diff --git a/example-get-started-experiments/.gitignore b/example-get-started-experiments/.gitignore
new file mode 100644
index 00000000..e81293d0
--- /dev/null
+++ b/example-get-started-experiments/.gitignore
@@ -0,0 +1,4 @@
+# Custom
+*.zip
+/tmp
+build/
diff --git a/example-get-started-experiments/README.md b/example-get-started-experiments/README.md
new file mode 100644
index 00000000..4555f110
--- /dev/null
+++ b/example-get-started-experiments/README.md
@@ -0,0 +1,61 @@
+Generate the actual repo by running: 
+
+```
+bash generate.sh
+```
+
+The repo generated in `build/example-get-started-experiments` is intended to be 
+published on https://github.com/iterative/example-get-started-experiments. 
+Make sure the Github repo exists first and that you have appropriate write 
+permissions.
+
+Run these commands to force push it:
+
+```
+cd build/example-get-started-experiments
+git remote add origin https://github.com/iterative/example-get-started-experiments.git
+git push --force origin main
+git push --force origin tune-architecture
+git push --force origin --tags
+```
+
+Run these to drop and then rewrite the experiment references on the repo:
+
+```
+dvc exp remove -A -g origin
+dvc exp push origin -A
+```
+
+And this to clean the remote cache to only contain the last iteration:
+
+```
+dvc gc -c --all-experiments
+```
+
+- `tune-architecture` P.R.
+
+To create a PR from the "tune-architecture" branch:
+
+```
+gh pr create -t "Run experiments tuning architecture" \
+   -B main -H tune-architecture
+```
+
+Finally, return to the directory where you started:
+
+```
+cd ../..
+```
+
+You may remove the generated repo with:
+
+```
+rm -fR build
+```
+
+- Manual Studio P.R.
+
+Once the repo has been generated and pushed, go to the 
+[corresponding Studio project](https://studio.iterative.ai/team/Iterative/projects/example-get-started-experiments-y8toqd433r) 
+and create a P.R. using the `Experiment` button, increasing epochs from `8` to 
+`12`.
diff --git a/example-get-started-experiments/code/.devcontainer.json b/example-get-started-experiments/code/.devcontainer.json
new file mode 100644
index 00000000..4c71e8b2
--- /dev/null
+++ b/example-get-started-experiments/code/.devcontainer.json
@@ -0,0 +1,18 @@
+{
+    "name": "example-cv",
+    "image": "mcr.microsoft.com/devcontainers/python:3.10",
+    "runArgs": ["--ipc=host"],
+    "features": {
+      "ghcr.io/devcontainers/features/nvidia-cuda:1": {
+        "installCudnn": true
+      },
+      "ghcr.io/iterative/features/dvc:1": {},
+      "ghcr.io/iterative/features/nvtop:1": {}
+    },
+    "extensions": [
+      "Iterative.dvc",
+      "ms-python.python",
+      "redhat.vscode-yaml"
+    ],
+    "postCreateCommand": "pip install --user -r requirements.txt"
+}
diff --git a/example-get-started-experiments/code/.gitattributes b/example-get-started-experiments/code/.gitattributes
new file mode 100644
index 00000000..56eb98f5
--- /dev/null
+++ b/example-get-started-experiments/code/.gitattributes
@@ -0,0 +1,2 @@
+*.dvc linguist-language=YAML
+dvc.lock linguist-language=YAML
diff --git a/example-get-started-experiments/code/.github/workflows/run-studio-experiment.yml b/example-get-started-experiments/code/.github/workflows/run-studio-experiment.yml
new file mode 100644
index 00000000..8309ffdf
--- /dev/null
+++ b/example-get-started-experiments/code/.github/workflows/run-studio-experiment.yml
@@ -0,0 +1,57 @@
+name: Run Studio Experiment
+on: 
+  push:
+    paths:
+      - ".github/workflows/run-experiment.yaml"
+      - "data/**"
+      - "src/**"
+      - "params.yaml"
+      - "dvc.*"
+jobs:
+  deploy-runner:
+    if: github.actor == 'iterative-studio[bot]'
+    environment: cloud
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: iterative/setup-cml@v1
+      - uses: aws-actions/configure-aws-credentials@v1
+        with:
+          aws-region: us-east-2
+          role-to-assume: arn:aws:iam::342840881361:role/SandboxUser
+          role-duration-seconds: 43200
+      - name: Create Runner
+        env:
+          REPO_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
+        run: |
+          cml runner launch --single \
+            --labels=cml \
+            --cloud=aws \
+            --cloud-region=us-east \
+            --cloud-hdd-size=40 \
+            --cloud-type=g5.2xlarge
+  runner-job:
+    needs: deploy-runner
+    runs-on: [ self-hosted, cml ]
+    container:
+      image: iterativeai/cml:0-dvc2-base1-gpu
+      options: --gpus all --ipc host
+    steps:
+      - uses: actions/checkout@v3
+      - uses: aws-actions/configure-aws-credentials@v1
+        with:
+          aws-region: us-east-1
+          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
+          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+      - name: training
+        env:
+          REPO_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
+          STUDIO_TOKEN: ${{ secrets.STUDIO_TOKEN }}
+          DVCLIVE_LOGLEVEL: DEBUG
+        run: |
+          pip install -r requirements.txt
+          dvc pull
+          cml comment create --publish --watch results/train/report.md & dvc exp run
+          dvc push
+          cml pr --squash --skip-ci .
