From dac8b8b62e44783a1c945e109724e210362bb753 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Wed, 3 Feb 2021 04:59:22 -0600 Subject: [PATCH 01/40] guide: intro (empty) EM guide + basic links --- content/docs/index.md | 7 ++++--- content/docs/sidebar.json | 1 + content/docs/start/experiments.md | 3 +++ content/docs/user-guide/experiment-management.md | 1 + content/docs/user-guide/related-technologies.md | 3 +++ content/docs/user-guide/what-is-dvc.md | 9 +++++---- 6 files changed, 17 insertions(+), 7 deletions(-) create mode 100644 content/docs/user-guide/experiment-management.md diff --git a/content/docs/index.md b/content/docs/index.md index 88d0303e6b..96acf10c81 100644 --- a/content/docs/index.md +++ b/content/docs/index.md @@ -1,8 +1,9 @@ # DVC Documentation -Data Version Control, or DVC, is a data and ML experiment management tool that -takes advantage of the existing engineering toolset that you're already familiar -with (Git, CI/CD, etc.). +Data Version Control, or DVC, is a data and ML +[experiment management](/doc/user-guide/experiment-management) tool that takes +advantage of the existing engineering toolset that you're already familiar with +(Git, CI/CD, etc.). diff --git a/content/docs/sidebar.json b/content/docs/sidebar.json index 720f0f9a14..b0f035aa52 100644 --- a/content/docs/sidebar.json +++ b/content/docs/sidebar.json @@ -127,6 +127,7 @@ "merge-conflicts" ] }, + "experiment-management", "setup-google-drive-remote", "large-dataset-optimization", "external-dependencies", diff --git a/content/docs/start/experiments.md b/content/docs/start/experiments.md index 33b270bcc5..0cc46a29ec 100644 --- a/content/docs/start/experiments.md +++ b/content/docs/start/experiments.md @@ -14,6 +14,9 @@ Read on or watch our video to see how it's done! https://youtu.be/iduHPtBncBk +> ๐Ÿ“– See [Experiment Management](/doc/user-guide/experiment-management) for more +> information on DVC's approach. + ## Collecting metrics First, let's see what is the mechanism to capture values for these ML experiment diff --git a/content/docs/user-guide/experiment-management.md b/content/docs/user-guide/experiment-management.md new file mode 100644 index 0000000000..0a6315bd2d --- /dev/null +++ b/content/docs/user-guide/experiment-management.md @@ -0,0 +1 @@ +# Experiment Management diff --git a/content/docs/user-guide/related-technologies.md b/content/docs/user-guide/related-technologies.md index b845cb7c67..9a913b029e 100644 --- a/content/docs/user-guide/related-technologies.md +++ b/content/docs/user-guide/related-technologies.md @@ -82,6 +82,9 @@ _Luigi_, etc. ## Experiment management software +> See also the [Experiment Management](/doc/user-guide/experiment-management) +> guide. + - DVC uses Git as the underlying version control layer for data, pipelines, and experiments. Data versions exist as metadata in Git, as opposed to using external databases or APIs, so no additional services are required. diff --git a/content/docs/user-guide/what-is-dvc.md b/content/docs/user-guide/what-is-dvc.md index b89df5ec9a..5b3ef69dfb 100644 --- a/content/docs/user-guide/what-is-dvc.md +++ b/content/docs/user-guide/what-is-dvc.md @@ -1,10 +1,11 @@ # What Is DVC? **Data Version Control** is a new type of data versioning, workflow, and -experiment management software, that builds upon [Git](https://git-scm.com/) -(although it can work stand-alone). DVC reduces the gap between established -engineering tool sets and data science needs, allowing users to take advantage -of new [features](#core-features) while reusing existing skills and intuition. +[experiment management](/doc/user-guide/experiment-management) software, that +builds upon [Git](https://git-scm.com/) (although it can work stand-alone). DVC +reduces the gap between established engineering tool sets and data science +needs, allowing users to take advantage of new [features](#core-features) while +reusing existing skills and intuition. ![](/img/reproducibility.png) _DVC codifies data and ML experiments_ From b1e4c580df7420fcbe81291e2aba8ee04e497964 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Wed, 3 Feb 2021 17:01:00 -0600 Subject: [PATCH 02/40] guide: intro, structure, and run-cache section in Exp Mgmt --- .../docs/user-guide/experiment-management.md | 38 +++++++++++++++++++ 1 file changed, 38 insertions(+) diff --git a/content/docs/user-guide/experiment-management.md b/content/docs/user-guide/experiment-management.md index 0a6315bd2d..8e47b16689 100644 --- a/content/docs/user-guide/experiment-management.md +++ b/content/docs/user-guide/experiment-management.md @@ -1 +1,39 @@ # Experiment Management + +Data science and ML are iterative processes that tend to require a large number +of attempts during their course, for example to develop data features, +hyperspace exploration, model accuracy optimization, etc. DVC is designed to +help you codify and manage all of your experiments. + +Kinds of exps... With DVC, no variation of your code or data is left +hyperparameters + +## Automatic log of stage runs + +DVC already caches every change to outputs when it can (see also +`dvc status`). Additionally, `dvc repro` and `dvc run` by default populate and +reutilize a log of stages that have been run in the project, known as the +run-cache. + +This means that every time you execute [stages](/doc/command-reference/run) with +DVC, the unique combination that identifies that "run" is saved internally (in +`.dvc/cache/runs` by default). The corresponding results (typically +cached) can later be retrieved in subsequent runs, even if you +didn't remember that the combination had been tried before! + +When this happens, the results are restored instantly, without wasting time or +computing resources. This can dramatically improve performance, and it's a +built-in feature that just works out-of-the-box (it can be disabled via the +`--no-run-cache` option). + +## Ephemeral experiments + +frequent, transient, brain storming + +## Persistent experiments + +selected, committed + +## Ways to organize experimentation + +Implicit vs. Git branches/tags vs. file structures From 92c3b1fc623cafcebf578fdd41dee3ca3ea7b1d5 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Thu, 4 Feb 2021 15:06:24 -0600 Subject: [PATCH 03/40] guide: finish Exp Mgmt intro, shorten run-cache section --- .../docs/user-guide/experiment-management.md | 64 ++++++++++++------- 1 file changed, 41 insertions(+), 23 deletions(-) diff --git a/content/docs/user-guide/experiment-management.md b/content/docs/user-guide/experiment-management.md index 8e47b16689..b488cdd4a6 100644 --- a/content/docs/user-guide/experiment-management.md +++ b/content/docs/user-guide/experiment-management.md @@ -2,31 +2,49 @@ Data science and ML are iterative processes that tend to require a large number of attempts during their course, for example to develop data features, -hyperspace exploration, model accuracy optimization, etc. DVC is designed to -help you codify and manage all of your experiments. +hyperspace exploration, deep learning optimization, etc. DVC is designed to help +you codify and manage all of your experiments. + +DVC considers certain levels at which the variants of your work are considered +_experiments_: + +0. Tests you do on you own without DVC knowing about them โ€” we can't help with + that! +1. An automatic log of every stage run through `dvc repro` is the entry point + for DVC. +2. _Ephemeral experiments_ can be setup in virtual project branches. This is + where you can start **automating** their execution and generate reports + comparing many of them. At some point a few are selected/promoted, and the + rest can be abandoned. +3. _Persistent experiments_ can be picked up from previous levels, or they can + be registered manually by **committed** their results to Git. This is where + you may want to start thinking about the different ways to + [organize](#organizing-experimentats) them in your project (branches, + folders, etc.). + +## Automatic log of stage runs (run-cache) + +Every time you `dvc repro` each stage [stages](/doc/command-reference/run), DVC +determines a unique identifier of each stage "run" (logged to `.dvc/cache/runs` +by default). If it never happened before, the stage command(s) are executed and +their outputs cached normally. Every subsequent time the stage runs +under the same conditions, those results can be restored instantly, without +wasting time or computing resources. + +This mechanism can dramatically improve performance, and it's a built-in +feature, enabled out-of-the-box (it can be disabled via the `--no-run-cache` +option). + +> Note that the run-cache assumes that stage commands are deterministic (see +> **Avoiding unexpected behavior** in `dvc run`). -Kinds of exps... With DVC, no variation of your code or data is left -hyperparameters - -## Automatic log of stage runs - -DVC already caches every change to outputs when it can (see also -`dvc status`). Additionally, `dvc repro` and `dvc run` by default populate and -reutilize a log of stages that have been run in the project, known as the -run-cache. - -This means that every time you execute [stages](/doc/command-reference/run) with -DVC, the unique combination that identifies that "run" is saved internally (in -`.dvc/cache/runs` by default). The corresponding results (typically -cached) can later be retrieved in subsequent runs, even if you -didn't remember that the combination had been tried before! +## Ephemeral experiments -When this happens, the results are restored instantly, without wasting time or -computing resources. This can dramatically improve performance, and it's a -built-in feature that just works out-of-the-box (it can be disabled via the -`--no-run-cache` option). +Unique stage runs can be identified by the combination of their dependencies +(including params) and the command(s) to execute. -## Ephemeral experiments +Every run of a stage or pipeline can be considered an experiment. These are +identified by the exact combination of dependencies, , and frequent, transient, brain storming @@ -34,6 +52,6 @@ frequent, transient, brain storming selected, committed -## Ways to organize experimentation +## Organizing experiments Implicit vs. Git branches/tags vs. file structures From 70e4f91d23d9fdffa851abfa71097a40da55a035 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Thu, 4 Feb 2021 19:39:06 -0600 Subject: [PATCH 04/40] guide: shorten Exp Mgmt intro and its run-cache section more, and remove WIP content --- .../user-guide/basic-concepts/experiment.md | 11 ++++ .../docs/user-guide/experiment-management.md | 65 ++++++++----------- .../project-structure/internal-files.md | 10 ++- 3 files changed, 45 insertions(+), 41 deletions(-) create mode 100644 content/docs/user-guide/basic-concepts/experiment.md diff --git a/content/docs/user-guide/basic-concepts/experiment.md b/content/docs/user-guide/basic-concepts/experiment.md new file mode 100644 index 0000000000..e3424d929c --- /dev/null +++ b/content/docs/user-guide/basic-concepts/experiment.md @@ -0,0 +1,11 @@ +--- +name: Experiment +match: [experiment, experiments] +--- + +An attempt to reach desired/better/interesting results during data pipelining or +ML model development. DVC is designed to help +[manage experiments](/doc/user-guide/experiment-management), having built-in +mechanisms like the +[run-cache](/doc/user-guide/project-structure/internal-files#run-cache) and the +`dvc experiments` commands (coming in DVC 2.0). diff --git a/content/docs/user-guide/experiment-management.md b/content/docs/user-guide/experiment-management.md index b488cdd4a6..336af5e5ce 100644 --- a/content/docs/user-guide/experiment-management.md +++ b/content/docs/user-guide/experiment-management.md @@ -2,56 +2,45 @@ Data science and ML are iterative processes that tend to require a large number of attempts during their course, for example to develop data features, -hyperspace exploration, deep learning optimization, etc. DVC is designed to help -you codify and manage all of your experiments. - -DVC considers certain levels at which the variants of your work are considered -_experiments_: - -0. Tests you do on you own without DVC knowing about them โ€” we can't help with - that! -1. An automatic log of every stage run through `dvc repro` is the entry point - for DVC. -2. _Ephemeral experiments_ can be setup in virtual project branches. This is - where you can start **automating** their execution and generate reports - comparing many of them. At some point a few are selected/promoted, and the - rest can be abandoned. -3. _Persistent experiments_ can be picked up from previous levels, or they can - be registered manually by **committed** their results to Git. This is where - you may want to start thinking about the different ways to - [organize](#organizing-experimentats) them in your project (branches, - folders, etc.). +hyperspace exploration, deep learning optimization, etc. DVC helps you codify +and manage all of your experiments, considering the following +levels at which they may exist: + +0. Tests you do on you own (without DVC knowing) โ€” that's all you! +1. DVC enters the scene with an automatic log of every stage `dvc repro` runs. +2. Create [ephemeral experiments](#ephemeral-experiments) that virtually branch + off your current workspace. You can start **automating** them at this point, + and quickly visualize and compare them from terminal. The best ones can be + promoted to the next level, and the rest archived. +3. [Persistent experiments](#persistent-experiments) have their results + **committed** to Git. They can be selected from previous levels, or created + from scratch. This is where you may want to consider the different + [ways to organize](#organizing-experimentats) them in your project (as + branches, folders, etc.). + +> Note that DVC assumes that all experiments are deterministic (see **Avoiding +> unexpected behavior** in `dvc run`). ## Automatic log of stage runs (run-cache) -Every time you `dvc repro` each stage [stages](/doc/command-reference/run), DVC -determines a unique identifier of each stage "run" (logged to `.dvc/cache/runs` -by default). If it never happened before, the stage command(s) are executed and -their outputs cached normally. Every subsequent time the stage runs -under the same conditions, those results can be restored instantly, without -wasting time or computing resources. +Every time you `dvc repro` stages, DVC logs the unique signature of that "run" +(to `.dvc/cache/runs` by default). If it never happened before, the stage +command(s) are executed normally. Every subsequent time the +[stage](/doc/command-reference/run) runs under the same conditions, the previous +results can be restored instantly, without wasting time or computing resources. -This mechanism can dramatically improve performance, and it's a built-in +โœ… This mechanism can dramatically improve performance, and it's a built-in feature, enabled out-of-the-box (it can be disabled via the `--no-run-cache` option). -> Note that the run-cache assumes that stage commands are deterministic (see -> **Avoiding unexpected behavior** in `dvc run`). - ## Ephemeral experiments -Unique stage runs can be identified by the combination of their dependencies -(including params) and the command(s) to execute. - -Every run of a stage or pipeline can be considered an experiment. These are -identified by the exact combination of dependencies, , and - -frequent, transient, brain storming +... ## Persistent experiments -selected, committed +... ## Organizing experiments -Implicit vs. Git branches/tags vs. file structures +... diff --git a/content/docs/user-guide/project-structure/internal-files.md b/content/docs/user-guide/project-structure/internal-files.md index 2d1f25eec1..fedee7095d 100644 --- a/content/docs/user-guide/project-structure/internal-files.md +++ b/content/docs/user-guide/project-structure/internal-files.md @@ -131,9 +131,10 @@ That's how DVC knows that the other two cached files belong in the directory. have been run in the project. It is found in the `runs/` directory inside the cache (or [remote storage](/doc/command-reference/remote)). -Runs are identified as combinations of dependencies, commands, and -outputs that correspond to each other. These combinations are -hashed into special values that make up the file paths inside the run-cache dir. +Runs are identified as combinations of exact dependency contents +(or [parameter](/doc/command-reference/params) values), and the literal +command(s) to execute. These combinations are represented by special hashes that +translate to the file paths inside the run-cache dir: ```dvc $ tree .dvc/cache/runs @@ -151,3 +152,6 @@ run. ๐Ÿ’ก `dvc push` and `dvc pull` (and `dvc fetch`) can download and upload the run-cache to remote storage for sharing and/or as a back up. + +> Note that the run-cache assumes that stage commands are deterministic (see +> **Avoiding unexpected behavior** in `dvc run`). From f73ae78730176e4a715b4207aa7ac4864275d06d Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Tue, 9 Feb 2021 13:29:24 -0600 Subject: [PATCH 05/40] guide: finish explaining ephimeral exps --- .../docs/user-guide/experiment-management.md | 36 ++++++++++++++----- 1 file changed, 28 insertions(+), 8 deletions(-) diff --git a/content/docs/user-guide/experiment-management.md b/content/docs/user-guide/experiment-management.md index 336af5e5ce..e05d5417f4 100644 --- a/content/docs/user-guide/experiment-management.md +++ b/content/docs/user-guide/experiment-management.md @@ -23,9 +23,9 @@ levels at which they may exist: ## Automatic log of stage runs (run-cache) -Every time you `dvc repro` stages, DVC logs the unique signature of that "run" -(to `.dvc/cache/runs` by default). If it never happened before, the stage -command(s) are executed normally. Every subsequent time the +Every time you `dvc repro` pipelines, DVC logs the unique signature of each +stage run (to `.dvc/cache/runs` by default). If it never happened before, the +stage command(s) are executed normally. Every subsequent time a [stage](/doc/command-reference/run) runs under the same conditions, the previous results can be restored instantly, without wasting time or computing resources. @@ -35,12 +35,32 @@ option). ## Ephemeral experiments -... +โš ๏ธ This feature is only available in DVC 2.0, and for Git-enabled +repositories โš ๏ธ -## Persistent experiments +`dvc experiments` commands let you run DVC +[pipelines](/doc/command-reference/dag) in a "virtual branch", so that each +experiment is captured automatically as a transient commit. Your parent Git repo +is not cluttered with all these commits. The base workflow goes like this: -... +- Establish or change the dependencies, parameters, or commands/source code of + your stages. +- Use `dvc exp run` (instead of `repro`) to execute the pipeline, which creates + a transient commit that represents this experiment. +- Visualize the experiments statistics with `dvc exp show`. Repeat. +- Use metrics in your pipeline to help you identify the best experiment(s), and + use `dvc exp apply` to promote them as persistent experiments (regular + commits) to the project. -## Organizing experiments +
-... +### What are virtual experiment branches? + +DVC uses actual commits under custom +[Git references](https://git-scm.com/book/en/v2/Git-Internals-Git-References) +(found in `.git/refs/exps`) to keep track of `dvc exp run` branches. The first +run has the current Git repo's `HEAD` as parent. + +
+ +> See `dvc exp` for more details. From 6b9b509882ec4163dcd46a31a63c521156645a4f Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Tue, 9 Feb 2021 13:52:48 -0600 Subject: [PATCH 06/40] guide: improves to ephimeral exps, intro persisten exps in Exp Mgmt --- .../docs/user-guide/experiment-management.md | 27 +++++++++++++------ 1 file changed, 19 insertions(+), 8 deletions(-) diff --git a/content/docs/user-guide/experiment-management.md b/content/docs/user-guide/experiment-management.md index e05d5417f4..29bd614453 100644 --- a/content/docs/user-guide/experiment-management.md +++ b/content/docs/user-guide/experiment-management.md @@ -43,24 +43,35 @@ option). experiment is captured automatically as a transient commit. Your parent Git repo is not cluttered with all these commits. The base workflow goes like this: -- Establish or change the dependencies, parameters, or commands/source code of - your stages. +- Establish or change the dependencies, parameters, + commands (`cmd` field of `dvc.yaml`), or the source code of your stages. - Use `dvc exp run` (instead of `repro`) to execute the pipeline, which creates a transient commit that represents this experiment. - Visualize the experiments statistics with `dvc exp show`. Repeat. -- Use metrics in your pipeline to help you identify the best experiment(s), and - use `dvc exp apply` to promote them as persistent experiments (regular - commits) to the project. +- Use [metrics](/doc/command-reference/metrics) in your pipeline to help you + identify the best experiment(s), and promote them to persistent experiments + (regular commits) with `dvc exp apply`. + +> See `dvc exp` for more options.
-### What are virtual experiment branches? +### What are _virtual branches_ and _transient commits_? DVC uses actual commits under custom + [Git references](https://git-scm.com/book/en/v2/Git-Internals-Git-References) (found in `.git/refs/exps`) to keep track of `dvc exp run` branches. The first -run has the current Git repo's `HEAD` as parent. +run has the current Git repo's `HEAD` as parent. Each reference has a unique +signature similar to the entries in the run-cache.
