guide: best-practices section

content/docs/sidebar.json

@@ -91,6 +91,7 @@
         "label": "DVC Files and Directories",
         "slug": "dvc-files-and-directories"
       },
+      "best-practices",
       "merge-conflicts",
       {
         "slug": "dvcignore",

content/docs/user-guide/best-practices.md

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+# Best Practices for DVC Projects
+
+DVC provides a systematic approach towards managing and collaborating on data
+science projects. Here are a few recommended practices to organize your workflow
+and project structure effectively:
+
+> See also these quick [tips & tricks](/doc/user-guide/tips-and-tricks).
+
+## Matching source code to data
+
+One of DVC's basic uses is to avoid a disconnection between
+[revisions](https://git-scm.com/docs/revisions) of source code and
+[versions](/doc/use-cases/versioning-data-and-model-files) of data. DVC replaces
+large data files and directories, models, etc. with small
+[metafiles](/doc/user-guide/dvc-files-and-directories), which you can track with
+Git, along with the corresponding code.
+
+These metafiles point to the original data, which is <abbr>cached</abbr>
+automatically. You can access it later by restoring that Git working tree (e.g.
+with `git checkout`) and using `dvc checkout` to update DVC tracked data
+files/dir:
+
+```dvc
+$ git checkout 95485f  # Git commit of a desired project version
+$ dvc checkout
+```
+
+> See
+> [Versioning Data and Model Files](/doc/use-cases/versioning-data-and-model-files)
+> for more details.
+
+## Using directories as single data units
+
+If your dataset consist of multiple files like images, etc. then the best way to
+track it is
+[as a directory](/doc/command-reference/add#adding-entire-directories), with a
+single `.dvc` file:
+
+```dvc
+$ dvc add data/images/
+```
+
+## Manually editing dvc.yaml or .dvc files
+
+It's safe to edit `dvc.yaml` and `.dvc` files. Here's a `dvc.yaml` example:
+
+```yaml
+stages:
+  prepare:
+    cmd: python src/prepare.py data/data.xml
+    deps:
+      - data/data.xml
+    params:
+      - prepare.split
+    outs:
+      - data/prepared
+```
+
+You can manually edit all the fields present in `dvc.yaml`. However, in `.dvc`
+files please remember not to change the `md5` or `checksum` fields as they
+contain hash values which DVC uses to track the file or directory.
+
+## Managing and sharing large data
+
+Traditional or cloud storage can be used to store the project's data. You can
+share the entire 147 GB of your ML project, with all of its data sources,
+intermediate data files, and models with others by setting up DVC
+[remote storage](doc/command-reference/remote) (optional).
+
+This way you can share models trained in a GPU environment with colleagues who
+don't have access to GPUs.
+
+## Never store secrets in the shared config file
+
+Do not put user credentials in the default config file (`.dvc/config`), which is
+tracked by Git. Use the `--local`, `--global`, or `--system` options of
+`dvc config` to provide sensitive or user-specific settings:
+
+```dvc
+$ dvc config --local remote.password mypassword  # just here
+$ dvc config --global core.checksum_jobs 16      # all my projest
+$ dvc config --system core.check_update false    # all users
+```
+
+## Tracking experiments with Git
+
+If you are training different models on your data files in the same project,
+using Git commits, tags, or branches makes it easy to manage the project.

content/docs/user-guide/tips-and-tricks.md

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+# Tips and tricks for DVC Projects
+
+Using the methods listed here, you can manage your DVC projects more
+efficiently.
+
+## Switching between datasets
+
+You can quickly switch between a large dataset and a small subset without
+modifying source code: Change the dependencies of stage, either by manually
+editing the stage in `dvc.yaml` or by using `dvc run` again with `-f`.
+
+<!-- TODO: needs actual example -->
+
+## Tracking small data with Git
+
+If your `output` files are small in size and you want to track them with Git
+then you can use `--outs-no-cache` option to define outputs while creating or
+modifying a stage. DVC will not track will not track outputs in this case:
+
+```dvc
+$ dvc run -n train -d src/train.py -d data/features \
+          ---outs-no-cache model.p \
+          python src/train.py data/features model.pkl
+```
+
+## Partial reproducibility
+
+You can run a model's evaluation process again without preprocessing a raw
+dataset again, or retraining the model. Pass a target stage to `dvc repro` to
+execute only the necessary parts of the pipeline:
+
+```dvc
+$ dvc repro evaluate
+```
+
+## User metadata in DVC metafiles
+
+DVC provides an optional `meta` field for `dvc.yaml` and `.dvc` metafiles
+(that's very meta!). It can be used to add any user information (as YAML content
+e.g. `"a string"`).