From 7350938abb03ea6b2318927e315e49589c782dd4 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Thu, 13 Oct 2022 02:13:19 -0400 Subject: [PATCH 01/76] guide: draft structure of Data Mgmt and some updates around the topic in existing docs --- content/docs/command-reference/config.md | 40 +++++++++++++------ content/docs/sidebar.json | 2 +- .../user-guide/basic-concepts/dvc-cache.md | 6 +-- .../docs/user-guide/data-management/index.md | 21 ++++++++++ .../project-structure/internal-files.md | 35 +++++++++------- 5 files changed, 72 insertions(+), 32 deletions(-) create mode 100644 content/docs/user-guide/data-management/index.md diff --git a/content/docs/command-reference/config.md b/content/docs/command-reference/config.md index a52bc06c9b..7b1411bae5 100644 --- a/content/docs/command-reference/config.md +++ b/content/docs/command-reference/config.md @@ -165,24 +165,31 @@ See `dvc remote add` and `dvc remote modify` for more information. value is `cache`, that resolves to `.dvc/cache` (relative to the project config file location). - > See also the helper command `dvc cache dir` to intuitively set this config - > option, properly transforming paths relative to the current working - > directory into paths relative to the config file location. + + + See also the helper command `dvc cache dir` to intuitively set this config + option, properly transforming paths relative to the current working directory + into paths relative to the config file location. + + - `cache.type` - link type that DVC should use to link data files from cache to the workspace. Possible values: `reflink`, `symlink`, `hardlink`, `copy` or an ordered combination of those, separated by commas e.g: `reflink,hardlink,copy`. Default: `reflink,copy` + + + There are pros and cons to different link types. Refer to [File link types] + for a full explanation of each one. + + + If you set `cache.type` to `hardlink` or `symlink`, manually modifying tracked data files in the workspace would corrupt the cache. To prevent this, DVC automatically protects those kinds of links (making them read-only). Use `dvc unprotect` to be able to modify them safely. - There are pros and cons to different link types. Refer to - [File link types](/doc/user-guide/large-dataset-optimization#file-link-types-for-the-dvc-cache) - for a full explanation of each one. - To apply changes to this config option in the workspace, restore all file links/copies from cache with `dvc checkout --relink`. @@ -192,16 +199,23 @@ See `dvc remote add` and `dvc remote modify` for more information. faster cache link types available than the defaults (`reflink,copy` – see `cache.type`). Accepts values `true` and `false`. - > These warnings are automatically turned off when `cache.type` is manually - > set. + + + These warnings are automatically turned off when `cache.type` is manually set. + + - `cache.shared` - permissions for newly created or downloaded cache files and directories. The only accepted value right now is `group`, which makes DVC use `664` (rw-rw-r--) for files and `775` (rwxrwxr-x) for directories. This is - useful when [sharing a cache](/doc/user-guide/how-to/share-a-dvc-cache) among - projects. The default permissions for cache files is system dependent. In - Linux and macOS for example, they're determined using - [`os.umask`](https://docs.python.org/3/library/os.html#os.umask). + useful when [sharing a cache] among projects. The default permissions for + cache files is system dependent. In Linux and macOS for example, they're + determined using [`os.umask`]. + +[file link types]: + /doc/user-guide/large-dataset-optimization#file-link-types-for-the-dvc-cache +[sharing a cache]: /doc/user-guide/how-to/share-a-dvc-cache +[`os.umask`]: https://docs.python.org/3/library/os.html#os.umask The following parameters allow setting an [external cache](/doc/user-guide/managing-external-data#setting-up-an-external-cache) diff --git a/content/docs/sidebar.json b/content/docs/sidebar.json index 30cd1fce0d..6aba2dc085 100644 --- a/content/docs/sidebar.json +++ b/content/docs/sidebar.json @@ -121,7 +121,7 @@ }, { "slug": "data-management", - "source": false, + "source": "data-management/index.md", "children": [ "large-dataset-optimization", "importing-external-data", diff --git a/content/docs/user-guide/basic-concepts/dvc-cache.md b/content/docs/user-guide/basic-concepts/dvc-cache.md index fb0cf2a1a3..51009da413 100644 --- a/content/docs/user-guide/basic-concepts/dvc-cache.md +++ b/content/docs/user-guide/basic-concepts/dvc-cache.md @@ -3,7 +3,7 @@ name: 'DVC Cache' match: ['DVC cache', cache, caches, cached, 'cache directory', caching] tooltip: >- The DVC cache is a hidden storage (by default in `.dvc/cache`) for files and - directories tracked by DVC, and their different versions. Learn more about its - structure - [here](/doc/user-guide/project-structure/internal-files#structure-of-the-cache-directory). + directories tracked by DVC, and their different versions. For efficiency, it + uses a content-addressable + [structure](/doc/user-guide/project-structure/internal-files#structure-of-the-cache-directory). --- diff --git a/content/docs/user-guide/data-management/index.md b/content/docs/user-guide/data-management/index.md new file mode 100644 index 0000000000..304514bf1a --- /dev/null +++ b/content/docs/user-guide/data-management/index.md @@ -0,0 +1,21 @@ +# Data Management with DVC + +Mechanisms of DVC around handling large files and directories + +## Separating data from code + +Git +Codification +Versioning + +## Local storage (DVC cache) + +... + +## Remote storage (DVC remotes) + +... + +## Cloud versioning + +... diff --git a/content/docs/user-guide/project-structure/internal-files.md b/content/docs/user-guide/project-structure/internal-files.md index f3f2776a83..3774d40271 100644 --- a/content/docs/user-guide/project-structure/internal-files.md +++ b/content/docs/user-guide/project-structure/internal-files.md @@ -15,24 +15,26 @@ operation. (credentials, private locations, etc). The local config file can be edited by hand or with the command `dvc config --local`. -- `.dvc/cache`: Default location of the cache directory. The cache - stores the project data in a special - [structure](#structure-of-the-cache-directory). The data files and directories - in the workspace will only contain links to the data files in the - cache (refer to - [Large Dataset Optimization](/doc/user-guide/large-dataset-optimization). See - `dvc config cache` for related configuration options, including changing its - location. - - > Note that DVC includes the cache directory in `.gitignore` during - > initialization. No data tracked by DVC should ever be pushed to the Git - > repository, only the DVC files that are needed to download or - > reproduce that data. +- `.dvc/cache`: Default location of the cache directory. The cache + stores the project data in a special content-addressable + [structure](#structure-of-the-cache-directory). The files and directories + visible in the workspace will typically be [links] to cached + data. See `dvc config cache` for related configuration options, including + changing this default location. + + + + DVC includes the cache directory in `.gitignore` during [initialization]. No + data tracked by DVC should ever be pushed to the Git repository, only the + DVC files that are needed to download or reproduce that data. + + [initialization]: /doc/command-reference/init + + - `.dvc/cache/runs`: Default location of the [run-cache](#run-cache). -- `.dvc/plots`: Directory for - [plot templates](/doc/user-guide/visualizing-plots#plot-templates-data-series-only) +- `.dvc/plots`: Directory for [plot templates] - `.dvc/tmp`: Directory for miscellaneous temporary files @@ -69,6 +71,9 @@ operation. - `.dvc/tmp/exps`: This directory will contain workspace copies used for temporary or [queued experiments]. +[links]: /doc/user-guide/large-dataset-optimization +[plot templates]: + /doc/user-guide/visualizing-plots#plot-templates-data-series-only [queued experiments]: /doc/user-guide/experiment-management/running-experiments#the-experiments-queue From 203f6a6a48c89382dff6f1c2de9e26cf9939db15 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Fri, 14 Oct 2022 16:40:02 -0400 Subject: [PATCH 02/76] guide: full text for draft intro to DM --- .../versioning-data-and-models/index.md | 2 +- .../docs/user-guide/data-management/index.md | 57 ++++++++++++++++--- 2 files changed, 49 insertions(+), 10 deletions(-) diff --git a/content/docs/use-cases/versioning-data-and-models/index.md b/content/docs/use-cases/versioning-data-and-models/index.md index adb976e561..4f63add6e9 100644 --- a/content/docs/use-cases/versioning-data-and-models/index.md +++ b/content/docs/use-cases/versioning-data-and-models/index.md @@ -36,7 +36,7 @@ learn how DVC looks and feels firsthand. As you use DVC, unique versions of your data files and directories are [cached](/doc/user-guide/project-structure/internal-files#structure-of-the-cache-directory) -in a systematic way (preventing file duplication). The working datastore is +in a systematic way (preventing file duplication). The working data store is separated from your workspace to keep the project light, but stays connected via file [links](/doc/user-guide/large-dataset-optimization#file-link-types-for-the-dvc-cache) diff --git a/content/docs/user-guide/data-management/index.md b/content/docs/user-guide/data-management/index.md index 304514bf1a..da085412ab 100644 --- a/content/docs/user-guide/data-management/index.md +++ b/content/docs/user-guide/data-management/index.md @@ -1,21 +1,60 @@ # Data Management with DVC -Mechanisms of DVC around handling large files and directories +DVC helps you manage and share arbitrarily large files, datasets, and ML models +anywhere: mounted drives, Network Attached Storage (NAS), external devices, or +remotely (SSH servers, cloud storage, etc.). Once the project is configured, you +can manipulate files normally in your local workspace, and DVC tracks, restores, +and synchronizes them across locations. -## Separating data from code +![]() _Figure 1_ -Git -Codification -Versioning +## Separating data from code (local cache) -## Local storage (DVC cache) +First, large files, data artifacts, ML models are replaced with small +[metafiles]; We call this process _codification_. The actual data files are then +cached by DVC in a separate data store, and linked to your project. -... +![]() _Figure 2_ + +All DVC projects need fast storage access to a data cache. By +default it's local (in `.dvc/cache`), but it can be moved to and external +location in the file system or to a network location, for example to [share it] +among several projects. + +A DVC cache could even be setup in a remote system and accessed through the +internet, but this is typically too slow for working with the data regularly. + +[metafiles]: /doc/user-guide/project-structure +[share it]: /doc/user-guide/how-to/share-a-dvc-cache ## Remote storage (DVC remotes) -... +DVC supports remote storage locations like SSH servers or cloud services (Amazon +S3, Google Drive, Azure Blob Storage, etc.). This is mainly useful to upload +copies of your project's cache in order to share and back up all or some of your +datasets and models. + +## Data Versioning + +DVC applies source code management (SCM) to data and machine learning. The +metafiles in your repo can be handled with standard [Git workflows] ([version +control], branching, pull requests, etc.). + +[git workflows]: https://www.atlassian.com/git/tutorials/comparing-workflows +[version control]: /doc/use-cases/versioning-data-and-models + + + +Refer to [Versioning Data and Models] to learn more. + +[versioning data and models]: /doc/use-cases/versioning-data-and-models + + ## Cloud versioning -... +_New in DVC 2.28.0 (see `dvc version`)_ + +To simplify remote data operations, DVC now supports native versioning of files +and directories on several cloud providers. This means that you can browse your +files normally as you would see them in your local workspace. From eb246bb39193e6775ae60d630dd2f97cae49dc3c Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Mon, 17 Oct 2022 19:19:08 -0400 Subject: [PATCH 03/76] guide: hide cloud versioning info per https://github.com/iterative/dvc.org/pull/4042#pullrequestreview-1142987292 --- content/docs/user-guide/data-management/index.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/content/docs/user-guide/data-management/index.md b/content/docs/user-guide/data-management/index.md index da085412ab..842609b665 100644 --- a/content/docs/user-guide/data-management/index.md +++ b/content/docs/user-guide/data-management/index.md @@ -51,10 +51,12 @@ Refer to [Versioning Data and Models] to learn more. + From a3687ec5fe933059fe29e7baf28100f313d6c6c7 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Tue, 18 Oct 2022 00:37:29 -0400 Subject: [PATCH 04/76] guide: clarify Data Mgmt parts and add prospective figure titles --- .../docs/user-guide/data-management/index.md | 75 +++++++++++++------ .../project-structure/internal-files.md | 39 ++++++---- 2 files changed, 78 insertions(+), 36 deletions(-) diff --git a/content/docs/user-guide/data-management/index.md b/content/docs/user-guide/data-management/index.md index 842609b665..db78a32228 100644 --- a/content/docs/user-guide/data-management/index.md +++ b/content/docs/user-guide/data-management/index.md @@ -1,47 +1,76 @@ # Data Management with DVC DVC helps you manage and share arbitrarily large files, datasets, and ML models -anywhere: mounted drives, Network Attached Storage (NAS), external devices, or +anywhere: mounted drives, network resources (e.g. NAS), external devices, or remotely (SSH servers, cloud storage, etc.). Once the project is configured, you -can manipulate files normally in your local workspace, and DVC tracks, restores, -and synchronizes them across locations. +can manipulate files normally in your local workspace. DVC tracks, restores, and +synchronizes them across locations. -![]() _Figure 1_ +![]() _Local, external, and remote storage locations_ -## Separating data from code (local cache) +## The data cache -First, large files, data artifacts, ML models are replaced with small -[metafiles]; We call this process _codification_. The actual data files are then -cached by DVC in a separate data store, and linked to your project. +DVC projects separate data from code by replacing large files, data +artifacts, ML models, etc. in your workspace with small +[metafiles]; We call this process _codification_ (of the data). The actual file +contents are cached in an independent data store and linked to your project. -![]() _Figure 2_ +![]() _Separating code from data_ -All DVC projects need fast storage access to a data cache. By -default it's local (in `.dvc/cache`), but it can be moved to and external -location in the file system or to a network location, for example to [share it] -among several projects. + -A DVC cache could even be setup in a remote system and accessed through the +In order to [avoid duplicate content], and to support +[versioning features](#data-versioning), files and directories are reorganized +in the cache into a [content-addressable structure]. + +[avoid duplicate content]: + /doc/user-guide/data-management/large-dataset-optimization + + + +DVC expects fast storage access to the cache, so it's local to the +project by default (found in `.dvc/cache`). It can, however, be moved to an +external location in the file system or network, for example to [share it] among +several projects. + + + +A DVC cache could even be set up in a remote system and accessed through the internet, but this is typically too slow for working with the data regularly. + + [metafiles]: /doc/user-guide/project-structure [share it]: /doc/user-guide/how-to/share-a-dvc-cache +[content-addressable structure]: + /doc/user-guide/project-structure/internal-files#structure-of-the-cache-directory + +## Remote storage + +Optionally, DVC supports additional storage locations such as cloud services +(Amazon S3, Google Drive, Azure Blob Storage, etc.), SSH servers, HDFS, and +others. [DVC remotes] are typically used to sync copies of all or some of your +datasets and models, for sharing or backup. + +![]() _Distributed collaboration on DVC projects_ -## Remote storage (DVC remotes) + + +Remote storage uses the same [structure][content-addressable structure] of the +data cache. + + -DVC supports remote storage locations like SSH servers or cloud services (Amazon -S3, Google Drive, Azure Blob Storage, etc.). This is mainly useful to upload -copies of your project's cache in order to share and back up all or some of your -datasets and models. +[dvc remotes]: /doc/command-reference/remote ## Data Versioning -DVC applies source code management (SCM) to data and machine learning. The -metafiles in your repo can be handled with standard [Git workflows] ([version -control], branching, pull requests, etc.). +DVC brings source code management (SCM) to data science. Specifically, the +metafiles in your repo can be handled with standard [Git workflows] (commits, +branching, pull requests, etc.). This way machine learning teams can apply +mature software engineering practices. [git workflows]: https://www.atlassian.com/git/tutorials/comparing-workflows -[version control]: /doc/use-cases/versioning-data-and-models diff --git a/content/docs/user-guide/project-structure/internal-files.md b/content/docs/user-guide/project-structure/internal-files.md index 3774d40271..2581c9305e 100644 --- a/content/docs/user-guide/project-structure/internal-files.md +++ b/content/docs/user-guide/project-structure/internal-files.md @@ -79,17 +79,26 @@ operation. ## Structure of the cache directory -The DVC cache is a -[content-addressable storage](https://en.wikipedia.org/wiki/Content-addressable_storage) -(by default in `.dvc/cache`), which adds a layer of indirection between code and -data. +The DVC cache