Add dataset registry repo/project generator

README.md

@@ -23,7 +23,11 @@ $ cd example-get-started
 $ ./deploy.sh
 ```
 
-<!-- ### dataset-registry -->
+### dataset-registry
+
+- `generate.sh`: Generates the `dataset-registry` DVC project from scratch. This
+  project is used by **example-get-started** below, so it should be generated
+  first.
 
 ### example-get-started
 

dataset-registry/code/README.md

@@ -0,0 +1,73 @@
+# DVC Dataset Registry
+
+This is an auto-generated repository for use in https://dvc.org/doc/. Please
+report any issues in its source project,
+[example-repos-dev](https://github.com/iterative/example-repos-dev).
+
+_Dataset Registry_ is a centralized place to manage raw data files for use in
+other example DVC projects, such as
+https://github.com/iterative/example-get-started.
+
+## Installation
+
+Start by cloning the project:
+
+```console
+$ git clone https://github.com/iterative/dataset-registry
+$ cd dataset-registry
+```
+
+This DVC project comes with a preconfigured DVC
+[remote storage](https://man.dvc.org/remote) to hold all of the datasets. This
+is a read-only HTTP remote.
+
+```console
+$ dvc remote list
+storage https://remote.dvc.org/dataset-registry
+```
+
+You can run [`dvc pull`](https://man.dvc.org/pull) to download specific datasets
+locally:
+
+```console
+$ dvc pull -r storage get-started/data.xml
+```
+
+## Testing data synchronization locally
+
+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
+```
+
+## Datasets
+
+The folder structure of this project groups datasets corresponding to the
+external projects they pertain to.
+After cloning and using [`dvc pull`](https://man.dvc.org/pull) to download data
+under DVC control, the workspace should look like this:
+
+
+```console
+$ tree
+.
+├── README.md
+└── get-started
+    ├── data.xml      # Dataset used in iterative/example-get-started
+    └── data.xml.dvc
+
+1 directory, 3 files
+```

dataset-registry/generate.sh

@@ -0,0 +1,80 @@
+#!/bin/sh
+
+# 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 "$0")" ; pwd -P )"
+REPO_NAME="dataset-registry"
+REPO_PATH="$HERE/build/$REPO_NAME"
+
+if [ -d "$REPO_PATH" ]; then
+    echo "Repo $REPO_PATH already exists, remove it first."
+    exit 1
+fi
+
+mkdir -p $REPO_PATH
+pushd $REPO_PATH
+
+# Create virtualenv, install `dvc`, initialize/config DVC project
+
+virtualenv -p python3 .env
+export VIRTUAL_ENV_DISABLE_PROMPT=true
+source .env/bin/activate
+echo '.env/' >> .gitignore
+
+pip install dvc[s3]
+
+git init
+dvc init
+
+# Remote active on this environment only for writing to HTTP redirect below.
+dvc remote add -d --local storage s3://dvc-public/remote/dataset-registry
+
+# Actual remote for generated project (read-only). Redirect of S3 bucket below.
+dvc remote add -d storage https://remote.dvc.org/dataset-registry
+
+cp $HERE/code/README.md $REPO_PATH
+
+git add .
+git commit -m "Init & config DVC project, add README"
+
+# Get Started
+
+mkdir get-started
+wget https://data.dvc.org/get-started/data.xml -O get-started/data.xml
+dvc add get-started/data.xml
+git add get-started/.gitignore get-started/data.xml.dvc
+git commit -m "Add Get Started dataset"
+dvc push
+
+# TODO: Gather more datasets!
+
+popd
+
+echo "`cat <<EOF-
+
+The Git repo generated by this script is intended to be published on
+https://github.com/iterative/dataset-registry. Make sure the Github repo
+exists firt.
+
+To create it with https://hub.github.com/ for example, run:
+
+hub create iterative/dataset-registry -d "Get Started DVC project" \
+-h "https://dvc.org/doc/get-started"
+
+If the Github repo already exists, run these commands to rewrite it:
+
+cd build/dataset-registry
+git remote add origin [email protected]:iterative/dataset-registry.git
+git push --force origin master
+cd ../..
+
+You may remove the generated repo with:
+
+rm -fR build
+
+`"

example-get-started/.gitignore

@@ -1,3 +1,4 @@
 # Custom
 *.zip
 /tmp
+build/

example-get-started/code/README.md

@@ -1,14 +1,18 @@
 # DVC Get Started
 
 This is an auto-generated repository for use in https://dvc.org/doc/get-started.
-Please report any issues in
+Please report any issues in its source project,
 [example-repos-dev](https://github.com/iterative/example-repos-dev).
 
 ![](https://dvc.org/static/img/example-flow-2x.png)
 
 _Get Started_ is a step by step introduction into basic DVC concepts. It doesn't
 go into details much, but provides links and expandable sections to learn more.
 
+> Note that this project
+[imports](https://dvc.org/doc/commands-reference/import) a dataset from
+https://github.com/iterative/dataset-registry.
+
 The idea of the project is a simplified version of the
 [Tutorial](https://dvc.org/doc/tutorial). It explores the natural language
 processing (NLP) problem of predicting tags for a given StackOverflow question.
@@ -34,8 +38,10 @@ $ source .env/bin/activate
 $ pip install -r src/requirements.txt
 ```
 