+          cml comment create --pr --publish results/evaluate/report.md
diff --git a/example-get-started-experiments/code/.gitignore b/example-get-started-experiments/code/.gitignore
new file mode 100644
index 00000000..b6e47617
--- /dev/null
+++ b/example-get-started-experiments/code/.gitignore
@@ -0,0 +1,129 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
diff --git a/example-get-started-experiments/code/LICENSE b/example-get-started-experiments/code/LICENSE
new file mode 100644
index 00000000..e0636bfa
--- /dev/null
+++ b/example-get-started-experiments/code/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 Iterative
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/example-get-started-experiments/code/README.md b/example-get-started-experiments/code/README.md
new file mode 100644
index 00000000..90ce4d7e
--- /dev/null
+++ b/example-get-started-experiments/code/README.md
@@ -0,0 +1,138 @@
+[![DVC](https://img.shields.io/badge/-Open_in_Studio-grey.svg?style=flat-square&logo=dvc)](https://studio.iterative.ai/team/Iterative/projects/example-get-started-experiments-y8toqd433r) 
+[![DVC-metrics](https://img.shields.io/badge/dynamic/json?style=flat-square&colorA=grey&colorB=F46737&label=Average%20Precision&url=https://github.com/iterative/example-get-started-experiments/raw/main/results/evaluate/metrics.json&query=dice_multi)](https://github.com/iterative/example-get-started-experiments/raw/main/results/evaluate/metrics.json)
+
+# DVC Get Started: Experiments
+
+This is an auto-generated repository for use in [DVC](https://dvc.org)
+[Get Started: Experiments](https://dvc.org/doc/start/experiment-management).
+
+This is a Computer Vision (CV) project that solves the problem of segmenting out 
+swimming pools from satellite images. 
+
+We use a slightly modified version of the [BH-Pools dataset](http://patreo.dcc.ufmg.br/2020/07/29/bh-pools-watertanks-datasets/):
+we split the original 4k images into tiles of 1024x1024 pixels.
+
+
+🐛 Please report any issues found in this project here -
+[example-repos-dev](https://github.com/iterative/example-repos-dev).
+
+## Installation
+
+Python 3.8+ is required to run code from this repo.
+
+```console
+$ git clone https://github.com/iterative/example-get-started-experiments
+$ cd example-get-started-experiments
+```
+
+Now let's install the requirements. But before we do that, we **strongly**
+recommend creating a virtual environment with a tool such as
+[virtualenv](https://virtualenv.pypa.io/en/stable/):
+
+```console
+$ python -m venv .venv
+$ source .venv/bin/activate
+$ pip install -r src/requirements.txt
+```
+
+This DVC project comes with a preconfigured DVC
+[remote storage](https://dvc.org/doc/commands-reference/remote) that holds raw
+data (input), intermediate, and final results that are produced. This is a
+read-only HTTP remote.
+
+```console
+$ dvc remote list
+storage  https://remote.dvc.org/get-started-pools
+```
+
+You can run [`dvc pull`](https://man.dvc.org/pull) to download the data:
+
+```console
+$ dvc pull
+```
+
+## Running in your environment
+
+Run [`dvc exp run`](https://man.dvc.org/exp/run) to reproduce the
+[pipeline](https://dvc.org/doc/user-guide/pipelines/defining-pipelinese):
+
+```console
+$ dvc exp run
+Data and pipelines are up to date.