is a [content-addressable storage] (found in `.dvc/cache` by +default), which adds a layer of [indirection] between code and data. -There are two ways in which the data is cached, depending on -whether it's a single file, or a directory (which may contain multiple files). +[content-addressable storage]: + https://en.wikipedia.org/wiki/Content-addressable_storage +[indirection]: https://en.wikipedia.org/wiki/Indirection -Note files are renamed, reorganized, and directory trees are flattened in the -cache, which always has exactly one depth level with 2-character directories -(based on hashes of the data contents, as explained next). + + +This structure is also used by [remote storage]. + +[remote storage]: /doc/user-guide/data-management#remote-storage + + + +There are two ways in which data is cached, depending on whether +it's a single file or a folder: Files are renamed and reorganized to prevent +duplication. Directory trees are flattened so that the cache always has exactly +one depth level containing 2-character directories (based on hashes of the data +contents, as explained next). ### Files @@ -99,10 +108,14 @@ rest become the file name of the cached file. For example, if a data file has a hash value of `ec1d2935f811b77cc49b031b999cbf17`, its path in the cache will be `.dvc/cache/ec/1d2935f811b77cc49b031b999cbf17`. -> Note that file hashes are calculated from file contents only. 2 or more files -> with different names but the same contents can exist in the workspace and be -> tracked by DVC, but only one copy is stored in the cache. This helps avoid -> data duplication. + + +File hashes are calculated from file contents only. 2 or more files with +different names but the same contents can exist in the workspace and be tracked +by DVC, but only one copy is stored in the cache. This helps avoid data +duplication. + + ### Directories From fad0bad4c6fb44f4ccb475be4680e4256183efe1 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Tue, 18 Oct 2022 21:09:39 -0400 Subject: [PATCH 05/76] guide: add figure drafts to Data Mgmt --- .../docs/user-guide/data-management/index.md | 8 +++++--- static/img/code-vs-data.png | Bin 0 -> 49742 bytes static/img/distributed-collaboration.png | Bin 0 -> 22952 bytes static/img/storage-locations.png | Bin 0 -> 59238 bytes 4 files changed, 5 insertions(+), 3 deletions(-) create mode 100644 static/img/code-vs-data.png create mode 100644 static/img/distributed-collaboration.png create mode 100644 static/img/storage-locations.png diff --git a/content/docs/user-guide/data-management/index.md b/content/docs/user-guide/data-management/index.md index db78a32228..b2a61c3583 100644 --- a/content/docs/user-guide/data-management/index.md +++ b/content/docs/user-guide/data-management/index.md @@ -6,7 +6,8 @@ remotely (SSH servers, cloud storage, etc.). Once the project is configured, you can manipulate files normally in your local workspace. DVC tracks, restores, and synchronizes them across locations. -![]() _Local, external, and remote storage locations_ +![Storage locations](/img/storage-locations.png) _Local, external, and remote +storage locations_ ## The data cache @@ -15,7 +16,7 @@ artifacts, ML models, etc. in your workspace with small [metafiles]; We call this process _codification_ (of the data). The actual file contents are cached in an independent data store and linked to your project. -![]() _Separating code from data_ +![Code vs. data](/img/code-vs-data.png) _Separating code from data_ @@ -52,7 +53,8 @@ Optionally, DVC supports additional storage locations such as cloud services others. 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b/content/docs/user-guide/data-management/index.md @@ -67,11 +67,13 @@ data cache. ## Data Versioning -DVC brings source code management (SCM) to data science. Specifically, the -metafiles in your repo can be handled with standard [Git workflows] (commits, -branching, pull requests, etc.). This way machine learning teams can apply -mature software engineering practices. +DVC brings [version control] to data science. Specifically, the metafiles in +your repo can be handled with standard [Git workflows] (commits, branching, pull +requests, etc.). This way machine learning teams can apply mature software +engineering practices. +[version control]: + https://www.atlassian.com/git/tutorials/what-is-version-control [git workflows]: https://www.atlassian.com/git/tutorials/comparing-workflows From 7f02c15f020695cea5c97d9da46d03190d918c1b Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Tue, 18 Oct 2022 21:48:20 -0400 Subject: [PATCH 07/76] guide: update 2 figs and add 1 more (Data Mgmt) --- .../docs/user-guide/data-management/index.md | 16 +++++++++------- static/img/code-vs-data.png | Bin 49742 -> 47896 bytes static/img/distributed-collaboration.png | Bin 22952 -> 50453 bytes static/img/versioned-project.png | Bin 0 -> 57882 bytes 4 files changed, 9 insertions(+), 7 deletions(-) create mode 100644 static/img/versioned-project.png diff --git a/content/docs/user-guide/data-management/index.md b/content/docs/user-guide/data-management/index.md index d903e8a73f..ace505c607 100644 --- a/content/docs/user-guide/data-management/index.md +++ b/content/docs/user-guide/data-management/index.md @@ -14,18 +14,17 @@ storage locations_ DVC projects separate data from code by replacing large files, data artifacts, ML models, etc. in your workspace with small [metafiles]; We call this process _codification_ (of the data). The actual file -contents are cached in an independent data store and linked to your project. +contents are cached in an independent data store and [linked] to your project. ![Code vs. data](/img/code-vs-data.png) _Separating code from data_ -In order to [avoid duplicate content], and to support +In order to [avoid duplicate content][linked], and to support [versioning features](#data-versioning), files and directories are reorganized in the cache into a [content-addressable structure]. -[avoid duplicate content]: - /doc/user-guide/data-management/large-dataset-optimization +[linked]: /doc/user-guide/data-management/large-dataset-optimization @@ -72,9 +71,8 @@ your repo can be handled with standard [Git workflows] (commits, branching, pull requests, etc.). 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files changed, 15 insertions(+), 38 deletions(-) delete mode 100644 static/img/distributed-collaboration.png diff --git a/content/docs/user-guide/data-management/index.md b/content/docs/user-guide/data-management/index.md index 460fbae348..8f96c37e4a 100644 --- a/content/docs/user-guide/data-management/index.md +++ b/content/docs/user-guide/data-management/index.md @@ -5,42 +5,25 @@ anywhere: cloud storage, SSH servers, network resources (e.g. NAS), mounted drives, local file systems, etc. You manipulate DVC project normally in your local workspace; DVC tracks, restores, and synchronizes them across locations. -![Storage locations](/img/storage-locations.png) _Local, external, and remote -storage locations_ - -## The data cache - -DVC projects separate data from code by replacing large files, data -artifacts, ML models, etc. in your workspace with small -[metafiles]; We call this process _codification_ (of the data). The actual file -contents are cached in an independent data store and [linked] to your project. - -![Code vs. data](/img/code-vs-data.png) _Separating code from data_ - - - -In order to [avoid duplicate content][linked], and to support -[versioning features](#data-versioning), files and directories are reorganized -in the cache into a [content-addressable structure]. - -[linked]: /doc/user-guide/data-management/large-dataset-optimization - - +![Storage locations](/img/storage-locations.png) _Local, external, and remote +storage locations_ -DVC expects fast storage access to the cache, so it's local to the -project by default (found in `.dvc/cache`). It can, however, be moved to an -external location in the file system or network, for example to [share it] among -several projects. +Every DVC project starts with 2 locations. The +workspace is the main project directory, containing your data, +models, source code, etc. DVC also creates a data cache (found +locally in `.dvc/cache` by default), which will be used as fast-access storage +for DVC operations. -A DVC cache could even be set up in a remote system and accessed through the -internet, but this is typically too slow for working with the data regularly. +The cache can be moved to an external location in the file system or network, +for example to [share it] among several projects. It could even be set up in a +remote system (Internet access), but this is typically too slow for working with +data regularly. @@ -49,20 +32,14 @@ internet, but this is typically too slow for working with the data regularly. [content-addressable structure]: /doc/user-guide/project-structure/internal-files#structure-of-the-cache-directory -## Remote storage - Optionally, DVC supports additional storage locations such as cloud services -(Amazon S3, Google Drive, Azure Blob Storage, etc.), SSH servers, HDFS, and -others. [DVC remotes] are typically used to sync copies of all or some of your -datasets and models, for sharing or backup. - -![Distributed collaboration](/img/distributed-collaboration.png) _Distributed -collaboration on DVC projects_ +(Amazon S3, Google Drive, Azure Blob Storage, etc.), SSH servers, +network-attached storage, etc. These are called [DVC remotes], and help you to +share or back up copies of your data assets. -Remote storage uses the same [structure][content-addressable structure] of the -data cache. +DVC remotes are similar to Git remotes, but for cached data. diff --git a/static/img/distributed-collaboration.png b/static/img/distributed-collaboration.png deleted file mode 100644 index 6b78922efd76165e40a8743a9ef662780075df3b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 50453 zcmeFZbzGHO^FB<2AcCaQjYvs%NlS;MbW3-asFZ{togyjST}pTN2BaI6*wVkX^_=sZ z=Xv$N_s=hU*lg~7ubNqF=9+8fCRABb8WWue9S#l-Q&vVo6%G!$0S*qn0u2RRq4ITl 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zw}8Jc9(+?QOFbS@WFhC$64 z8MO*EAb_B0nsbkRXcIvohwo6E8^aXLlWMSzIZ_<{=Ymtc1gA=We0B7{mMciwq2&+Y zHeVb#B7}@|$o@S{FoAq9W53 Date: Sat, 22 Oct 2022 00:49:56 -0400 Subject: [PATCH 11/76] guide: udpates to DM/ DV moved from https://github.com/iterative/dvc.org/pull/4053#pullrequestreview-1149770432 --- .../docs/user-guide/data-management/index.md | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/content/docs/user-guide/data-management/index.md b/content/docs/user-guide/data-management/index.md index 8f96c37e4a..2ad4761cd4 100644 --- a/content/docs/user-guide/data-management/index.md +++ b/content/docs/user-guide/data-management/index.md @@ -47,14 +47,20 @@ DVC remotes are similar to Git remotes, but for cached data. ## Data Versioning -DVC brings [version control] to data science. Specifically, the metafiles in -your repo can be handled with standard [Git workflows] (commits, branching, pull -requests, etc.). This way machine learning teams can apply mature software -engineering practices. +DVC enables [version control] for data science. But DVC does not actually +implement versioning features! Instead, DVC focuses on codification, generating +small [metafiles] that you can handle with standard [Git workflows] (commits, +branching, pull requests, etc.). This way machine learning teams can apply +mature software engineering practices. ![Versioned ML project](/img/versioned-project.png) _Navigate versions with Git commits_ +The resulting projects are neatly organized in the "space dimension", having +only the files and directories needed at the time and without complicated, ad +hoc file names like `2022-10-20_linear-model_v2-Carl`. Project versions live in +the "time dimension" ([Git history]). + Refer to [Versioning Data and Models] to learn more. @@ -64,8 +70,10 @@ Refer to [Versioning Data and Models] to learn more. [version control]: - https://www.atlassian.com/git/tutorials/what-is-version-control + https://git-scm.com/book/en/v2/Getting-Started-About-Version-Control [git workflows]: https://www.atlassian.com/git/tutorials/comparing-workflows +[git history]: + https://git-scm.com/book/en/v2/Git-Basics-Viewing-the-Commit-History diff --git a/content/docs/user-guide/data-management/index.md b/content/docs/user-guide/data-management/index.md index 2ad4761cd4..911bf80f7c 100644 --- a/content/docs/user-guide/data-management/index.md +++ b/content/docs/user-guide/data-management/index.md @@ -27,7 +27,6 @@ data regularly. -[metafiles]: /doc/user-guide/project-structure [share it]: /doc/user-guide/how-to/share-a-dvc-cache [content-addressable structure]: /doc/user-guide/project-structure/internal-files#structure-of-the-cache-directory @@ -44,43 +43,3 @@ DVC remotes are similar to Git remotes, but for cached data. [dvc remotes]: /doc/command-reference/remote - -## Data Versioning - -DVC enables [version control] for data science. But DVC does not actually -implement versioning features! Instead, DVC focuses on codification, generating -small [metafiles] that you can handle with standard [Git workflows] (commits, -branching, pull requests, etc.). This way machine learning teams can apply -mature software engineering practices. - -![Versioned ML project](/img/versioned-project.png) _Navigate versions with Git -commits_ - -The resulting projects are neatly organized in the "space dimension", having -only the files and directories needed at the time and without complicated, ad -hoc file names like `2022-10-20_linear-model_v2-Carl`. Project versions live in -the "time dimension" ([Git history]). - - - -Refer to [Versioning Data and Models] to learn more. - -[versioning data and models]: /doc/use-cases/versioning-data-and-models - - - -[version control]: - https://git-scm.com/book/en/v2/Getting-Started-About-Version-Control -[git workflows]: https://www.atlassian.com/git/tutorials/comparing-workflows -[git history]: - https://git-scm.com/book/en/v2/Git-Basics-Viewing-the-Commit-History - - From 1a8ca610eb8b0720af303678ae85215e3badbce5 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Sat, 22 Oct 2022 02:22:50 -0400 Subject: [PATCH 13/76] guide: update outdated link --- content/docs/user-guide/project-structure/internal-files.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/docs/user-guide/project-structure/internal-files.md b/content/docs/user-guide/project-structure/internal-files.md index 2581c9305e..9eaf2a59d9 100644 --- a/content/docs/user-guide/project-structure/internal-files.md +++ b/content/docs/user-guide/project-structure/internal-files.md @@ -90,7 +90,7 @@ default), which adds a layer of [indirection] between code and data. This structure is also used by [remote storage]. -[remote storage]: /doc/user-guide/data-management#remote-storage +[remote storage]: /doc/command-reference/remote From 27be87f5e1f0b301ff7b818d0d664a82d8b33685 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Sat, 22 Oct 2022 03:21:56 -0400 Subject: [PATCH 14/76] guide: revert more unrelatedly chaqnged files per https://github.com/iterative/dvc.org/pull/4042#pullrequestreview-1150321143 --- .../user-guide/basic-concepts/dvc-cache.md | 6 +- .../project-structure/internal-files.md | 74 +++++++------------ 2 files changed, 31 insertions(+), 49 deletions(-) diff --git a/content/docs/user-guide/basic-concepts/dvc-cache.md b/content/docs/user-guide/basic-concepts/dvc-cache.md index 51009da413..fb0cf2a1a3 100644 --- a/content/docs/user-guide/basic-concepts/dvc-cache.md +++ b/content/docs/user-guide/basic-concepts/dvc-cache.md @@ -3,7 +3,7 @@ name: 'DVC Cache' match: ['DVC cache', cache, caches, cached, 'cache directory', caching] tooltip: >- The DVC cache is a hidden storage (by default in `.dvc/cache`) for files and - directories tracked by DVC, and their different versions. For efficiency, it - uses a content-addressable - [structure](/doc/user-guide/project-structure/internal-files#structure-of-the-cache-directory). + directories tracked by DVC, and their different versions. Learn more about its + structure + [here](/doc/user-guide/project-structure/internal-files#structure-of-the-cache-directory). --- diff --git a/content/docs/user-guide/project-structure/internal-files.md b/content/docs/user-guide/project-structure/internal-files.md index 9eaf2a59d9..05d25d2f0e 100644 --- a/content/docs/user-guide/project-structure/internal-files.md +++ b/content/docs/user-guide/project-structure/internal-files.md @@ -15,26 +15,24 @@ operation. (credentials, private locations, etc). The local config file can be edited by hand or with the command `dvc config --local`. -- `.dvc/cache`: Default location of the cache directory. The cache - stores the project data in a special content-addressable - [structure](#structure-of-the-cache-directory). The files and directories - visible in the workspace will typically be [links] to cached - data. See `dvc config cache` for related configuration options, including - changing this default location. - - - - DVC includes the cache directory in `.gitignore` during [initialization]. No - data tracked by DVC should ever be pushed to the Git repository, only the - DVC files that are needed to download or reproduce that data. - - [initialization]: /doc/command-reference/init - - +- `.dvc/cache`: Default location of the cache directory. The cache + stores the project data in a special + [structure](#structure-of-the-cache-directory). The data files and directories + in the workspace will only contain links to the data files in the + cache (refer to + [Large Dataset Optimization](/doc/user-guide/data-management/large-dataset-optimization). + See `dvc config cache` for related configuration options, including changing + its location. + + > Note that DVC includes the cache directory in `.gitignore` during + > initialization. No data tracked by DVC should ever be pushed to the Git + > repository, only the DVC files that are needed to