-> See `dvc exp` for more details. +## Persistent experiments + +> See [Get Started: Experiments](/doc/start/experiments) for a primer. + +When your experiments are good enough to save or share, you may want to store +them persistently as commits in your (Git-enabled) repository. The +results may have been produced with `dvc repro` directly, or as a `dvc exp run` +that gets promoted with `dvc exp apply`. From 203fa3c3bab3f31931039ad0c6d48b77016a84ea Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Tue, 9 Feb 2021 15:10:47 -0600 Subject: [PATCH 07/40] guide: more info on ephimeral exps and wrap up persistent exps section --- .../user-guide/basic-concepts/run-cache.md | 8 +-- .../docs/user-guide/experiment-management.md | 56 +++++++++++-------- 2 files changed, 37 insertions(+), 27 deletions(-) diff --git a/content/docs/user-guide/basic-concepts/run-cache.md b/content/docs/user-guide/basic-concepts/run-cache.md index 0eab76c538..d749f62ad7 100644 --- a/content/docs/user-guide/basic-concepts/run-cache.md +++ b/content/docs/user-guide/basic-concepts/run-cache.md @@ -3,9 +3,9 @@ name: 'Run-cache' match: ['run-cache'] --- -The DVC run-cache is a log of stages that have been run in the project. It's -comprised of `dvc.lock` file backups, identified as combinations of -dependencies, commands, and outputs that correspond to each other. `dvc repro` -and `dvc run` populate and reutilize the run-cache. See +A log of stages that have been run in the project. It's comprised of `dvc.lock` +file backups, identified as combinations of dependencies, commands, and outputs +that correspond to each other. `dvc repro` and `dvc run` populate and reutilize +the run-cache. See [Run-cache](/doc/user-guide/project-structure/internal-files#run-cache) for more details. diff --git a/content/docs/user-guide/experiment-management.md b/content/docs/user-guide/experiment-management.md index 29bd614453..7ed01e62fb 100644 --- a/content/docs/user-guide/experiment-management.md +++ b/content/docs/user-guide/experiment-management.md @@ -29,49 +29,59 @@ stage command(s) are executed normally. Every subsequent time a [stage](/doc/command-reference/run) runs under the same conditions, the previous results can be restored instantly, without wasting time or computing resources. -โœ… This mechanism can dramatically improve performance, and it's a built-in -feature, enabled out-of-the-box (it can be disabled via the `--no-run-cache` -option). +โœ… This built-in feature is called run-cache and it can +dramatically improve performance. It's enabled out-of-the-box (but can be +disabled with the `--no-run-cache` command option). ## Ephemeral experiments โš ๏ธ This feature is only available in DVC 2.0, and for Git-enabled repositories โš ๏ธ -`dvc experiments` commands let you run DVC -[pipelines](/doc/command-reference/dag) in a "virtual branch", so that each -experiment is captured automatically as a transient commit. Your parent Git repo -is not cluttered with all these commits. The base workflow goes like this: +`dvc experiments` commands let you work on DVC +[pipelines](/doc/command-reference/dag) in a virtual branch, so that each +experiment is captured automatically in a way that you can visualize later. The +workspace may reflect each experiment's results for you to check, +but there's no need to save them manually. The base workflow goes like this: -- Establish or change the dependencies, parameters, - commands (`cmd` field of `dvc.yaml`), or the source code of your stages. +- Establish or change the dependencies (e.g. input data or source + code), parameters, or commands (`cmd` field of `dvc.yaml`) of + your stages. - Use `dvc exp run` (instead of `repro`) to execute the pipeline, which creates - a transient commit that represents this experiment. + a transient commit that records this experiment. - Visualize the experiments statistics with `dvc exp show`. Repeat. -- Use [metrics](/doc/command-reference/metrics) in your pipeline to help you - identify the best experiment(s), and promote them to persistent experiments - (regular commits) with `dvc exp apply`. - -> See `dvc exp` for more options. +- Use [metrics](/doc/command-reference/metrics) in your pipeline to identify the + best experiment(s), and promote them to persistent experiments (regular + commits) with `dvc exp apply`.
### What are _virtual branches_ and _transient commits_? DVC uses actual commits under custom - [Git references](https://git-scm.com/book/en/v2/Git-Internals-Git-References) (found in `.git/refs/exps`) to keep track of `dvc exp run` branches. The first -run has the current Git repo's `HEAD` as parent. Each reference has a unique -signature similar to the entries in the run-cache. +experiment has the Git repo's `HEAD` as parent. Each reference has a unique +signature similar to the +[entries in the run-cache](/doc/user-guide/project-structure/internal-files#run-cache).
-## Persistent experiments +Note that `dvc exp run` also logs and reuses +[stage runs](#automatic-log-of-stage-runs-run-cache) in the +run-cache by default. -> See [Get Started: Experiments](/doc/start/experiments) for a primer. +> See `dvc exp` for more details and other commands. + +## Persistent experiments When your experiments are good enough to save or share, you may want to store -them persistently as commits in your (Git-enabled) repository. The -results may have been produced with `dvc repro` directly, or as a `dvc exp run` -that gets promoted with `dvc exp apply`. +them persistently as commits in your (Git-enabled) repository. + +The results may have been produced with `dvc repro` directly, or after an +[ephemeral workflow](#ephemeral-experiments). In any case the workspace will +have the right `dvc.yaml` and `dvc.lock` file pair, as well as the corresponding +outputs (via `dvc checkout`). + +See [Get Started: Experiments](/doc/start/experiments) for a hands-on intro +guide on regular experiments. From fd344d6f46a1395781b31b1cf2c390235545ac92 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Tue, 9 Feb 2021 15:18:05 -0600 Subject: [PATCH 08/40] guide: begin Orging exps in Exp Mgmt --- content/docs/user-guide/experiment-management.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/content/docs/user-guide/experiment-management.md b/content/docs/user-guide/experiment-management.md index 7ed01e62fb..dc4ae6983f 100644 --- a/content/docs/user-guide/experiment-management.md +++ b/content/docs/user-guide/experiment-management.md @@ -85,3 +85,17 @@ have the right `dvc.yaml` and `dvc.lock` file pair, as well as the corresponding See [Get Started: Experiments](/doc/start/experiments) for a hands-on intro guide on regular experiments. + +## Organizing experiments + +Automatic [stage run logs](#automatic-log-of-stage-runs-run-cache) are dumped +without a structure in the run-cache. +[Ephemeral experiments](#ephemeral-experiments) always have a linear branch +structure (a queue) based on current `HEAD` commit. But when it comes to +full-blown [persistent experiments](#persistent-experiments), it's up to you to +decide how to organize them in your project. These are the main alternatives: + +- Branches +- Tags +- Directories +- Hybrid From c7cc38848559a08a4f8e01fd6992726ede1c8fbb Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Tue, 9 Feb 2021 15:44:28 -0600 Subject: [PATCH 09/40] guide: copy edits Ephemeral experiments --- .../docs/user-guide/experiment-management.md | 23 +++++++++++-------- 1 file changed, 13 insertions(+), 10 deletions(-) diff --git a/content/docs/user-guide/experiment-management.md b/content/docs/user-guide/experiment-management.md index dc4ae6983f..4904fee9bd 100644 --- a/content/docs/user-guide/experiment-management.md +++ b/content/docs/user-guide/experiment-management.md @@ -40,13 +40,14 @@ disabled with the `--no-run-cache` command option). `dvc experiments` commands let you work on DVC [pipelines](/doc/command-reference/dag) in a virtual branch, so that each -experiment is captured automatically in a way that you can visualize later. The -workspace may reflect each experiment's results for you to check, -but there's no need to save them manually. The base workflow goes like this: - -- Establish or change the dependencies (e.g. input data or source - code), parameters, or commands (`cmd` field of `dvc.yaml`) of - your stages. +experiment is captured automatically in a way that you can review and roll back +later. The workspace may reflect each experiment's results for you +to check, but there's no need to save them manually. The base workflow goes like +this: + +- Modify dependencies (e.g. input data or source code), + parameters, or commands (`cmd` field of `dvc.yaml`) of a + committed stage. - Use `dvc exp run` (instead of `repro`) to execute the pipeline, which creates a transient commit that records this experiment. - Visualize the experiments statistics with `dvc exp show`. Repeat. @@ -60,9 +61,11 @@ but there's no need to save them manually. The base workflow goes like this: DVC uses actual commits under custom [Git references](https://git-scm.com/book/en/v2/Git-Internals-Git-References) -(found in `.git/refs/exps`) to keep track of `dvc exp run` branches. The first -experiment has the Git repo's `HEAD` as parent. Each reference has a unique -signature similar to the +(found in `.git/refs/exps`) to keep track of `dvc exp run` branches. These are +not pushed to the Git remote by default (see `dvc exp push`). + +The first transient commits has the Git repo's `HEAD` as parent, and the rest +branch off there. Each reference has a unique signature similar to the [entries in the run-cache](/doc/user-guide/project-structure/internal-files#run-cache). From ea260f6d14e5d005cc2db4d39961eeb1955117aa Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Wed, 10 Feb 2021 11:43:47 -0600 Subject: [PATCH 10/40] guide: remove 0. Tests and add add Checkpoints section --- .../docs/user-guide/experiment-management.md | 18 +++++++++++------- 1 file changed, 11 insertions(+), 7 deletions(-) diff --git a/content/docs/user-guide/experiment-management.md b/content/docs/user-guide/experiment-management.md index 4904fee9bd..7bd6d6e530 100644 --- a/content/docs/user-guide/experiment-management.md +++ b/content/docs/user-guide/experiment-management.md @@ -6,12 +6,12 @@ hyperspace exploration, deep learning optimization, etc. DVC helps you codify and manage all of your experiments, considering the following levels at which they may exist: -0. Tests you do on you own (without DVC knowing) โ€” that's all you! 1. DVC enters the scene with an automatic log of every stage `dvc repro` runs. -2. Create [ephemeral experiments](#ephemeral-experiments) that virtually branch - off your current workspace. You can start **automating** them at this point, - and quickly visualize and compare them from terminal. The best ones can be - promoted to the next level, and the rest archived. +2. Create [ephemeral experiments](#ephemeral-experiments) and + [in-code checkpoints](#checkpoints-in-python-code) that virtually branch off + your current workspace. DVC lets you quickly visualize and compare them from + terminal. The best ones can be promoted to the next level, and the rest + archived. 3. [Persistent experiments](#persistent-experiments) have their results **committed** to Git. They can be selected from previous levels, or created from scratch. This is where you may want to consider the different @@ -48,8 +48,8 @@ this: - Modify dependencies (e.g. input data or source code), parameters, or commands (`cmd` field of `dvc.yaml`) of a committed stage. -- Use `dvc exp run` (instead of `repro`) to execute the pipeline, which creates - a transient commit that records this experiment. +- Use `dvc exp run` (instead of `repro`) to execute the pipeline. This creates a + transient commit that records this experiment. - Visualize the experiments statistics with `dvc exp show`. Repeat. - Use [metrics](/doc/command-reference/metrics) in your pipeline to identify the best experiment(s), and promote them to persistent experiments (regular @@ -76,6 +76,10 @@ Note that `dvc exp run` also logs and reuses > See `dvc exp` for more details and other commands. +## Checkpoints in Python code + +... + ## Persistent experiments When your experiments are good enough to save or share, you may want to store From 3099d1acd35a5f3a2b96996767e037d2d06a85d5 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Wed, 10 Feb 2021 12:12:01 -0600 Subject: [PATCH 11/40] guide: 3 main exp forms in Exp Mgmt --- .../docs/user-guide/experiment-management.md | 73 +++++++++++-------- 1 file changed, 42 insertions(+), 31 deletions(-) diff --git a/content/docs/user-guide/experiment-management.md b/content/docs/user-guide/experiment-management.md index 7bd6d6e530..3f4482e52e 100644 --- a/content/docs/user-guide/experiment-management.md +++ b/content/docs/user-guide/experiment-management.md @@ -3,35 +3,23 @@ Data science and ML are iterative processes that tend to require a large number of attempts during their course, for example to develop data features, hyperspace exploration, deep learning optimization, etc. DVC helps you codify -and manage all of your experiments, considering the following -levels at which they may exist: - -1. DVC enters the scene with an automatic log of every stage `dvc repro` runs. -2. Create [ephemeral experiments](#ephemeral-experiments) and - [in-code checkpoints](#checkpoints-in-python-code) that virtually branch off - your current workspace. DVC lets you quickly visualize and compare them from - terminal. The best ones can be promoted to the next level, and the rest - archived. +and manage all of your experiments, supporting these main +experimentation approaches: + +1. Create [ephemeral experiments](#ephemeral-experiments) that derive from your + latest project version, without having to keep track manually. DVC does that + for you, letting you list and compare them. The best ones can be promoted, + and the rest archived. +2. Place [in-code checkpoints](#checkpoints-in-python-code) that form series of + (ephemeral) experiments. DVC helps you capture them at runtime, and manage + them as batches. 3. [Persistent experiments](#persistent-experiments) have their results - **committed** to Git. They can be selected from previous levels, or created - from scratch. This is where you may want to consider the different - [ways to organize](#organizing-experimentats) them in your project (as - branches, folders, etc.). + **committed** to Git. They can be selected from existing attempts, or created + from scratch. -> Note that DVC assumes that all experiments are deterministic (see **Avoiding -> unexpected behavior** in `dvc run`). - -## Automatic log of stage runs (run-cache) - -Every time you `dvc repro` pipelines, DVC logs the unique signature of each -stage run (to `.dvc/cache/runs` by default). If it never happened before, the -stage command(s) are executed normally. Every subsequent time a -[stage](/doc/command-reference/run) runs under the same conditions, the previous -results can be restored instantly, without wasting time or computing resources. - -โœ… This built-in feature is called run-cache and it can -dramatically improve performance. It's enabled out-of-the-box (but can be -disabled with the `--no-run-cache` command option). + At this point you may also want to consider the different + [ways to organize](#organizing-experimentats) experiments in your project (as + Git branches, as folders, etc.). ## Ephemeral experiments @@ -70,16 +58,26 @@ branch off there. Each reference has a unique signature similar to the -Note that `dvc exp run` also logs and reuses -[stage runs](#automatic-log-of-stage-runs-run-cache) in the -run-cache by default. - > See `dvc exp` for more details and other commands. ## Checkpoints in Python code +โš ๏ธ This feature is only available in DVC 2.0, and for Git-enabled +repositories โš ๏ธ + ... +
+ +### How are checkpoints captured by DVC? + +When DVC runs checkpoint-enabled stages, a new transient commit is generated in +the experiment's virtual branch each time the code calls +`dvc.api.make_checkpoint()` or writes a `.dvc/tmp/DVC_CHECKPOINT` signal file. +See `dvc exp run` for more details + +
+ ## Persistent experiments When your experiments are good enough to save or share, you may want to store @@ -93,6 +91,19 @@ have the right `dvc.yaml` and `dvc.lock` file pair, as well as the corresponding See [Get Started: Experiments](/doc/start/experiments) for a hands-on intro guide on regular experiments. +## Automatic log of stage runs (run-cache) + +Every time you `dvc repro` pipelines or `dvc exp run` experiments, DVC logs the +unique signature of each stage run (to `.dvc/cache/runs` by default). If it +never happened before, the stage command(s) are executed normally. Every +subsequent time a [stage](/doc/command-reference/run) runs under the same +conditions, the previous results can be restored instantly, without wasting time +or computing resources. + +โœ… This built-in feature is called run-cache and it can +dramatically improve performance. It's enabled out-of-the-box (but can be +disabled with the `--no-run-cache` command option). + ## Organizing experiments Automatic [stage run logs](#automatic-log-of-stage-runs-run-cache) are dumped From d012334882750180beaa7b9d61b5ff805c5cc2dd Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Wed, 10 Feb 2021 13:05:56 -0600 Subject: [PATCH 12/40] guide: simplify Exp Mgmt tech details --- .../docs/user-guide/experiment-management.md | 46 +++++++++---------- 1 file changed, 22 insertions(+), 24 deletions(-) diff --git a/content/docs/user-guide/experiment-management.md b/content/docs/user-guide/experiment-management.md index 3f4482e52e..b6cd967907 100644 --- a/content/docs/user-guide/experiment-management.md +++ b/content/docs/user-guide/experiment-management.md @@ -23,38 +23,36 @@ experimentation approaches: ## Ephemeral experiments -โš ๏ธ This feature is only available in DVC 2.0, and for Git-enabled -repositories โš ๏ธ - -`dvc experiments` commands let you work on DVC -[pipelines](/doc/command-reference/dag) in a virtual branch, so that each -experiment is captured automatically in a way that you can review and roll back -later. The workspace may reflect each experiment's results for you -to check, but there's no need to save them manually. The base workflow goes like +โš ๏ธ This feature is only available in DVC 2.0 โš ๏ธ + +`dvc exp` commands let you automatically track variations to established DVC +[pipelines](/doc/command-reference/dag), so you can review, compare, and restore +them at any time, or roll back to the baseline. The base workflow goes like this: - Modify dependencies (e.g. input data or source code), parameters, or commands (`cmd` field of `dvc.yaml`) of a committed stage. -- Use `dvc exp run` (instead of `repro`) to execute the pipeline. This creates a - transient commit that records this experiment. -- Visualize the experiments statistics with `dvc exp show`. Repeat. +- Use `dvc exp run` (instead of `repro`) to execute the pipeline. This puts the + experiment's results in your workspace, and records it under the + hood. +- Visualize experiment configurations and results with `dvc exp show`. Repeat. - Use [metrics](/doc/command-reference/metrics) in your pipeline to identify the best experiment(s), and promote them to persistent experiments (regular commits) with `dvc exp apply`.
-### What are _virtual branches_ and _transient commits_? +### How does DVC capture these experiments? DVC uses actual commits under custom [Git references](https://git-scm.com/book/en/v2/Git-Internals-Git-References) -(found in `.git/refs/exps`) to keep track of `dvc exp run` branches. These are -not pushed to the Git remote by default (see `dvc exp push`). +(found in `.git/refs/exps`) to keep track of ephemeral experiments. Each commit +has the Git repo's `HEAD` as parent. These are not pushed to the Git remote by +default (see `dvc exp push`). -The first transient commits has the Git repo's `HEAD` as parent, and the rest -branch off there. Each reference has a unique signature similar to the -[entries in the run-cache](/doc/user-guide/project-structure/internal-files#run-cache). +> References have a unique signature similar to the +> [entries in the run-cache](/doc/user-guide/project-structure/internal-files#run-cache).
