinny
 ```
 
 Then, run the following Python script to log a parameter and metric to FastTrackML:
@@ -35,14 +82,16 @@ mlflow.set_tracking_uri("http://localhost:5000")
 # Set the experiment name
 mlflow.set_experiment("my-first-experiment")
 
-# Log a parameter
-mlflow.log_param("param1", random.randint(0, 100))
+# Start a run
+with mlflow.start_run():
+    # Log a parameter
+    mlflow.log_param("param1", random.randint(0, 100))
 
-# Log a metric
-mlflow.log_metric("foo", random.random())
-# metrics can be updated throughout the run
-mlflow.log_metric("foo", random.random() + 1)
-mlflow.log_metric("foo", random.random() + 2)
+    # Log a metric
+    mlflow.log_metric("foo", random.random())
+    # metrics can be updated throughout the run
+    mlflow.log_metric("foo", random.random() + 1)
+    mlflow.log_metric("foo", random.random() + 2)
 ```
 
 After running this script, you should see the following output