iterative · shcheklein · Oct 4, 2019 · Oct 2, 2019 · Oct 2, 2019 · Oct 3, 2019
diff --git a/server.js b/server.js
@@ -55,6 +55,12 @@ app.prepare().then(() => {
         Location: req.url.replace('commands-reference', 'command-reference')
       })
       res.end()
+    } else if (pathname == '/doc/tutorial') {
+      // path /doc/tutorial -> /doc/tutorials
+      res.writeHead(302, {
+        Location: req.url.replace('/doc/tutorial', '/doc/tutorials')
+      })
+      res.end()
     } else if (/^\/doc.*/i.test(pathname)) {
       // path /doc* -> /doc
       let normalized_pathname = pathname.replace(/^\/doc[^?\/]*/i, '/doc')

diff --git a/src/Documentation/sidebar.json b/src/Documentation/sidebar.json
@@ -24,14 +24,42 @@
       {
         "label": "Get Older Files",
         "slug": "older-versions"
+      }
+    ]
+  },
+  {
+    "slug": "tutorials",
+    "source": "tutorials/index.md",
+    "children": [
+      {
+        "slug": "interactive",
+        "label": "Interactive Tutorials"
+      },
+      {
+        "slug": "versioning",
+        "label": "Data Versioning"
+      },
+      {
+        "slug": "pipelines",
+        "label": "Stages and Pipelines"
       },
       {
-        "label": "Example: Versioning",
-        "slug": "example-versioning"
+        "slug": "tutorial",
+        "label": "Longer Tutorial",
+        "source": "tutorial/index.md",
+        "children": [
+          "preparation",
+          {
+            "label": "Define ML Pipeline",
+            "slug": "define-ml-pipeline"
+          },
+          "reproducibility",
+          "sharing-data"
+        ]
       },
       {
-        "label": "Example: Pipelines",
-        "slug": "example-pipeline"
+        "slug": "community",
+        "label": "Community Tutorials"
       }
     ]
   },
@@ -284,19 +312,6 @@
       }
     ]
   },
-  {
-    "slug": "tutorial",
-    "source": "tutorial/index.md",
-    "children": [
-      "preparation",
-      {
-        "label": "Define ML Pipeline",
-        "slug": "define-ml-pipeline"
-      },
-      "reproducibility",
-      "sharing-data"
-    ]
-  },
   {
     "label": "Understanding DVC",
     "slug": "understanding-dvc",

diff --git a/static/docs/command-reference/add.md b/static/docs/command-reference/add.md
@@ -241,7 +241,7 @@ $ dvc run -f train.dvc \
 ```
 
 To see this whole example go to
-[Example: Versioning](/doc/get-started/example-versioning).
+[Tutorial: Versioning](/doc/tutorials/versioning).
 
 Since no top-level DVC-file is generated with the `--recursive` option we cannot
 use the directory structure as a whole.
diff --git a/static/docs/command-reference/checkout.md b/static/docs/command-reference/checkout.md
@@ -125,7 +125,7 @@ $ cd example-get-started
 </details>
 
 The workspace looks almost like in this
-[pipeline setup](/doc/get-started/example-pipeline):
+[pipeline setup](/doc/tutorials/pipelines):
 
 ```dvc
 .

diff --git a/static/docs/command-reference/fetch.md b/static/docs/command-reference/fetch.md
@@ -136,7 +136,7 @@ $ cd example-get-started
 </details>
 
 The workspace looks almost like in this
-[pipeline setup](/doc/get-started/example-pipeline):
+[pipeline setup](/doc/tutorials/pipelines):
 
 ```dvc
 .

diff --git a/static/docs/command-reference/repro.md b/static/docs/command-reference/repro.md
@@ -118,8 +118,7 @@ specified), and updates stage files with the new checksum information.
 
 For simplicity, let's build a pipeline defined below. (If you want get your
 hands-on something more real, see this shot
-[pipeline tutorial](/doc/get-started/example-pipeline)). It takes this
-`text.txt` file:
+[pipeline tutorial](/doc/tutorials/pipelines)). It takes this `text.txt` file:
 
 ```
 dvc

diff --git a/static/docs/command-reference/run.md b/static/docs/command-reference/run.md
@@ -46,11 +46,10 @@ creating a new stage. For example, for every output there should be only one
 stage that explicitly specifies it. There should be no cycles, etc.
 