@@ -62,8 +60,7 @@ branch off there. Each reference has a unique signature similar to the ## Checkpoints in Python code -โš ๏ธ This feature is only available in DVC 2.0, and for Git-enabled -repositories โš ๏ธ +โš ๏ธ This feature is only available in DVC 2.0 โš ๏ธ ... @@ -71,17 +68,18 @@ branch off there. Each reference has a unique signature similar to the ### How are checkpoints captured by DVC? -When DVC runs checkpoint-enabled stages, a new transient commit is generated in -the experiment's virtual branch each time the code calls -`dvc.api.make_checkpoint()` or writes a `.dvc/tmp/DVC_CHECKPOINT` signal file. -See `dvc exp run` for more details +When DVC runs checkpoint-enabled stages, a new commit is generated in a custom +Git branch (found in `.git/refs/exps`) each time the code calls +`dvc.api.make_checkpoint()` or writes a `.dvc/tmp/DVC_CHECKPOINT` signal file +(see `dvc exp run` for info). These are not pushed to the Git remote by default +(see `dvc exp push`). ## Persistent experiments When your experiments are good enough to save or share, you may want to store -them persistently as commits in your (Git-enabled) repository. +them persistently as commits in your repository. The results may have been produced with `dvc repro` directly, or after an [ephemeral workflow](#ephemeral-experiments). In any case the workspace will From 006068cdd023b4390961c7c91db98594444acf8a Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Wed, 10 Feb 2021 20:32:00 -0600 Subject: [PATCH 13/40] guide: finish Checkpoints section in Exp Mgmt --- .../docs/user-guide/experiment-management.md | 37 +++++++++++-------- 1 file changed, 21 insertions(+), 16 deletions(-) diff --git a/content/docs/user-guide/experiment-management.md b/content/docs/user-guide/experiment-management.md index b6cd967907..f8da6549aa 100644 --- a/content/docs/user-guide/experiment-management.md +++ b/content/docs/user-guide/experiment-management.md @@ -10,7 +10,7 @@ experimentation approaches: latest project version, without having to keep track manually. DVC does that for you, letting you list and compare them. The best ones can be promoted, and the rest archived. -2. Place [in-code checkpoints](#checkpoints-in-python-code) that form series of +2. Place [in-code checkpoints](#checkpoints-in-source-code) that form series of (ephemeral) experiments. DVC helps you capture them at runtime, and manage them as batches. 3. [Persistent experiments](#persistent-experiments) have their results @@ -25,10 +25,10 @@ experimentation approaches: โš ๏ธ This feature is only available in DVC 2.0 โš ๏ธ -`dvc exp` commands let you automatically track variations to established DVC -[pipelines](/doc/command-reference/dag), so you can review, compare, and restore -them at any time, or roll back to the baseline. The base workflow goes like -this: +`dvc exp` commands let you automatically track multiple variations to +established DVC [pipelines](/doc/command-reference/dag), so you can review, +compare, and restore them at any time, or roll back to the baseline. The basic +workflow goes like this: - Modify dependencies (e.g. input data or source code), parameters, or commands (`cmd` field of `dvc.yaml`) of a @@ -48,31 +48,36 @@ this: DVC uses actual commits under custom [Git references](https://git-scm.com/book/en/v2/Git-Internals-Git-References) (found in `.git/refs/exps`) to keep track of ephemeral experiments. Each commit -has the Git repo's `HEAD` as parent. These are not pushed to the Git remote by -default (see `dvc exp push`). +has the repo `HEAD` as parent. These are not pushed to the Git remote by default +(see `dvc exp push`). > References have a unique signature similar to the > [entries in the run-cache](/doc/user-guide/project-structure/internal-files#run-cache). -> See `dvc exp` for more details and other commands. - -## Checkpoints in Python code +## Checkpoints in source code โš ๏ธ This feature is only available in DVC 2.0 โš ๏ธ -... +To track successive steps in a longer experiment, you can write your code so it +registers checkpoints with DVC at runtime. This allows you, for example, to +track the progress in deep learning techniques such as evolving neural networks. + +This kind of experiment is also derived fom your latest project version, but it +can contain a series of variations (the checkpoints). You mainly interact with +them using `dvc exp run`, `dvc exp resume`, and `dvc exp reset`. See also the +`checkpoint` field of `dvc.yaml`.
### How are checkpoints captured by DVC? -When DVC runs checkpoint-enabled stages, a new commit is generated in a custom -Git branch (found in `.git/refs/exps`) each time the code calls -`dvc.api.make_checkpoint()` or writes a `.dvc/tmp/DVC_CHECKPOINT` signal file -(see `dvc exp run` for info). These are not pushed to the Git remote by default -(see `dvc exp push`). +When DVC runs a checkpoint-enabled pipeline, a custom Git branch (in +`.git/refs/exps`) is started off the repo `HEAD`. A new commit is appended each +time the code calls `dvc.api.make_checkpoint()` or writes a +`.dvc/tmp/DVC_CHECKPOINT` signal file. These are not pushed to the Git remote by +default (see `dvc exp push`).
From 520563bbd6f7f0e08d0f170ef4c03ef1b3ee24f8 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Wed, 10 Feb 2021 21:05:34 -0600 Subject: [PATCH 14/40] guide: mention params, metrics, plots, and other copy edits to Exp Mgmt --- .../docs/user-guide/experiment-management.md | 32 ++++++++++++------- 1 file changed, 20 insertions(+), 12 deletions(-) diff --git a/content/docs/user-guide/experiment-management.md b/content/docs/user-guide/experiment-management.md index f8da6549aa..66323b91ef 100644 --- a/content/docs/user-guide/experiment-management.md +++ b/content/docs/user-guide/experiment-management.md @@ -1,10 +1,10 @@ # Experiment Management -Data science and ML are iterative processes that tend to require a large number -of attempts during their course, for example to develop data features, -hyperspace exploration, deep learning optimization, etc. DVC helps you codify -and manage all of your experiments, supporting these main -experimentation approaches: +Data science and ML are iterative processes that require a large number of +attempts to reach a certain level of a metric. Experimentation is part of the +development of data features, hyperspace exploration, deep learning +optimization, etc. DVC helps you codify and manage all of your +experiments, supporting these main approaches: 1. Create [ephemeral experiments](#ephemeral-experiments) that derive from your latest project version, without having to keep track manually. DVC does that @@ -21,6 +21,13 @@ experimentation approaches: [ways to organize](#organizing-experimentats) experiments in your project (as Git branches, as folders, etc.). +DVC also provides specialized features to codify and analyze experiments. +[Parameters](/doc/command-reference/params) are simple values you can tweak in a +human-readable text file, which cause different behaviors in your code and +models. On the other end, [metrics](/doc/command-reference/metrics) (and +[plots](/doc/command-reference/plots)) let you define, visualize, and compare +meaningful measures for the experimental results. + ## Ephemeral experiments โš ๏ธ This feature is only available in DVC 2.0 โš ๏ธ @@ -31,7 +38,7 @@ compare, and restore them at any time, or roll back to the baseline. The basic workflow goes like this: - Modify dependencies (e.g. input data or source code), - parameters, or commands (`cmd` field of `dvc.yaml`) of a + hyperparameters, or commands (`cmd` field of `dvc.yaml`) of a committed stage. - Use `dvc exp run` (instead of `repro`) to execute the pipeline. This puts the experiment's results in your workspace, and records it under the @@ -86,13 +93,14 @@ default (see `dvc exp push`). When your experiments are good enough to save or share, you may want to store them persistently as commits in your repository. -The results may have been produced with `dvc repro` directly, or after an -[ephemeral workflow](#ephemeral-experiments). In any case the workspace will -have the right `dvc.yaml` and `dvc.lock` file pair, as well as the corresponding -outputs (via `dvc checkout`). +Whether the results were produced with `dvc repro` directly, or after an +[ephemeral workflow](#ephemeral-experiments), the workspace will +have a corresponding `dvc.yaml` and `dvc.lock` file pair. The right +outputs (including [metrics](/doc/command-reference/metrics)) will +also be present, or available via `dvc checkout`. -See [Get Started: Experiments](/doc/start/experiments) for a hands-on intro -guide on regular experiments. +See [Get Started: Experiments](/doc/start/experiments) for a hands-on +introduction to regular experiments. ## Automatic log of stage runs (run-cache) From b571f509f539bd3ba02d8443b3d4333c328106c5 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Tue, 16 Feb 2021 08:10:30 -0600 Subject: [PATCH 15/40] guide: wrap up comprehensive Ecx Mgmt doc, add checkpoint to dvc.yaml spec --- .../docs/user-guide/experiment-management.md | 104 ++++++++++-------- .../project-structure/pipelines-files.md | 7 +- 2 files changed, 62 insertions(+), 49 deletions(-) diff --git a/content/docs/user-guide/experiment-management.md b/content/docs/user-guide/experiment-management.md index 66323b91ef..b16fa47499 100644 --- a/content/docs/user-guide/experiment-management.md +++ b/content/docs/user-guide/experiment-management.md @@ -6,19 +6,19 @@ development of data features, hyperspace exploration, deep learning optimization, etc. DVC helps you codify and manage all of your experiments, supporting these main approaches: -1. Create [ephemeral experiments](#ephemeral-experiments) that derive from your - latest project version, without having to keep track manually. DVC does that - for you, letting you list and compare them. The best ones can be promoted, - and the rest archived. -2. Place [in-code checkpoints](#checkpoints-in-source-code) that form series of - (ephemeral) experiments. DVC helps you capture them at runtime, and manage - them as batches. -3. [Persistent experiments](#persistent-experiments) have their results - **committed** to Git. They can be selected from existing attempts, or created - from scratch. +1. Create [experiments](#experiments) that derive from your latest project + version without having to track them manually. DVC does that automatically, + letting you list and compare them. The best ones can be promoted, and the + rest archived. +2. Place in-code [checkpoints](#checkpoints-in-source-code) that mark a series + of variations, forming an in-depth experiment. DVC helps you capture them at + runtime, and manage them in batches. +3. Apply experiments or checkpoints as [persistent](#persistent-experiments) + commits in your repository. Or create these versions from + scratch like typical project changes. At this point you may also want to consider the different - [ways to organize](#organizing-experimentats) experiments in your project (as + [ways to organize](#organization-patterns) experiments in your project (as Git branches, as folders, etc.). DVC also provides specialized features to codify and analyze experiments. @@ -28,20 +28,20 @@ models. On the other end, [metrics](/doc/command-reference/metrics) (and [plots](/doc/command-reference/plots)) let you define, visualize, and compare meaningful measures for the experimental results. -## Ephemeral experiments +## Experiments โš ๏ธ This feature is only available in DVC 2.0 โš ๏ธ -`dvc exp` commands let you automatically track multiple variations to -established DVC [pipelines](/doc/command-reference/dag), so you can review, -compare, and restore them at any time, or roll back to the baseline. The basic -workflow goes like this: +`dvc exp` commands let you automatically track a variation to an established +[data pipeline](/doc/command-reference/dag). You can create multiple isolated +experiments this way, as well as review, compare, and restore them later, or +roll back to the baseline. The basic workflow goes like this: - Modify dependencies (e.g. input data or source code), - hyperparameters, or commands (`cmd` field of `dvc.yaml`) of a - committed stage. + hyperparameters, or commands (`cmd` field of `dvc.yaml`) of + committed stages. - Use `dvc exp run` (instead of `repro`) to execute the pipeline. This puts the - experiment's results in your workspace, and records it under the + experiment's results in your workspace, and tracks it under the hood. - Visualize experiment configurations and results with `dvc exp show`. Repeat. - Use [metrics](/doc/command-reference/metrics) in your pipeline to identify the @@ -50,13 +50,13 @@ workflow goes like this:
-### How does DVC capture these experiments? +### How does DVC track experiments? DVC uses actual commits under custom [Git references](https://git-scm.com/book/en/v2/Git-Internals-Git-References) -(found in `.git/refs/exps`) to keep track of ephemeral experiments. Each commit -has the repo `HEAD` as parent. These are not pushed to the Git remote by default -(see `dvc exp push`). +(found in `.git/refs/exps`) to keep track of experiments created with `dvc exp`. +Each commit has the repo `HEAD` as parent. These are not pushed to the Git +remote by default (see `dvc exp push`). > References have a unique signature similar to the > [entries in the run-cache](/doc/user-guide/project-structure/internal-files#run-cache). @@ -72,9 +72,9 @@ registers checkpoints with DVC at runtime. This allows you, for example, to track the progress in deep learning techniques such as evolving neural networks. This kind of experiment is also derived fom your latest project version, but it -can contain a series of variations (the checkpoints). You mainly interact with -them using `dvc exp run`, `dvc exp resume`, and `dvc exp reset`. See also the -`checkpoint` field of `dvc.yaml`. +tracks a series of variations (the checkpoints). You interact with them using +`dvc exp run`, `dvc exp resume`, and `dvc exp reset` (see also the `checkpoint` +field of `dvc.yaml`).
@@ -93,14 +93,36 @@ default (see `dvc exp push`). When your experiments are good enough to save or share, you may want to store them persistently as commits in your repository. -Whether the results were produced with `dvc repro` directly, or after an -[ephemeral workflow](#ephemeral-experiments), the workspace will -have a corresponding `dvc.yaml` and `dvc.lock` file pair. The right -outputs (including [metrics](/doc/command-reference/metrics)) will -also be present, or available via `dvc checkout`. - -See [Get Started: Experiments](/doc/start/experiments) for a hands-on -introduction to regular experiments. +Whether the results were produced with `dvc repro` directly, or after a +`dvc exp` workflow (refer to previous sections), the `dvc.yaml` and `dvc.lock` +pair in the workspace will codify the experiment as a new project +version. The right outputs (including +[metrics](/doc/command-reference/metrics)) should also be present, or available +via `dvc checkout`. + +> ๐Ÿ‘จโ€๐Ÿ’ป See [Get Started: Experiments](/doc/start/experiments) for a hands-on +> introduction to regular experiments. + +### Organization patterns + +DVC takes care of arranging `dvc exp` experiments and the data +cache under the hood. But when it comes to full-blown persistent +experiments, it's up to you to decide how to organize them in your project. +These are the main alternatives: + +- **Git tags and branches** - use the repo's "time dimension" to distribute your + experiments. This makes the most sense for experiments that build on each + other. Helpful if the Git [revisions](https://git-scm.com/docs/revisions) can + be easily visualized, for example with tools + [like GitHub](https://docs.github.com/en/github/visualizing-repository-data-with-graphs/viewing-a-repositorys-network). +- **Directories** - the project's "space dimension" can be structured with + directories (folders) to organize experiments. Useful when you want to see all + your experiments at the same time (without switching versions) by just + exploring the file system. +- **Hybrid** - combining an intuitive directory structure with a good repo + branching strategy tends to be the best option for complex projects. + Completely independent experiments live in separate directories, while their + progress can be found in different branches. ## Automatic log of stage runs (run-cache) @@ -114,17 +136,3 @@ or computing resources. โœ… This built-in feature is called run-cache and it can dramatically improve performance. It's enabled out-of-the-box (but can be disabled with the `--no-run-cache` command option). - -## Organizing experiments - -Automatic [stage run logs](#automatic-log-of-stage-runs-run-cache) are dumped -without a structure in the run-cache. -[Ephemeral experiments](#ephemeral-experiments) always have a linear branch -structure (a queue) based on current `HEAD` commit. But when it comes to -full-blown [persistent experiments](#persistent-experiments), it's up to you to -decide how to organize them in your project. These are the main alternatives: - -- Branches -- Tags -- Directories -- Hybrid diff --git a/content/docs/user-guide/project-structure/pipelines-files.md b/content/docs/user-guide/project-structure/pipelines-files.md index 8fb5315fc6..b1ac66c018 100644 --- a/content/docs/user-guide/project-structure/pipelines-files.md +++ b/content/docs/user-guide/project-structure/pipelines-files.md @@ -44,6 +44,8 @@ changed to decide whether the stage requires re-execution (see `dvc status`). If it writes files or dirs, they can be defined as outputs (`outs`). DVC will track them going forward (similar to using `dvc add`). +> See the full stage entry [specification](#stage-entries). + ### Parameter dependencies [Parameters](/doc/command-reference/params) are a special type of stage @@ -337,7 +339,9 @@ stages: > Note that this feature is not compatible with [templating](#templating) at the > moment. -## Specification +## Stage entries + +These are the fields that are accepted in each stage: | Field | Description | | ---------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | @@ -349,6 +353,7 @@ stages: | `metrics` | List of [metrics files](/doc/command-reference/metrics), and optionally, whether or not this metrics file is cached (`true` by default). See the `--metrics-no-cache` (`-M`) option of `dvc run`. | | `plots` | List of [plot metrics](/doc/command-reference/plots), and optionally, their default configuration (subfields matching the options of `dvc plots modify`), and whether or not this plots file is cached ( `true` by default). See the `--plots-no-cache` option of `dvc run`. | | `frozen` | Whether or not this stage is frozen from reproduction | +| `checkpoint` | Set to `true` to let DVC know this stage registers [in-code checkpoint](/doc/user-guide/experiment-management#checkpoints-in-source-code) experiments. | | `always_changed` | Whether or not this stage is considered as changed by commands such as `dvc status` and `dvc repro`. `false` by default | | `meta` | (Optional) arbitrary metadata can be added manually with this field. Any YAML content is supported. `meta` contents are ignored by DVC, but they can be meaningful for user processes that read or write `.dvc` files directly. | | `desc` | (Optional) user description for this stage. This doesn't affect any DVC operations. | From 143a2b2e8843770c0360203d1077f018acb5e9ec Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Tue, 16 Feb 2021 21:22:56 +0300 Subject: [PATCH 16/40] glossary: create "stage" entry Fixes #1579 --- .../docs/user-guide/basic-concepts/stage.md | 24 +++++++++++++++++++ 1 file changed, 24 insertions(+) create mode 100644 content/docs/user-guide/basic-concepts/stage.md diff --git a/content/docs/user-guide/basic-concepts/stage.md b/content/docs/user-guide/basic-concepts/stage.md new file mode 100644 index 0000000000..91bb3971fc --- /dev/null +++ b/content/docs/user-guide/basic-concepts/stage.md @@ -0,0 +1,24 @@ +--- +name: 'Stage' +match: ['stage', 'stages'] +tooltip: >- + A stage is a single unit of execution in a pipeline that produces some + (intermediate or final) output. [รฐยŸย“ย–](/doc/user-guide/concepts/stage) +--- + +# Stage + +A stage is a single unit of execution in a pipeline that takes some +input and produces some output. A stage is defined by its +dependencies, its command and its outputs. + +Stages that depend on other stages can be invalidated by the change in their +dependencies. A stage may have multiple dependencies, multiple commands and +multiple outputs. A stage can also have parameter dependencies to +represent model hyperparameters and similar changes during execution. + +A stage may also represent an import from outside sources. + +## Further Reading + +- `dvc run` command reference From f1bfda3d0851d9f54764e942a757a2e530b6a87c Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Tue, 16 Feb 2021 12:25:11 -0600 Subject: [PATCH 17/40] ref: move checkpoint field into dvc.yaml output spec --- content/docs/user-guide/experiment-management.md | 2 +- .../user-guide/project-structure/pipelines-files.md | 12 ++++++------ 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/content/docs/user-guide/experiment-management.md b/content/docs/user-guide/experiment-management.md index b16fa47499..7cda085317 100644 --- a/content/docs/user-guide/experiment-management.md +++ b/content/docs/user-guide/experiment-management.md @@ -74,7 +74,7 @@ track the progress in deep learning techniques such as evolving neural networks. This kind of experiment is also derived fom your latest project version, but it tracks a series of variations (the checkpoints). You interact with them using `dvc exp run`, `dvc exp resume`, and `dvc exp reset` (see also the `checkpoint` -field of `dvc.yaml`). +field of `dvc.yaml` outputs).