+```
+
+If you'd like to test commands like [`dvc push`](https://man.dvc.org/push),
+that require write access to the remote storage, the easiest way would be to set
+up a "local remote" on your file system:
+
+> This kind of remote is located in the local file system, but is external to
+> the DVC project.
+
+```console
+$ mkdir -p /tmp/dvc-storage
+$ dvc remote add local /tmp/dvc-storage
+```
+
+You should now be able to run:
+
+```console
+$ dvc push -r local
+```
+
+## Existing stages
+
+There is a couple of git tags in this project :
+
+### [1-notebook-dvclive](https://github.com/iterative/example-get-started-experiments/tree/1-notebook-dvclive)
+
+Contains an end-to-end Jupyter notebook that loads data, trains a model and 
+reports model performance. 
+[DVCLive](https://dvc.org/doc/dvclive) is used for experiment tracking. 
+See this [blog post](https://iterative.ai/blog/exp-tracking-dvc-python) for more
+details.
+
+### [2-dvc-pipeline](https://github.com/iterative/example-get-started-experiments/tree/2-dvc-pipeline)
+
+Contains a DVC pipeline `dvc.yaml` that was created by refactoring the above 
+notebook into individual pipeline stages. 
+
+The pipeline artifacts (processed data, model file, etc) are automatically 
+versioned. 
+
+This tag also contains a GitHub Actions workflow that reruns the pipeline if any
+ changes are introduced to the pipeline-related files. 
+[CML](https://cml.dev/) is used in this workflow to provision a cloud-based GPU 
+machine as well as report model performance results in Pull Requests.
+
+## Project structure
+
+The data files, DVC files, and results change as stages are created one by one.
+After cloning and using [`dvc pull`](https://man.dvc.org/pull) to download
+data, models, and plots tracked by DVC, the workspace should look like this:
+
+```console
+$ tree -L 2
+.
+├── LICENSE
+├── README.md
+├── data.            # <-- Directory with raw and intermediate data
+│   ├── pool_data    # <-- Raw image data
+│   ├── pool_data.dvc # <-- .dvc file - a placeholder/pointer to raw data
+│   ├── test_data    # <-- Processed test data
+│   └── train_data   # <-- Processed train data
+├── dvc.lock
+├── dvc.yaml         # <-- DVC pipeline file
+├── models
+│   └── model.pkl    # <-- Trained model file
+├── notebooks
+│   └── TrainSegModel.ipynb # <-- Initial notebook (refactored into `dvc.yaml`) 
+├── params.yaml      # <-- Parameters file
+├── requirements.txt # <-- Python dependencies needed in the project
+├── results          # <-- DVCLive reports and plots
+│   ├── evaluate
+│   └── train
+└── src              # <-- Source code to run the pipeline stages
+    ├── data_split.py
+    ├── evaluate.py
+    └── train.py
+```
diff --git a/example-get-started-experiments/code/data/.gitignore b/example-get-started-experiments/code/data/.gitignore
new file mode 100644
index 00000000..6830f1fe
--- /dev/null
+++ b/example-get-started-experiments/code/data/.gitignore
@@ -0,0 +1,3 @@
+/pool_data
+/test_data
+/train_data
diff --git a/example-get-started-experiments/code/data/pool_data.dvc b/example-get-started-experiments/code/data/pool_data.dvc
new file mode 100644
index 00000000..e83b976c
--- /dev/null
+++ b/example-get-started-experiments/code/data/pool_data.dvc
@@ -0,0 +1,5 @@
+outs:
+- md5: 14d187e749ee5614e105741c719fa185.dir
+  size: 18999874
+  nfiles: 183
+  path: pool_data
diff --git a/example-get-started-experiments/code/notebooks/TrainSegModel.ipynb b/example-get-started-experiments/code/notebooks/TrainSegModel.ipynb
new file mode 100644
index 00000000..d8392782
--- /dev/null
+++ b/example-get-started-experiments/code/notebooks/TrainSegModel.ipynb
@@ -0,0 +1,290 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "ROOT = Path(\"../\")\n",
+    "DATA = ROOT / \"data\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "from functools import partial\n",