 Note that `dvc repro` provides an interface to check state and reproduce this
-graph (pipeline) later. This concept is similar to that of
+graph (pipeline) later. This concept is similar to the one of the
 [Make](https://www.gnu.org/software/make/) in software build automation, but DVC
 captures data and caches <abbr>data artifacts</abbr> along the way. See this
-[example](/doc/get-started/example-pipeline) to learn more and try to create a
-pipeline.
+[example](/doc/tutorials/pipelines) to learn more and try to create a pipeline.
 
 ## Options
 

diff --git a/static/docs/get-started/connect-code-and-data.md b/static/docs/get-started/connect-code-and-data.md
@@ -60,7 +60,7 @@ $ git commit -m "Add source code files to repo"
 Having installed the `src/prepare.py` script in your repo, the following command
 transforms it into a reproducible [stage](/doc/command-reference/run) for the ML
 pipeline we're building (described in the
-[next chapter](/doc/get-started/example-pipeline)).
+[next chapter](/doc/tutorials/pipelines)).
 
 ```dvc
 $ dvc run -f prepare.dvc \

diff --git a/static/docs/get-started/index.md b/static/docs/get-started/index.md
@@ -5,8 +5,8 @@ go into details much, but provides links and expandable sections to learn more.
 
 At the very end there are a few complete examples to give you more hands-on
 experience with real life scenarios. The first one is about model and dataset
-[versioning](/doc/get-started/example-versioning), and the second one is focused
-on [pipelines and reproducibility](/doc/get-started/example-pipeline).
+[versioning](/doc/tutorials/versioning), and the second one is focused on
+[pipelines and reproducibility](/doc/tutorials/pipelines).
 
 ✅ Please, join our [community](/chat) or see these [support](/support) options
 if you have any questions or need any help. We are very responsive ⚡.

diff --git a/static/docs/get-started/older-versions.md b/static/docs/get-started/older-versions.md
@@ -50,4 +50,4 @@ $ dvc checkout
 ```
 