download or + > reproduce that data. - `.dvc/cache/runs`: Default location of the [run-cache](#run-cache). -- `.dvc/plots`: Directory for [plot templates] +- `.dvc/plots`: Directory for + [plot templates](/doc/user-guide/experiment-management/visualizing-plots#plot-templates-data-series-only) - `.dvc/tmp`: Directory for miscellaneous temporary files @@ -71,34 +69,22 @@ operation. - `.dvc/tmp/exps`: This directory will contain workspace copies used for temporary or [queued experiments]. -[links]: /doc/user-guide/large-dataset-optimization -[plot templates]: - /doc/user-guide/visualizing-plots#plot-templates-data-series-only [queued experiments]: /doc/user-guide/experiment-management/running-experiments#the-experiments-queue ## Structure of the cache directory -The DVC cache is a [content-addressable storage] (found in `.dvc/cache` by -default), which adds a layer of [indirection] between code and data. - -[content-addressable storage]: - https://en.wikipedia.org/wiki/Content-addressable_storage -[indirection]: https://en.wikipedia.org/wiki/Indirection +The DVC cache is a +[content-addressable storage](https://en.wikipedia.org/wiki/Content-addressable_storage) +(by default in `.dvc/cache`), which adds a layer of indirection between code and +data. - +There are two ways in which the data is cached, depending on +whether it's a single file, or a directory (which may contain multiple files). -This structure is also used by [remote storage]. - -[remote storage]: /doc/command-reference/remote - - - -There are two ways in which data is cached, depending on whether -it's a single file or a folder: Files are renamed and reorganized to prevent -duplication. Directory trees are flattened so that the cache always has exactly -one depth level containing 2-character directories (based on hashes of the data -contents, as explained next). +Note files are renamed, reorganized, and directory trees are flattened in the +cache, which always has exactly one depth level with 2-character directories +(based on hashes of the data contents, as explained next). ### Files @@ -108,14 +94,10 @@ rest become the file name of the cached file. For example, if a data file has a hash value of `ec1d2935f811b77cc49b031b999cbf17`, its path in the cache will be `.dvc/cache/ec/1d2935f811b77cc49b031b999cbf17`. - - -File hashes are calculated from file contents only. 2 or more files with -different names but the same contents can exist in the workspace and be tracked -by DVC, but only one copy is stored in the cache. This helps avoid data -duplication. - - +> Note that file hashes are calculated from file contents only. 2 or more files +> with different names but the same contents can exist in the workspace and be +> tracked by DVC, but only one copy is stored in the cache. This helps avoid +> data duplication. ### Directories From aaee7af29cf30f0831c73ee956f014b0afcb8c1e Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Sat, 22 Oct 2022 03:26:16 -0400 Subject: [PATCH 15/76] guide: remove unused ref link --- content/docs/user-guide/data-management/index.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/content/docs/user-guide/data-management/index.md b/content/docs/user-guide/data-management/index.md index 911bf80f7c..008cb2b09c 100644 --- a/content/docs/user-guide/data-management/index.md +++ b/content/docs/user-guide/data-management/index.md @@ -28,8 +28,6 @@ data regularly. [share it]: /doc/user-guide/how-to/share-a-dvc-cache -[content-addressable structure]: - /doc/user-guide/project-structure/internal-files#structure-of-the-cache-directory Optionally, DVC supports additional storage locations such as cloud services (Amazon S3, Google Drive, Azure Blob Storage, etc.), SSH servers, From 118e3eb5c061f6ee852ef49bd13d54b39ed65d68 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Sat, 22 Oct 2022 04:06:47 -0400 Subject: [PATCH 16/76] guide: DM/ Remote Storage (not just Setup) and and some links from cmd refs and avoid term "data remote" and some admons nearby... --- content/docs/command-reference/remote/add.md | 10 +++++++--- content/docs/command-reference/remote/default.md | 6 ++---- content/docs/command-reference/remote/index.md | 4 +++- content/docs/command-reference/remote/list.md | 3 ++- content/docs/command-reference/remote/modify.md | 12 ++++++++---- content/docs/command-reference/remote/remove.md | 11 ++++++++--- content/docs/command-reference/remote/rename.md | 9 +++++++-- content/docs/sidebar.json | 2 +- content/docs/user-guide/data-management/index.md | 2 +- .../data-management/remote-storage-setup.md | 1 - .../user-guide/data-management/remote-storage.md | 11 +++++++++++ 11 files changed, 50 insertions(+), 21 deletions(-) delete mode 100644 content/docs/user-guide/data-management/remote-storage-setup.md create mode 100644 content/docs/user-guide/data-management/remote-storage.md diff --git a/content/docs/command-reference/remote/add.md b/content/docs/command-reference/remote/add.md index 28d14b46db..17dfbda779 100644 --- a/content/docs/command-reference/remote/add.md +++ b/content/docs/command-reference/remote/add.md @@ -1,9 +1,13 @@ # remote add -Add a new [data remote](/doc/command-reference/remote). +Register a new [DVC remote](/doc/user-guide/data-management/remote-storage). -> Depending on your storage type, you may also need `dvc remote modify` to -> provide credentials and/or configure other remote parameters. + + +Depending on your storage type, you may also need `dvc remote modify` to provide +credentials and/or configure other remote parameters. + + ## Synopsis diff --git a/content/docs/command-reference/remote/default.md b/content/docs/command-reference/remote/default.md index 60175a7ed8..7f390a5ec6 100644 --- a/content/docs/command-reference/remote/default.md +++ b/content/docs/command-reference/remote/default.md @@ -1,9 +1,7 @@ # remote default -Set/unset the default [data remote](/doc/command-reference/remote). - -> Depending on your remote storage type, you may also need `dvc remote modify` -> to provide credentials and/or configure other remote parameters. +Set/unset the default +[remote storage](/doc/user-guide/data-management/remote-storage). ## Synopsis diff --git a/content/docs/command-reference/remote/index.md b/content/docs/command-reference/remote/index.md index 6b3cbb723c..16c20f5cd3 100644 --- a/content/docs/command-reference/remote/index.md +++ b/content/docs/command-reference/remote/index.md @@ -1,6 +1,6 @@ # remote -A set of commands to set up and manage data remotes: +A set of commands to set up and manage [remote storage]: [add](/doc/command-reference/remote/add), [default](/doc/command-reference/remote/default), [list](/doc/command-reference/remote/list), @@ -8,6 +8,8 @@ A set of commands to set up and manage data remotes: [remove](/doc/command-reference/remote/remove), and [rename](/doc/command-reference/remote/rename). +[remote storage]: /doc/user-guide/data-management/remote-storage + ## Synopsis ```usage diff --git a/content/docs/command-reference/remote/list.md b/content/docs/command-reference/remote/list.md index a7b8c819f9..cb478cdfb9 100644 --- a/content/docs/command-reference/remote/list.md +++ b/content/docs/command-reference/remote/list.md @@ -1,6 +1,7 @@ # remote list -List all available [data remotes](/doc/command-reference/remote). +List all available +[DVC remotes](/doc/user-guide/data-management/remote-storage). ## Synopsis diff --git a/content/docs/command-reference/remote/modify.md b/content/docs/command-reference/remote/modify.md index bd05697445..b9d9c4b142 100644 --- a/content/docs/command-reference/remote/modify.md +++ b/content/docs/command-reference/remote/modify.md @@ -1,10 +1,14 @@ # remote modify -Modify the configuration of a [data remote](/doc/command-reference/remote). +Configure a [DVC remote](/doc/user-guide/data-management/remote-storage). -> This command is commonly needed after `dvc remote add` or -> [default](/doc/command-reference/remote/default) to set up credentials or -> other customizations to each remote storage type. + + +This command is commonly needed after `dvc remote add` or `dvc remote default` +to set up credentials or for other customizations specific to the +[storage type](#available-parameters-per-storage-type). + + ## Synopsis diff --git a/content/docs/command-reference/remote/remove.md b/content/docs/command-reference/remote/remove.md index 5a26838336..aaddb4c0a4 100644 --- a/content/docs/command-reference/remote/remove.md +++ b/content/docs/command-reference/remote/remove.md @@ -1,8 +1,13 @@ # remote remove -Remove a [data remote](/doc/command-reference/remote). This command affects DVC -configuration files only, it does not physically remove data files stored -remotely. +Remove a [DVC remote](/doc/user-guide/data-management/remote-storage). + + + +This command affects DVC configuration files only. It does not physically remove +data files stored remotely. See `dvc gc --cloud` for that. + + ## Synopsis diff --git a/content/docs/command-reference/remote/rename.md b/content/docs/command-reference/remote/rename.md index ae90f94502..4c470a185d 100644 --- a/content/docs/command-reference/remote/rename.md +++ b/content/docs/command-reference/remote/rename.md @@ -1,7 +1,12 @@ # remote rename -Rename a [data remote](/doc/command-reference/remote). The remote's URL is not -changed by this command. +Rename a [DVC remote](/doc/user-guide/data-management/remote-storage). + + + +The remote storage URL is not changed by this command. + + ## Synopsis diff --git a/content/docs/sidebar.json b/content/docs/sidebar.json index f0b6dec195..2fd68a977d 100644 --- a/content/docs/sidebar.json +++ b/content/docs/sidebar.json @@ -125,7 +125,7 @@ "source": "data-management/index.md", "children": [ "data-versioning", - "remote-storage-setup", + "remote-storage", "large-dataset-optimization", "importing-external-data", "managing-external-data" diff --git a/content/docs/user-guide/data-management/index.md b/content/docs/user-guide/data-management/index.md index 008cb2b09c..05860d2172 100644 --- a/content/docs/user-guide/data-management/index.md +++ b/content/docs/user-guide/data-management/index.md @@ -40,4 +40,4 @@ DVC remotes are similar to Git remotes, but for cached data. -[dvc remotes]: /doc/command-reference/remote +[dvc remotes]: /doc/user-guide/data-management/remote-storage diff --git a/content/docs/user-guide/data-management/remote-storage-setup.md b/content/docs/user-guide/data-management/remote-storage-setup.md deleted file mode 100644 index f6a4ccf103..0000000000 --- a/content/docs/user-guide/data-management/remote-storage-setup.md +++ /dev/null @@ -1 +0,0 @@ -# Remote Storage Setup diff --git a/content/docs/user-guide/data-management/remote-storage.md b/content/docs/user-guide/data-management/remote-storage.md new file mode 100644 index 0000000000..865f3ee59b --- /dev/null +++ b/content/docs/user-guide/data-management/remote-storage.md @@ -0,0 +1,11 @@ +# Remote Storage + + + +... + + + +## Configuration + +... From 24c331a3b8a80dcbc6df65a700713b2a7c8705c3 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Sat, 22 Oct 2022 04:08:15 -0400 Subject: [PATCH 17/76] guide: remove a comment --- content/docs/user-guide/data-management/index.md | 4 ---- 1 file changed, 4 deletions(-) diff --git a/content/docs/user-guide/data-management/index.md b/content/docs/user-guide/data-management/index.md index 008cb2b09c..e933329426 100644 --- a/content/docs/user-guide/data-management/index.md +++ b/content/docs/user-guide/data-management/index.md @@ -5,10 +5,6 @@ anywhere: cloud storage, SSH servers, network resources (e.g. NAS), mounted drives, local file systems, etc. You manipulate DVC project normally in your local workspace; DVC tracks, restores, and synchronizes them across locations. - - ![Storage locations](/img/storage-locations.png) _Local, external, and remote storage locations_ From 266a8f76448f3731f2881af3d4caf1b3816d21c8 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Sat, 22 Oct 2022 04:54:00 -0400 Subject: [PATCH 18/76] guide: draft for DM/ Remote Storage content --- content/docs/sidebar.json | 5 +- .../data-management/remote-storage.md | 49 +++++++++++++++++-- 2 files changed, 48 insertions(+), 6 deletions(-) diff --git a/content/docs/sidebar.json b/content/docs/sidebar.json index 2fd68a977d..25c84f66b7 100644 --- a/content/docs/sidebar.json +++ b/content/docs/sidebar.json @@ -125,7 +125,10 @@ "source": "data-management/index.md", "children": [ "data-versioning", - "remote-storage", + { + "label": "Remote Storage (Optional)", + "slug": "remote-storage" + }, "large-dataset-optimization", "importing-external-data", "managing-external-data" diff --git a/content/docs/user-guide/data-management/remote-storage.md b/content/docs/user-guide/data-management/remote-storage.md index 865f3ee59b..8d38df60c5 100644 --- a/content/docs/user-guide/data-management/remote-storage.md +++ b/content/docs/user-guide/data-management/remote-storage.md @@ -1,11 +1,50 @@ -# Remote Storage +# Remote Storage (Optional) - +_DVC remotes_ provide additional storage for your data and ML models, and allow +sharing them. For example, you can pull data artifacts created by colleagues +without spending time and resources to recreate them locally. See `dvc push` and +`dvc pull`. -... +This is similar to how GitHub provides hosting for source code repositories (Git +repos). However, DVC does not provide or recommend a specific storage service. +Instead, it adopts a bring-your-own-platform approach, supporting a +[wide variety of storage types](#supported-storage-types). - + + +DVC remotes are similar to [Git remotes], but for cached data. + +[git remotes]: https://git-scm.com/book/en/v2/Git-Basics-Working-with-Remotes + + + +The main uses of remote storage are: + +- Synchronize cached data assets (pull and push). +- Share data in a distributed way to better collaborate. +- Back up your data and its different versions. +- Save space in your working environment (by deleting data that has been + pushed). ## Configuration -... +To enable additional storage locations, you can configure remote storage with +the `dvc remote add` and `dvc remote modify` commands (see `dvc remote` for more +options). + +## Supported storage types + +### Cloud providers + +- Google Drive +- Amazon S3 (AWS) +- S3-compatible e.g. MinIO +- Google Cloud Storage (GCP) +- Azure Blob Storage + +### Self-hosted + +- SSH servers; Like `scp` +- Local directories, mounted drives; Like `rsync` +- Network resources e.g. network-attached storage (NAS) or other external + devices From b04f20aee74cbd2b7e50d53685dea18f4ec03664 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Sun, 23 Oct 2022 16:58:36 -0400 Subject: [PATCH 19/76] ref: expand config.remote and link to/from Remotes guide --- content/docs/command-reference/config.md | 29 ++++++++++++++++--- .../data-management/remote-storage.md | 9 ++++-- 2 files changed, 31 insertions(+), 7 deletions(-) diff --git a/content/docs/command-reference/config.md b/content/docs/command-reference/config.md index b311fddd3b..48a7ab5dab 100644 --- a/content/docs/command-reference/config.md +++ b/content/docs/command-reference/config.md @@ -119,7 +119,7 @@ within: ### core -- `core.remote` - name of the remote storage to use by default. +- `core.remote` - name of the [remote storage](#remote) to use by default. - `core.interactive` - whether to always ask for confirmation before reproducing each [stage](/doc/command-reference/run) in `dvc repro`. (Normally, this @@ -154,9 +154,30 @@ within: ### remote -All `remote` sections contain a `url` value and can also specify `user`, `port`, -`keyfile`, `timeout`, `ask_password`, and other cloud-specific key/value pairs. -See `dvc remote add` and `dvc remote modify` for more information. +Unlike most other sections, configuration files may have more than one +`'remote'`. All of them require a unique `"name"` and a `url` value. They can +also specify `jobs`, `verify`, and many platform-specific key/value pairs like +`port` and `password`. + + + +See [Remote Storage Configuration] for more details. + +[remote storage configuration]: + /doc/user-guide/data-management/remote-storage#configuration + + + +For example, the following config file defines a `temp` remote in the local file +system (located in `/tmp/dvcstore`), and marked as default (via [`core`](#core) +section): + +```ini +['remote "temp"'] + url = /tmp/dvcstore +[core] + remote = temp +``` ### cache diff --git a/content/docs/user-guide/data-management/remote-storage.md b/content/docs/user-guide/data-management/remote-storage.md index 8d38df60c5..a871cd341c 100644 --- a/content/docs/user-guide/data-management/remote-storage.md +++ b/content/docs/user-guide/data-management/remote-storage.md @@ -28,9 +28,12 @@ The main uses of remote storage are: ## Configuration -To enable additional storage locations, you can configure remote storage with -the `dvc remote add` and `dvc remote modify` commands (see `dvc remote` for more -options). +To enable additional storage, you can set up one or more `dvc remote` locations, +especially with the `dvc remote add` and `dvc remote modify` commands. These +read and write to the [`remote` section] of the project's configuration file +(`.dvc/config`). + +[`remote` section]: /doc/command-reference/config#remote ## Supported storage types From 1c77de42491be9f3c3246db8f461258345b4b1bf Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Sun, 23 Oct 2022 17:02:32 -0400 Subject: [PATCH 20/76] ref: fix remote config file examples --- content/docs/command-reference/remote/add.md | 26 +++++++++---------- .../docs/command-reference/remote/index.md | 12 ++++----- .../docs/command-reference/remote/modify.md | 6 ++--- 3 files changed, 22 insertions(+), 22 deletions(-) diff --git a/content/docs/command-reference/remote/add.md b/content/docs/command-reference/remote/add.md index 17dfbda779..a01a9259fd 100644 --- a/content/docs/command-reference/remote/add.md +++ b/content/docs/command-reference/remote/add.md @@ -30,9 +30,9 @@ for the first remote): ```ini ['remote "myremote"'] -url = /tmp/dvcstore + url = /tmp/dvcstore [core] -remote = myremote + remote = myremote ``` > 💡 Default remotes are expected by commands that accept a `-r`/`--remote` @@ -383,10 +383,10 @@ Using an absolute path (recommended): ```cli $ dvc remote add -d myremote /tmp/dvcstore $ cat .dvc/config - ... - ['remote "myremote"'] - url = /tmp/dvcstore - ... +... +['remote "myremote"'] + url = /tmp/dvcstore +... ``` > Note that the absolute path `/tmp/dvcstore` is saved as is. @@ -397,10 +397,10 @@ directory, but saved **relative to the config file location**: ```cli $ dvc remote add -d myremote ../dvcstore $ cat .dvc/config - ... - ['remote "myremote"'] - url = ../../dvcstore - ... +... +['remote "myremote"'] + url = ../../dvcstore +... ``` > Note that `../dvcstore` has been resolved relative to the `.dvc/` dir, @@ -427,10 +427,10 @@ The project's config file (`.dvc/config`) now looks like this: ```ini ['remote "myremote"'] -url = s3://mybucket/path -region = us-east-2 + url = s3://mybucket/path + region = us-east-2 [core] -remote = myremote + remote = myremote ``` The list of remotes should now be: diff --git a/content/docs/command-reference/remote/index.md b/content/docs/command-reference/remote/index.md index 16c20f5cd3..8e0dd78aaf 100644 --- a/content/docs/command-reference/remote/index.md +++ b/content/docs/command-reference/remote/index.md @@ -103,9 +103,9 @@ The project's config file should now look like this: ```ini ['remote "myremote"'] -url = /path/to/remote + url = /path/to/remote [core] -remote = myremote + remote = myremote ``` ## Example: List all remotes in the project @@ -130,12 +130,12 @@ The project's config file should now look something like this: ```ini ['remote "myremote"'] -url = /path/to/remote + url = /path/to/remote [core] -remote = myremote + remote = myremote ['remote "newremote"'] -url = s3://mybucket/path -endpointurl = https://object-storage.example.com + url = s3://mybucket/path + endpointurl = https://object-storage.example.com ``` ## Example: Change the name of a remote diff --git a/content/docs/command-reference/remote/modify.md b/content/docs/command-reference/remote/modify.md index b9d9c4b142..a9ddabe630 100644 --- a/content/docs/command-reference/remote/modify.md +++ b/content/docs/command-reference/remote/modify.md @@ -1201,10 +1201,10 @@ Now the project config file should look like this: ```ini ['remote "myremote"'] -url = s3://mybucket/path -profile = myuser + url = s3://mybucket/path + profile = myuser [core] -remote = myremote + remote = myremote ``` ## Example: Some Azure authentication methods From 8e7c3206f0e6fc8fc17093abba8f531965b098b1 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Sun, 23 Oct 2022 19:05:25 -0400 Subject: [PATCH 21/76] guide: complete Remote Config section and and add Project config section to DM/ DV guide --- .../data-management/data-versioning.md | 30 ++++++++ .../data-management/remote-storage.md | 69 +++++++++++++++++-- 2 files changed, 93 insertions(+), 6 deletions(-) diff --git a/content/docs/user-guide/data-management/data-versioning.md b/content/docs/user-guide/data-management/data-versioning.md index c8730a0009..ea041696f7 100644 --- a/content/docs/user-guide/data-management/data-versioning.md +++ b/content/docs/user-guide/data-management/data-versioning.md @@ -41,3 +41,33 @@ To simplify remote data operations, DVC now supports native versioning of files and directories on several cloud providers. This means that you can browse your files normally as you would see them in your local workspace. --> + +## Project configuration + +Besides metafiles, DVC projects may contain a config file +(`.dvc/config`) that can also be treated as code when it comes to version +control. + + + +See `dvc config` for more information on DVC config. + + + +Some times it's important to version configuration changes along with +corresponding data updates. Most notably, if you [set up remote storage] and +`dvc push` data for others to `dvc pull` later, you should `git commit` both the +metafile(s) and `.dvc/config` to the repo. + +Advanced situations where this is may also be necessary: + +- When migrating to a [shared cache] +- If you enable `dvc cache hydra` and then create an [experiment queue] with + [param composition] to run from another environment + +[set up remote storage]: + /doc/user-guide/data-management/remote-storage#configuration +[shared cache]: doc/user-guide/how-to/share-a-dvc-cache +[experiment queue]: + /doc/user-guide/experiment-management/running-experiments#the-experiments-queue +[param composition]: /doc/user-guide/experiment-management/hydra-composition diff --git a/content/docs/user-guide/data-management/remote-storage.md b/content/docs/user-guide/data-management/remote-storage.md index a871cd341c..d5fb3528be 100644 --- a/content/docs/user-guide/data-management/remote-storage.md +++ b/content/docs/user-guide/data-management/remote-storage.md @@ -1,6 +1,6 @@ # Remote Storage (Optional) -_DVC remotes_ provide additional storage for your data and ML models, and allow +_DVC remotes_ provide additional storage for your data or ML models and allow sharing them. For example, you can pull data artifacts created by colleagues without spending time and resources to recreate them locally. See `dvc push` and `dvc pull`. @@ -28,15 +28,72 @@ The main uses of remote storage are: ## Configuration -To enable additional storage, you can set up one or more `dvc remote` locations, -especially with the `dvc remote add` and `dvc remote modify` commands. These -read and write to the [`remote` section] of the project's configuration file -(`.dvc/config`). +You can set up one or more remote storage locations, mainly with the +`dvc remote add` and `dvc remote modify` commands. These read and write to the +[`remote`] section of the project's configuration file (`.dvc/config`), which +you could edit manually as well. -[`remote` section]: /doc/command-reference/config#remote +Typically, you'll first register a DVC remote by adding its name and URL (or +file path), e.g.: + +```cli +$ dvc remote add mybucket s3://my-bucket +``` + +Then, you may need or want to further configure the remote by authentication +information or other properties, etc. For example: + +```cli +$ dvc remote modify --local \ + mybucket credentialpath ~/.aws/alt + +$ dvc remote modify mybucket connect_timeout 300 +``` + + + +Make sure to use the `--local` flag when writing secrets to configuration. This +creates a second config file in `.dvc/config.local` that is ignored by Git. This +way your secrets do not get to the repository. See `dvc config` for more info. + +This also means each copy of the DVC repository may have to +re-configure remote storage authentication. + + + +
+ +### Click to see the resulting config files. + +```ini +# .dvc/config +['remote "mybucket"'] + url = s3://my-bucket + connect_timeout = 300 +``` + +```ini +# .dvc/config.local +['remote "mybucket"'] + credentialpath = ~/.aws/alt +``` + +```ini +# .gitignore +.dvc/config.local +``` + +
+ +Finally, you can `git add` and `git commit` the changes to share the general +configuration of your remote (in `.dvc/config`) via the Git repo. + +[`remote`]: /doc/command-reference/config#remote ## Supported storage types +> See more [details](/doc/command-reference/remote/add#supported-storage-types). + ### Cloud providers - Google Drive From bc9f588dd25d2aa8167eae574692200fbd0a82b9 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Sun, 23 Oct 2022 21:00:57 -0400 Subject: [PATCH 22/76] ref: rewrite remote add and modify Descs --- content/docs/command-reference/config.md | 2 +- content/docs/command-reference/remote/add.md | 79 ++++++++++++------- .../docs/command-reference/remote/modify.md | 48 ++++++++--- 3 files changed, 89 insertions(+), 40 deletions(-) diff --git a/content/docs/command-reference/config.md b/content/docs/command-reference/config.md index 48a7ab5dab..7c4f31f6e0 100644 --- a/content/docs/command-reference/config.md +++ b/content/docs/command-reference/config.md @@ -63,7 +63,7 @@ multiple projects or users, respectively. > Note that the `--show-origin` flag can show you where a given config option > `value` is currently stored. -## Command options (flags) +## Command options/flags - `-u`, `--unset` - remove the specified config option `name` from a config file. Don't provide a `value` argument when employing this flag. diff --git a/content/docs/command-reference/remote/add.md b/content/docs/command-reference/remote/add.md index a01a9259fd..4645ac2ae5 100644 --- a/content/docs/command-reference/remote/add.md +++ b/content/docs/command-reference/remote/add.md @@ -1,11 +1,15 @@ # remote add -Register a new [DVC remote](/doc/user-guide/data-management/remote-storage). +Add a new `dvc remote` to the DVC project configuration. -Depending on your storage type, you may also need `dvc remote modify` to provide -credentials and/or configure other remote parameters. +You may also need `dvc remote modify` to provide credentials and/or configure +other remote parameters. See [Remote storage configuration] for more +information. + +[remote storage configuration]: + /doc/user-guide/data-management/remote-storage#configuration @@ -23,41 +27,62 @@ positional arguments: ## Description -This command creates a `remote` section in the DVC project's -[config file](/doc/command-reference/config) and optionally assigns a _default -remote_ in the `core` section, if the `--default` option is used (recommended -for the first remote): +Registers an [additional storage] location to save data files (besides the +cache) and optionally sets it as the `--default` remote. DVC +remotes can point to a cloud storage service, an SSH server, network-attached +storage, or even a directory in the local file system. + + + +A [default remote] is expected by `dvc push`, `dvc pull`, `dvc status`, +`dvc gc`, and `dvc fetch` unless their `--remote` option is used. + +[default remote]: /doc/command-reference/remote/default + + + +The remote `name` (required) is used to identify the remote and must be unique. +DVC will determine the [type of remote](#supported-storage-types) based on the +provided `url` (also required), a URL or path for the location. + + + +The storage type determines which config parameters you can access via +`dvc remote modify`. Note that the `url` itself can be modified. + + + +This command creates a [`remote`] section in the project's [config file] +(`.dvc/config`). The `--default` (`-d`) flag uses the [`core`] config section: + +```cli +$ dvc remote add -d temp /tmp/dvcstore +``` ```ini -['remote "myremote"'] +# .dvc/config +['remote "temp"'] url = /tmp/dvcstore [core] remote = myremote ``` -> 💡 Default remotes are expected by commands that accept a `-r`/`--remote` -> option (`dvc pull`, `dvc push`, `dvc status`, `dvc gc`, `dvc fetch`) when that -> option is omitted. - -`name` and `url` are required. The `name` is used to identify the remote and -must be unique for the project. + -`url` specifies a location to store your data. It can represent a cloud storage -service, an SSH server, network-attached storage, or even a directory in the -local file system (see all the supported remote storage types in the examples -below). +If you installed DVC via `pip` and plan to use cloud services as remote storage, +you might need to install these optional dependencies: `[s3]`, `[azure]`, +`[gdrive]`, `[gs]`, `[oss]`, `[ssh]`. Alternatively, use `[all]` to include them +all. The command should look like this: `pip install "dvc[s3]"`. (This example +installs `boto3` library along with DVC to support S3 storage.) -DVC will determine the [type of remote](#supported-storage-types) based on the -`url` provided. This may affect which parameters you can access later via -`dvc remote modify` (note that the `url` itself can be modified). + -> If you installed DVC via `pip` and plan to use cloud services as remote -> storage, you might need to install these optional dependencies: `[s3]`, -> `[azure]`, `[gdrive]`, `[gs]`, `[oss]`, `[ssh]`. Alternatively, use `[all]` to -> include them all. The command should look like this: `pip install "dvc[s3]"`. -> (This example installs `boto3` library along with DVC to support S3 storage.) +[additional storage]: /doc/user-guide/data-management/remote-storage +[config file]: /doc/command-reference/config +[`remote`]: /doc/command-reference/config#remote +[`core`]: /doc/command-reference/config#core -## Options +## Command options/flags - `--system` - save remote configuration to the system config file (e.g. `/etc/xdg/dvc/config`) instead of `.dvc/config`. diff --git a/content/docs/command-reference/remote/modify.md b/content/docs/command-reference/remote/modify.md index a9ddabe630..fa18b0cdbb 100644 --- a/content/docs/command-reference/remote/modify.md +++ b/content/docs/command-reference/remote/modify.md @@ -1,12 +1,14 @@ # remote modify -Configure a [DVC remote](/doc/user-guide/data-management/remote-storage). +Configure an existing `dvc remote`. -This command is commonly needed after `dvc remote add` or `dvc remote default` -to set up credentials or for other customizations specific to the -[storage type](#available-parameters-per-storage-type). +This command is commonly needed after `dvc remote add` to set up credentials or +other customizations. See [Remote storage configuration] for more information. + +[remote storage configuration]: + /doc/user-guide/data-management/remote-storage#configuration @@ -25,16 +27,38 @@ positional arguments: ## Description -Remote `name` and `option` name are required. Config option names are specific -to the remote type. See `dvc remote add` and -[Available parameters](#available-parameters-per-storage-type) below for a list -of remote storage types. +The DVC remote's `name` and a valid `option` to modify are required. Remote +options or [config parameters](#available-parameters-per-storage-type) are +specific to the storage type and typically require a `value` as well. + +This command updates a [`remote`] section in the [config file] (`.dvc/config`): + +```cli +$ dvc remote modify temp url /mnt/c/tmp/dvcstore +``` + +```git +# .dvc/config +['remote "temp"'] +- url = /tmp/dvcstore ++ url = /mnt/c/tmp/dvcstore +``` + + + +If you installed DVC via `pip` and plan to use cloud services as remote storage, +you might need to install these optional dependencies: `[s3]`, `[azure]`, +`[gdrive]`, `[gs]`, `[oss]`, `[ssh]`. Alternatively, use `[all]` to include them +all. The command should look like this: `pip install "dvc[s3]"`. (This example +installs `boto3` library along with DVC to support S3 storage.) + + -This command modifies a `remote` section in the project's -[config file](/doc/command-reference/config). Alternatively, `dvc config` or -manual editing could be used to change the configuration. +[config file]: /doc/command-reference/config +[`remote`]: /doc/command-reference/config#remote +[`core`]: /doc/command-reference/config#core -## Command options (flags) +## Command options/flags - `-u`, `--unset` - remove the configuration `option` from a config file. Don't provide a `value` argument when employing this flag. From 9b904f5f421fd413d53c57610eabe05c6f732611 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Mon, 24 Oct 2022 00:59:21 -0400 Subject: [PATCH 23/76] guide: complete list of supported storage types --- .../user-guide/data-management/remote-storage.md | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/content/docs/user-guide/data-management/remote-storage.md b/content/docs/user-guide/data-management/remote-storage.md index d5fb3528be..08733c64ba 100644 --- a/content/docs/user-guide/data-management/remote-storage.md +++ b/content/docs/user-guide/data-management/remote-storage.md @@ -96,15 +96,19 @@ configuration of your remote (in `.dvc/config`) via the Git repo. ### Cloud providers -- Google Drive - Amazon S3 (AWS) - S3-compatible e.g. MinIO +- Microsoft Azure Blob Storage +- Google Drive - Google Cloud Storage (GCP) -- Azure Blob Storage +- Aliyun OSS -### Self-hosted +### Self-hosted/ On-prem/ Others - SSH servers; Like `scp` +- HDFS & WebHDFS +- HTTP +- WebDAV - Local directories, mounted drives; Like `rsync` -- Network resources e.g. network-attached storage (NAS) or other external - devices + > Includes network resources e.g. network-attached storage (NAS) or other + > external devices From 33f46fc2165832fd9c7247bc22ccf9979029218f Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Mon, 24 Oct 2022 01:01:27 -0400 Subject: [PATCH 24/76] ref: rewrite remote index page from extracted from https://github.com/iterative/dvc.org/pull/4053 --- content/docs/command-reference/remote/add.md | 15 +++-- .../docs/command-reference/remote/index.md | 63 ++++++------------- .../docs/command-reference/remote/modify.md | 15 +++-- 3 files changed, 38 insertions(+), 55 deletions(-) diff --git a/content/docs/command-reference/remote/add.md b/content/docs/command-reference/remote/add.md index 4645ac2ae5..191f6c1a33 100644 --- a/content/docs/command-reference/remote/add.md +++ b/content/docs/command-reference/remote/add.md @@ -69,11 +69,16 @@ $ dvc remote add -d temp /tmp/dvcstore -If you installed DVC via `pip` and plan to use cloud services as remote storage, -you might need to install these optional dependencies: `[s3]`, `[azure]`, -`[gdrive]`, `[gs]`, `[oss]`, `[ssh]`. Alternatively, use `[all]` to include them -all. The command should look like this: `pip install "dvc[s3]"`. (This example -installs `boto3` library along with DVC to support S3 storage.) +If you [installed DVC] via `pip` and plan to use cloud services as remote +storage, you might need to install these optional dependencies: `[s3]`, +`[azure]`, `[gdrive]`, `[gs]`, `[oss]`, `[ssh]`. Use `[all]` to include them +all. For example: + +```cli +$ pip install "dvc[s3]" +``` + +[installed dvc]: /doc/install diff --git a/content/docs/command-reference/remote/index.md