+    "\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "from box import ConfigBox\n",
+    "from dvclive import Live\n",
+    "from dvclive.fastai import DVCLiveCallback\n",
+    "from fastai.data.all import Normalize, get_files\n",
+    "from fastai.metrics import DiceMulti\n",
+    "from fastai.vision.all import (Resize, SegmentationDataLoaders, aug_transforms,\n",
+    "                               imagenet_stats, models, unet_learner)\n",
+    "from ruamel.yaml import YAML\n",
+    "from PIL import Image"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load data and split it into train/test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_pct = 0.25\n",
+    "\n",
+    "img_fpaths = get_files(DATA / \"pool_data\" / \"images\", extensions=\".jpg\")\n",
+    "\n",
+    "train_data_dir = DATA / \"train_data\"\n",
+    "train_data_dir.mkdir(exist_ok=True)\n",
+    "test_data_dir = DATA / \"test_data\"\n",
+    "test_data_dir.mkdir(exist_ok=True)\n",
+    "for img_path in img_fpaths:\n",
+    "    msk_path = DATA / \"pool_data\" / \"masks\" / f\"{img_path.stem}.png\"\n",
+    "    if np.random.uniform() <= test_pct:\n",
+    "        shutil.copy(img_path, test_data_dir)\n",
+    "        shutil.copy(msk_path, test_data_dir)\n",
+    "    else:\n",
+    "        shutil.copy(img_path, train_data_dir)\n",
+    "        shutil.copy(msk_path, train_data_dir)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a data loader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_mask_path(x, train_data_dir):\n",
+    "    return Path(train_data_dir) / f\"{Path(x).stem}.png\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs = 8\n",
+    "valid_pct = 0.20\n",
+    "img_size = 256\n",
+    "\n",
+    "data_loader = SegmentationDataLoaders.from_label_func(\n",
+    "        path=train_data_dir,\n",
+    "        fnames=get_files(train_data_dir, extensions=\".jpg\"),\n",
+    "        label_func=partial(get_mask_path, train_data_dir=train_data_dir),\n",
+    "        codes=[\"not-pool\", \"pool\"],\n",
+    "        bs=bs,\n",
+    "        valid_pct=valid_pct,\n",
+    "        item_tfms=Resize(img_size),\n",
+    "        batch_tfms=[\n",
+    "            *aug_transforms(size=img_size),\n",
+    "            Normalize.from_stats(*imagenet_stats),\n",
+    "        ],\n",
+    "    )"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Review a sample batch of data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_loader.show_batch(alpha=0.7)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train multiple models with different learning rates using `DVCLiveCallback`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def dice(mask_pred, mask_true, classes=[0, 1], eps=1e-6):\n",
+    "    dice_list = []\n",
+    "    for c in classes:\n",
+    "        y_true = mask_true == c\n",
+    "        y_pred = mask_pred == c\n",
+    "        intersection = 2.0 * np.sum(y_true * y_pred)\n",
+    "        dice = intersection / (np.sum(y_true) + np.sum(y_pred) + eps)\n",
+    "        dice_list.append(dice)\n",
+    "    return np.mean(dice_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_arch = 'resnet18'\n",
+    "\n",
+    "for base_lr in [0.001, 0.005, 0.01]:\n",
+    "    with Live(str(ROOT / \"results\" / \"train\"), save_dvc_exp=True) as live:\n",
+    "        live.log_param(\"train_arch\", train_arch)\n",
+    "        fine_tune_args = {\n",
+    "            'epochs': 8,\n",
+    "            'base_lr': base_lr\n",
+    "        }\n",
+    "        live.log_params(fine_tune_args)\n",
+    "\n",
+    "        learn = unet_learner(data_loader, \n",
+    "                            arch=getattr(models, train_arch), \n",
+    "                            metrics=DiceMulti)\n",
+    "        learn.fine_tune(\n",
+    "            **fine_tune_args,\n",
+    "            cbs=[DVCLiveCallback(live=live)])\n",
+    "\n",
+    "\n",