 Read the `dvc checkout` command reference and a dedicated data versioning
-[example](/doc/get-started/example-versioning) for more information.
+[example](/doc/tutorials/versioning) for more information.
diff --git a/static/docs/get-started/pipeline.md b/static/docs/get-started/pipeline.md
@@ -36,7 +36,7 @@ $ dvc push
 ```
 
 This example is simplified just to show you a basic pipeline, see a more
-advanced [example](/doc/get-started/example-pipeline) or complete
+advanced [example](/doc/tutorials/pipelines) or complete
 [tutorial](/doc/tutorial) to create a
 [NLP](https://en.wikipedia.org/wiki/Natural_language_processing) pipeline
 end-to-end.

diff --git a/static/docs/tutorials/community.md b/static/docs/tutorials/community.md
@@ -0,0 +1,25 @@
+# Community Tutorials
+
+Blog posts from the community which show how they use DVC in their ML scenarios:
+
+- [Data Version Control Tutorial](https://blog.dataversioncontrol.com/data-version-control-tutorial-9146715eda46)
+
+- [DVC tutorial: improving CNN model](https://medium.com/@fullstackml/aab8162f8e3f)
+
+- [Creating an awesome project using DVC and DAGsHub](https://dagshub.com/docs/overview/)
+
+- [Using DVC to create an efficient version control system for data projects](https://medium.com/qonto-engineering/using-dvc-to-create-an-efficient-version-control-system-for-data-projects-96efd94355fe)
+
+- [Introduction to using DVC to manage machine learning project datasets](https://techsparx.com/software-development/ai/dvc/simple-example.html)
+
+- [Managing versioned machine learning datasets in DVC, and easily share ML projects with colleagues](https://techsparx.com/software-development/ai/dvc/versioning-example.html)
+
+- [A walkthrough of DVC](https://blog.codecentric.de/en/2019/03/walkthrough-dvc/)
+
+- [DVC dependency management](https://blog.codecentric.de/en/2019/08/dvc-dependency-management/)
+
+- [How to use data version control (dvc) in a machine learning project](https://towardsdatascience.com/how-to-use-data-version-control-dvc-in-a-machine-learning-project-a78245c0185)
+
+- [My first try at DVC](https://stdiff.net/MB2019051301.html)
+
+- [Effective Management of your Machine Learning Laboratory](https://www.linkedin.com/pulse/effective-management-your-machine-learning-laboratory-ulaganathan/)
diff --git a/static/docs/tutorials/index.md b/static/docs/tutorials/index.md
@@ -0,0 +1,18 @@
+# Tutorials
+
+- [Interactive Tutorials](/docs/tutorials/interactive) <br/> Interactive lessons
+  and tutorials on [Katacoda](https://katacoda.com/dvc) that explain the basic
+  concepts of DVC and show how to use it in simple ML scenarios.
+
+- [Data Versioning](/docs/tutorials/versioning) <br/> Using DVC commands to work
+  with multiple versions of datasets and ML models.
+
+- [Stages and Pipelines](/docs/tutorials/pipelines) <br/> Using DVC commands to
+  build a simple ML pipeline.
+
+- [Comprehensive Tutorial](/docs/tutorials/tutorial) <br/> Introduces DVC
+  step-by-step, while additionally explaining in great detail the motivation and
+  what's happening internally.
+
+- [Community Tutorials](/docs/tutorials/community) <br/> Blog posts from the
+  community which show how they use DVC in their ML scenarios.
diff --git a/static/docs/tutorials/interactive.md b/static/docs/tutorials/interactive.md
@@ -0,0 +1,46 @@
+# Interactive Tutorials
+
+Interactive lessons and tutorials on [Katacoda](https://katacoda.com/dvc) that
+explain the basic concepts of DVC and show how to use it in simple ML scenarios.
+
+## Basic Concepts
+
+Learn basic concepts and features of DVC with interactive lessons:
+
+1. [Data Management](https://katacoda.com/dvc/courses/basics/data) <br/> The
+   core function of DVC is data tracking and management. Let's see how to do it.
+
+2. [Getting the Best Performance](https://katacoda.com/dvc/courses/basics/performance)
+   <br/> It is important to optimize the DVC setup for having the best
+   performance with handling big data files.
+
+3. [Tracking Data Versions](https://katacoda.com/dvc/courses/basics/versioning)
+   <br/> DVC takes advantage of GIT's versioning features to keep track of the
+   data versions.
+
+4. [Sharing Data](https://katacoda.com/dvc/courses/basics/sharing) <br/> DVC
+   facilitates sharing of data between different people that work on the same
+   project.
+
+5. [Stages And Pipelines](https://katacoda.com/dvc/courses/basics/pipelines)