b/content/docs/command-reference/remote/index.md index 8e0dd78aaf..863b6b679d 100644 --- a/content/docs/command-reference/remote/index.md +++ b/content/docs/command-reference/remote/index.md @@ -26,49 +26,22 @@ positional arguments: ## Description -What is data remote? +DVC remotes are distributed storage locations for your data sets and ML models +(similar to Git remotes, but for cached assets). This optional +feature is typically used to share or back up copies of all or some of your +data. Several types are supported: Amazon S3, Google Drive, SSH, HTTP, local +file systems, among others. -The same way as GitHub provides storage hosting for Git repositories, DVC -remotes provide a location to store and share data and models. You can pull data -assets created by colleagues from DVC remotes without spending time and -resources to build or process them locally. Remote storage can also save space -on your local environment – DVC can [fetch](/doc/command-reference/fetch) into -the cache directory only the data you need for a specific -branch/commit. + -Using DVC with remote storage is optional. DVC commands use the local cache -(usually in dir `.dvc/cache`) as data storage by default. This enables the main -DVC usage scenarios out of the box. - -DVC supports several types of remote storage: local file system, SSH, Amazon S3, -Google Cloud Storage, HTTP, HDFS, among others. Refer to `dvc remote add` for -more details. - - - -If you installed DVC via `pip` and plan to use cloud services as remote storage, -you might need to install these optional dependencies: `[s3]`, `[azure]`, -`[gdrive]`, `[gs]`, `[oss]`, `[ssh]`. Alternatively, use `[all]` to include them -all. The command should look like this: `pip install "dvc[s3]"`. (This example -installs `boto3` library along with DVC to support S3 storage.) - - - -### Managing remote storage - - - -For an intro on DVC remote usage see [Storing and sharing data]. - -[storing and sharing data]: - /doc/start/data-management/data-versioning#storing-and-sharing +Learn more about [remote storage]. -`dvc remote` subcommands read or modify DVC [config files], where DVC remotes -are set up. Alternatively, `dvc config` can be used, or the config files can be -edited manually. +`dvc remote` subcommands read or modify DVC [config files] (`.dvc/config` by +default). Alternatively, the config files can be edited manually. +[types of storage]: /doc/command-reference/remote/add#supported-storage-types [config files]: /doc/command-reference/config ## Options @@ -88,8 +61,8 @@ edited manually. While the term may seem contradictory, it doesn't have to be. The "local" part refers to the type of location where the storage is: another directory in the -same file system. "Remote" is how we call storage for DVC projects. -It's essentially a local backup for data tracked by DVC. +same file system. "Remote" is what we call storage for DVC +projects. It's essentially a local backup for data tracked by DVC. @@ -103,9 +76,9 @@ The project's config file should now look like this: ```ini ['remote "myremote"'] - url = /path/to/remote +url = /path/to/remote [core] - remote = myremote +remote = myremote ``` ## Example: List all remotes in the project @@ -130,12 +103,12 @@ The project's config file should now look something like this: ```ini ['remote "myremote"'] - url = /path/to/remote +url = /path/to/remote [core] - remote = myremote +remote = myremote ['remote "newremote"'] - url = s3://mybucket/path - endpointurl = https://object-storage.example.com +url = s3://mybucket/path +endpointurl = https://object-storage.example.com ``` ## Example: Change the name of a remote diff --git a/content/docs/command-reference/remote/modify.md b/content/docs/command-reference/remote/modify.md index fa18b0cdbb..89be5b8d20 100644 --- a/content/docs/command-reference/remote/modify.md +++ b/content/docs/command-reference/remote/modify.md @@ -46,11 +46,16 @@ $ dvc remote modify temp url /mnt/c/tmp/dvcstore -If you installed DVC via `pip` and plan to use cloud services as remote storage, -you might need to install these optional dependencies: `[s3]`, `[azure]`, -`[gdrive]`, `[gs]`, `[oss]`, `[ssh]`. Alternatively, use `[all]` to include them -all. The command should look like this: `pip install "dvc[s3]"`. (This example -installs `boto3` library along with DVC to support S3 storage.) +If you [installed DVC] via `pip` and plan to use cloud services as remote +storage, you might need to install these optional dependencies: `[s3]`, +`[azure]`, `[gdrive]`, `[gs]`, `[oss]`, `[ssh]`. Use `[all]` to include them +all. For example: + +```cli +$ pip install "dvc[s3]" +``` + +[installed dvc]: /doc/install From 3b5e520dc6615047ab8336d679bac2b57eb9197e Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Mon, 24 Oct 2022 01:18:00 -0400 Subject: [PATCH 25/76] guide: clarify `remote modify` phrase in in the Remote config section of DM/ Remote Storage --- content/docs/user-guide/data-management/remote-storage.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/docs/user-guide/data-management/remote-storage.md b/content/docs/user-guide/data-management/remote-storage.md index 08733c64ba..e06ce50518 100644 --- a/content/docs/user-guide/data-management/remote-storage.md +++ b/content/docs/user-guide/data-management/remote-storage.md @@ -40,8 +40,8 @@ file path), e.g.: $ dvc remote add mybucket s3://my-bucket ``` -Then, you may need or want to further configure the remote by authentication -information or other properties, etc. For example: +Then, you'll usually need or want to configure the remote's authentication +credentials or other properties, etc. For example: ```cli $ dvc remote modify --local \ From ff7e66683bcfbe7bef1b3bb2b51764d773bc4c11 Mon Sep 17 00:00:00 2001 From: Dave Berenbaum Date: Thu, 27 Oct 2022 09:27:02 -0400 Subject: [PATCH 26/76] Update content/docs/user-guide/data-management/data-versioning.md --- content/docs/user-guide/data-management/data-versioning.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/docs/user-guide/data-management/data-versioning.md b/content/docs/user-guide/data-management/data-versioning.md index ea041696f7..353c5b0ae9 100644 --- a/content/docs/user-guide/data-management/data-versioning.md +++ b/content/docs/user-guide/data-management/data-versioning.md @@ -59,7 +59,7 @@ corresponding data updates. Most notably, if you [set up remote storage] and `dvc push` data for others to `dvc pull` later, you should `git commit` both the metafile(s) and `.dvc/config` to the repo. -Advanced situations where this is may also be necessary: +Advanced situations where this may also be necessary: - When migrating to a [shared cache] - If you enable `dvc cache hydra` and then create an [experiment queue] with From c0026fc087e39975a51679b3bc339b6fce65c8a4 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Thu, 27 Oct 2022 14:54:49 -0400 Subject: [PATCH 27/76] guide: update versioning config per https://github.com/iterative/dvc.org/pull/4058#pullrequestreview-1158285601 --- content/docs/user-guide/data-management/data-versioning.md | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/content/docs/user-guide/data-management/data-versioning.md b/content/docs/user-guide/data-management/data-versioning.md index 353c5b0ae9..88534dc6fd 100644 --- a/content/docs/user-guide/data-management/data-versioning.md +++ b/content/docs/user-guide/data-management/data-versioning.md @@ -62,12 +62,9 @@ metafile(s) and `.dvc/config` to the repo. Advanced situations where this may also be necessary: - When migrating to a [shared cache] -- If you enable `dvc cache hydra` and then create an [experiment queue] with - [param composition] to run from another environment +- If you change a `dvc config parsing` option, which impact how `dvc.yaml` files + get parsed. [set up remote storage]: /doc/user-guide/data-management/remote-storage#configuration [shared cache]: doc/user-guide/how-to/share-a-dvc-cache -[experiment queue]: - /doc/user-guide/experiment-management/running-experiments#the-experiments-queue -[param composition]: /doc/user-guide/experiment-management/hydra-composition From 71b599cf314dbe9fabb5b5fadb6ef79576fa7d5b Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Thu, 27 Oct 2022 14:06:24 -0500 Subject: [PATCH 28/76] guide: don't call remote storage "additional" here (in the DM/ Remote Storage guide) per https://github.com/iterative/dvc.org/pull/4058#pullrequestreview-1158288895 Co-authored-by: Dave Berenbaum --- .../docs/user-guide/data-management/remote-storage.md | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/content/docs/user-guide/data-management/remote-storage.md b/content/docs/user-guide/data-management/remote-storage.md index e06ce50518..c7a49c0e1a 100644 --- a/content/docs/user-guide/data-management/remote-storage.md +++ b/content/docs/user-guide/data-management/remote-storage.md @@ -1,9 +1,8 @@ # Remote Storage (Optional) -_DVC remotes_ provide additional storage for your data or ML models and allow -sharing them. For example, you can pull data artifacts created by colleagues -without spending time and resources to recreate them locally. See `dvc push` and -`dvc pull`. +_DVC remotes_ provide storage to backup and share your data or ML models. For +example, you can pull data artifacts created by colleagues without spending time +and resources to recreate them locally. See `dvc push` and `dvc pull`. This is similar to how GitHub provides hosting for source code repositories (Git repos). However, DVC does not provide or recommend a specific storage service. @@ -22,7 +21,7 @@ The main uses of remote storage are: - Synchronize cached data assets (pull and push). - Share data in a distributed way to better collaborate. -- Back up your data and its different versions. +- Back up different versions of your data and models. - Save space in your working environment (by deleting data that has been pushed). From 9774855f8ff55367d5a0b5d8b0fa464df6ead89c Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Thu, 27 Oct 2022 15:15:22 -0400 Subject: [PATCH 29/76] guide: pull -> download (DM/ RS intro) --- content/docs/user-guide/data-management/remote-storage.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/docs/user-guide/data-management/remote-storage.md b/content/docs/user-guide/data-management/remote-storage.md index c7a49c0e1a..71a709da5f 100644 --- a/content/docs/user-guide/data-management/remote-storage.md +++ b/content/docs/user-guide/data-management/remote-storage.md @@ -1,8 +1,8 @@ # Remote Storage (Optional) _DVC remotes_ provide storage to backup and share your data or ML models. For -example, you can pull data artifacts created by colleagues without spending time -and resources to recreate them locally. See `dvc push` and `dvc pull`. +example, you can download data artifacts created by colleagues without spending +time and resources to recreate them locally. See `dvc push` and `dvc pull`. This is similar to how GitHub provides hosting for source code repositories (Git repos). However, DVC does not provide or recommend a specific storage service. From e5c6f13494f0673d956f019196143d1403624282 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Thu, 27 Oct 2022 15:38:46 -0400 Subject: [PATCH 30/76] guide: remove "optional" from Remote Storage nav & title per https://github.com/iterative/dvc.org/pull/4058#pullrequestreview-1158301379 --- content/docs/sidebar.json | 5 +---- .../docs/user-guide/data-management/remote-storage.md | 9 +++++---- 2 files changed, 6 insertions(+), 8 deletions(-) diff --git a/content/docs/sidebar.json b/content/docs/sidebar.json index 25c84f66b7..2fd68a977d 100644 --- a/content/docs/sidebar.json +++ b/content/docs/sidebar.json @@ -125,10 +125,7 @@ "source": "data-management/index.md", "children": [ "data-versioning", - { - "label": "Remote Storage (Optional)", - "slug": "remote-storage" - }, + "remote-storage", "large-dataset-optimization", "importing-external-data", "managing-external-data" diff --git a/content/docs/user-guide/data-management/remote-storage.md b/content/docs/user-guide/data-management/remote-storage.md index 71a709da5f..d217bdc4f6 100644 --- a/content/docs/user-guide/data-management/remote-storage.md +++ b/content/docs/user-guide/data-management/remote-storage.md @@ -1,8 +1,9 @@ -# Remote Storage (Optional) +# Remote Storage -_DVC remotes_ provide storage to backup and share your data or ML models. For -example, you can download data artifacts created by colleagues without spending -time and resources to recreate them locally. See `dvc push` and `dvc pull`. +_DVC remotes_ provide storage to backup and share your data or ML models +(optional). For example, you can download data artifacts created by colleagues +without spending time and resources to recreate them locally. See `dvc push` and +`dvc pull`. This is similar to how GitHub provides hosting for source code repositories (Git repos). However, DVC does not provide or recommend a specific storage service. From 2f31bb64e39173fb546fa4eae562a1f1284b6892 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Fri, 28 Oct 2022 17:40:54 -0400 Subject: [PATCH 31/76] guide: splits and notes around Data Mgmt index page rel. https://github.com/iterative/dvc.org/pull/4042#discussion_r1008451492 --- .../docs/user-guide/data-management/index.md | 38 +++++++++++++++---- 1 file changed, 30 insertions(+), 8 deletions(-) diff --git a/content/docs/user-guide/data-management/index.md b/content/docs/user-guide/data-management/index.md index e933329426..a1fe2f96ab 100644 --- a/content/docs/user-guide/data-management/index.md +++ b/content/docs/user-guide/data-management/index.md @@ -1,9 +1,31 @@ # Data Management with DVC -DVC helps you manage and share arbitrarily large files, datasets, and ML models -anywhere: cloud storage, SSH servers, network resources (e.g. NAS), mounted -drives, local file systems, etc. You manipulate DVC project normally in your -local workspace; DVC tracks, restores, and synchronizes them across locations. + + +Managing datasets and ML models tends to be a manual and different process for +each team and project. + + + +With DVC, you manipulate the project files normally in your local workspace; DVC +tracks, restores, and synchronizes them across locations. + +## How it works + + + +DVC helps you manage and share arbitrarily large files anywhere: cloud storage, +SSH servers, network resources (e.g. NAS), mounted drives, local file systems, +etc. To do so, several storage locations can be defined. + + ![Storage locations](/img/storage-locations.png) _Local, external, and remote storage locations_ @@ -25,10 +47,10 @@ data regularly. [share it]: /doc/user-guide/how-to/share-a-dvc-cache -Optionally, DVC supports additional storage locations such as cloud services -(Amazon S3, Google Drive, Azure Blob Storage, etc.), SSH servers, -network-attached storage, etc. These are called [DVC remotes], and help you to -share or back up copies of your data assets. +DVC supports additional storage locations such as cloud services (Amazon S3, +Google Drive, Azure Blob Storage, etc.), SSH servers, network-attached storage, +etc. These are called [DVC remotes], and help you to share or back up copies of +your data assets. From a84c4425ba49a348ca0d66949c3d54756a82da4d Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Sat, 29 Oct 2022 15:17:47 -0400 Subject: [PATCH 32/76] guide: Data Mgmt intro + note updates --- .../docs/user-guide/data-management/index.md | 58 ++++++++++++++----- 1 file changed, 42 insertions(+), 16 deletions(-) diff --git a/content/docs/user-guide/data-management/index.md b/content/docs/user-guide/data-management/index.md index a1fe2f96ab..d964885d8a 100644 --- a/content/docs/user-guide/data-management/index.md +++ b/content/docs/user-guide/data-management/index.md @@ -1,29 +1,55 @@ # Data Management with DVC - +You work with data normally in a local workspace. DVC tracks, +restores, and synchronize everything with a few, straightforward commands +(similar to Git) that do not change regardless of the underlying file systems, +transfer protocols, etc. -Managing datasets and ML models tends to be a manual and different process for -each team and project. +![]() _Separating data from code_ - +To achieve this, DVC relies on data _codification_: replacing large files and +directories with small [metafiles] that describe the assets. Data files are +moved to a separate cache but kept virtually (linked) in the +workspace. This **separates your data from code** (including metafiles). -With DVC, you manipulate the project files normally in your local workspace; DVC -tracks, restores, and synchronizes them across locations. + + +This also allows you to [version] all project files with Git, a battle-tested +[SCM] tool. + + + +DVC operations stay the same because they work [indirectly], by going through +the metafiles and [configuration] of your project to find out where +and how to handle files. This is transparent to you as user, but it's important +to understand the mechanics in general. + +## Workflow and benefits + + + +... + + + +[metafiles]: /doc/user-guide/project-structure +[indirectly]: https://en.wikipedia.org/wiki/Indirection +[configuration]: /doc/command-reference/config +[version]: /doc/user-guide/data-management/data-versioning +[scm]: https://www.atlassian.com/git/tutorials/source-code-management -## How it works +## Storage locations -DVC helps you manage and share arbitrarily large files anywhere: cloud storage, -SSH servers, network resources (e.g. NAS), mounted drives, local file systems, -etc. To do so, several storage locations can be defined. +DVC can manage data anywhere: cloud storage, SSH servers, network resources +(e.g. NAS), mounted drives, local file systems, etc. These locations can be +separated into three groups. From ab55389aa763372a3d89cf22b62649d637e72bd0 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Sat, 29 Oct 2022 16:26:55 -0400 Subject: [PATCH 33/76] guide: draft of all contents + + remove comments --- .../docs/user-guide/data-management/index.md | 70 ++++++++++++------- 1 file changed, 44 insertions(+), 26 deletions(-) diff --git a/content/docs/user-guide/data-management/index.md b/content/docs/user-guide/data-management/index.md index d964885d8a..62f4f69e8d 100644 --- a/content/docs/user-guide/data-management/index.md +++ b/content/docs/user-guide/data-management/index.md @@ -6,53 +6,71 @@ permissions on cloud storage, sync tools and schedules, back up snapshots, etc. and focus on machine learning. You work with data normally in a local workspace. DVC tracks, -restores, and synchronize everything with a few, straightforward commands -(similar to Git) that do not change regardless of the underlying file systems, -transfer protocols, etc. +restores, and synchronize everything with a few, straightforward operations that +do not change regardless of the underlying file systems, transfer protocols, +etc. -![]() _Separating data from code_ +![]() _Separating data from code (codification)_ -To achieve this, DVC relies on data _codification_: replacing large files and -directories with small [metafiles] that describe the assets. Data files are -moved to a separate cache but kept virtually (linked) in the -workspace. This **separates your data from code** (including metafiles). +