+    "        test_img_fpaths = get_files(DATA / \"test_data\", extensions=\".jpg\")\n",
+    "        test_dl = learn.dls.test_dl(test_img_fpaths)\n",
+    "        preds, _ = learn.get_preds(dl=test_dl)\n",
+    "        masks_pred = np.array(preds[:, 1, :] > 0.5, dtype=int)\n",
+    "        test_mask_fpaths = [\n",
+    "            get_mask_path(fpath, DATA / \"test_data\") for fpath in test_img_fpaths\n",
+    "        ]\n",
+    "        masks_true = [Image.open(mask_path) for mask_path in test_mask_fpaths]\n",
+    "        masks_true = [\n",
+    "            np.array(img.resize((img_size, img_size)), dtype=int) for img in masks_true\n",
+    "        ]\n",
+    "        dice_multi = 0.0\n",
+    "        for ii in range(len(masks_true)):\n",
+    "            mask_pred, mask_true = masks_pred[ii], masks_true[ii]\n",
+    "            dice_multi += dice(mask_true, mask_pred) / len(masks_true)\n",
+    "        live.summary[\"evaluate/dice_multi\"] = dice_multi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compare experiments\n",
+    "!dvc exp show --only-changed"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Review sample preditions vs ground truth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn.show_results(max_n=6, alpha=0.7)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Review instances where loss function values are the highest (i.e. model is likely to be wrong)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastai.vision.all import SegmentationInterpretation\n",
+    "\n",
+    "interp = SegmentationInterpretation.from_learner(learn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "interp.plot_top_losses(k=5, alpha=0.7)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.10.4 (main, Jan 25 2023, 00:13:50) [GCC 9.4.0]"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "3ad933181bd8a04b432d3370b9dc3b0662ad032c4dfaa4e4f1596c548f763858"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/example-get-started-experiments/code/params.yaml b/example-get-started-experiments/code/params.yaml
new file mode 100644
index 00000000..b6977226
--- /dev/null
+++ b/example-get-started-experiments/code/params.yaml
@@ -0,0 +1,17 @@
+base:
+  random_seed: 42
+
+data_split:
+  test_pct: 0.15
+
+train:
+  valid_pct: 0.1
+  arch: resnet18
+  img_size: 256
+  batch_size: 8
+  fine_tune_args:
+    epochs: 8
+    base_lr: 0.01
+
+evaluate:
+  n_samples_to_save: 10
diff --git a/example-get-started-experiments/code/requirements.txt b/example-get-started-experiments/code/requirements.txt
new file mode 100644
index 00000000..fa40d9c7
--- /dev/null
+++ b/example-get-started-experiments/code/requirements.txt
@@ -0,0 +1,4 @@
+dvc[s3]>=2.43.1
+dvclive>=1.3.4
+fastai
+python-box
diff --git a/example-get-started-experiments/code/src/data_split.py b/example-get-started-experiments/code/src/data_split.py
new file mode 100644
index 00000000..4c046d07
--- /dev/null
+++ b/example-get-started-experiments/code/src/data_split.py
@@ -0,0 +1,33 @@
+import shutil
+from pathlib import Path
+
+import numpy as np
+from box import ConfigBox
+from fastai.vision.all import get_files
+from ruamel.yaml import YAML
+
+
+yaml = YAML(typ="safe")
+
+
+def data_split():
+    params = ConfigBox(yaml.load(open("params.yaml", encoding="utf-8")))
+    np.random.seed(params.base.random_seed)
+    img_fpaths = get_files(Path("data") / "pool_data" / "images", extensions=".jpg")
+
+    train_data_dir = Path("data") / "train_data"
+    train_data_dir.mkdir(exist_ok=True)
+    test_data_dir = Path("data") / "test_data"
+    test_data_dir.mkdir(exist_ok=True)
+    for img_path in img_fpaths:
+        msk_path = Path("data") / "pool_data" / "masks" / f"{img_path.stem}.png"
+        if np.random.uniform() <= params.data_split.test_pct:
+            shutil.copy(img_path, test_data_dir)
+            shutil.copy(msk_path, test_data_dir)
+        else:
+            shutil.copy(img_path, train_data_dir)
+            shutil.copy(msk_path, train_data_dir)
+
+
+if __name__ == "__main__":
+    data_split()
diff --git a/example-get-started-experiments/code/src/evaluate.py b/example-get-started-experiments/code/src/evaluate.py
new file mode 100644
index 00000000..cd40a9ca
--- /dev/null
+++ b/example-get-started-experiments/code/src/evaluate.py
@@ -0,0 +1,99 @@
+from pathlib import Path
+
+import numpy as np
+from box import ConfigBox
+from dvclive import Live
+from fastai.vision.all import get_files, load_learner
+from PIL import Image
+from ruamel.yaml import YAML
+
+
+yaml = YAML(typ="safe")
+
+
+def dice(mask_pred, mask_true, classes=[0, 1], eps=1e-6):
+    dice_list = []
+    for c in classes:
+        y_true = mask_true == c
+        y_pred = mask_pred == c
+        intersection = 2.0 * np.sum(y_true * y_pred)
+        dice = intersection / (np.sum(y_true) + np.sum(y_pred) + eps)
+        dice_list.append(dice)
+    return np.mean(dice_list)
+
+
+def paint_mask(mask, color_map={0: (0, 0, 0), 1: (0, 0, 255)}):
+    vis_shape = mask.shape + (3,)
+    vis = np.zeros(vis_shape)
+    for i, c in color_map.items():
+        vis[mask == i] = color_map[i]
+    return Image.fromarray(vis.astype(np.uint8))
+
+
+def stack_images(im1, im2):
+    dst = Image.new("RGB", (im1.width + im2.width, im1.height))
+    dst.paste(im1, (0, 0))
+    dst.paste(im2, (im1.width, 0))
+    return dst
+
+
+def get_overlay_image(img_fpath, mask_true, mask_pred, img_size):
+    img_pil = Image.open(img_fpath).resize((img_size, img_size))
+    overlay_img_true = Image.blend(
+        img_pil.convert("RGBA"), paint_mask(mask_true).convert("RGBA"), 0.5
+    )
+
+    new_color_map = {
+        0: (0, 0, 0),  # no color - TN
+        1: (255, 0, 255),  # purple - FN
+        2: (255, 255, 0),  # yellow - FP
+        3: (0, 0, 255),  # blue - TP
+    }
+    combined_mask = mask_true + 2 * mask_pred
+
+    overlay_img_pred = Image.blend(
+        img_pil.convert("RGBA"),
+        paint_mask(combined_mask, color_map=new_color_map).convert("RGBA"),
+        0.5,
+    )
+    stacked_image = stack_images(overlay_img_true, overlay_img_pred)
+    return stacked_image
+
+
+def get_mask_path(x, train_data_dir):
+    return Path(train_data_dir) / f"{Path(x).stem}.png"
+
+
+def evaluate():
+    params = ConfigBox(yaml.load(open("params.yaml", encoding="utf-8")))
+    img_size = params.train.img_size
+    model_fpath = Path("models") / "model.pkl"
+    learn = load_learner(model_fpath, cpu=False)
+    test_img_fpaths = get_files(Path("data") / "test_data", extensions=".jpg")
+    test_dl = learn.dls.test_dl(test_img_fpaths)
+    preds, _ = learn.get_preds(dl=test_dl)
+    masks_pred = np.array(preds[:, 1, :] > 0.5, dtype=int)
+    test_mask_fpaths = [
+        get_mask_path(fpath, Path("data") / "test_data") for fpath in test_img_fpaths
+    ]
+    masks_true = [Image.open(mask_path) for mask_path in test_mask_fpaths]
+    masks_true = [
+        np.array(img.resize((img_size, img_size)), dtype=int) for img in masks_true
+    ]
+    with Live("results/evaluate", report="md") as live:
+        dice_multi = 0.0
+        for ii in range(len(masks_true)):
+            mask_pred, mask_true = masks_pred[ii], masks_true[ii]
+            dice_multi += dice(mask_true, mask_pred) / len(masks_true)
+
+            if ii < params.evaluate.n_samples_to_save:
+                stacked_image = get_overlay_image(
+                    test_img_fpaths[ii], mask_true, mask_pred, img_size
+                )
+                live.log_image(f"{Path(test_img_fpaths[ii]).stem}.png", stacked_image)
+
+        live.summary["dice_multi"] = dice_multi
+
+
+if __name__ == "__main__":
+    evaluate()
diff --git a/example-get-started-experiments/code/src/train.py b/example-get-started-experiments/code/src/train.py
new file mode 100644
index 00000000..21ed91e4