+   <br/> DVC has a built-in way to connect ML steps into a DAG and run the full
+   pipeline end-to-end.
+
+## Simple ML Scenarios
+
+Learn how DVC can be used in simple ML scenarios:
+
+- [Data Versioning](https://katacoda.com/dvc/courses/tutorials/versioning) <br/>
+  Using DVC commands to work with multiple versions of datasets and ML models.
+
+- [Stages and Pipelines](https://katacoda.com/dvc/courses/tutorials/pipelines)
+  <br/> Using DVC commands to build a simple ML pipeline.
+
+## Examples
+
+Interactive examples about using DVC commands and other features of DVC.
+
+- [dvc fetch](https://katacoda.com/dvc/courses/examples/fetch) <br/> We will use
+  an example project with some data, code, ML models, pipeline stages, as well
+  as a few Git tags. Then we will see what happens with dvc fetch as we switch
+  from tag to tag.
diff --git a/static/docs/get-started/example-pipeline.md → static/docs/tutorials/pipelines.md b/static/docs/get-started/example-pipeline.md → static/docs/tutorials/pipelines.md
@@ -1,4 +1,4 @@
-# Example: Pipelines
+# Tutorial: Pipelines
 
 To show DVC in action, let's play with an actual machine learning scenario.
 Let's explore the natural language processing

diff --git a/static/docs/tutorial/define-ml-pipeline.md → .../tutorials/tutorial/define-ml-pipeline.md b/static/docs/tutorial/define-ml-pipeline.md → .../tutorials/tutorial/define-ml-pipeline.md
diff --git a/static/docs/tutorial/index.md → static/docs/tutorials/tutorial/index.md b/static/docs/tutorial/index.md → static/docs/tutorials/tutorial/index.md
diff --git a/static/docs/tutorial/preparation.md → ...ic/docs/tutorials/tutorial/preparation.md b/static/docs/tutorial/preparation.md → ...ic/docs/tutorials/tutorial/preparation.md
diff --git a/static/docs/tutorial/reproducibility.md → ...ocs/tutorials/tutorial/reproducibility.md b/static/docs/tutorial/reproducibility.md → ...ocs/tutorials/tutorial/reproducibility.md
diff --git a/static/docs/tutorial/sharing-data.md → ...c/docs/tutorials/tutorial/sharing-data.md b/static/docs/tutorial/sharing-data.md → ...c/docs/tutorials/tutorial/sharing-data.md
diff --git a/...ic/docs/get-started/example-versioning.md → static/docs/tutorials/versioning.md b/...ic/docs/get-started/example-versioning.md → static/docs/tutorials/versioning.md
@@ -1,4 +1,4 @@
-# Example: Versioning
+# Tutorial: Versioning
 
 > Reading time is 10-13 minutes. Running the training is 30-40 minutes
 > (including downloading the dataset). Running the code is optional, and reading
@@ -362,8 +362,8 @@ Here's where the [pipelines](/doc/command-reference/pipeline) feature of DVC
 comes very handy and was designed for. We touched it briefly when we described
 `dvc run` and `dvc repro` at the very end. The next step here would be splitting
 the script into two parts, and utilizing pipelines. See
-[this example](/doc/get-started/example-pipeline) to get a hands-on experience
-with pipelines and try to apply it here. Don't hesitate to join our
+[this example](/doc/tutorials/pipelines) to get a hands-on experience with
+pipelines and try to apply it here. Don't hesitate to join our
 [community](/chat) to ask any questions!
 
 Another detail we only brushed on here is the way we captured the `metrics.json`

diff --git a/static/docs/use-cases/data-and-model-files-versioning.md b/static/docs/use-cases/data-and-model-files-versioning.md
@@ -2,7 +2,7 @@
 
 > This document provides an overview the file versioning workflow with DVC. To
 > get more hands-on experience on this we recommend following along the
-> [Versioning](/doc/get-started/example-versioning) example.
+> [Versioning](/doc/tutorials/versioning) tutorial.
 
 DVC allows versioning data files and directories, intermediate results, and ML
 models using Git, but without storing the file contents in the repository. It's
@@ -117,6 +117,5 @@ To share your data with others you need to setup a
 [Share Data And Model Files](/doc/use-cases/share-data-and-model-files) use case
 to get an overview on how to do this.
 
-Please also don't forget to see the
-[Versioning](/doc/get-started/example-versioning) example to get a hands-on
-experience with datasets and models versioning.
+Please also don't forget to see the [Versioning](/doc/tutorials/versioning)
+example to get a hands-on experience with datasets and models versioning.