diff --git a/content/docs/user-guide/project-structure/pipelines-files.md b/content/docs/user-guide/project-structure/pipelines-files.md index b1ac66c018..c335151e36 100644 --- a/content/docs/user-guide/project-structure/pipelines-files.md +++ b/content/docs/user-guide/project-structure/pipelines-files.md @@ -353,7 +353,6 @@ These are the fields that are accepted in each stage: | `metrics` | List of [metrics files](/doc/command-reference/metrics), and optionally, whether or not this metrics file is cached (`true` by default). See the `--metrics-no-cache` (`-M`) option of `dvc run`. | | `plots` | List of [plot metrics](/doc/command-reference/plots), and optionally, their default configuration (subfields matching the options of `dvc plots modify`), and whether or not this plots file is cached ( `true` by default). See the `--plots-no-cache` option of `dvc run`. | | `frozen` | Whether or not this stage is frozen from reproduction | -| `checkpoint` | Set to `true` to let DVC know this stage registers [in-code checkpoint](/doc/user-guide/experiment-management#checkpoints-in-source-code) experiments. | | `always_changed` | Whether or not this stage is considered as changed by commands such as `dvc status` and `dvc repro`. `false` by default | | `meta` | (Optional) arbitrary metadata can be added manually with this field. Any YAML content is supported. `meta` contents are ignored by DVC, but they can be meaningful for user processes that read or write `.dvc` files directly. | | `desc` | (Optional) user description for this stage. This doesn't affect any DVC operations. | @@ -374,11 +373,12 @@ validation and auto-completion. > Notice that these are a subset of those in `.dvc` file > [output entries](/doc/user-guide/project-structure/dvc-files#output-entries). -| Field | Description | -| --------- | ------------------------------------------------------------------------------------------------------------------------------------------ | -| `cache` | Whether or not this file or directory is cached (`true` by default). See the `--no-commit` option of `dvc add`. | -| `persist` | Whether the output file/dir should remain in place while `dvc repro` runs (`false` by default: outputs are deleted when `dvc repro` starts | -| `desc` | (Optional) user description for this output. This doesn't affect any DVC operations. | +| Field | Description | +| ------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `cache` | Whether or not this file or directory is cached (`true` by default). See the `--no-commit` option of `dvc add`. | +| `persist` | Whether the output file/dir should remain in place while `dvc repro` runs (`false` by default: outputs are deleted when `dvc repro` starts | +| `desc` | (Optional) user description for this output. This doesn't affect any DVC operations. | +| `checkpoint` | Set to `true` to let DVC know that this output is associated with [in-code checkpoints](/doc/user-guide/experiment-management#checkpoints-in-source-code) (for `dvc experiments`). | ## dvc.lock file From 51fa3278a55b9f247130d37be5928f96cf0744e2 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Tue, 16 Feb 2021 17:35:21 -0600 Subject: [PATCH 18/40] glossary: migrate exp tooltip to new format (fontmatter) --- .../docs/user-guide/basic-concepts/experiment.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/docs/user-guide/basic-concepts/experiment.md b/content/docs/user-guide/basic-concepts/experiment.md index e3424d929c..e4130a9683 100644 --- a/content/docs/user-guide/basic-concepts/experiment.md +++ b/content/docs/user-guide/basic-concepts/experiment.md @@ -1,11 +1,11 @@ --- name: Experiment match: [experiment, experiments] +tooltip: >- + An attempt to reach desired/better/interesting results during data pipelining + or ML model development. DVC is designed to help [manage + experiments](/doc/user-guide/experiment-management), having built-in + mechanisms like the + [run-cache](/doc/user-guide/project-structure/internal-files#run-cache) and + the `dvc experiments` commands (coming in DVC 2.0). --- - -An attempt to reach desired/better/interesting results during data pipelining or -ML model development. DVC is designed to help -[manage experiments](/doc/user-guide/experiment-management), having built-in -mechanisms like the -[run-cache](/doc/user-guide/project-structure/internal-files#run-cache) and the -`dvc experiments` commands (coming in DVC 2.0). From 1ea2da363fd4c8271641593a69470a34a8f0e53a Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Wed, 17 Feb 2021 14:46:37 +0300 Subject: [PATCH 19/40] modified to have a more informal language and removed BC text from the tooltip --- .../docs/user-guide/basic-concepts/stage.md | 22 +++---------------- 1 file changed, 3 insertions(+), 19 deletions(-) diff --git a/content/docs/user-guide/basic-concepts/stage.md b/content/docs/user-guide/basic-concepts/stage.md index 91bb3971fc..d83a2673fa 100644 --- a/content/docs/user-guide/basic-concepts/stage.md +++ b/content/docs/user-guide/basic-concepts/stage.md @@ -2,23 +2,7 @@ name: 'Stage' match: ['stage', 'stages'] tooltip: >- - A stage is a single unit of execution in a pipeline that produces some - (intermediate or final) output. [รฐยŸย“ย–](/doc/user-guide/concepts/stage) + A single step in a pipeline which may import data, run experiments and produce + some (intermediate or final) output. Stages may depend other stages and they + can be automatically invalidated when their dependencies change. --- - -# Stage - -A stage is a single unit of execution in a pipeline that takes some -input and produces some output. A stage is defined by its -dependencies, its command and its outputs. - -Stages that depend on other stages can be invalidated by the change in their -dependencies. A stage may have multiple dependencies, multiple commands and -multiple outputs. A stage can also have parameter dependencies to -represent model hyperparameters and similar changes during execution. - -A stage may also represent an import from outside sources. - -## Further Reading - -- `dvc run` command reference From 3d7aed36d0084ba175cccf531a7f2cf785d2d9c7 Mon Sep 17 00:00:00 2001 From: Batuhan Taskaya Date: Thu, 18 Feb 2021 03:03:34 +0300 Subject: [PATCH 20/40] cmd: add dvc update --to-remote docs (#2175) * cmd: add dvc update --to-remote docs * Update content/docs/command-reference/update.md Co-authored-by: Jorge Orpinel Co-authored-by: Ivan Shcheklein Co-authored-by: Jorge Orpinel --- content/docs/command-reference/update.md | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/content/docs/command-reference/update.md b/content/docs/command-reference/update.md index 9b8157707e..78b66e45c7 100644 --- a/content/docs/command-reference/update.md +++ b/content/docs/command-reference/update.md @@ -6,8 +6,8 @@ repositories, and the corresponding import stage `.dvc` files. ## Synopsis ```usage -usage: dvc update [-h] [-q | -v] [--rev ] [-R] - targets [targets ...] +usage: dvc update [-h] [-q | -v] [--rev ] [-R] [--to-remote] + [-r ] [-j ] targets [targets ...] positional arguments: targets Import stage .dvc files to update. Using -R, directories @@ -49,6 +49,20 @@ $ dvc update --rev master directory and its subdirectories for import stage `.dvc` files to inspect. If there are no directories among the targets, this option is ignored. +- `--to-remote` - update the import `.dvc` file and + [transfer](/doc/command-reference/import-url#example-import-straight-to-the-remote) + the new data directly to remote storage (the default one unless `-r` is used). + No changes are done in the workspace. Use `dvc pull` to get the + data locally. + +- `-r `, `--remote ` - name of the + [remote storage](/doc/command-reference/remote) (can only be used with + `--to-remote`). + +- `-j `, `--jobs ` - parallelism level for DVC to download data + from the source. The default value is `4 * cpu_count()`. For SSH remotes, the + default is `4`. Using more jobs may speed up the operation. + - `-h`, `--help` - prints the usage/help message, and exit. - `-q`, `--quiet` - do not write anything to standard output. Exit with 0 if no From ba8178ee102990882540da7db98f3ef576eb0f2d Mon Sep 17 00:00:00 2001 From: Jenifer De Figueiredo Date: Wed, 17 Feb 2021 18:08:17 -0800 Subject: [PATCH 21/40] Draft of Feb Heartbeat (#2190) * Draft of Feb Heartbeat * please work * add images * fix HB image and author path * typos and spacy pic change * typos * updates to Heartbeat * revised dagshub image and link * images shrunk * dashub-logo * corrcting Roger and Dagshub logo * adjust roger * added Alt text * cleanup: delete redundant files * Update 2021-02-12-february-21-dvc-heartbeat.md * Delete 2021-02-12-february-21-dvc-heartbeat.md * Update jeny_defigueiredo.md * Update jeny_defigueiredo.md * Delete spacy_integration.png * Delete dags_diagram.png Co-authored-by: elleobrien Co-authored-by: Ivan Shcheklein --- content/authors/jeny_defigueiredo.md | 8 + .../2021-02-16-february-21-dvc-heartbeat.md | 177 ++++++++++++++++++ static/uploads/avatars/jeny_defigueiredo.png | Bin 0 -> 92314 bytes .../images/2021-02-16/dagshub-logo.png | Bin 0 -> 60616 bytes .../uploads/images/2021-02-16/feb21cover.png | Bin 0 -> 655504 bytes .../images/2021-02-16/newstack_image.png | Bin 0 -> 427429 bytes .../images/2021-02-16/spacy_integration.jpg | Bin 0 -> 157213 bytes 7 files changed, 185 insertions(+) create mode 100644 content/authors/jeny_defigueiredo.md create mode 100644 content/blog/2021-02-16-february-21-dvc-heartbeat.md create mode 100644 static/uploads/avatars/jeny_defigueiredo.png create mode 100644 static/uploads/images/2021-02-16/dagshub-logo.png create mode 100644 static/uploads/images/2021-02-16/feb21cover.png create mode 100644 static/uploads/images/2021-02-16/newstack_image.png create mode 100644 static/uploads/images/2021-02-16/spacy_integration.jpg diff --git a/content/authors/jeny_defigueiredo.md b/content/authors/jeny_defigueiredo.md new file mode 100644 index 0000000000..dcf00954ec --- /dev/null +++ b/content/authors/jeny_defigueiredo.md @@ -0,0 +1,8 @@ +--- +name: Jeny De Figueiredo +avatar: jeny_defigueiredo.png +links: + - https://twitter.com/jendefig +--- + +Community Manager at [DVC](https://dvc.org) diff --git a/content/blog/2021-02-16-february-21-dvc-heartbeat.md b/content/blog/2021-02-16-february-21-dvc-heartbeat.md new file mode 100644 index 0000000000..5c54bb19fc --- /dev/null +++ b/content/blog/2021-02-16-february-21-dvc-heartbeat.md @@ -0,0 +1,177 @@ +๏ปฟ--- +title: February '21 Heartbeat +date: 2021-02-16 +description: | + Monthly updates are here! Read all about our growing team, + our CEO's interview on The New Stack, integration with spaCy and more! +descriptionLong: | + Monthly updates are here! Read all about our growing team, + our CEO's interview on The New Stack, integration with spaCy and more! +picture: 2021-02-16/feb21cover.png +author: jeny_defigueiredo +commentsUrl: https://discuss.dvc.org/t/february-21-heartbeat/669 +tags: + - Heartbeat + - CML + - DVC + - DAGsHub + - spaCy + - ML Summit 2021 + - Spell + - MLOps +--- + +## News + +Happy February! Here's all the news to keep you up to date. + +## We've hired and are still hiring! + +We have four new team members this month! + +[**Dave Berenbaum**](https://www.linkedin.com/in/david-berenbaum-20b6b424/) came +to Iterative.ai by way of a +[previous contribution](https://github.com/iterative/dvc/pull/2107) to our open +source products while working as a Data Science Manager at Captial One. He joins +the team as a Technical Product Manager. We are thrilled he's here! + +[**Batuhan Taskaya**](https://www.linkedin.com/in/batuhan-osman-taskaya-7803b61a0/) +joins us as a DVC Software Engineer working on the Python core. Batuhan is +excited to work on open source full time and we are excited to have him do so! + +[**Jeny De Figueiredo**](https://www.linkedin.com/in/jenifer-de-figueiredo/) is +involved in the Seattle area data science community at Data Circles and is a +WiDS Puget Sound Ambassador. She joins us as our new Community Manager and is +looking forward to further building and engaging the community in MLOps! (Hi! +This is me. ๐Ÿ™‹๐Ÿปโ€โ™€๏ธ I'll be writing Heartbeat!) + +[**Roger Parent**](https://www.linkedin.com/in/rogermparent/) has already been a +big part of building DVC and [CML](https://cml.dev/). He has been a primary +developer of a UI that interfaces with the DVC Python application to provide an +interface with the Experiments feature that's coming out with DVC 2.0. We are so +excited to have him joining us full time as Software Engineer. + +![Search](https://media.giphy.com/media/vAvWgk3NCFXTa/giphy.gif) + +## Open Positions + +We are on the hunt for a +[TypeScript Front-End Engineer](https://docs.google.com/document/d/1aT5HZYt4kAUxXqD4JNTe3jPDlVUwSmnEWDPR2QoKdvo/edit) +to build SaaS and a VS Code UI for our popular machine learning tools: DVC and +CML. The ML tools ecosystem is what JS space was 10 years ago. Come join us on +this exciting project! + +Our search continues for a +[Developer Advocate](https://weworkremotely.com/remote-jobs/iterative-developer-advocate) +to support and inspire developers by creating new content like blogs, tutorials, +and videos - plus lead outreach through meetups and conferences. + +Does this sound like you or someone you know? Be in touch! + +## Iterative.ai Featured on The New Stack + +[Susan Hall](https://thenewstack.io/author/susanhall/) of +[The New Stack.io](https://thenewstack.io/) interviewed our very own CEO, +[Dmitry Petrov](https://twitter.com/fullstackml), discussing the needs of ML +engineers and how Iterative.ai makes tools to enable version control and CI/CD +for versioning data and ML models. + +> "ML engineers, they still need collaboration. They need GitHub for +> collaboration, they need this CI/CD system to resolve [issues] between each +> other, between the team and productions system." - Dmitry Petrov + + + +## Workshops and Talks + +### Developer Advocacy for Data Science + +So you saw the post further up. ๐Ÿ‘†๐Ÿฝ Curious about developer advocacy or what to +look for in a hire for this position? +[Elle O'Brien](https://twitter.com/drelleobrien) dove into this recently with +[Alexey Grigorev](https://twitter.com/Al_Grigor) (author of a +[Data Science Bookcamp](https://mlbookcamp.com/)) +[in this podcast](https://www.youtube.com/watch?v=jv5W4jXk4P4) on +[DataTalks.club](http://datatalks.club/) You can watch it here below. ๐Ÿ‘‡๐Ÿผ + +https://www.youtube.com/watch?v=jv5W4jXk4P4 + +## From the Community + +As ever, we have much to share from the great citizens of the DVC community. + +### spaCy and DVC Integration + +If your NLP team uses spaCy to manage your projects, with spaCy's release of +v3.0, you can now enjoy DVC integration to manage your workflow like Git! Check +out the [documentation here](https://spacy.io/usage/projects#integrations) to +streamline and track your process! ๐Ÿ† + + + +### DagsHub and DVC Integrations + +This month two great articles came out regarding the integration of DAGsHub and +DVC. First, this article: [Datasets Should Behave Like Git Repo walks you +through the steps to use DVC in your data versioning. The following image shows +the dependencies and how you simply need to do a `dvc update` each time your +dataset or model changes to track the process. + + + +### Did you say "Works Out of the Box?" + +Also from DAGsHub, by CEO [Dean Pleban](https://twitter.com/DeanPlbn), +[Free Dataset & Model Hosting with Zero Configuration - Launching DAGsHub Storage](https://dagshub.com/blog/dagshub-storage-zero-configuration-dataset-model-hosting/) +tells how their new DAGsHub storage is a DVC remote that requires zero +configuration (!) and will allow for team and organization access controls as +well as easy visibility. + +![Friends](https://media.giphy.com/media/Ftz07proVX6Rq/giphy.gif) + +### Model Management and ML Workflow Orchestration with DVC and Apache Airflow ๐Ÿ‡ฉ๐Ÿ‡ช โ—๏ธ + +We're really excited about a German language workshop led by +[Matthias Niehoff](https://twitter.com/matthiasniehoff)! The workshop will be a +part of the ML Summit 2021 taking place April 19-21st, but registration closes +February 18th. So time is ticking. โฐ The Conference is online, but will be in +German. For more info, head here ๐Ÿ‘‰๐Ÿฝ for the +[Workshop Details](https://ml-summit.de/machine-learing/modellmanagement-und-ml-workflow-orchestrierung-mit-dvc-und-apache-airflow/). + +### "_The_ most popular 'N+1' tool used by teams on Spell" + +[Using DVC as a Lightweight Feature Store on Spell](https://spell.ml/blog/using-dvc-with-spell-YBHOChEAACgAaSmV) +by [Aleksey Bilogur](https://twitter.com/ResidentMario) , reviews the process of +using DVC with Spell for managing changing datasets, enabling team-wide data +reproducibility and why Spell fans are DVC fans, and vice versa. ๐Ÿ”„ + +![Fans](https://media.giphy.com/media/GM8PrUsm92hRC/giphy.gif) + +## Tweet Love โค๏ธ + +https://twitter.com/mihail_eric/status/1357014486377324547?s=20 + +You're all caught up! See you at the next Community Gems ๐Ÿ’Ž! + +--- + +_Do you have any use case questions or need support? 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z|NJAd$NX1p@#jbNum3@@^6G{3b2|6_7xt)e A#{d8T literal 0 HcmV?d00001 From 520958522b66726b05577e48593577382709b54b Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Thu, 18 Feb 2021 16:05:34 -0600 Subject: [PATCH 34/40] Update content/blog/2021-02-18-dvc-2-0-pre-release.md --- content/blog/2021-02-18-dvc-2-0-pre-release.