-This DVC project comes with a preconfigured remote DVC storage that has raw data
-(input), intermediate, and final results that are produced.
+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
@@ -48,7 +54,7 @@ You can run [`dvc pull`](https://man.dvc.org/pull) to download the data:
 $ dvc pull -r storage
 ```
 
-## Running in Your Environment
+## Running in your environment
 
 Run [`dvc repro`](https://man.dvc.org/repro) to reproduce the
 [pipeline](https://dvc.org/doc/commands-reference/pipeline):
@@ -80,52 +86,54 @@ You should now be able to run:
 $ dvc push -r local
 ```
 
-## Existing Stages
+## Existing stages
 
 This project with the help of the Git tags reflects the sequence of actions that
 are run in the DVC [get started](https://dvc.org/doc/get-started) guide. Feel
 free to checkout one of them and play with the DVC commands having the
 playground ready.
 
-- `0-empty` - empty Git repository.
-- `1-initialize` - DVC has been initialized. The `.dvc` with the cache directory
+- `0-empty`: Empty Git repository initialized.
+- `1-initialize`: DVC has been initialized. `.dvc/` with the cache directory
   created.
-- `2-remote` - remote HTTP storage initialized. It is a shared read only storage
+- `2-remote`: Remote HTTP storage initialized. It's a shared read only storage
   that contains all data artifacts produced during next steps.
-- `3-add-file` - raw data file `data.xml` downloaded and put under DVC
-  control with [`dvc add`](https://man.dvc.org/add). First `.dvc` meta-file
-  created.
-- `4-source` - source code downloaded and put under Git control.
-- `5-preparation` - first DVC stage created using
+- `3-add-file`: Raw data file `data.xml` downloaded and put under DVC control
+  with [`dvc add`](https://man.dvc.org/add). First DVC-file (`.dvc` file
+  extension) created.
+- `4-source`: Source code downloaded and put under Git control.
+- `5-preparation`: First stage file (DVC-file) created using
   [`dvc run`](https://man.dvc.org/run). It transforms XML data into TSV.
-- `6-featurization` - feature extraction step added. It also includes the split
-  step for simplicity. It takes data in TSV format and produces two `.pkl` files
-  that contain serialized feature matrices.
-- `7-train` - the model training stage added. It produces `model.pkl` file - the
-  actual result that can be then deployed somewhere and classify questions.
-- `8-evaluate` - evaluate stage, we run it on a test dataset to see the AUC
-  value for the model. The result is dumped into a DVC metric file so that we
-  can compare it with other experiments later.
-- `9-bigrams` - bigrams experiment, code has been modified to extract more
+- `6-featurization`: Feature extraction stage created. It takes data in TSV
+  format and produces two `.pkl` files that contain serialized feature matrices.
+- `7-train`: Model training stage created. It produces `model.pkl` file – the
+  actual result that can then get deployed to an app that implements NLP
+  classification.
+- `8-evaluate`: Evaluation stage. Runs the model on a test dataset to produce
+  its performance AUC value. The result is dumped into a DVC metric file so that
+  we can compare it with other experiments later.
+- `9-bigrams-model`: Bigrams experiment, code has been modified to extract more
   features. We run [`dvc repro`](https://man.dvc.org/repro) for the first time
   to illustrate how DVC can reuse cached files and detect changes along the
-  computational graph.
+  computational graph, regenerating the model with the updated data.
+- `10-bigrams-experiment`: Reproduce the evaluation stage with the bigrams based
+  model.
 
 There are two additional tags:
 
-- `baseline-experiment` - the first end-to-end result that we performance metric
+- `baseline-experiment`: First end-to-end result that we have performance metric
   for.
-- `bigrams-experiment` - second version of the experiment.
+- `bigrams-experiment`: Second experiment (model trained using bigrams
+  features).
 
-Both these tags could be used to illustrate `-a` or `-T` options across
-different [DVC commands](https://man.dvc.org/).
+These tags can be used to illustrate `-a` or `-T` options across different
+[DVC commands](https://man.dvc.org/).
 
-## Project Structure
+## Project structure
 
-The data files, DVC-files, and results change as stages are created one by one,
-but right after you for Git clone and [`dvc pull`](https://man.dvc.org/pull) to
-download files that are under DVC control, the structure of the project should
-look like this:
+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
+under DVC control, the workspace should look like this:
 
 ```console
 $ tree