- +## Click to learn more about data _codification_ -This also allows you to [version] all project files with Git, a battle-tested -[SCM] tool. +To achieve this, DVC replaces large files and directories with small [metafiles] +that describe the assets. Data files are moved to a separate cache +but kept virtually (linked) in the workspace. This separates your data from code +(including metafiles). - + -DVC operations stay the same because they work [indirectly], by going through -the metafiles and [configuration] of your project to find out where -and how to handle files. This is transparent to you as user, but it's important -to understand the mechanics in general. +This also allows you to [version] project files with Git, a battle-tested [SCM] +tool. -## Workflow and benefits + - +[version]: /doc/user-guide/data-management/data-versioning +[scm]: https://www.atlassian.com/git/tutorials/source-code-management -... +
- +Your experience can stay consistent because DVC works [indirectly], by checking +the [metafiles] and [configuration] of your project to find out +where and how to handle files. This is transparent to you as user, but it's +important to understand the mechanics in general. [metafiles]: /doc/user-guide/project-structure [indirectly]: https://en.wikipedia.org/wiki/Indirection [configuration]: /doc/command-reference/config -[version]: /doc/user-guide/data-management/data-versioning -[scm]: https://www.atlassian.com/git/tutorials/source-code-management -## Storage locations +## Workflow and benefits + +**Before**: Files are scattered in the cloud; You use low-level operations +specific to each storage (e.g. `aws s3 cp`); Ad hoc file names are used to save +versions; It's easy to lose track of which data produced which results; Everyone +can read and write. + +**After**: Stored objects are organized by DVC and you don't need to touch them +directly; DVC exposes a few commands to manage them; Everything is happening +though a code repository that can be controlled with Git; Project versions (Git +commits) guarantee reproducibility of ML processes (e.g. training models with +the same datasets, hyperparametes, features, etc.). + + - +**Benefits**: You always work with project-specific paths; Efficient usage of +storage space (file deduplication); Small repository; [Data versioning]; [Fast +caching], [GitOps]. + +[data versioning]: /doc/use-cases/versioning-data-and-models +[fast caching]: /doc/use-cases/fast-data-caching-hub +[gitops]: https://www.gitops.tech/ + +## Storage locations DVC can manage data anywhere: cloud storage, SSH servers, network resources (e.g. NAS), mounted drives, local file systems, etc. These locations can be separated into three groups. - - ![Storage locations](/img/storage-locations.png) _Local, external, and remote storage locations_ From 601c99e71731d876c9ee338a790c7cd045eae472 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Wed, 2 Nov 2022 16:35:14 -0600 Subject: [PATCH 34/76] guide: small impros to Data Mgmt in prep for https://github.com/iterative/dvc.org/pull/4042#pullrequestreview-1164460714 --- .../docs/user-guide/data-management/index.md | 27 ++++++++++--------- 1 file changed, 15 insertions(+), 12 deletions(-) diff --git a/content/docs/user-guide/data-management/index.md b/content/docs/user-guide/data-management/index.md index 62f4f69e8d..a8a7ec2ce0 100644 --- a/content/docs/user-guide/data-management/index.md +++ b/content/docs/user-guide/data-management/index.md @@ -16,9 +16,9 @@ etc. ## Click to learn more about data _codification_ -To achieve this, DVC replaces large files and directories with small [metafiles] -that describe the assets. Data files are moved to a separate cache -but kept virtually (linked) in the workspace. This separates your data from code +DVC replaces large files and directories with small [metafiles] that describe +the assets. Data files are moved to a separate cache but kept +virtually (linked) in the workspace. This separates your data from code (including metafiles). @@ -44,16 +44,17 @@ important to understand the mechanics in general. ## Workflow and benefits -**Before**: Files are scattered in the cloud; You use low-level operations -specific to each storage (e.g. `aws s3 cp`); Ad hoc file names are used to save -versions; It's easy to lose track of which data produced which results; Everyone -can read and write. +**Before**: Files are scattered in the cloud; You use have to access each +storage platform directly (e.g. `aws s3 cp`, Python libraries, etc.) and know +the final URI of the assets; Ad hoc file names are used to save versions; It's +easy to lose track of which data produced which results; Everyone can read and +write. -**After**: Stored objects are organized by DVC and you don't need to touch them -directly; DVC exposes a few commands to manage them; Everything is happening -though a code repository that can be controlled with Git; Project versions (Git -commits) guarantee reproducibility of ML processes (e.g. training models with -the same datasets, hyperparametes, features, etc.). +**After**: Stored objects are organized by DVC (not intended for handling +manually); Everything is happening though a code repository that can be +controlled with Git; Project versions (Git commits) guarantee reproducibility of +ML processes (e.g. training models with the same datasets, hyperparametes, +features, etc.). @@ -61,6 +62,8 @@ the same datasets, hyperparametes, features, etc.). storage space (file deduplication); Small repository; [Data versioning]; [Fast caching], [GitOps]. + + [data versioning]: /doc/use-cases/versioning-data-and-models [fast caching]: /doc/use-cases/fast-data-caching-hub [gitops]: https://www.gitops.tech/ From a8bad84873fba218e41b60e09428b707e494a271 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Wed, 2 Nov 2022 23:40:44 -0600 Subject: [PATCH 35/76] guide: rewrite Data Mgmt index in before/after form per https://github.com/iterative/dvc.org/pull/4042#pullrequestreview-1164460714 --- .../data-management/data-versioning.md | 3 +- .../docs/user-guide/data-management/index.md | 118 ++++++++++-------- 2 files changed, 68 insertions(+), 53 deletions(-) diff --git a/content/docs/user-guide/data-management/data-versioning.md b/content/docs/user-guide/data-management/data-versioning.md index c8730a0009..0d7cd720bc 100644 --- a/content/docs/user-guide/data-management/data-versioning.md +++ b/content/docs/user-guide/data-management/data-versioning.md @@ -32,8 +32,7 @@ Refer to [Versioning Data and Models] to learn more. [git history]: https://git-scm.com/book/en/v2/Git-Basics-Viewing-the-Commit-History - +1. Everything happens though a code repository that can be controlled with Git. + +[data versions]: /doc/use-cases/versioning-data-and-models +[reorganized]: + /doc/user-guide/project-structure/internal-files#structure-of-the-cache-directory + +## Benefits + +Besides the basic solutions outlined before, there are several more benefits to +DVC's approach: + +- You work with data in a local workspace, as with any other files; + You deal with project-specific file paths and forget about complicated [URIs]. +- DVC tracks, restores, and synchronize everything with a few straightforward + operations that do not change regardless of file systems, transfer protocols, + etc. +- Standard [project versions] (Git commits) guarantee reproducibility of ML + processes (e.g. training models with the same datasets, hyperparametes, and + features). +- Your storage space is [used efficiently] (file deduplication); Your project + repo stays small. +- [Fast caching], [data registries], [model registries], [CI/CD for ML], and + more! + +[uris]: https://en.wikipedia.org/wiki/Uniform_Resource_Identifier +[project versions]: /doc/user-guide/data-management/data-versioning +[used efficiently]: /doc/user-guide/data-management/large-dataset-optimization +[fast caching]: /doc/use-cases/fast-data-caching-hub +[data registries]: /doc/use-cases/data-registry +[model registries]: /doc/use-cases/model-registry +[ci/cd for ml]: https://cml.dev/ -## Click to learn more about data _codification_ +## Separating data from code (codification) DVC replaces large files and directories with small [metafiles] that describe -the assets. Data files are moved to a separate cache but kept -virtually (linked) in the workspace. This separates your data from code -(including metafiles). +the assets. We call this _data codification_. Data files are moved to a separate +cache but kept virtually (linked) in the workspace. This separates +your data from code (including metafiles). -This also allows you to [version] project files with Git, a battle-tested [SCM] +This also allows you to version project files with Git, a battle-tested [SCM] tool. - - -[version]: /doc/user-guide/data-management/data-versioning [scm]: https://www.atlassian.com/git/tutorials/source-code-management - + Your experience can stay consistent because DVC works [indirectly], by checking -the [metafiles] and [configuration] of your project to find out -where and how to handle files. This is transparent to you as user, but it's -important to understand the mechanics in general. +the metafiles and [configuration] of your project to find out where +and how to handle files. This is transparent to you as user, but it's important +to understand the mechanics in general. [metafiles]: /doc/user-guide/project-structure [indirectly]: https://en.wikipedia.org/wiki/Indirection [configuration]: /doc/command-reference/config -## Workflow and benefits - -**Before**: Files are scattered in the cloud; You use have to access each -storage platform directly (e.g. `aws s3 cp`, Python libraries, etc.) and know -the final URI of the assets; Ad hoc file names are used to save versions; It's -easy to lose track of which data produced which results; Everyone can read and -write. - -**After**: Stored objects are organized by DVC (not intended for handling -manually); Everything is happening though a code repository that can be -controlled with Git; Project versions (Git commits) guarantee reproducibility of -ML processes (e.g. training models with the same datasets, hyperparametes, -features, etc.). - - - -**Benefits**: You always work with project-specific paths; Efficient usage of -storage space (file deduplication); Small repository; [Data versioning]; [Fast -caching], [GitOps]. - - - -[data versioning]: /doc/use-cases/versioning-data-and-models -[fast caching]: /doc/use-cases/fast-data-caching-hub -[gitops]: https://www.gitops.tech/ - ## Storage locations DVC can manage data anywhere: cloud storage, SSH servers, network resources From c8cc17b6c12b91da36bae57aecb0e55a63151d1d Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Thu, 3 Nov 2022 19:13:38 -0600 Subject: [PATCH 36/76] guide: add draft figure for Data Mgmt --- .../docs/user-guide/data-management/index.md | 4 ++++ static/img/before-after.png | Bin 0 -> 174082 bytes 2 files changed, 4 insertions(+) create mode 100644 static/img/before-after.png diff --git a/content/docs/user-guide/data-management/index.md b/content/docs/user-guide/data-management/index.md index c81795de76..cfa7581584 100644 --- a/content/docs/user-guide/data-management/index.md +++ b/content/docs/user-guide/data-management/index.md @@ -17,6 +17,8 @@ problems include: - It's easy to lose track of which data produced what results, hindering reproducibility. +![Before and after DVC](/img/before-after.png) _DVC simplifies your project._ + To address these pain points and help you focus on machine learning work, DVC introduces a layer of _indirection_: raw data, models, and other artifacts are [separated](#separating-data-from-code-codification) from your project's code. @@ -66,6 +68,8 @@ the assets. 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deletions(-) diff --git a/content/docs/user-guide/data-management/index.md b/content/docs/user-guide/data-management/index.md index cfa7581584..bf6fe04eb0 100644 --- a/content/docs/user-guide/data-management/index.md +++ b/content/docs/user-guide/data-management/index.md @@ -1,59 +1,42 @@ # Data Management with DVC -DVC fundamentally changes the way you work with datasets and ML models. Let's -look at how this workflow evolves, and why it's important. - -The mechanisms to store and transfer large files and directories directly can -vary wildly. For example, you may need to use more than one storage platform, -ending up with data scattered in the cloud and on-prem locations. Other common -problems include: - -- You have to access each location with specific tools (AWS CLI, code libraries, - SCP, etc.) -- This requires knowing the final URL or path of every asset (often hardcoded). -- Controlling who can read and write at folder or file level is difficult. -- Tracking [data versions] typically involves ad hoc file naming schemes that - don't scale. -- It's easy to lose track of which data produced what results, hindering - reproducibility. - -![Before and after DVC](/img/before-after.png) _DVC simplifies your project._ - -To address these pain points and help you focus on machine learning work, DVC -introduces a layer of _indirection_: raw data, models, and other artifacts are -[separated](#separating-data-from-code-codification) from your project's code. -This has a number of implications, but it's a small cost to improve your -productivity: +DVC changes the way you work with datasets and ML models in order to enable data +[versioning] and reproducibility, among other +[benefits](#benefits-and-implications). Let's look at how the workflow evolves. -1. You have to set up a DVC project and its - [storage locations](#storage-locations). -1. Stored objects are [reorganized] by DVC (not intended for manual handling). - -1. Everything happens though a code repository that can be controlled with Git. +![Before and after DVC](/img/before-after.png) _DVC codifies data access._ -[data versions]: /doc/use-cases/versioning-data-and-models -[reorganized]: - /doc/user-guide/project-structure/internal-files#structure-of-the-cache-directory +Traditionally, you would access storage platforms directly, with dedicated tools +such as AWS CLI, code libraries, SCP, etc. This requires knowing the final URL +of every asset. It also means that having data scattered across the cloud or +on-prem locations complicates your workflow. -## Benefits +DVC introduces a layer of [indirection]: raw data, models, and other artifacts +are _codified_ in small [metafiles] that describe their unique characteristics. +Storage locations (and other aspects of your project setup) are also saved to +human-readable config files. You can control all these with Git along with the +rest of your project's code and configuration files. -Besides the basic solutions outlined before, there are several more benefits to -DVC's approach: +[versioning]: /doc/use-cases/versioning-data-and-models +[indirection]: https://en.wikipedia.org/wiki/Indirection +[metafiles]: /doc/user-guide/project-structure + +## Benefits and implications + +DVC's approach lets you focus on machine learning and includes other advantages: -- You work with data in a local workspace, as with any other files; - You deal with project-specific file paths and forget about complicated [URIs]. -- DVC tracks, restores, and synchronize everything with a few straightforward - operations that do not change regardless of file systems, transfer protocols, - etc. - Standard [project versions] (Git commits) guarantee reproducibility of ML processes (e.g. training models with the same datasets, hyperparametes, and features). +- You work with data in a local workspace, as with any other files; + DVC tracks, restores, and synchronize everything with a few operations that do + not change per storage system. - Your storage space is [used efficiently] (file deduplication); Your project repo stays small. +- Controlling who can read and write at folder or file level is easier. - [Fast caching], [data registries], [model registries], [CI/CD for ML], and more! -[uris]: https://en.wikipedia.org/wiki/Uniform_Resource_Identifier [project versions]: /doc/user-guide/data-management/data-versioning [used efficiently]: /doc/user-guide/data-management/large-dataset-optimization [fast caching]: /doc/use-cases/fast-data-caching-hub @@ -61,6 +44,18 @@ DVC's approach: [model registries]: /doc/use-cases/model-registry [ci/cd for ml]: https://cml.dev/ +This implies getting used to a new workflow. But it's a small cost to improve +productivity: + +1. You have to set up a DVC project and its storage locations. + +1. Stored objects are [reorganized] by DVC (not intended for manual handling). +1. Everything happens though a code repository that can be controlled with Git. + +[reorganized]: + /doc/user-guide/project-structure/internal-files#structure-of-the-cache-directory + + + From c29d9ec28d7ec2aad1ac76293df8ffd6264715cc Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Thu, 17 Nov 2022 14:14:02 -0600 Subject: [PATCH 38/76] work around commented header bug --- content/docs/user-guide/data-management/index.