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/blog/2021-02-18-dvc-2-0-pre-release.md b/content/blog/2021-02-18-dvc-2-0-pre-release.md index 0eb9d123fd..d4ddd1db1f 100644 --- a/content/blog/2021-02-18-dvc-2-0-pre-release.md +++ b/content/blog/2021-02-18-dvc-2-0-pre-release.md @@ -115,7 +115,7 @@ of `dvc run` for more details. ### Iterating over params with foreach stages Iterating over params was a frequently requested feature. Now users can define -multiple similar stages with a templetized command. +multiple similar stages with a templatized command. ```yaml stages: From 308c825adbe0a7e283f5bd4714b74eb83e9983b2 Mon Sep 17 00:00:00 2001 From: Dmitry Petrov Date: Thu, 18 Feb 2021 15:06:32 -0800 Subject: [PATCH 35/40] show live metrics --- .../blog/2021-02-18-dvc-2-0-pre-release.md | 42 +- .../blog/2021-02-18-dvc-2-0-pre-release.md__ | 461 ++++++++++++++++++ .../images/2021-02-18/dvclive-diff-html.png | Bin 0 -> 175962 bytes 3 files changed, 497 insertions(+), 6 deletions(-) create mode 100644 content/blog/2021-02-18-dvc-2-0-pre-release.md__ create mode 100644 static/uploads/images/2021-02-18/dvclive-diff-html.png diff --git a/content/blog/2021-02-18-dvc-2-0-pre-release.md b/content/blog/2021-02-18-dvc-2-0-pre-release.md index d4ddd1db1f..c7a7575398 100644 --- a/content/blog/2021-02-18-dvc-2-0-pre-release.md +++ b/content/blog/2021-02-18-dvc-2-0-pre-release.md @@ -384,7 +384,7 @@ summary metrics in a single page: Note, the HTML and the summary metrics files are generating automatically for each. So, you can monitor model performance in realtime. -### Navigation with the metrics file +### Git-navigation with the metrics file DVC repository is NOT required to use the live metrics functionality from the above. It works independently from DVC. @@ -392,18 +392,48 @@ above. It works independently from DVC. DVC repository become usefule when the metrics and plots are commited in your Git repository and you need navigation around the metrics. +Metrics difference between workspace and the last Git commit: + ```dvc -$ dvc metrics diff -???? +$ git status -s + M logs.json + M logs/accuracy.tsv + M logs/loss.tsv + M logs/val_accuracy.tsv + M logs/val_loss.tsv + M train.py +?? model.h5 + +$ dvc metrics diff --target logs.json +Path Metric Old New Change +logs.json accuracy 0.995 0.99 -0.005 +logs.json loss 0.01596 0.03036 0.0144 +logs.json step 41 36 -5 +logs.json val_accuracy 0.515 0.5175 0.0025 +logs.json val_loss 13.70596 3.29033 -10.41563 +``` + +The difference between a particular commit/branch/tag or between two commits: + +``` +$ dvc metrics diff --target logs.json HEAD^ 47b85c +Path Metric Old New Change +logs.json accuracy 0.995 0.998 0.003 +logs.json loss 0.01596 0.01951 0.00355 +logs.json step 41 82 41 +logs.json val_accuracy 0.515 0.51 -0.005 +logs.json val_loss 13.70596 5.83056 -7.8754 ``` -The same with the plots: +The same Git-navigation works with the plots: ```dvc -$ dvc plots diff .... -???? +$ dvc plots diff --target logs +file:///Users/dmitry/src/exp-dc/plots.html ``` +![](/uploads/images/2021-02-18/dvclive-diff-html.png) + Another nice thing about the live metrics - they work across ML experiments and checkpoints if properly set up in dvc stages. To set up live metrics you need to specify the metrics directory in `live` section of a stage: diff --git a/content/blog/2021-02-18-dvc-2-0-pre-release.md__ b/content/blog/2021-02-18-dvc-2-0-pre-release.md__ new file mode 100644 index 0000000000..c7a7575398 --- /dev/null +++ b/content/blog/2021-02-18-dvc-2-0-pre-release.md__ @@ -0,0 +1,461 @@ +--- +title: DVC 2.0 Pre-Release +date: 2021-02-17 +description: | + Today, we're announcing DVC 2.0 pre-release. We'll share lessons from our + journey and how these will be reflected in the coming release. + +descriptionLong: | + The new release is a result of our learning from our users. There are four + major features are coming: + + ๐Ÿ”— ML pipeline templating and iterative foreach stages + + ๐Ÿงช Lightweight ML experiments + + ๐Ÿ“ ML model checkpoints + + ๐Ÿ“ˆ Dvc-live - new open-source library for metrics logging + +picture: 2021-02-18/dvc-2-0-pre-release.png +pictureComment: DVC 2.0 Pre-Release +author: dmitry_petrov +commentsUrl: https://discuss.dvc.org/t/dvc-3-years-anniversary-and-1-0-pre-release/374 +tags: + - Release + - MLOps + - DataOps +--- + +## Install + +First things first. You can install the 2.0 pre-release from the master branch +in our repo (instruction [here](https://dvc.org/doc/install/pre-release)) or +through pip: + +```dvc +$ pip install --upgrade --pre dvc +``` + +## ML pipelines parameterization and foreach stages + +After introducing the multi-stage pipeline file `dvc.yaml`, it was quickly +adopted among our users. The DVC team got tons of positive feedback from them, +as well as feature requests. + +### Pipeline parameters from `vars` + +The most requested feature was the ability to use parameters in `dvc.yaml`. For +example. So, you can pass the same seed value or filename to multiple stages in +the pipeline. + +```yaml +vars: + train_matrix: train.pkl + test_matrix: test.pkl + seed: 20210215 + +... + +stages: + process: + cmd: python process.py \ + --seed ${seed} \ + --train ${train_matrix} \ + --test ${test_matrix} + outs: + - ${test_matrix} + - ${train_matrix} + + ... + + train: + cmd: python train.py ${train_matrix} --seed ${seed} + deps: + - ${train_matrix} +``` + +Also, it gives an ability to localize all important parameters in a single +`vars` block, and play with them. This is a natural thing to do for scenarios +like NLP or when hyperparameter optimization is happening not only in the model +training code but in the data processing as well. + +### Pipeline parameters from params files + +It is quite common to define pipeline parameters in a config file or a +parameters file (like `params.yaml`) instead of in the pipeline file `dvc.yaml` +itself. These parameters defined in `params.yaml` can also be used in +`dvc.yaml`. + +```yaml +# params.yaml +models: + us: + thresh: 10 + filename: 'model-us.hdf5' +``` + +```yaml +# dvc.yaml +stages: + build-us: + cmd: >- + python script.py + --out ${models.us.filename} + --thresh ${models.us.thresh} + outs: + - ${models.us.filename} +``` + +DVC properly tracks params dependencies for each stage starting from the +previous DVC version 1.0. See the +[`--params` option](/doc/command-reference/run#for-displaying-and-comparing-data-science-experiments) +of `dvc run` for more details. + +### Iterating over params with foreach stages + +Iterating over params was a frequently requested feature. Now users can define +multiple similar stages with a templatized command. + +```yaml +stages: + build: + foreach: + gb: + thresh: 15 + filename: 'model-gb.hdf5' + us: + thresh: 10 + filename: 'model-us.hdf5' + do: + cmd: >- + python script.py --out ${item.filename} --thresh ${item.thresh} + outs: + - ${item.filename} +``` + +## Lightweight ML experiments + +DVC uses Git as a foundation for ML experiments. This solid foundation makes +each ML experiment reproducible and accessible from Git history. This Git-based +approach works very well for ML projects with mature ML models when only a few +new experiments per day are running. However, in more active development when +dozens or hundreds of experiments need to be run in a single day, Git creates +overhead - each experiment run requires additional Git commands +`git add/commit`, and comparing all experiments is difficult. + +We introduce lightweight experiments in DVC 2.0! This is the way of +auto-tracking without any overhead from ML engineers. + +โš ๏ธ Note, ML experiment is an experimental feature in the coming release. It +means the commands might change a bit even after the release. + +Run an ML experiment with a new hyperparameter from `params.yaml`: + +```dvc +$ dvc exp run --set-param featurize.max_features=3000 + +Reproduced experiment(s): exp-bb55c +Experiment results have been applied to your workspace. + +$ dvc exp diff +Path Metric Value Change +scores.json auc 0.57462 0.0072197 + +Path Param Value Change +params.yaml featurize.max_features 3000 1500 +``` + +More experiments: + +```dvc +$ dvc exp run --set-param featurize.max_features=4000 +Reproduced experiment(s): exp-9bf22 +Experiment results have been applied to your workspace. + +$ dvc exp run --set-param featurize.max_features=5000 +Reproduced experiment(s): exp-63ee0 +Experiment results have been applied to your workspace. + +$ dvc exp run --set-param featurize.max_features=5000 \ + --set-param featurize.ngrams=3 +Reproduced experiment(s): exp-80655 +Experiment results have been applied to your workspace. +``` + +In the examples above, hyperparamteres were changed automaticaly by option +`--set-param`. User can make this changes manualy by modifying the file. The +same way _any code or data files can be changed_ and `dvc exp run` will capture +the changes. + +See all the runs: + +```dvc +$ dvc exp show --no-pager --no-timestamp \ + --include-params featurize.max_features,featurize.ngrams +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ Experiment โ”ƒ auc โ”ƒ featurize.max_features โ”ƒ featurize.ngrams โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ workspace โ”‚ 0.56359 โ”‚ 5000 โ”‚ 3 โ”‚ +โ”‚ master โ”‚ 0.5674 โ”‚ 1500 โ”‚ 2 โ”‚ +โ”‚ โ”œโ”€โ”€ exp-80655 โ”‚ 0.56359 โ”‚ 5000 โ”‚ 3 โ”‚ +โ”‚ โ”œโ”€โ”€ exp-63ee0 โ”‚ 0.5515 โ”‚ 5000 โ”‚ 2 โ”‚ +โ”‚ โ”œโ”€โ”€ exp-9bf22 โ”‚ 0.56448 โ”‚ 4000 โ”‚ 2 โ”‚ +โ”‚ โ””โ”€โ”€ exp-bb55c โ”‚ 0.57462 โ”‚ 3000 โ”‚ 2 โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` + +No artificial branches, only custome references `exps` (do not worry if you +don't understand this part - it is an implementation detail): + +```dvc +$ git branch +* master + +$ git show-ref +5649f62d845fdc29e28ea6f7672dd729d3946940 refs/exps/exec/EXEC_APPLY +5649f62d845fdc29e28ea6f7672dd729d3946940 refs/exps/exec/EXEC_BRANCH +5649f62d845fdc29e28ea6f7672dd729d3946940 refs/exps/71/67904d89e116f28daf7a6e4c0878268117c893/exp-80655 +f16e7b7c804cf52d91d1d11850c15963fb2a8d7b refs/exps/97/d69af70c6fb4bc59aefb9a87437dcd28b3bde4/exp-63ee0 +0566d42cddb3a8c4eb533f31027f0febccbbc2dd refs/exps/91/94265d5acd847e1c439dd859aa74b1fc3d73ad/exp-bb55c +9bb067559583990a8c5d499d7435c35a7c9417b7 refs/exps/49/5c835cd36772123e82e812d96eabcce320f7ec/exp-9bf22 +``` + +The best experiment can be promoted to the workspace and commited to Git. + +```dvc +$ dvc exp apply exp-bb55c +$ git add . +$ git commit -m 'optimize max feature size' +``` + +Alternatively, an experiment can be promoted to a branch (`big_fr_size` branch +in this case): + +```dvc +$ dvc exp branch exp-80655 big_fr_size +Git branch 'big_fr_size' has been created from experiment 'exp-c695f'. +To switch to the new branch run: + + git checkout big_fr_size +``` + +Remove all the experiments that were not used: + +```dvc +$ dvc exp gc --workspace --force +``` + +## Model checkpoints tracking + +ML model checkpoints are an essential part of deep learning. ML engineers prefer +to save the model files (or weights) at checkpoints during a training process +and return back when metrics start diverging or learning is not fast enough. + +The checkpoints create a different dynamic around ML modeling process and need a +special support from the toolset: + +1. Track and save model checkpoints (DVC outputs) periodically, not only the + final result or training epoch. +2. Save metrics corresponding to each of the checkpoints. +3. Reuse checkpoints - warm-start training with an existing model file, + corresponding code, dataset version and metrics. + +This new behaviour is supported in DVC 2.0. Now, DVC can version all your +checkpoints with corresponding code and data. It brings reproducibility of DL +processes to the next level - every checkpoint is reproducible. + +This is how you define checkpoints with live-metrics: + +``` + +``` + +Start and interrupt training process: + +``` + +``` + +Navigate in checkpoints: + +``` + +``` + +Get back to any version: + +``` + +``` + +Under the hood DVC uses Git to store the checkpoint meta-information. +Straight-forward implementation of checkpoints on top of Git should include +branches and auto-commits in the branches. This approach over-pollutes the +branch namespace very quickly. To avoid this issue, we introduced Git custom +references `exps` the same way as GitHub uses Git custom references `pulls` to +track pull requests. This is an interesting technical topic that deserves a +separate blog post. Please follow us if you are interested. + +## Metrics logging + +Continuously logging ML metrics is a very common practice in the ML world. +Instead of a simple command line output with the metrics values many ML +engineers prefer visuals and plots. These plots can be organized in a "database" +of ML experiments to keep track of a project. There are many special solutions +for metrics collecting and experiment tracking such as sacred, mlflow, weight +and biases, neptune.ai or other. + +With DVC 2.0 we are releasing new open-source library +[DVC-Live](https://github.com/iterative/dvclive) that provide functionality for +tracking model metrics and organizing metrics in simple text files in a way that +DVC can visualize the metrics with navigation in Git histroy. So, DVC can show +you a metrics difference between current model and a model in `master` or any +other branch. + +This approach is similar to the other metrics tracking tools with the difference +that Git becomes a "database" or of ML experiments. + +### Generate metrics file + +Install the library: + +```dvc +$ pip install dvclive +``` + +Instrument your code: + +```python +import dvclive +from dvclive.keras import DvcLiveCallback + +dvclive.init("logs") #, summarize=True) + +... + +model.fit(... + # Set up DVC-Live callback: + callbacks=[ DvcLiveCallback() ] + ) + +``` + +During the training you will see the metrics files that are continiously +populated each epoches: + +```dvc +$ ls logs/ +accuracy.tsv loss.tsv val_accuracy.tsv val_loss.tsv + +$ head logs/accuracy.tsv +timestamp step accuracy +1613645582716 0 0.7360000014305115 +1613645585478 1 0.8349999785423279 +1613645587322 2 0.8830000162124634 +1613645589125 3 0.9049999713897705 +1613645590891 4 0.9070000052452087 +1613645592681 5 0.9279999732971191 +1613645594490 6 0.9430000185966492 +1613645596232 7 0.9369999766349792 +1613645598034 8 0.9430000185966492 +``` + +In addition to the continious metrics files you will see the summary metrics +file and html file with the same file prefix. The summary file conteins the +result of the latest epoch: + +```dvc +$ cat logs.json | python -m json.tool +{ + "step": 41, + "loss": 0.015958430245518684, + "accuracy": 0.9950000047683716, + "val_loss": 13.705962181091309, + "val_accuracy": 0.5149999856948853 +} +``` + +The html file contains all the visuals for continious metrics as well as the +summary metrics in a single page: + +![](/uploads/images/2021-02-18/dvclive-html.png) + +Note, the HTML and the summary metrics files are generating automatically for +each. So, you can monitor model performance in realtime. + +### Git-navigation with the metrics file + +DVC repository is NOT required to use the live metrics functionality from the +above. It works independently from DVC. + +DVC repository become usefule when the metrics and plots are commited in your +Git repository and you need navigation around the metrics. + +Metrics difference between workspace and the last Git commit: + +```dvc +$ git status -s + M logs.json + M logs/accuracy.tsv + M logs/loss.tsv + M logs/val_accuracy.tsv + M logs/val_loss.tsv + M train.py +?? model.h5 + +$ dvc metrics diff --target logs.json +Path Metric Old New Change +logs.json accuracy 0.995 0.99 -0.005 +logs.json loss 0.01596 0.03036 0.0144 +logs.json step 41 36 -5 +logs.json val_accuracy 0.515 0.5175 0.0025 +logs.json val_loss 13.70596 3.29033 -10.41563 +``` + +The difference between a particular commit/branch/tag or between two commits: + +``` +$ dvc metrics diff --target logs.json HEAD^ 47b85c +Path Metric Old New Change +logs.json accuracy 0.995 0.998 0.003 +logs.json loss 0.01596 0.01951 0.00355 +logs.json step 41 82 41 +logs.json val_accuracy 0.515 0.51 -0.005 +logs.json val_loss 13.70596 5.83056 -7.8754 +``` + +The same Git-navigation works with the plots: + +```dvc +$ dvc plots diff --target logs +file:///Users/dmitry/src/exp-dc/plots.html +``` + +![](/uploads/images/2021-02-18/dvclive-diff-html.png) + +Another nice thing about the live metrics - they work across ML experiments and +checkpoints if properly set up in dvc stages. To set up live metrics you need to +specify the metrics directory in `live` section of a stage: + +```yaml +stages: + train: + cmd: python train.py + live: + logs: + cache: false + summary: true + report: true + deps: + - data +``` + +## Thank you! + +I'd like to thank all of you DVC community members for the feedback that we are +constantly getting. This feedback helps us build new functionalities in DVC and +make it more stable. + +Please be in touch with us on [Twitter](https://twitter.com/DVCorg) and our +[Discord channel](https://dvc.org/chat). diff --git a/static/uploads/images/2021-02-18/dvclive-diff-html.png b/static/uploads/images/2021-02-18/dvclive-diff-html.png new file mode 100644 index 0000000000000000000000000000000000000000..be78742c99765d9f6e1d350efa61e8fdb93e0543 GIT binary patch literal 175962 zcma&N1z1#D+dn)sNQ;1UC`cn9-2x&YT|<|2H#np;NJw{gcXuj-ba!