--- /dev/null
+++ b/example-get-started-experiments/code/src/train.py
@@ -0,0 +1,74 @@
+import random
+from functools import partial
+from pathlib import Path
+
+import numpy as np
+import torch
+from box import ConfigBox
+from dvclive.fastai import DVCLiveCallback
+from fastai.data.all import Normalize, get_files
+from fastai.metrics import DiceMulti
+from fastai.vision.all import (
+    Resize,
+    SegmentationDataLoaders,
+    aug_transforms,
+    imagenet_stats,
+    models,
+    unet_learner,
+)
+from ruamel.yaml import YAML
+
+yaml = YAML(typ="safe")
+
+
+def get_mask_path(x, train_data_dir):
+    return Path(train_data_dir) / f"{Path(x).stem}.png"
+
+
+def train():
+    params = ConfigBox(yaml.load(open("params.yaml", encoding="utf-8")))
+
+    np.random.seed(params.base.random_seed)
+    torch.manual_seed(params.base.random_seed)
+    random.seed(params.base.random_seed)
+    train_data_dir = Path("data") / "train_data"
+
+    data_loader = SegmentationDataLoaders.from_label_func(
+        path=train_data_dir,
+        fnames=get_files(train_data_dir, extensions=".jpg"),
+        label_func=partial(get_mask_path, train_data_dir=train_data_dir),
+        codes=["not-pool", "pool"],
+        bs=params.train.batch_size,
+        valid_pct=params.train.valid_pct,
+        item_tfms=Resize(params.train.img_size),
+        batch_tfms=[
+            *aug_transforms(size=params.train.img_size),
+            Normalize.from_stats(*imagenet_stats),
+        ],
+    )
+
+    model_names = [
+        name
+        for name in dir(models)
+        if not name.startswith("_")
+        and name.islower()
+        and name not in ("all", "tvm", "unet", "xresnet")
+    ]
+    if params.train.arch not in model_names:
+        raise ValueError(f"Unsupported model, must be one of:\n{model_names}")
+
+    learn = unet_learner(
+        data_loader, arch=getattr(models, params.train.arch), metrics=DiceMulti
+    )
+
+    learn.fine_tune(
+        **params.train.fine_tune_args,
+        cbs=[DVCLiveCallback(dir="results/train", report="md")],
+    )
+    models_dir = Path("models")
+    models_dir.mkdir(exist_ok=True)
+    learn.export(fname=(models_dir / "model.pkl").absolute())
+
+
+if __name__ == "__main__":
+    train()
diff --git a/example-get-started-experiments/generate.sh b/example-get-started-experiments/generate.sh
new file mode 100755
index 00000000..8aa30710
--- /dev/null
+++ b/example-get-started-experiments/generate.sh
@@ -0,0 +1,165 @@
+#!/bin/bash
+
+# Setup script env:
+#   e   Exit immediately if a command exits with a non-zero exit status.
+#   u   Treat unset variables as an error when substituting.
+#   x   Print commands and their arguments as they are executed.
+set -eux
+HERE="$( cd "$(dirname "$0")" ; pwd -P )"
+REPO_NAME="example-get-started-experiments"
+REPO_PATH="$HERE/build/$REPO_NAME"
+PROD=${1:-false}
+
+if [ -d "$REPO_PATH" ]; then
+  echo "Repo $REPO_PATH already exists, please remove it first."
+  exit 1
+fi
+
+TOTAL_TAGS=3
+STEP_TIME=100000
+BEGIN_TIME=$(( $(date +%s) - ( ${TOTAL_TAGS} * ${STEP_TIME}) ))
+export TAG_TIME=${BEGIN_TIME}
+export GIT_AUTHOR_DATE="${TAG_TIME} +0000"
+export GIT_COMMITTER_DATE="${TAG_TIME} +0000"
+tick(){
+  export TAG_TIME=$(( ${TAG_TIME} + ${STEP_TIME} ))
+  export GIT_AUTHOR_DATE="${TAG_TIME} +0000"
+  export GIT_COMMITTER_DATE="${TAG_TIME} +0000"
+}
+
+export GIT_AUTHOR_NAME="Alex Kim"
+export GIT_AUTHOR_EMAIL="alex000kim@gmail.com"
+export GIT_COMMITTER_NAME="$GIT_AUTHOR_NAME"
+export GIT_COMMITTER_EMAIL="$GIT_AUTHOR_EMAIL"
+
+mkdir -p $REPO_PATH
+pushd $REPO_PATH
+
+virtualenv -p python3 .venv
+export VIRTUAL_ENV_DISABLE_PROMPT=true
+source .venv/bin/activate
+echo '.venv/' > .gitignore
+
+# Installing from main since we'd like to update repo before
+# the release
+pip install "git+https://github.com/iterative/dvc#egg=dvc[s3]"
+
+git init
+cp $HERE/code/README.md .