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/docs/user-guide/data-management/index.md b/content/docs/user-guide/data-management/index.md index bf6fe04eb0..6205cdf397 100644 --- a/content/docs/user-guide/data-management/index.md +++ b/content/docs/user-guide/data-management/index.md @@ -55,15 +55,14 @@ productivity: [reorganized]: /doc/user-guide/project-structure/internal-files#structure-of-the-cache-directory - -## Storage locations + diff --git a/static/img/versioned-project.png b/static/img/versioned-project.png deleted file 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40/76] guide: rewrite DM intro and - hide benefits (for now) - remove codification comment block --- .../docs/user-guide/data-management/index.md | 67 ++++++------------- 1 file changed, 22 insertions(+), 45 deletions(-) diff --git a/content/docs/user-guide/data-management/index.md b/content/docs/user-guide/data-management/index.md index 6205cdf397..e2c46dfffc 100644 --- a/content/docs/user-guide/data-management/index.md +++ b/content/docs/user-guide/data-management/index.md @@ -1,15 +1,18 @@ # Data Management with DVC -DVC changes the way you work with datasets and ML models in order to enable data -[versioning] and reproducibility, among other -[benefits](#benefits-and-implications). Let's look at how the workflow evolves. +Recognizing the challenges of managing data in real-life machine learning, DVC +establishes a new standard for working with file-based datasets and ML models. +This approach lets you focus on more important tasks like data exploration, +validation, preparation, etc. You'll just have to get used to an evolved +workflow and adopt basic [version control] principles. -![Before and after DVC](/img/before-after.png) _DVC codifies data access._ +![]() _A better way to manage data_ -Traditionally, you would access storage platforms directly, with dedicated tools -such as AWS CLI, code libraries, SCP, etc. This requires knowing the final URL -of every asset. It also means that having data scattered across the cloud or -on-prem locations complicates your workflow. +Traditionally, you would access storage platforms directly with dedicated tools +such as SSH, manually tracking the whereabouts (URL) of every asset. Repeated +files get scattered across clouds or on-prem (inefficient storage). Your project +directory gets bloated quickly, needing regular manual cleaning. Copying and +transferring files over and over takes time. DVC introduces a layer of [indirection]: raw data, models, and other artifacts are _codified_ in small [metafiles] that describe their unique characteristics. @@ -17,13 +20,13 @@ Storage locations (and other aspects of your project setup) are also saved to human-readable config files. You can control all these with Git along with the rest of your project's code and configuration files. -[versioning]: /doc/use-cases/versioning-data-and-models +[version control]: + https://www.atlassian.com/git/tutorials/what-is-version-control [indirection]: https://en.wikipedia.org/wiki/Indirection [metafiles]: /doc/user-guide/project-structure -## Benefits and implications - -DVC's approach lets you focus on machine learning and includes other advantages: + -This implies getting used to a new workflow. But it's a small cost to improve -productivity: +In order to get there, you'll have to do some preparation and keep a few things +in mind: -1. You have to set up a DVC project and its storage locations. +1. Set up a DVC project and its storage locations. 1. Stored objects are [reorganized] by DVC (not intended for manual handling). -1. Everything happens though a code repository that can be controlled with Git. +1. Everything happens though a code repository, typically controlled with Git. [reorganized]: /doc/user-guide/project-structure/internal-files#structure-of-the-cache-directory - - [version control]: https://www.atlassian.com/git/tutorials/what-is-version-control [indirection]: https://en.wikipedia.org/wiki/Indirection [metafiles]: /doc/user-guide/project-structure +To adopt this approach, you'll need some preparation and to keep a few things in +mind: + +1. Set up a DVC project and its storage locations. +1. Stored objects are [reorganized] by DVC (not intended for manual handling). +1. Everything happens through a code repository, typically controlled with Git. + +[reorganized]: + /doc/user-guide/project-structure/internal-files#structure-of-the-cache-directory + -In order to get there, you'll have to do some preparation and keep a few things -in mind: - -1. Set up a DVC project and its storage locations. - -1. Stored objects are [reorganized] by DVC (not intended for manual handling). -1. Everything happens though a code repository, typically controlled with Git. - -[reorganized]: - /doc/user-guide/project-structure/internal-files#structure-of-the-cache-directory - - -[version control]: - https://www.atlassian.com/git/tutorials/what-is-version-control [indirection]: https://en.wikipedia.org/wiki/Indirection [metafiles]: /doc/user-guide/project-structure +[with git]: https://git-scm.com/book/en/v2/Getting-Started-About-Version-Control -To adopt this approach, you'll need some preparation and to keep a few things in -mind: - -1. Set up a DVC project and its storage locations. +1. Start by setting up up a DVC project and its storage locations. 1. Stored objects are [reorganized] by DVC (not intended for manual handling). -1. Everything happens through a code repository, typically controlled with Git. +1. Data operations happen through a code repository (typically on Git). [reorganized]: /doc/user-guide/project-structure/internal-files#structure-of-the-cache-directory - + +[properly versioned]: /doc/use-cases/versioning-data-and-models +[reproducibility]: /doc/user-guide/pipelines +[cli]: /doc/command-reference +[api]: /doc/api-reference +[ide]: /doc/vs-code-extension +[web]: /doc/studio ![DVC data access](/img/dvc-data-access.png) _Standard approach with DVC_ -To achieve this, DVC introduces a layer of [indirection] where large files and -directories get _codified_ into small [metafiles] that describe them. Storage -locations are also saved to human-readable [config files]. You can capture this -[with Git] along with the rest of your project's code, while data storage is -separated automatically. Key workflow changes to keep in mind: +To get there, DVC introduces a layer of [indirection] where large files and +directories get _codified_ into small [metafiles], and storage locations defined +in [config files]. You can capture all these [with Git] along with the rest of +your project's code, while data storage is separated automatically. + + + +Key workflow changes to keep in mind: 1. You must set up a DVC project and its storage locations. 1. Stored objects are [reorganized] by DVC (not intended for manual handling). @@ -45,9 +57,9 @@ separated automatically. Key workflow changes to keep in mind: - Work in a small and clean workspace linked to one or more storage locations. DVC tracks, restores, and synchronizes everything with a few operations that do not change. -- Content-addressable caching maximizes storage efficiency and +- Content-addressable caching maximizes [storage efficiency] and transfer speed, enabling near-instantaneous switching among datasets. -- [Data versioning] with Git on top of your existing storage solutions (SSH, S3, +- Data versioning with Git on top of your existing storage solutions (SSH, S3, [etc.]) - Immutable project history with an append-only storage and authors (auditability) @@ -55,14 +67,6 @@ separated automatically. Key workflow changes to keep in mind: registries], data security, [CI/CD for ML], [productization], and other advanced scenarios are unlocked. -[project versions]: /doc/user-guide/data-management/data-versioning -[fast caching]: /doc/use-cases/fast-data-caching-hub -[data registries]: /doc/use-cases/data-registry -[etc.]: /doc/command-reference/remote/add#supported-storage-types -[model registries]: /doc/use-cases/model-registry -[ci/cd for ml]: https://cml.dev/ -[productization]: https://mlem.ai/ - +[project versions]: /doc/user-guide/data-management/data-versioning +[fast caching]: /doc/use-cases/fast-data-caching-hub +[data registries]: /doc/use-cases/data-registry +[storage efficiency]: /doc/user-guide/data-management/large-dataset-optimization +[etc.]: /doc/command-reference/remote/add#supported-storage-types +[model registries]: /doc/use-cases/model-registry +[ci/cd for ml]: https://cml.dev/ +[productization]: https://mlem.ai/ + + + -[properly versioned]: /doc/use-cases/versioning-data-and-models -[reproducibility]: /doc/user-guide/pipelines -[cli]: /doc/command-reference -[api]: /doc/api-reference -[ide]: /doc/vs-code-extension -[web]: /doc/studio +Where and how to store data and model files is one of the first decisions your +team will have to make. Typically, files end up scattered throughout multiple +buckets. Everyone can read and write which is not secure. Duplicated content is +hard to find, so outdated datasets overlap with the latest version. You start +formatting folder names with dates, but others still can't reproduce your +results... -![DVC data access](/img/dvc_managed_storage.png) _Simplified approach with DVC_ +![Direct storage access](/img/manual_data_management.png) _Managing data +manually does not scale and quickly becomes a consuming job._ -To get there, DVC introduces a layer of [indirection] where large files and -directories get _codified_ into small [metafiles], and storage locations defined -in [config files]. You can capture all these [with Git] along with the rest of -your project's code, while data storage is separated automatically. +DVC gives you a structure that lets you take control of your existing [storage +platforms]. It provides visibility over all your data and helps secure their +access. - +![DVC data access](/img/dvc_managed_storage.png) _DVC's approach_ + +Your storage is managed from an immutable repository, implementing +append-only data stores. Content changes can be [properly versioned], +guaranteeing the [reproducibility] of any state of your project. DVC tracks +where the underlying files reside, avoiding duplicates along the way. + +[storage platforms]: /doc/command-reference/remote/add#supported-storage-types +[properly versioned]: /doc/use-cases/versioning-data-and-models +[reproducibility]: /doc/user-guide/pipelines Key workflow changes to keep in mind: 1. You must set up a DVC project and its storage locations. 1. Stored objects are [reorganized] by DVC (not intended for manual handling). -1. Data operations happen through a code repository (typically on Git). +1. Data operations happen indirectly, through a code repository (typically on + Git). -[indirection]: https://en.wikipedia.org/wiki/Indirection -[metafiles]: /doc/user-guide/project-structure -[config files]: /doc/user-guide/project-structure/internal-files -[with git]: https://git-scm.com/book/en/v2/Getting-Started-About-Version-Control [reorganized]: /doc/user-guide/project-structure/internal-files#structure-of-the-cache-directory +[indirectly]: https://en.wikipedia.org/wiki/Indirection -## Benefits +More benefits: - Work in a small and clean workspace linked to one or more storage - locations. DVC tracks, restores, and synchronizes everything with a few + locations. +- Automatic caching decouples your project code and configuration + from storage and maximizes [space efficiency]. +- DVC backs up, restores, and [synchronizes] everything with a few fast operations that do not change. -- Content-addressable caching maximizes [storage efficiency] and - transfer speed, enabling near-instantaneous switching among datasets. -- Data versioning with Git on top of your existing storage solutions (SSH, S3, - [etc.]) -- Immutable project history with an append-only storage and authors - (auditability) -- [Data registries], distributed collaboration, [data pipelines], [model - registries], data security, [CI/CD for ML], [productization], and other - advanced scenarios are unlocked. - - - -[project versions]: /doc/user-guide/data-management/data-versioning -[fast caching]: /doc/use-cases/fast-data-caching-hub +- You get an authored project history that ban be traced and audited. +- Use a consistent interface to access data anywhere (via [CLI], [API], [IDE], + or [web]). +- Enable advanced features such as [data registries], [ML pipelines], [CI/CD for + ML], [productization], and more! + +[space efficiency]: /doc/user-guide/data-management/large-dataset-optimization +[synchronizes]: /doc/command-reference/remote +[cli]: /doc/command-reference +[api]: /doc/api-reference +[ide]: /doc/vs-code-extension +[web]: /doc/studio [data registries]: /doc/use-cases/data-registry -[storage efficiency]: /doc/user-guide/data-management/large-dataset-optimization -[etc.]: /doc/command-reference/remote/add#supported-storage-types -[model registries]: /doc/use-cases/model-registry +[ml pipelines]: /doc/user-guide/pipelines [ci/cd for ml]: https://cml.dev/ [productization]: https://mlem.ai/ - - - diff --git a/content/docs/user-guide/data-management/storage-locations.md b/content/docs/user-guide/data-management/storage-locations.md new file mode 100644 index 0000000000..9f780e7813 --- /dev/null +++ b/content/docs/user-guide/data-management/storage-locations.md @@ -0,0 +1,38 @@ +# Storage locations + +DVC can manage data anywhere: cloud storage, SSH servers, network resources +(e.g. NAS), mounted drives, local file systems, etc. These locations can be put +into three groups. + +![Storage locations](/img/storage-locations.png) _Local, external, and remote +storage locations_ + +Every DVC project starts with 2 locations. The +workspace is the main project directory, containing your data, +models, source code, etc. DVC also creates a data cache (found +locally in `.dvc/cache` by default), which will be used as fast-access storage +for DVC operations. + + + +The cache can be moved to an external location in the file system or network, +for example to [share it] among several projects. It could even be set up in a +remote system (Internet access), but this is typically too slow for working with +data regularly. + + + +[share it]: /doc/user-guide/how-to/share-a-dvc-cache + +DVC supports additional storage locations such as cloud services (Amazon S3, +Google Drive, Azure Blob Storage, etc.), SSH servers, network-attached storage, +etc. These are called [DVC remotes], and help you to share or back up copies of +your data assets. + + + +DVC remotes are similar to Git remotes, but for cached data. + + + +[dvc remotes]: /doc/command-reference/remote From 4b67b6481acbe8debb397022856654a399d17ea7 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Wed, 14 Dec 2022 15:16:57 -0800 Subject: [PATCH 50/76] guide: update Data Mgmt figs --- .../docs/user-guide/data-management/index.md | 22 +++++++++--------- static/img/dvc_managed_storage.png | Bin 182791 -> 317868 bytes static/img/manual_data_management.png | Bin 269811 -> 176431 bytes 3 files changed, 11 insertions(+), 11 deletions(-) diff --git a/content/docs/user-guide/data-management/index.md b/content/docs/user-guide/data-management/index.md index 675609caa4..36425057db 100644 --- a/content/docs/user-guide/data-management/index.md +++ b/content/docs/user-guide/data-management/index.md @@ -5,20 +5,20 @@ --> Where and how to store data and model files is one of the first decisions your -team will have to make. Typically, files end up scattered throughout multiple -buckets. Everyone can read and write which is not secure. Duplicated content is -hard to find, so outdated datasets overlap with the latest version. You start -formatting folder names with dates, but others still can't reproduce your -results... +team will have to make. But as the project needs grow and change, files may end +up scattered throughout multiple buckets. Everyone can read and write, which is +error-prone and not secure. Duplicated content is hard to prevent, so outdated +datasets overlap with the latest version. You start formatting folder names with +dates, but others still can't reproduce your results... -![Direct storage access](/img/manual_data_management.png) _Managing data -manually does not scale and quickly becomes a consuming job._ +![Direct storage access](/img/manual_data_management.png) _Managing code, data, +and models manually does not scale and quickly becomes a consuming job._ -DVC gives you a structure that lets you take control of your existing [storage -platforms]. It provides visibility over all your data and helps secure their -access. +DVC provides a structure that lets you take control of your existing [storage +platforms]. It gives visibility over all your data and helps secure its access. -![DVC data access](/img/dvc_managed_storage.png) _DVC's approach_ +![DVC data access](/img/dvc_managed_storage.png) _DVC centralizes file and +directory management for you._ Your storage is managed from an immutable repository, implementing append-only data stores. 