{Rh35So#x zh=`nohzPlyowbpvg&_#^J}5>NNlEcLVXB6Lgo(FYw(lbRj0oR4=0gX@E! z^a*H`*>k!0G4voi`SSdh+8T>ldoayr){T0aG_ADCoh)iMZlp=f*Az~;$DNrJ z9Nt`SBk_r{HrcAWu{8r%%!KoTQ5z(%h~75K(O}9d?ODW1%gEin-K+daT$?D^7lnSU zeDf*&o3v5UK^rzogV}AoQ3VYxu0CD&q}XaSIFOyf>FXBtyCTPTcp+RBK0K^qpIJT3 z195yKzsbe87t=`LeNCTahQ?uThFIbSHB^|nrOQP>%PGWk3LYW~B-{L4^Mq=DU6}Xn z&*}IMP591o8|A}k`;3>|@|Jn3>dzC?6GIepTu;p0=N01d3)UGaE~D|O3b$5i{cLOt z_t(@$JFj0VC%f2USSvNtk-8BC!VqI2f`wo-sN>P>yy|m`T2UVqLPbHO5#AQ$@PlSX 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zfL#>ss?0BB_geQ Date: Thu, 18 Feb 2021 15:54:09 -0800 Subject: [PATCH 36/40] add tables in checkpoint section --- .../blog/2021-02-18-dvc-2-0-pre-release.md | 137 +++++++++++++++--- 1 file changed, 119 insertions(+), 18 deletions(-) diff --git a/content/blog/2021-02-18-dvc-2-0-pre-release.md b/content/blog/2021-02-18-dvc-2-0-pre-release.md index c7a7575398..ec5a00c4f5 100644 --- a/content/blog/2021-02-18-dvc-2-0-pre-release.md +++ b/content/blog/2021-02-18-dvc-2-0-pre-release.md @@ -7,7 +7,7 @@ description: | descriptionLong: | The new release is a result of our learning from our users. There are four - major features are coming: + major features coming: ๐Ÿ”— ML pipeline templating and iterative foreach stages @@ -20,7 +20,7 @@ descriptionLong: | picture: 2021-02-18/dvc-2-0-pre-release.png pictureComment: DVC 2.0 Pre-Release author: dmitry_petrov -commentsUrl: https://discuss.dvc.org/t/dvc-3-years-anniversary-and-1-0-pre-release/374 +commentsUrl: https://discuss.dvc.org/t/dvc-2-0-pre-release/681 tags: - Release - MLOps @@ -183,7 +183,7 @@ Reproduced experiment(s): exp-80655 Experiment results have been applied to your workspace. ``` -In the examples above, hyperparamteres were changed automaticaly by option +In the examples above, hyperparamters were changed automaticaly by option `--set-param`. User can make this changes manualy by modifying the file. The same way _any code or data files can be changed_ and `dvc exp run` will capture the changes. @@ -205,6 +205,14 @@ $ dvc exp show --no-pager --no-timestamp \ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ ``` +Under the hood DVC uses Git to store the experiments meta-information. +Straight-forward implementation on top of Git should include branches and +auto-commits in the branches. This approach over-pollutes the branch namespace +very quickly. To avoid this issue, we introduced Git custom references `exps` +the same way as GitHub uses Git custom references `pulls` to track pull +requests. This is an interesting technical topic that deserves a separate blog +post. Below you can see how it works. + No artificial branches, only custome references `exps` (do not worry if you don't understand this part - it is an implementation detail): @@ -246,7 +254,7 @@ Remove all the experiments that were not used: $ dvc exp gc --workspace --force ``` -## Model checkpoints tracking +## Model checkpoints ML model checkpoints are an essential part of deep learning. ML engineers prefer to save the model files (or weights) at checkpoints during a training process @@ -267,35 +275,128 @@ processes to the next level - every checkpoint is reproducible. This is how you define checkpoints with live-metrics: +```dvc +$ dvc stage add -n train \ + -d users.csv -d train.py \ + -p dropout,epochs,lr,process \ + --checkpoint model.h5 \ + --live logs \ + python train.py + +Creating 'dvc.yaml' +Adding stage 'train' in 'dvc.yaml' ``` -``` - -Start and interrupt training process: +Note, we use `dvc stage add` command instead of `dvc run`. Starting from DVC 2.0 +we extracting all stage specific functionality under `dvc stage` unbrella. +`dvc run` is still working but it wll be depricated in the following DVC version +(most likely in 3.0). -``` +Start the training process and interrupt it after 5 epoches: +```dvc +$ dvc exp run +'users.csv.dvc' didn't change, skipping +Running stage 'train': +> python train.py +... +^CTraceback (most recent call last): +... +KeyboardInterrupt ``` Navigate in checkpoints: +```dvc +$ dvc exp show --no-pager --no-timestamp +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”“ +โ”ƒ Experiment โ”ƒ step โ”ƒ loss โ”ƒ accuracy โ”ƒ val_loss โ”ƒ โ€ฆ โ”ƒ epochs โ”ƒ โ€ฆ โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”ฉ +โ”‚ workspace โ”‚ 4 โ”‚ 2.0702 โ”‚ 0.30388 โ”‚ 2.025 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ master โ”‚ - โ”‚ 5 โ”‚ 2.1e-07 โ”‚ logs โ”‚ โ€ฆ โ”‚ 0.124 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•“ exp-e15bc โ”‚ 4 โ”‚ 2.0702 โ”‚ 0.30388 โ”‚ 2.025 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ 5ea8327 โ”‚ 4 โ”‚ 2.0702 โ”‚ 0.30388 โ”‚ 2.025 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ bc0cf02 โ”‚ 3 โ”‚ 2.1338 โ”‚ 0.23988 โ”‚ 2.0883 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ f8cf03f โ”‚ 2 โ”‚ 2.1989 โ”‚ 0.17932 โ”‚ 2.1542 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ 7575a44 โ”‚ 1 โ”‚ 2.2694 โ”‚ 0.12833 โ”‚ 2.223 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”œโ”€โ•จ a72c526 โ”‚ 0 โ”‚ 2.3416 โ”‚ 0.0959 โ”‚ 2.2955 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”˜ ``` -``` +Each of the checkpoint above is a separate experiment with all data, code, +paramaters and metrics. You can use the same `dvc exp apply` command to extract +any of these. -Get back to any version: +Another run just continues this process. You can see how accuracy metrics is +increasing - DVC does not remove the model/checkpoint and training code trains +on top of it: +```dvc +$ dvc exp run +Existing checkpoint experiment 'exp-e15bc' will be resumed +... +^C +KeyboardInterrupt + +$ dvc exp show --no-pager --no-timestamp +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”“ +โ”ƒ Experiment โ”ƒ step โ”ƒ loss โ”ƒ accuracy โ”ƒ val_loss โ”ƒ โ€ฆ โ”ƒ epochs โ”ƒ โ€ฆ โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”ฉ +โ”‚ workspace โ”‚ 9 โ”‚ 1.7845 โ”‚ 0.58125 โ”‚ 1.7381 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ master โ”‚ - โ”‚ 5 โ”‚ 2.1e-07 โ”‚ logs โ”‚ โ€ฆ โ”‚ 0.124 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•“ exp-e15bc โ”‚ 9 โ”‚ 1.7845 โ”‚ 0.58125 โ”‚ 1.7381 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ 205a8d3 โ”‚ 9 โ”‚ 1.7845 โ”‚ 0.58125 โ”‚ 1.7381 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ dd23d96 โ”‚ 8 โ”‚ 1.8369 โ”‚ 0.54173 โ”‚ 1.7919 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ 5bb3a1f โ”‚ 7 โ”‚ 1.8929 โ”‚ 0.49108 โ”‚ 1.8474 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ 6dc5610 โ”‚ 6 โ”‚ 1.951 โ”‚ 0.43433 โ”‚ 1.9046 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ a79cf29 โ”‚ 5 โ”‚ 2.0088 โ”‚ 0.36837 โ”‚ 1.9637 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ bf276cf โ”‚ 4 โ”‚ 2.0702 โ”‚ 0.30388 โ”‚ 2.025 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ 5ea8327 โ”‚ 4 โ”‚ 2.0702 โ”‚ 0.30388 โ”‚ 2.025 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ bc0cf02 โ”‚ 3 โ”‚ 2.1338 โ”‚ 0.23988 โ”‚ 2.0883 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ f8cf03f โ”‚ 2 โ”‚ 2.1989 โ”‚ 0.17932 โ”‚ 2.1542 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ 7575a44 โ”‚ 1 โ”‚ 2.2694 โ”‚ 0.12833 โ”‚ 2.223 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”œโ”€โ•จ a72c526 โ”‚ 0 โ”‚ 2.3416 โ”‚ 0.0959 โ”‚ 2.2955 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”˜ ``` +Afrer modifing code, data or params the same process can be resumed. DVC +recognizes the change and shows it (see experiment `b363267`): + +```dvc +$ vi train.py # modify code +$ vi params.yaml # modify params + +$ dvc exp run +Modified checkpoint experiment based on 'exp-e15bc' will be created +... + +$ dvc exp show --no-pager --no-timestamp +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”“ +โ”ƒ Experiment โ”ƒ step โ”ƒ loss โ”ƒ accuracy โ”ƒ val_loss โ”ƒ โ€ฆ โ”ƒ epochs โ”ƒ โ€ฆ โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”ฉ +โ”‚ workspace โ”‚ 13 โ”‚ 1.5841 โ”‚ 0.69262 โ”‚ 1.5381 โ”‚ โ€ฆ โ”‚ 15 โ”‚ โ€ฆ โ”‚ +โ”‚ master โ”‚ - โ”‚ 5 โ”‚ 2.1e-07 โ”‚ logs โ”‚ โ€ฆ โ”‚ 0.124 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•“ exp-7ff06 โ”‚ 13 โ”‚ 1.5841 โ”‚ 0.69262 โ”‚ 1.5381 โ”‚ โ€ฆ โ”‚ 15 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ 6c62fec โ”‚ 12 โ”‚ 1.6325 โ”‚ 0.67248 โ”‚ 1.5857 โ”‚ โ€ฆ โ”‚ 15 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ 4baca3c โ”‚ 11 โ”‚ 1.6817 โ”‚ 0.64855 โ”‚ 1.6349 โ”‚ โ€ฆ โ”‚ 15 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ b363267 (2b06de7) โ”‚ 10 โ”‚ 1.7323 โ”‚ 0.61925 โ”‚ 1.6857 โ”‚ โ€ฆ โ”‚ 15 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•“ 2b06de7 โ”‚ - โ”‚ - โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +โ”‚ โ”‚ โ•Ÿ 205a8d3 โ”‚ - โ”‚ - โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +โ”‚ โ”‚ โ•Ÿ dd23d96 โ”‚ - โ”‚ - โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +โ”‚ โ”‚ โ•Ÿ 5bb3a1f โ”‚ - โ”‚ - โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +โ”‚ โ”‚ โ•Ÿ 6dc5610 โ”‚ - โ”‚ - โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +โ”‚ โ”‚ โ•Ÿ a79cf29 โ”‚ - โ”‚ - โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +โ”‚ โ”‚ โ•Ÿ bf276cf โ”‚ - โ”‚ - โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +โ”‚ โ”‚ โ•Ÿ 5ea8327 โ”‚ - โ”‚ - โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +โ”‚ โ”‚ โ•Ÿ bc0cf02 โ”‚ - โ”‚ - โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +โ”‚ โ”‚ โ•Ÿ f8cf03f โ”‚ - โ”‚ - โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +โ”‚ โ”‚ โ•Ÿ 7575a44 โ”‚ - โ”‚ - โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +โ”‚ โ”œโ”€โ•จ a72c526 โ”‚ - โ”‚ - โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”˜ ``` -Under the hood DVC uses Git to store the checkpoint meta-information. -Straight-forward implementation of checkpoints on top of Git should include -branches and auto-commits in the branches. This approach over-pollutes the -branch namespace very quickly. To avoid this issue, we introduced Git custom -references `exps` the same way as GitHub uses Git custom references `pulls` to -track pull requests. This is an interesting technical topic that deserves a -separate blog post. Please follow us if you are interested. +Sometimes you might need training the model from scratch. Reset option removes +the checkpoint file before the traning: `dvc exp run --reset` ## Metrics logging @@ -415,7 +516,7 @@ logs.json val_loss 13.70596 3.29033 -10.41563 The difference between a particular commit/branch/tag or between two commits: -``` +```dvc $ dvc metrics diff --target logs.json HEAD^ 47b85c Path Metric Old New Change logs.json accuracy 0.995 0.998 0.003 From 4398f7caceff38850613929aed66016e5dfe8a89 Mon Sep 17 00:00:00 2001 From: Dmitry Petrov Date: Thu, 18 Feb 2021 19:47:20 -0800 Subject: [PATCH 37/40] pre-release blog fixes --- .../blog/2021-02-18-dvc-2-0-pre-release.md | 80 +-- .../blog/2021-02-18-dvc-2-0-pre-release.md__ | 461 ------------------ 2 files changed, 42 insertions(+), 499 deletions(-) delete mode 100644 content/blog/2021-02-18-dvc-2-0-pre-release.md__ diff --git a/content/blog/2021-02-18-dvc-2-0-pre-release.md b/content/blog/2021-02-18-dvc-2-0-pre-release.md index ec5a00c4f5..febc603387 100644 --- a/content/blog/2021-02-18-dvc-2-0-pre-release.md +++ b/content/blog/2021-02-18-dvc-2-0-pre-release.md @@ -136,21 +136,25 @@ stages: ## Lightweight ML experiments -DVC uses Git as a foundation for ML experiments. This solid foundation makes -each ML experiment reproducible and accessible from Git history. This Git-based -approach works very well for ML projects with mature ML models when only a few -new experiments per day are running. However, in more active development when -dozens or hundreds of experiments need to be run in a single day, Git creates -overhead - each experiment run requires additional Git commands -`git add/commit`, and comparing all experiments is difficult. +DVC uses Git versioning as the basis for ML experiments. This solid foundation +makes each experiment reproducible and accessible from the project's history. +This Git-based approach works very well for ML projects with mature models when +only a few new experiments per day are run. -We introduce lightweight experiments in DVC 2.0! This is the way of -auto-tracking without any overhead from ML engineers. +However, in more active development when dozens or hundreds of experiments need +to be run in a single day, Git creates overhead โ€” each experiment run requires +additional Git commands `git add/commit`, and comparing all experiments is +difficult. -โš ๏ธ Note, ML experiment is an experimental feature in the coming release. It -means the commands might change a bit even after the release. +We introduce lightweight experiments in DVC 2.0! This is how you can auto-track +ML experiments without any overhead from ML engineers. -Run an ML experiment with a new hyperparameter from `params.yaml`: +โš ๏ธ Note, our new ML experiment features (`dvc exp`) are experimental in the +coming release. This means that the commands might change a bit in following +minor releases. + +`dvc exp run` can run an ML experiment with a new hyperparameter from +`params.yaml` while `dvc exp diff` shows metrics and params difference: ```dvc $ dvc exp run --set-param featurize.max_features=3000 @@ -183,10 +187,10 @@ Reproduced experiment(s): exp-80655 Experiment results have been applied to your workspace. ``` -In the examples above, hyperparamters were changed automaticaly by option -`--set-param`. User can make this changes manualy by modifying the file. The -same way _any code or data files can be changed_ and `dvc exp run` will capture -the changes. +In the examples above, hyperparamters were changed with the `--set-param` +option, but you can make these changes by modifying the params file instead. In +fact _any code or data files can be changed_ and `dvc exp run` will capture the +variations. See all the runs: @@ -205,16 +209,16 @@ $ dvc exp show --no-pager --no-timestamp \ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ ``` -Under the hood DVC uses Git to store the experiments meta-information. -Straight-forward implementation on top of Git should include branches and -auto-commits in the branches. This approach over-pollutes the branch namespace -very quickly. To avoid this issue, we introduced Git custom references `exps` -the same way as GitHub uses Git custom references `pulls` to track pull -requests. This is an interesting technical topic that deserves a separate blog -post. Below you can see how it works. +Under the hood DVC uses Git to store the experiments meta-information. A +straight-forward implementation would create visible branches and auto-commit in +them, but that approach would over-pollute the branch namespace very quickly. To +avoid this issue, we introduced custom Git references `exps`, the same way as +GitHub uses custom references `pulls` to track pull requests (this is an +interesting technical topic that deserves a separate blog post). Below you can +see how it works. -No artificial branches, only custome references `exps` (do not worry if you -don't understand this part - it is an implementation detail): +No artificial branches, only custom references `exps` (do not worry if you don't +understand this part - it is an implementation detail): ```dvc $ git branch @@ -288,11 +292,11 @@ Adding stage 'train' in 'dvc.yaml' ``` Note, we use `dvc stage add` command instead of `dvc run`. Starting from DVC 2.0 -we extracting all stage specific functionality under `dvc stage` unbrella. -`dvc run` is still working but it wll be depricated in the following DVC version +we extracting all stage specific functionality under `dvc stage` umbrella. +`dvc run` is still working but it wll be deprecated in the following DVC version (most likely in 3.0). -Start the training process and interrupt it after 5 epoches: +Start the training process and interrupt it after 5 epochs: ```dvc $ dvc exp run @@ -313,7 +317,7 @@ $ dvc exp show --no-pager --no-timestamp โ”ƒ Experiment โ”ƒ step โ”ƒ loss โ”ƒ accuracy โ”ƒ val_loss โ”ƒ โ€ฆ โ”ƒ epochs โ”ƒ โ€ฆ โ”ƒ โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”ฉ โ”‚ workspace โ”‚ 4 โ”‚ 2.0702 โ”‚ 0.30388 โ”‚ 2.025 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ -โ”‚ master โ”‚ - โ”‚ 5 โ”‚ 2.1e-07 โ”‚ logs โ”‚ โ€ฆ โ”‚ 0.124 โ”‚ โ€ฆ โ”‚ +โ”‚ master โ”‚ - โ”‚ - โ”‚ - โ”‚ - โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ โ”‚ โ”‚ โ•“ exp-e15bc โ”‚ 4 โ”‚ 2.0702 โ”‚ 0.30388 โ”‚ 2.025 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ โ”‚ โ”‚ โ•Ÿ 5ea8327 โ”‚ 4 โ”‚ 2.0702 โ”‚ 0.30388 โ”‚ 2.025 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ โ”‚ โ”‚ โ•Ÿ bc0cf02 โ”‚ 3 โ”‚ 2.1338 โ”‚ 0.23988 โ”‚ 2.0883 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ @@ -343,7 +347,7 @@ $ dvc exp show --no-pager --no-timestamp โ”ƒ Experiment โ”ƒ step โ”ƒ loss โ”ƒ accuracy โ”ƒ val_loss โ”ƒ โ€ฆ โ”ƒ epochs โ”ƒ โ€ฆ โ”ƒ โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”ฉ โ”‚ workspace โ”‚ 9 โ”‚ 1.7845 โ”‚ 0.58125 โ”‚ 1.7381 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ -โ”‚ master โ”‚ - โ”‚ 5 โ”‚ 2.1e-07 โ”‚ logs โ”‚ โ€ฆ โ”‚ 0.124 โ”‚ โ€ฆ โ”‚ +โ”‚ master โ”‚ - โ”‚ - โ”‚ - โ”‚ - โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ โ”‚ โ”‚ โ•“ exp-e15bc โ”‚ 9 โ”‚ 1.7845 โ”‚ 0.58125 โ”‚ 1.7381 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ โ”‚ โ”‚ โ•Ÿ 205a8d3 โ”‚ 9 โ”‚ 1.7845 โ”‚ 0.58125 โ”‚ 1.7381 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ โ”‚ โ”‚ โ•Ÿ dd23d96 โ”‚ 8 โ”‚ 1.8369 โ”‚ 0.54173 โ”‚ 1.7919 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ @@ -359,7 +363,7 @@ $ dvc exp show --no-pager --no-timestamp โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”˜ ``` -Afrer modifing code, data or params the same process can be resumed. DVC +Afrer modifyng code, data or params the same process can be resumed. DVC recognizes the change and shows it (see experiment `b363267`): ```dvc @@ -375,7 +379,7 @@ $ dvc exp show --no-pager --no-timestamp โ”ƒ Experiment โ”ƒ step โ”ƒ loss โ”ƒ accuracy โ”ƒ val_loss โ”ƒ โ€ฆ โ”ƒ epochs โ”ƒ โ€ฆ โ”ƒ โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”ฉ โ”‚ workspace โ”‚ 13 โ”‚ 1.5841 โ”‚ 0.69262 โ”‚ 1.5381 โ”‚ โ€ฆ โ”‚ 15 โ”‚ โ€ฆ โ”‚ -โ”‚ master โ”‚ - โ”‚ 5 โ”‚ 2.1e-07 โ”‚ logs โ”‚ โ€ฆ โ”‚ 0.124 โ”‚ โ€ฆ โ”‚ +โ”‚ master โ”‚ - โ”‚ - โ”‚ - โ”‚ - โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ โ”‚ โ”‚ โ•“ exp-7ff06 โ”‚ 13 โ”‚ 1.5841 โ”‚ 0.69262 โ”‚ 1.5381 โ”‚ โ€ฆ โ”‚ 15 โ”‚ โ€ฆ โ”‚ โ”‚ โ”‚ โ•Ÿ 6c62fec โ”‚ 12 โ”‚ 1.6325 โ”‚ 0.67248 โ”‚ 1.5857 โ”‚ โ€ฆ โ”‚ 15 โ”‚ โ€ฆ โ”‚ โ”‚ โ”‚ โ•Ÿ 4baca3c โ”‚ 11 โ”‚ 1.6817 โ”‚ 0.64855 โ”‚ 1.6349 โ”‚ โ€ฆ โ”‚ 15 โ”‚ โ€ฆ โ”‚ @@ -395,8 +399,8 @@ $ dvc exp show --no-pager --no-timestamp โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”˜ ``` -Sometimes you might need training the model from scratch. Reset option removes -the checkpoint file before the traning: `dvc exp run --reset` +Sometimes you might need to train the model from scratch. The reset option +removes the checkpoint file before training: `dvc exp run --reset`. ## Metrics logging @@ -463,7 +467,7 @@ timestamp step accuracy ``` In addition to the continious metrics files you will see the summary metrics -file and html file with the same file prefix. The summary file conteins the +file and html file with the same file prefix. The summary file contains the result of the latest epoch: ```dvc @@ -477,7 +481,7 @@ $ cat logs.json | python -m json.tool } ``` -The html file contains all the visuals for continious metrics as well as the +The html file contains all the visuals for continuous metrics as well as the summary metrics in a single page: ![](/uploads/images/2021-02-18/dvclive-html.png) @@ -490,8 +494,8 @@ each. So, you can monitor model performance in realtime. DVC repository is NOT required to use the live metrics functionality from the above. It works independently from DVC. -DVC repository become usefule when the metrics and plots are commited in your -Git repository and you need navigation around the metrics. +DVC repository become useful when the metrics and plots are commited in your Git +repository and you need navigation around the metrics. Metrics difference between workspace and the last Git commit: diff --git a/content/blog/2021-02-18-dvc-2-0-pre-release.md__ b/content/blog/2021-02-18-dvc-2-0-pre-release.md__ deleted file mode 100644 index c7a7575398..0000000000 --- a/content/blog/2021-02-18-dvc-2-0-pre-release.md__ +++ /dev/null @@ -1,461 +0,0 @@ ---- -title: DVC 2.0 Pre-Release -date: 2021-02-17 -description: | - Today, we're announcing DVC 2.0 pre-release. We'll share lessons from our - journey and how these will be reflected in the coming release. - -descriptionLong: | - The new release is a result of our learning from our users. There are four - major features are coming: - - ๐Ÿ”— ML pipeline templating and iterative foreach stages - - ๐Ÿงช Lightweight ML experiments - - ๐Ÿ“ ML model checkpoints - - ๐Ÿ“ˆ Dvc-live - new open-source library for metrics logging - -picture: 2021-02-18/dvc-2-0-pre-release.png -pictureComment: DVC 2.0 Pre-Release -author: dmitry_petrov -commentsUrl: https://discuss.dvc.org/t/dvc-3-years-anniversary-and-1-0-pre-release/374 -tags: - - Release - - MLOps - - DataOps ---- - -## Install - -First