+cp $HERE/code/.devcontainer.json .
+cp $HERE/code/.gitattributes .
+cp $HERE/code/requirements.txt .
+cp -r $HERE/code/.github .
+git add .
+tick
+git commit -m "Initialize Git repository"
+git branch -M main
+
+
+dvc init
+# Remote active on this env only, for writing to HTTP redirect below.
+dvc remote add -d --local storage s3://dvc-public/remote/get-started-pools
+# Actual remote for generated project (read-only). Redirect of S3 bucket above.
+dvc remote add -d storage https://remote.dvc.org/get-started-pools
+git add .
+tick
+git commit -m "Initialize DVC project"
+
+
+cp -r $HERE/code/data .
+git add data/.gitignore data/pool_data.dvc
+tick
+git commit -m "Add data"
+dvc pull
+
+
+cp -r $HERE/code/notebooks .
+git add .
+git commit -m "Add notebook using DVCLive"
+
+pip install -r requirements.txt
+pip install jupyter
+jupyter nbconvert --execute 'notebooks/TrainSegModel.ipynb' --inplace
+# Apply best experiment
+BEST_EXP_ROW=$(dvc exp show --drop '.*' --keep 'Experiment|evaluate/dice_multi|base_lr' --csv --sort-by evaluate/dice_multi | tail -n 1)
+BEST_EXP_NAME=$(echo $BEST_EXP_ROW | cut -d, -f 1)
+BEST_EXP_BASE_LR=$(echo $BEST_EXP_ROW | cut -d, -f 3)
+dvc exp apply $BEST_EXP_NAME
+git add .
+tick
+git commit -m "Run notebook and apply best experiment"
+git tag -a "1-notebook-dvclive" -m "Experiment using Notebook"
+
+
+cp -r $HERE/code/src .
+cp $HERE/code/params.yaml .
+ sed -e "s/base_lr: 0.01/base_lr: $BEST_EXP_BASE_LR/" -i".bkp" params.yaml
+
+dvc stage add -n data_split \
+  -p base,data_split \
+  -d src/data_split.py -d data/pool_data \
+  -o data/train_data -o  data/test_data \
+  python src/data_split.py
+
+dvc stage add -n train \
+  -p base,train \
+  -d src/train.py -d data/train_data \
+  -o models/model.pkl \
+  -M results/train/metrics.json \
+  --plots-no-cache results/train/plots \
+  python src/train.py
+
+dvc stage add -n evaluate \
+  -p base,evaluate \
+  -d src/evaluate.py -d models/model.pkl -d data/test_data \
+  -M results/evaluate/metrics.json \
+  --plots-no-cache results/evaluate/plots \
+  python src/evaluate.py
+git add .
+tick
+git commit -m "Convert Notebook to dvc.yaml pipeline"
+
+
+dvc exp run
+git add .
+tick
+git commit -m "Run dvc.yaml pipeline"
+git tag -a "2-dvc-pipeline" -m "Experiment using dvc pipeline"
+dvc push
+
+
+export GIT_AUTHOR_NAME="David de la Iglesia"
+export GIT_AUTHOR_EMAIL="daviddelaiglesiacastro@gmail.com"
+export GIT_COMMITTER_NAME="$GIT_AUTHOR_NAME"
+export GIT_COMMITTER_EMAIL="$GIT_AUTHOR_EMAIL"
+
+git checkout -b "tune-architecture"
+
+unset GIT_AUTHOR_DATE
+unset GIT_COMMITTER_DATE
+
+dvc exp run --queue --set-param train.arch=alexnet
+dvc exp run --queue --set-param train.arch=resnet34
+dvc exp run --queue --set-param train.arch=squeezenet1_1
+
+dvc exp run --run-all
+# Apply best experiment
+EXP=$(dvc exp show --csv --sort-by results/evaluate/metrics.json:dice_multi | tail -n 1 | cut -d , -f 1)
+dvc exp apply $EXP
+tick
+git commit -am "Run experiments tuning architecture. Apply best one"
+dvc push
+
+git checkout main
+
+popd
+
+unset TAG_TIME
+unset GIT_AUTHOR_DATE
+unset GIT_COMMITTER_DATE
+unset GIT_AUTHOR_NAME
+unset GIT_AUTHOR_EMAIL
+unset GIT_COMMITTER_NAME
+unset GIT_COMMITTER_EMAIL
+
+cat README.md