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9eb7143e758a4cb40f58893df10b6e57e77c8cac Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Sat, 17 Dec 2022 16:22:02 -0800 Subject: [PATCH 51/76] guide: more supporting info about Data Mgmt --- .../docs/user-guide/data-management/index.md | 125 ++++++++++++------ 1 file changed, 83 insertions(+), 42 deletions(-) diff --git a/content/docs/user-guide/data-management/index.md b/content/docs/user-guide/data-management/index.md index 36425057db..4d1cc9a1cb 100644 --- a/content/docs/user-guide/data-management/index.md +++ b/content/docs/user-guide/data-management/index.md @@ -4,12 +4,12 @@ ## Data Management for Machine Learning --> -Where and how to store data and model files is one of the first decisions your -team will have to make. But as the project needs grow and change, files may end -up scattered throughout multiple buckets. Everyone can read and write, which is -error-prone and not secure. Duplicated content is hard to prevent, so outdated -datasets overlap with the latest version. You start formatting folder names with -dates, but others still can't reproduce your results... +Where and how to store data and ML model files is one of the first decisions +your team will have to make. But as the project grows, files may end up +scattered throughout multiple buckets. Everyone can read and write (error-prone +and not secure). Duplicated content is hard to prevent, causing data leakage and +outdated datasets that overlap with the latest version. You start formatting +folder names with dates, but others still can't reproduce your results... ![Direct storage access](/img/manual_data_management.png) _Managing code, data, and models manually does not scale and quickly becomes a consuming job._ @@ -20,27 +20,87 @@ platforms]. It gives visibility over all your data and helps secure its access. ![DVC data access](/img/dvc_managed_storage.png) _DVC centralizes file and directory management for you._ -Your storage is managed from an immutable repository, implementing -append-only data stores. Content changes can be [properly versioned], -guaranteeing the [reproducibility] of any state of your project. DVC tracks -where the underlying files reside, avoiding duplicates along the way. +Your storage is managed from a Git repository, so you can go back to any +previous state of a project and the data will match the code and +parameters you used at that time. In other words, [data versioning] guarantees +[reproducibility]. DVC tracks where the underlying files reside, avoiding +duplicates along the way. This requires a few key changes to your workflow: + +1. Data and models must be registered in a code repository (typically on Git). +1. Stored objects are [reorganized] by DVC (not intended for manual handling). +1. Data operations (drop, update, transfer, etc.) happen indirectly -- through + the repo. [storage platforms]: /doc/command-reference/remote/add#supported-storage-types -[properly versioned]: /doc/use-cases/versioning-data-and-models +[data versioning]: /doc/use-cases/versioning-data-and-models [reproducibility]: /doc/user-guide/pipelines +[reorganized]: + /doc/user-guide/project-structure/internal-files#structure-of-the-cache-directory -Key workflow changes to keep in mind: +## How it works -1. You must set up a DVC project and its storage locations. -1. Stored objects are [reorganized] by DVC (not intended for manual handling). -1. Data operations happen indirectly, through a code repository (typically on - Git). +DVC repositories contain [metafiles] that act as pointers, +[linking] to your data files and directories. This keeps your project folder +small and comprised mainly of text-like code and configuration files you can +[version with Git] normally. DVC moves your assets (large files, binaries, etc.) +to a content-addressable cache, eliminating any duplicates. -[reorganized]: - /doc/user-guide/project-structure/internal-files#structure-of-the-cache-directory +[metafiles]: /doc/user-guide/project-structure +[linking]: /doc/user-guide/data-management/large-dataset-optimization +[register]: /doc/command-reference/add +[generate]: /doc/command-reference/repro +[version with git]: + https://git-scm.com/book/en/v2/Getting-Started-About-Version-Control + + + +
+ +### Click for deeper learning: data _codification_ and _indirection_. + +These are two important concepts behind **data version control**. First, large +files and directories are replaced by [file links] and tiny code-like metafiles. +We can also call this "data as code". + +This DVC-generated code contains references to the underlying files in the cache +(which have been renamed). All data operations going forward happen +**[indirectly]**, reading/writing metafiles in the repo first (handled +automatically by `dvc` operations). + +[file links]: + https://towardsdatascience.com/reflinks-vs-symlinks-vs-hard-links-and-how-they-can-help-machine-learning-projects-b77b89cdbab1 [indirectly]: https://en.wikipedia.org/wiki/Indirection -More benefits: +
+ +Now that your data is tracked by DVC (and its code with Git) there's no need to +come up with special file names or directory structures to handle dataset +variations -- you can use [Git branching] instead. This keeps your project clean +and manageable at any scale. + +[git branching]: + https://git-scm.com/book/en/v2/Git-Branching-Basic-Branching-and-Merging + + + +The built-in cache included in every DVC project isolates your +project logic from storage needs. Files are renamed and [reorganized] based on +their contents (using simple hashes) to flatten any directory structure and +prevent duplication. + +The cache is the first storage layer for you and your team to share and +collaborate, but more can be defined in DVC [config files]. This is where you +set up one or more locations to back up and share data, features, ML models, +etc. Supported platforms include SSH, Amazon S3, Google Cloud Storage, Microsoft +Azure, among [many more]. + +[config files]: /doc/user-guide/project-structure/internal-files +[many more]: /doc/command-reference/remote/add#supported-storage-types + +## Why it's better + +DVC's approach has a low cost (indirect access, work with Git) and many +benefits: - Work in a small and clean workspace linked to one or more storage locations. @@ -48,7 +108,7 @@ More benefits: from storage and maximizes [space efficiency]. - DVC backs up, restores, and [synchronizes] everything with a few fast operations that do not change. -- You get an authored project history that ban be traced and audited. +- You get an authored history of data changes that can be traced and audited. - Use a consistent interface to access data anywhere (via [CLI], [API], [IDE], or [web]). - Enable advanced features such as [data registries], [ML pipelines], [CI/CD for @@ -65,25 +125,6 @@ More benefits: [ci/cd for ml]: https://cml.dev/ [productization]: https://mlem.ai/ - +In summary, DVC establishes a standard way to manage data for ML projects, +letting you focus on more important tasks like data exploration, preparation, +cross validation, etc. From dd4466ed2295ee1ff39cb01ac205f77413fd966c Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Tue, 20 Dec 2022 23:45:41 -0700 Subject: [PATCH 52/76] guide: update figures (much more concrete) and and matching text updates --- .../docs/user-guide/data-management/index.md | 27 +++++++++--------- static/img/direct_access_storage.png | Bin 0 -> 214159 bytes static/img/dvc_managed_storage.png | Bin 317868 -> 261635 bytes static/img/manual_data_management.png | Bin 176431 -> 0 bytes 4 files changed, 14 insertions(+), 13 deletions(-) create mode 100644 static/img/direct_access_storage.png delete mode 100644 static/img/manual_data_management.png diff --git a/content/docs/user-guide/data-management/index.md b/content/docs/user-guide/data-management/index.md index 4d1cc9a1cb..38fb7f0bda 100644 --- a/content/docs/user-guide/data-management/index.md +++ b/content/docs/user-guide/data-management/index.md @@ -5,26 +5,27 @@ --> Where and how to store data and ML model files is one of the first decisions -your team will have to make. But as the project grows, files may end up -scattered throughout multiple buckets. Everyone can read and write (error-prone -and not secure). Duplicated content is hard to prevent, causing data leakage and -outdated datasets that overlap with the latest version. You start formatting -folder names with dates, but others still can't reproduce your results... +your team will have to make. But as projects grow, files may end up scattered +throughout multiple buckets and folders. Datasets repeat and overlap, causing +data leakage and inefficient storage. Other people's work coexists with yours +(error-prone and not secure). How do you find the data you need? What was the +name of the best model? Will others be able to reproduce your results? -![Direct storage access](/img/manual_data_management.png) _Managing code, data, -and models manually does not scale and quickly becomes a consuming job._ +![Direct storage access](/img/direct_access_storage.png) _Managing code, data, +and models manually does not scale and quickly becomes a problem._ -DVC provides a structure that lets you take control of your existing [storage -platforms]. It gives visibility over all your data and helps secure its access. +DVC gives your data stores a structure, helping you take control of existing +[storage platforms]. It provides clear visibility over all your data and helps +secure its access. -![DVC data access](/img/dvc_managed_storage.png) _DVC centralizes file and -directory management for you._ +![DVC data access](/img/dvc_managed_storage.png) _DVC keeps track of which data +files you need in the current version of a project._ Your storage is managed from a Git repository, so you can go back to any previous state of a project and the data will match the code and parameters you used at that time. In other words, [data versioning] guarantees -[reproducibility]. DVC tracks where the underlying files reside, avoiding -duplicates along the way. This requires a few key changes to your workflow: +[reproducibility]. DVC knows where the underlying files reside and avoids +duplicates. This requires a few key changes to your workflow: 1. Data and models must be registered in a code repository (typically on Git). 1. 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zYD`c2K&J*UPm|~C@1OqtY12d@k20x4C-d*G_WQpMc|(d*{6;+K`(OSL0R7J&NM(@6 z-DYx8`KJ%*r;pH57&JMRRVM$?)&JS{{o!|?#USu_v-)t$W)RcAcxxt^8fz+z0Sc~J{#wUG5vFKXy@n^odG#;rgWSC zi~FBL*me9F$E^PXFZi#TlHlB zY{hnVj`|^}0Fo*3{`>p)a5xMZS(K7b@xR|R?o4n3{yQ}P9h(0R&3}hx$6WtYRQR8# z`Onk*=V|^6&;AR~{tM6k|H^5$yE3T*_dx5KbY`&U=oKPm)QCDA%6$(GE=xgg?} zqf5WKkCbNjSAYG3!(q#Kr&|)r-`@GxFZ=zY4&&TNeV};$x9|C Date: Wed, 21 Dec 2022 00:20:47 -0700 Subject: [PATCH 53/76] guide: edits to How it works (Data Mgmt) --- .../docs/user-guide/data-management/index.md | 22 +++++++++---------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/content/docs/user-guide/data-management/index.md b/content/docs/user-guide/data-management/index.md index 38fb7f0bda..36f6efac05 100644 --- a/content/docs/user-guide/data-management/index.md +++ b/content/docs/user-guide/data-management/index.md @@ -40,11 +40,11 @@ duplicates. This requires a few key changes to your workflow: ## How it works -DVC repositories contain [metafiles] that act as pointers, -[linking] to your data files and directories. This keeps your project folder -small and comprised mainly of text-like code and configuration files you can -[version with Git] normally. DVC moves your assets (large files, binaries, etc.) -to a content-addressable cache, eliminating any duplicates. +DVC repositories contain [metafiles] (e.g. `data.dvc`) that act as +pointers, [linking] to your data files and directories. This keeps your project +folder small and comprised mainly of text-like code and configuration files you +can [version with Git] normally. DVC moves your assets (large files, binaries, +etc.) to a content-addressable cache, eliminating any duplicates. [metafiles]: /doc/user-guide/project-structure [linking]: /doc/user-guide/data-management/large-dataset-optimization @@ -90,10 +90,10 @@ their contents (using simple hashes) to flatten any directory structure and prevent duplication. The cache is the first storage layer for you and your team to share and -collaborate, but more can be defined in DVC [config files]. This is where you -set up one or more locations to back up and share data, features, ML models, -etc. Supported platforms include SSH, Amazon S3, Google Cloud Storage, Microsoft -Azure, among [many more]. +collaborate, but more can be defined in DVC [config files] (using `dvc remote` +commands). These storage locations let you back up and share data, features, ML +models, etc. Supported platforms include SSH, Amazon S3, Google Cloud Storage, +Microsoft Azure, among [many more]. [config files]: /doc/user-guide/project-structure/internal-files [many more]: /doc/command-reference/remote/add#supported-storage-types @@ -113,7 +113,7 @@ benefits: - Use a consistent interface to access data anywhere (via [CLI], [API], [IDE], or [web]). - Enable advanced features such as [data registries], [ML pipelines], [CI/CD for - ML], [productization], and more! + ML], [productize] your models, and more! [space efficiency]: /doc/user-guide/data-management/large-dataset-optimization [synchronizes]: /doc/command-reference/remote @@ -124,7 +124,7 @@ benefits: [data registries]: /doc/use-cases/data-registry [ml pipelines]: /doc/user-guide/pipelines [ci/cd for ml]: https://cml.dev/ -[productization]: https://mlem.ai/ +[productize]: https://mlem.ai/ In summary, DVC establishes a standard way to manage data for ML projects, letting you focus on more important tasks like data exploration, preparation, From 3eb81ff26adc7e476640d3879527683f4382ba27 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Perez Date: Thu, 22 Dec 2022 13:18:30 -0700 Subject: [PATCH 54/76] guide: update Data Mgmt figures Rel. https://github.com/iterative/dvc.org/pull/4042#issuecomment-1363307677 --- static/img/direct_access_storage.png | Bin 214159 -> 314021 bytes static/img/dvc_managed_storage.png | Bin 261635 -> 295328 bytes 2 files changed, 0 insertions(+), 0 deletions(-) diff --git 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insertions(+), 18 deletions(-) diff --git a/content/docs/user-guide/data-management/index.md b/content/docs/user-guide/data-management/index.md index 36f6efac05..cc635c946d 100644 --- a/content/docs/user-guide/data-management/index.md +++ b/content/docs/user-guide/data-management/index.md @@ -4,28 +4,35 @@ ## Data Management for Machine Learning --> -Where and how to store data and ML model files is one of the first decisions -your team will have to make. But as projects grow, files may end up scattered -throughout multiple buckets and folders. Datasets repeat and overlap, causing -data leakage and inefficient storage. Other people's work coexists with yours -(error-prone and not secure). How do you find the data you need? What was the -name of the best model? Will others be able to reproduce your results? +Where and how to store datasets and ML model files is one of the first decisions +your team will face. But as time progresses, unnecessary files may end up +scattered throughout multiple buckets and folders. Overlapping contents cause +data leakage and inefficient storage. The project evolution is not easily to +track, so multiple data versions coexist (error-prone and not secure). What was +the name of the best model? Will others be able to reproduce your results? ![Direct storage access](/img/direct_access_storage.png) _Managing code, data, -and models manually does not scale and quickly becomes a problem._ +and models manually does not scale: The S3 bucket on the left is shared by +various people and projects; The user on the right needs to know the exact +location of the correct files, and uses cloud-specific tools (e.g. AWS CLI) to +access them directly._ DVC gives your data stores a structure, helping you take control of existing -[storage platforms]. It provides clear visibility over all your data and helps -secure its access. +[storage platforms]. It maintains visibility over all your data and helps secure +its access. -![DVC data access](/img/dvc_managed_storage.png) _DVC keeps track of which data -files you need in the current version of a project._ +![DVC data access](/img/dvc_managed_storage.png) _DVC captures the structure of +your datasets in a code repository, which can be versioned with Git. The storage +(left) can now be reorganized to eliminate duplication; You access data with +`dvc` (multi-cloud support) -- it checks the metadata to locate files in both +sides._ -Your storage is managed from a Git repository, so you can go back to any -previous state of a project and the data will match the code and -parameters you used at that time. In other words, [data versioning] guarantees -[reproducibility]. DVC knows where the underlying files reside and avoids -duplicates. This requires a few key changes to your workflow: +DVC lets you describe the entire project in a Git repository, so +you can go back to any previous state and find the right data, code, parameters, +etc. used at that time. In other words: [data versioning] guarantees [ML +reproducibility]. + +This requires a few key changes to your workflow: 1. Data and models must be registered in a code repository (typically on Git). 1. Stored objects are [reorganized] by DVC (not intended for manual handling). @@ -34,14 +41,14 @@ duplicates. This requires a few key changes to your workflow: [storage platforms]: /doc/command-reference/remote/add#supported-storage-types [data versioning]: /doc/use-cases/versioning-data-and-models -[reproducibility]: /doc/user-guide/pipelines +[ml reproducibility]: /doc/user-guide/pipelines [reorganized]: /doc/user-guide/project-structure/internal-files#structure-of-the-cache-directory ## How it works DVC repositories contain [metafiles] (e.g. `data.dvc`) that act as -pointers, [linking] to your data files and directories. This keeps your project +pointers, [linking]? to your data files and directories. This keeps your project folder small and comprised mainly of text-like code and configuration files you can [version with Git] normally. 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