things first. You can install the 2.0 pre-release from the master branch -in our repo (instruction [here](https://dvc.org/doc/install/pre-release)) or -through pip: - -```dvc -$ pip install --upgrade --pre dvc -``` - -## ML pipelines parameterization and foreach stages - -After introducing the multi-stage pipeline file `dvc.yaml`, it was quickly -adopted among our users. The DVC team got tons of positive feedback from them, -as well as feature requests. - -### Pipeline parameters from `vars` - -The most requested feature was the ability to use parameters in `dvc.yaml`. For -example. So, you can pass the same seed value or filename to multiple stages in -the pipeline. - -```yaml -vars: - train_matrix: train.pkl - test_matrix: test.pkl - seed: 20210215 - -... - -stages: - process: - cmd: python process.py \ - --seed ${seed} \ - --train ${train_matrix} \ - --test ${test_matrix} - outs: - - ${test_matrix} - - ${train_matrix} - - ... - - train: - cmd: python train.py ${train_matrix} --seed ${seed} - deps: - - ${train_matrix} -``` - -Also, it gives an ability to localize all important parameters in a single -`vars` block, and play with them. This is a natural thing to do for scenarios -like NLP or when hyperparameter optimization is happening not only in the model -training code but in the data processing as well. - -### Pipeline parameters from params files - -It is quite common to define pipeline parameters in a config file or a -parameters file (like `params.yaml`) instead of in the pipeline file `dvc.yaml` -itself. These parameters defined in `params.yaml` can also be used in -`dvc.yaml`. - -```yaml -# params.yaml -models: - us: - thresh: 10 - filename: 'model-us.hdf5' -``` - -```yaml -# dvc.yaml -stages: - build-us: - cmd: >- - python script.py - --out ${models.us.filename} - --thresh ${models.us.thresh} - outs: - - ${models.us.filename} -``` - -DVC properly tracks params dependencies for each stage starting from the -previous DVC version 1.0. See the -[`--params` option](/doc/command-reference/run#for-displaying-and-comparing-data-science-experiments) -of `dvc run` for more details. - -### Iterating over params with foreach stages - -Iterating over params was a frequently requested feature. Now users can define -multiple similar stages with a templatized command. - -```yaml -stages: - build: - foreach: - gb: - thresh: 15 - filename: 'model-gb.hdf5' - us: - thresh: 10 - filename: 'model-us.hdf5' - do: - cmd: >- - python script.py --out ${item.filename} --thresh ${item.thresh} - outs: - - ${item.filename} -``` - -## Lightweight ML experiments - -DVC uses Git as a foundation for ML experiments. This solid foundation makes -each ML experiment reproducible and accessible from Git history. This Git-based -approach works very well for ML projects with mature ML models when only a few -new experiments per day are running. However, in more active development when -dozens or hundreds of experiments need to be run in a single day, Git creates -overhead - each experiment run requires additional Git commands -`git add/commit`, and comparing all experiments is difficult. - -We introduce lightweight experiments in DVC 2.0! This is the way of -auto-tracking without any overhead from ML engineers. - -โš ๏ธ Note, ML experiment is an experimental feature in the coming release. It -means the commands might change a bit even after the release. - -Run an ML experiment with a new hyperparameter from `params.yaml`: - -```dvc -$ dvc exp run --set-param featurize.max_features=3000 - -Reproduced experiment(s): exp-bb55c -Experiment results have been applied to your workspace. - -$ dvc exp diff -Path Metric Value Change -scores.json auc 0.57462 0.0072197 - -Path Param Value Change -params.yaml featurize.max_features 3000 1500 -``` - -More experiments: - -```dvc -$ dvc exp run --set-param featurize.max_features=4000 -Reproduced experiment(s): exp-9bf22 -Experiment results have been applied to your workspace. - -$ dvc exp run --set-param featurize.max_features=5000 -Reproduced experiment(s): exp-63ee0 -Experiment results have been applied to your workspace. - -$ dvc exp run --set-param featurize.max_features=5000 \ - --set-param featurize.ngrams=3 -Reproduced experiment(s): exp-80655 -Experiment results have been applied to your workspace. -``` - -In the examples above, hyperparamteres were changed automaticaly by option -`--set-param`. User can make this changes manualy by modifying the file. The -same way _any code or data files can be changed_ and `dvc exp run` will capture -the changes. - -See all the runs: - -```dvc -$ dvc exp show --no-pager --no-timestamp \ - --include-params featurize.max_features,featurize.ngrams -โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ -โ”ƒ Experiment โ”ƒ auc โ”ƒ featurize.max_features โ”ƒ featurize.ngrams โ”ƒ -โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ -โ”‚ workspace โ”‚ 0.56359 โ”‚ 5000 โ”‚ 3 โ”‚ -โ”‚ master โ”‚ 0.5674 โ”‚ 1500 โ”‚ 2 โ”‚ -โ”‚ โ”œโ”€โ”€ exp-80655 โ”‚ 0.56359 โ”‚ 5000 โ”‚ 3 โ”‚ -โ”‚ โ”œโ”€โ”€ exp-63ee0 โ”‚ 0.5515 โ”‚ 5000 โ”‚ 2 โ”‚ -โ”‚ โ”œโ”€โ”€ exp-9bf22 โ”‚ 0.56448 โ”‚ 4000 โ”‚ 2 โ”‚ -โ”‚ โ””โ”€โ”€ exp-bb55c โ”‚ 0.57462 โ”‚ 3000 โ”‚ 2 โ”‚ -โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ -``` - -No artificial branches, only custome references `exps` (do not worry if you -don't understand this part - it is an implementation detail): - -```dvc -$ git branch -* master - -$ git show-ref -5649f62d845fdc29e28ea6f7672dd729d3946940 refs/exps/exec/EXEC_APPLY -5649f62d845fdc29e28ea6f7672dd729d3946940 refs/exps/exec/EXEC_BRANCH -5649f62d845fdc29e28ea6f7672dd729d3946940 refs/exps/71/67904d89e116f28daf7a6e4c0878268117c893/exp-80655 -f16e7b7c804cf52d91d1d11850c15963fb2a8d7b refs/exps/97/d69af70c6fb4bc59aefb9a87437dcd28b3bde4/exp-63ee0 -0566d42cddb3a8c4eb533f31027f0febccbbc2dd refs/exps/91/94265d5acd847e1c439dd859aa74b1fc3d73ad/exp-bb55c -9bb067559583990a8c5d499d7435c35a7c9417b7 refs/exps/49/5c835cd36772123e82e812d96eabcce320f7ec/exp-9bf22 -``` - -The best experiment can be promoted to the workspace and commited to Git. - -```dvc -$ dvc exp apply exp-bb55c -$ git add . -$ git commit -m 'optimize max feature size' -``` - -Alternatively, an experiment can be promoted to a branch (`big_fr_size` branch -in this case): - -```dvc -$ dvc exp branch exp-80655 big_fr_size -Git branch 'big_fr_size' has been created from experiment 'exp-c695f'. -To switch to the new branch run: - - git checkout big_fr_size -``` - -Remove all the experiments that were not used: - -```dvc -$ dvc exp gc --workspace --force -``` - -## Model checkpoints tracking - -ML model checkpoints are an essential part of deep learning. ML engineers prefer -to save the model files (or weights) at checkpoints during a training process -and return back when metrics start diverging or learning is not fast enough. - -The checkpoints create a different dynamic around ML modeling process and need a -special support from the toolset: - -1. Track and save model checkpoints (DVC outputs) periodically, not only the - final result or training epoch. -2. Save metrics corresponding to each of the checkpoints. -3. Reuse checkpoints - warm-start training with an existing model file, - corresponding code, dataset version and metrics. - -This new behaviour is supported in DVC 2.0. Now, DVC can version all your -checkpoints with corresponding code and data. It brings reproducibility of DL -processes to the next level - every checkpoint is reproducible. - -This is how you define checkpoints with live-metrics: - -``` - -``` - -Start and interrupt training process: - -``` - -``` - -Navigate in checkpoints: - -``` - -``` - -Get back to any version: - -``` - -``` - -Under the hood DVC uses Git to store the checkpoint meta-information. -Straight-forward implementation of checkpoints on top of Git should include -branches and auto-commits in the branches. This approach over-pollutes the -branch namespace very quickly. To avoid this issue, we introduced Git custom -references `exps` the same way as GitHub uses Git custom references `pulls` to -track pull requests. This is an interesting technical topic that deserves a -separate blog post. Please follow us if you are interested. - -## Metrics logging - -Continuously logging ML metrics is a very common practice in the ML world. -Instead of a simple command line output with the metrics values many ML -engineers prefer visuals and plots. These plots can be organized in a "database" -of ML experiments to keep track of a project. There are many special solutions -for metrics collecting and experiment tracking such as sacred, mlflow, weight -and biases, neptune.ai or other. - -With DVC 2.0 we are releasing new open-source library -[DVC-Live](https://github.com/iterative/dvclive) that provide functionality for -tracking model metrics and organizing metrics in simple text files in a way that -DVC can visualize the metrics with navigation in Git histroy. So, DVC can show -you a metrics difference between current model and a model in `master` or any -other branch. - -This approach is similar to the other metrics tracking tools with the difference -that Git becomes a "database" or of ML experiments. - -### Generate metrics file - -Install the library: - -```dvc -$ pip install dvclive -``` - -Instrument your code: - -```python -import dvclive -from dvclive.keras import DvcLiveCallback - -dvclive.init("logs") #, summarize=True) - -... - -model.fit(... - # Set up DVC-Live callback: - callbacks=[ DvcLiveCallback() ] - ) - -``` - -During the training you will see the metrics files that are continiously -populated each epoches: - -```dvc -$ ls logs/ -accuracy.tsv loss.tsv val_accuracy.tsv val_loss.tsv - -$ head logs/accuracy.tsv -timestamp step accuracy -1613645582716 0 0.7360000014305115 -1613645585478 1 0.8349999785423279 -1613645587322 2 0.8830000162124634 -1613645589125 3 0.9049999713897705 -1613645590891 4 0.9070000052452087 -1613645592681 5 0.9279999732971191 -1613645594490 6 0.9430000185966492 -1613645596232 7 0.9369999766349792 -1613645598034 8 0.9430000185966492 -``` - -In addition to the continious metrics files you will see the summary metrics -file and html file with the same file prefix. The summary file conteins the -result of the latest epoch: - -```dvc -$ cat logs.json | python -m json.tool -{ - "step": 41, - "loss": 0.015958430245518684, - "accuracy": 0.9950000047683716, - "val_loss": 13.705962181091309, - "val_accuracy": 0.5149999856948853 -} -``` - -The html file contains all the visuals for continious metrics as well as the -summary metrics in a single page: - -![](/uploads/images/2021-02-18/dvclive-html.png) - -Note, the HTML and the summary metrics files are generating automatically for -each. So, you can monitor model performance in realtime. - -### Git-navigation with the metrics file - -DVC repository is NOT required to use the live metrics functionality from the -above. It works independently from DVC. - -DVC repository become usefule when the metrics and plots are commited in your -Git repository and you need navigation around the metrics. - -Metrics difference between workspace and the last Git commit: - -```dvc -$ git status -s - M logs.json - M logs/accuracy.tsv - M logs/loss.tsv - M logs/val_accuracy.tsv - M logs/val_loss.tsv - M train.py -?? model.h5 - -$ dvc metrics diff --target logs.json -Path Metric Old New Change -logs.json accuracy 0.995 0.99 -0.005 -logs.json loss 0.01596 0.03036 0.0144 -logs.json step 41 36 -5 -logs.json val_accuracy 0.515 0.5175 0.0025 -logs.json val_loss 13.70596 3.29033 -10.41563 -``` - -The difference between a particular commit/branch/tag or between two commits: - -``` -$ dvc metrics diff --target logs.json HEAD^ 47b85c -Path Metric Old New Change -logs.json accuracy 0.995 0.998 0.003 -logs.json loss 0.01596 0.01951 0.00355 -logs.json step 41 82 41 -logs.json val_accuracy 0.515 0.51 -0.005 -logs.json val_loss 13.70596 5.83056 -7.8754 -``` - -The same Git-navigation works with the plots: - -```dvc -$ dvc plots diff --target logs -file:///Users/dmitry/src/exp-dc/plots.html -``` - -![](/uploads/images/2021-02-18/dvclive-diff-html.png) - -Another nice thing about the live metrics - they work across ML experiments and -checkpoints if properly set up in dvc stages. To set up live metrics you need to -specify the metrics directory in `live` section of a stage: - -```yaml -stages: - train: - cmd: python train.py - live: - logs: - cache: false - summary: true - report: true - deps: - - data -``` - -## Thank you! - -I'd like to thank all of you DVC community members for the feedback that we are -constantly getting. This feedback helps us build new functionalities in DVC and -make it more stable. - -Please be in touch with us on [Twitter](https://twitter.com/DVCorg) and our -[Discord channel](https://dvc.org/chat). From d651fc0a6c8214625a48bba9d84b48ae4f00bbf1 Mon Sep 17 00:00:00 2001 From: Dmitry Petrov Date: Thu, 18 Feb 2021 19:51:54 -0800 Subject: [PATCH 38/40] exp run - fix resume table --- .../blog/2021-02-18-dvc-2-0-pre-release.md | 24 +++++++++---------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/content/blog/2021-02-18-dvc-2-0-pre-release.md b/content/blog/2021-02-18-dvc-2-0-pre-release.md index febc603387..e2cac54cdd 100644 --- a/content/blog/2021-02-18-dvc-2-0-pre-release.md +++ b/content/blog/2021-02-18-dvc-2-0-pre-release.md @@ -384,18 +384,18 @@ $ dvc exp show --no-pager --no-timestamp โ”‚ โ”‚ โ•Ÿ 6c62fec โ”‚ 12 โ”‚ 1.6325 โ”‚ 0.67248 โ”‚ 1.5857 โ”‚ โ€ฆ โ”‚ 15 โ”‚ โ€ฆ โ”‚ โ”‚ โ”‚ โ•Ÿ 4baca3c โ”‚ 11 โ”‚ 1.6817 โ”‚ 0.64855 โ”‚ 1.6349 โ”‚ โ€ฆ โ”‚ 15 โ”‚ โ€ฆ โ”‚ โ”‚ โ”‚ โ•Ÿ b363267 (2b06de7) โ”‚ 10 โ”‚ 1.7323 โ”‚ 0.61925 โ”‚ 1.6857 โ”‚ โ€ฆ โ”‚ 15 โ”‚ โ€ฆ โ”‚ -โ”‚ โ”‚ โ•“ 2b06de7 โ”‚ - โ”‚ - โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ -โ”‚ โ”‚ โ•Ÿ 205a8d3 โ”‚ - โ”‚ - โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ -โ”‚ โ”‚ โ•Ÿ dd23d96 โ”‚ - โ”‚ - โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ -โ”‚ โ”‚ โ•Ÿ 5bb3a1f โ”‚ - โ”‚ - โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ -โ”‚ โ”‚ โ•Ÿ 6dc5610 โ”‚ - โ”‚ - โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ -โ”‚ โ”‚ โ•Ÿ a79cf29 โ”‚ - โ”‚ - โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ -โ”‚ โ”‚ โ•Ÿ bf276cf โ”‚ - โ”‚ - โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ -โ”‚ โ”‚ โ•Ÿ 5ea8327 โ”‚ - โ”‚ - โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ -โ”‚ โ”‚ โ•Ÿ bc0cf02 โ”‚ - โ”‚ - โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ -โ”‚ โ”‚ โ•Ÿ f8cf03f โ”‚ - โ”‚ - โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ -โ”‚ โ”‚ โ•Ÿ 7575a44 โ”‚ - โ”‚ - โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ -โ”‚ โ”œโ”€โ•จ a72c526 โ”‚ - โ”‚ - โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +โ”‚ โ”‚ โ•“ 2b06de7 โ”‚ 9 โ”‚ 1.7845 โ”‚ 0.58125 โ”‚ 1.7381 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ 205a8d3 โ”‚ 9 โ”‚ 1.7845 โ”‚ 0.58125 โ”‚ 1.7381 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ dd23d96 โ”‚ 8 โ”‚ 1.8369 โ”‚ 0.54173 โ”‚ 1.7919 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ 5bb3a1f โ”‚ 7 โ”‚ 1.8929 โ”‚ 0.49108 โ”‚ 1.8474 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ 6dc5610 โ”‚ 6 โ”‚ 1.951 โ”‚ 0.43433 โ”‚ 1.9046 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ a79cf29 โ”‚ 5 โ”‚ 2.0088 โ”‚ 0.36837 โ”‚ 1.9637 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ bf276cf โ”‚ 4 โ”‚ 2.0702 โ”‚ 0.30388 โ”‚ 2.025 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ 5ea8327 โ”‚ 4 โ”‚ 2.0702 โ”‚ 0.30388 โ”‚ 2.025 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ bc0cf02 โ”‚ 3 โ”‚ 2.1338 โ”‚ 0.23988 โ”‚ 2.0883 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ f8cf03f โ”‚ 2 โ”‚ 2.1989 โ”‚ 0.17932 โ”‚ 2.1542 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”‚ โ•Ÿ 7575a44 โ”‚ 1 โ”‚ 2.2694 โ”‚ 0.12833 โ”‚ 2.223 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ +โ”‚ โ”œโ”€โ•จ a72c526 โ”‚ 0 โ”‚ 2.3416 โ”‚ 0.0959 โ”‚ 2.2955 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”˜ ``` From e3e36538018d1e8ececd26ef4dd8c00c80a88645 Mon Sep 17 00:00:00 2001 From: Dmitry Petrov Date: Thu, 18 Feb 2021 20:39:01 -0800 Subject: [PATCH 39/40] fix duplication issue in table --- content/blog/2021-02-18-dvc-2-0-pre-release.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/content/blog/2021-02-18-dvc-2-0-pre-release.md b/content/blog/2021-02-18-dvc-2-0-pre-release.md index e2cac54cdd..b5650256c2 100644 --- a/content/blog/2021-02-18-dvc-2-0-pre-release.md +++ b/content/blog/2021-02-18-dvc-2-0-pre-release.md @@ -354,7 +354,6 @@ $ dvc exp show --no-pager --no-timestamp โ”‚ โ”‚ โ•Ÿ 5bb3a1f โ”‚ 7 โ”‚ 1.8929 โ”‚ 0.49108 โ”‚ 1.8474 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ โ”‚ โ”‚ โ•Ÿ 6dc5610 โ”‚ 6 โ”‚ 1.951 โ”‚ 0.43433 โ”‚ 1.9046 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ โ”‚ โ”‚ โ•Ÿ a79cf29 โ”‚ 5 โ”‚ 2.0088 โ”‚ 0.36837 โ”‚ 1.9637 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ -โ”‚ โ”‚ โ•Ÿ bf276cf โ”‚ 4 โ”‚ 2.0702 โ”‚ 0.30388 โ”‚ 2.025 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ โ”‚ โ”‚ โ•Ÿ 5ea8327 โ”‚ 4 โ”‚ 2.0702 โ”‚ 0.30388 โ”‚ 2.025 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ โ”‚ โ”‚ โ•Ÿ bc0cf02 โ”‚ 3 โ”‚ 2.1338 โ”‚ 0.23988 โ”‚ 2.0883 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ โ”‚ โ”‚ โ•Ÿ f8cf03f โ”‚ 2 โ”‚ 2.1989 โ”‚ 0.17932 โ”‚ 2.1542 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ @@ -390,7 +389,6 @@ $ dvc exp show --no-pager --no-timestamp โ”‚ โ”‚ โ•Ÿ 5bb3a1f โ”‚ 7 โ”‚ 1.8929 โ”‚ 0.49108 โ”‚ 1.8474 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ โ”‚ โ”‚ โ•Ÿ 6dc5610 โ”‚ 6 โ”‚ 1.951 โ”‚ 0.43433 โ”‚ 1.9046 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ โ”‚ โ”‚ โ•Ÿ a79cf29 โ”‚ 5 โ”‚ 2.0088 โ”‚ 0.36837 โ”‚ 1.9637 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ -โ”‚ โ”‚ โ•Ÿ bf276cf โ”‚ 4 โ”‚ 2.0702 โ”‚ 0.30388 โ”‚ 2.025 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ โ”‚ โ”‚ โ•Ÿ 5ea8327 โ”‚ 4 โ”‚ 2.0702 โ”‚ 0.30388 โ”‚ 2.025 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ โ”‚ โ”‚ โ•Ÿ bc0cf02 โ”‚ 3 โ”‚ 2.1338 โ”‚ 0.23988 โ”‚ 2.0883 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ โ”‚ โ”‚ โ•Ÿ f8cf03f โ”‚ 2 โ”‚ 2.1989 โ”‚ 0.17932 โ”‚ 2.1542 โ”‚ โ€ฆ โ”‚ 5 โ”‚ โ€ฆ โ”‚ @@ -412,7 +410,7 @@ for metrics collecting and experiment tracking such as sacred, mlflow, weight and biases, neptune.ai or other. With DVC 2.0 we are releasing new open-source library -[DVC-Live](https://github.com/iterative/dvclive) that provide functionality for +[DVC-Live](https://github.com/iterative/dvclive) that provides functionality for tracking model metrics and organizing metrics in simple text files in a way that DVC can visualize the metrics with navigation in Git histroy. So, DVC can show you a metrics difference between current model and a model in `master` or any From 87942d00f706a76cfb1241d12b8a80c5f1f7872e Mon Sep 17 00:00:00 2001 From: Saugat Pachhai Date: Fri, 19 Feb 2021 12:06:19 +0545 Subject: [PATCH 40/40] document stage:add (#2208) * document stage:add * fix typos, indent flags in usage, add tooltip * add syntax highlighting for stage:add command * minor fixup * Update content/docs/command-reference/stage/add.md * Update content/docs/command-reference/stage/add.md * Apply suggestions from code review from @jorgeorpinel Co-authored-by: Jorge Orpinel * indent examples * put command details before dependencies and outputs section Co-authored-by: Jorge Orpinel --- config/prismjs/dvc-commands.js | 2 + content/docs/command-reference/stage/add.md | 427 ++++++++++++++++++ content/docs/command-reference/stage/index.md | 25 + content/docs/sidebar.json | 11 + .../docs/user-guide/basic-concepts/stage.md | 8 + 5 files changed, 473 insertions(+) create mode 100644 content/docs/command-reference/stage/add.md create mode 100644 content/docs/command-reference/stage/index.md create mode 100644 content/docs/user-guide/basic-concepts/stage.md diff --git a/config/prismjs/dvc-commands.js b/config/prismjs/dvc-commands.js index 845f8dcc68..ece849ebf6 100644 --- a/config/prismjs/dvc-commands.js +++ b/config/prismjs/dvc-commands.js @@ -7,6 +7,8 @@ module.exports = [ 'unfreeze', 'tag', 'status', + 'stage add', + 'stage', 'run', 'root', 'repro', diff --git a/content/docs/command-reference/stage/add.md b/content/docs/command-reference/stage/add.md new file mode 100644 index 0000000000..270f64887f --- /dev/null +++ b/content/docs/command-reference/stage/add.md @@ -0,0 +1,427 @@ +# stage add + +Helper command to create or update stages in `dvc.yaml`. + +## Synopsis + +```usage +usage: dvc stage add [-h] [-q | -v] -n [-d ] [-o ] + [-O ] [-p [:]] + [-m ] [-M ] [--plots ] + [--plots-no-cache ] [-w ] [-f] + [--outs-persist ] + [--outs-persist-no-cache ] + [--always-changed] [--external] [--desc ] + command + +positional arguments: + command Command to execute +``` + +## Description + +Creates or updates stages in a [pipeline](/doc/command-reference/dag) (saved to +`dvc.yaml` in the current working directory). + +A stage name is required and can be provided using the `-n` (`--name`) option. +Most of the other [options](#options) help with defining different kinds of +[dependencies and outputs](#dependencies-and-outputs) for the stage. The +remaining terminal input provided to `dvc stage add` after `-`/`--` flags will +become the required [`command` argument](#the-command-argument). + +`dvc repro` can be used to execute pipelines after their stages have been +defined. + +

+ +### ๐Ÿ’ก Avoiding unexpected behavior + +We don't want to tell anyone how to write their code or what programs to use! +However, please be aware that in order to prevent unexpected results when DVC +reproduces pipeline stages, the underlying code should ideally follow these +rules: + +- Read/write exclusively from/to the specified dependencies and + outputs (including parameters files, metrics, and plots). + +- Completely rewrite outputs. Do not append or edit. + +- Stop reading and writing files when the `command` exits. + +Also, if your pipeline reproducibility goals include consistent output data, its +code should be +[deterministic](https://en.wikipedia.org/wiki/Deterministic_algorithm) (produce +the same output for any given input): avoid code that increases +[entropy](https://en.wikipedia.org/wiki/Software_entropy) (e.g. random numbers, +time functions, hardware dependencies, etc.). + +
+ +### The `command` argument + +The `command` sent to `dvc stage add` can be anything your terminal would accept +and run directly, for example a shell built-in, expression, or binary found in +`PATH`. Please remember that any flags sent after the `command` are considered +part of the command itself, not of `dvc stage add`. + +โš ๏ธ While DVC is platform-agnostic, the commands defined in your +[pipeline](/doc/command-reference/dag) stages may only work on some operating +systems and require certain software packages to be installed. + +Wrap the command with double quotes `"` if there are special characters in it +like `|` (pipe) or `<`, `>` (redirection), otherwise they would apply to +`dvc stage add` itself. Use single quotes `'` instead if there are environment +variables in it that should be evaluated dynamically. Examples: + +```dvc +$ dvc stage add -n first_stage "./a_script.sh > /dev/null 2>&1" +$ dvc stage add -n second_stage './another_script.sh $MYENVVAR' +``` + +### Dependencies and outputs + +By specifying lists of dependencies (`-d` option) and/or +outputs (`-o` and `-O` options) for each stage, we can create a +_dependency graph_ ([DAG](https://en.wikipedia.org/wiki/Directed_acyclic_graph)) +that connects them, i.e. the output of a stage becomes the input of another, and +so on (see `dvc dag`). This graph can be restored by DVC later to modify or +[reproduce](/doc/command-reference/repro) the full pipeline. For example: + +```dvc +$ dvc stage add -n printer -d write.sh -o pages ./write.sh +$ dvc stage add -n scanner -d read.sh -d pages -o signed.pdf ./read.sh pages +``` + +Stage dependencies can be any file or directory, either untracked, or more +commonly tracked by DVC or Git. Outputs will be tracked and cached +by DVC when the stage is run. Every output version will be cached when the stage +is reproduced (see also `dvc gc`). + +Relevant notes: + +- Typically, scripts to run (or possibly a directory containing the source code) + are included among the specified `-d` dependencies. This ensures that when the + source code changes, DVC knows that the stage needs to be reproduced. (You can + chose whether to do this.) + +- `dvc stage add` checks the dependency graph integrity before creating a new + stage. For example: two stage cannot specify the same output or overlapping + output paths, there should be no cycles, etc. + +- DVC does not feed dependency files to the command being run. The program will + have to read by itself the files specified with `-d`. + +- Entire directories produced by the stage can be tracked as outputs by DVC, + which generates a single `.dir` entry in the cache (refer to + [Structure of cache directory](/doc/user-guide/project-structure/internal-files#structure-of-the-cache-directory) + for more info.) + +- [external dependencies](/doc/user-guide/external-dependencies) and + [external outputs](/doc/user-guide/managing-external-data) (outside of the + workspace) are also supported (except metrics and plots). + +- Outputs are deleted from the workspace before executing the command (including + at `dvc repro`) if their paths are found as existing files/directories (unless + `--outs-persist` is used). This also means that the stage command needs to + recreate any directory structures defined as outputs every time its executed + by DVC. + +- In some situations, we have previously executed a stage, and later notice that + some of the files/directories used by the stage as dependencies, or created as + outputs are missing from `dvc.yaml`. It is possible to + [add missing dependencies/outputs to an existing stage](/docs/user-guide/how-to/add-deps-or-outs-to-a-stage) + without having to execute it again. + +- Renaming dependencies or outputs requires a + [manual process](/doc/command-reference/move#renaming-stage-outputs) to update + `dvc.yaml` and the project's cache accordingly. + +### For displaying and comparing data science experiments + +[parameters](/doc/command-reference/params) (`-p`/`--params` option) are a +special type of key/value dependencies. Multiple parameter dependencies can be +specified from within one or more YAML, JSON, TOML, or Python parameters files +(e.g. `params.yaml`). This allows tracking experimental hyperparameters easily. + +Special types of output files, [metrics](/doc/command-reference/metrics) (`-m` +and `-M` options) and [plots](/doc/command-reference/plots) (`--plots` and +`--plots-no-cache` options), are also supported. Metrics and plots files have +specific formats (JSON, YAML, CSV, or TSV) and allow displaying and comparing +data science experiments. + +## Options + +- `-n `, `--name ` (**required**) - specify a name for the stage + generated by this command (e.g. `-n train`). Stage names can only contain + letters, numbers, dash `-` and underscore `_`. + +- `-d `, `--deps ` - specify a file or a directory the stage depends + on. Multiple dependencies can be specified like this: + `-d data.csv -d process.py`. Usually, each dependency is a file or a directory + with data, or a code file, or a configuration file. DVC also supports certain + [external dependencies](/doc/user-guide/external-dependencies). + + When you use `dvc repro`, the list of dependencies helps DVC analyze whether + any dependencies have changed and thus executing stages required to regenerate + their outputs. + +- `-o `, `--outs ` - specify a file or directory that is the result + of running the `command`. Multiple outputs can be specified: + `-o model.pkl -o output.log`. DVC builds a dependency graph (pipeline) to + connect different stages with each other based on this list of outputs and + dependencies (see `-d`). DVC tracks all output files and directories and puts + them into the cache (this is similar to what's happening when you use + `dvc add`). + +- `-O `, `--outs-no-cache ` - the same as `-o` except that outputs + are not tracked by DVC. This means that they are never cached, so it's up to + the user to manage them separately. This is useful if the outputs are small + enough to be tracked by Git directly; or large, yet you prefer to regenerate + them every time (see `dvc repro`); or unwanted in storage for any other + reason. + +- `--outs-persist ` - declare output file or directory that will not be + removed when `dvc repro` starts (but it can still be modified, overwritten, or + even deleted by the stage command(s)). + +- `--outs-persist-no-cache ` - the same as `-outs-persist` except that + outputs are not tracked by DVC (same as with `-O` above). + +- `-p [:]`, `--params [:]` - specify a set + of [parameter dependencies](/doc/command-reference/params) the stage depends + on, from a parameters file. This is done by sending a comma separated list as + argument, e.g. `-p learning_rate,epochs`. The default parameters file name is + `params.yaml`, but this can be redefined with a prefix in the argument sent to + this option, e.g. `-p parse_params.yaml:threshold`. See `dvc params` to learn + more about parameters. + +- `-m `, `--metrics ` - specify a metrics file produced by this + stage. This option behaves like `-o` but registers the file in a `metrics` + field inside the `dvc.yaml` stage. Metrics are usually small, human readable + files (JSON or YAML) with scalar numbers or other simple information that + describes a model (or any other data artifact). See `dvc metrics` to learn + more about _metrics_. + +- `-M `, `--metrics-no-cache ` - the same as `-m` except that DVC + does not track the metrics file (same as with `-O` above). This means that + they are never cached, so it's up to the user to manage them separately. This + is typically desirable with _metrics_ because they are small enough to be + tracked with Git directly. + +- `--plots ` - specify a plot metrics file produces by this stage. This + option behaves like `-o` but registers the file in a `plots` field inside the + `dvc.yaml` stage. Plot metrics are data series stored in tabular (CSV or TSV) + or hierarchical (JSON or YAML) files, with complex information that describes + a model (or any other data artifact). See `dvc plots` to learn more about + plots. + +- `--plots-no-cache ` - the same as `--plots` except that DVC does not + track the plots file (same as with `-O` and `-M` above). This may be desirable + with _plots_, if they are small enough to be tracked with Git directly. + +- `-w `, `--wdir ` - specifies a working directory for the `command` + to run in (uses the `wdir` field in `dvc.yaml`). Dependency and output files + (including metrics and plots) should be specified relative to this directory. + It's used by `dvc repro` to change the working directory before executing the + `command`. + +- `-f`, `--force` - overwrite an existing stage in `dvc.yaml` file without + asking for confirmation. + +- `--always-changed` - always consider this stage as changed (uses the + `always_changed` field in `dvc.yaml`). As a result `dvc status` will report it + as `always changed` and `dvc repro` will always execute it. + + > Note that regular `.dvc` files (without dependencies) are automatically + > considered "always changed", so this option has no effect in those cases. + +- `--external` - allow writing outputs outside of the DVC repository. See + [Managing External Data](/doc/user-guide/managing-external-data). + +- `--desc ` - user description of the stage (optional). This doesn't + affect any DVC operations. + +- `-h`, `--help` - prints the usage/help message, and exit. + +- `-q`, `--quiet` - do not write anything to standard output. Exit with 0 if no + problems arise, otherwise 1. + +- `-v`, `--verbose` - displays detailed tracing information. + +## Examples + +Let's create a DVC project and a stage (that counts the number of +lines in a `test.txt` file): + +```dvc +$ mkdir example && cd example +$ git init +$ dvc init +$ mkdir data +$ dvc stage add -n count \ + -d test.txt \ + -o lines \ + "cat test.txt | wc -l > lines" +Creating 'dvc.yaml' +Adding stage 'count' in 'dvc.yaml' + +To track the changes with git, run: + + git add .gitignore dvc.yaml + +$ tree +. +โ”œโ”€โ”€ dvc.yaml +โ””โ”€โ”€ test.txt +``` + +This results in the following stage entry in `dvc.yaml`: + +```yaml +stages: + count: + cmd: 'cat test.txt | wc -l > lines' + deps: + - test.txt + outs: + - lines +``` + +There's no `lines` file in the workspace as the stage is not run yet. It'll be +created and tracked whenever `dvc repro` is run. + +## Example: Overwrite an existing stage + +The following stage runs a Python script that trains an ML model on the training +dataset (`20180226` is a seed value): + +```dvc +$ dvc stage add -n train \ + -d train_model.py -d matrix-train.p -o model.p \ + python train_model.py 20180226 model.p +``` + +To update a stage that is already defined, the `-f` (`--force`) option is +needed. Let's update the seed for the `train` stage: + +```dvc +$ dvc stage add -n train --force \ + -d train_model.p -d matrix-train.p -o model.p \ + python train_model.py 18494003 model.p +``` + +## Example: Separate stages in a subdirectory + +Let's move to a subdirectory and create a stage there. This generates a separate +`dvc.yaml` file in that location. The stage command itself counts the lines in +`test.txt` and writes the number to `lines`. + +```dvc +$ cd more_stages/ +$ dvc stage add -n process_data \ + -d data.in \ + -o result.out \ + ./my_script.sh data.in result.out +$ tree .. +. +โ”œโ”€โ”€ dvc.yaml +โ”œโ”€โ”€ dvc.lock +โ”œโ”€โ”€ file1 +โ”œโ”€โ”€ ... +โ””โ”€โ”€ more_stages/ + โ”œโ”€โ”€ data.in + โ””โ”€โ”€ dvc.yaml +``` + +## Example: Chaining stages + +DVC [pipelines](/doc/command-reference/dag) are constructed by connecting the +outputs of a stage to the dependencies of the following one(s). + +Let's create a stage that extracts an XML file from an archive to the `data/` +folder: + +```dvc +$ mkdir data +$ dvc stage add -n extract \ + -d Posts.xml.zip \ + -o data/Posts.xml \ + unzip Posts.xml.zip -d data/ +``` + +> Note that the last `-d` applies to the stage's command (`unzip`), not to +> `dvc stage add`. + +Also, let's add another stage that executes an R script that parses the XML +file: + +```dvc +$ dvc stage add -n parse \ + -d parsingxml.R -d data/Posts.xml \ + -o data/Posts.csv \ + Rscript parsingxml.R data/Posts.xml data/Posts.csv +``` + +These stages are not run yet, so there are no outputs. But we can still see how +they are connected into a pipeline (given their outputs and dependencies) with +`dvc dag`: + +```dvc +$ dvc dag ++---------+ +| extract | ++---------+ + * + * + * ++---------+ +| parse | ++---------+ +``` + +We can use `dvc repro` to execute this pipeline to get the outputs. + +## Example: Using parameter dependencies + +To use specific values inside a parameters file as dependencies, create a simple +YAML file named `params.yaml` (default params file name, see `dvc params` to +learn more): + +```yaml +seed: 20180226 + +train: + lr: 0.0041 + epochs: 75 + layers: 9 + +processing: + threshold: 0.98 + bow_size: 15000 +``` + +Define a stage with both regular dependencies as well as parameter dependencies: + +```dvc +$ dvc stage add -n train \ + -d train_model.py -d matrix-train.p -o model.p \ + -p seed,train.lr,train.epochs + python train_model.py 20200105 model.p +``` + +`train_model.py` will include some code to open and parse the parameters: + +```py +import yaml + +with open("params.yaml", 'r') as fd: + params = yaml.safe_load(fd) + +seed = params['seed'] +lr = params['train']['lr'] +epochs = params['train']['epochs'] +``` + +DVC will keep an eye on these param values (same as with the regular dependency +files) and know that the stage should be reproduced if/when they change. See +`dvc params` for more details. diff --git a/content/docs/command-reference/stage/index.md b/content/docs/command-reference/stage/index.md new file mode 100644 index 0000000000..d9c4d98fe4 --- /dev/null +++ b/content/docs/command-reference/stage/index.md @@ -0,0 +1,25 @@ +# stage + +A set of commands to add and list stages: +[add](/doc/command-reference/stage/add). + +## Synopsis + +```usage +usage: dvc stage [-h] [-q | -v] {add,list} ... + +positional arguments: + COMMAND + add Create stage. + list List stages. +``` + +## Description + +_Stages_ represent individual data processes, including their input and +resulting outputs. They can be combined to capture simple data workflows, +organize data science projects, or build detailed machine learning pipelines. + +`dvc stage add` can be used to create/update stages in the `dvc.yaml` file. +Similarly, `dvc stage list` helps listing the stages present in the `dvc.yaml` +file. diff --git a/content/docs/sidebar.json b/content/docs/sidebar.json index 2d94c6e173..73a436f487 100644 --- a/content/docs/sidebar.json +++ b/content/docs/sidebar.json @@ -361,6 +361,17 @@ "label": "run", "slug": "run" }, + { + "label": "stage", + "slug": "stage", + "source": "stage/index.md", + "children": [ + { + "label": "stage add", + "slug": "add" + } + ] + }, { "label": "status", "slug": "status" diff --git a/content/docs/user-guide/basic-concepts/stage.md b/content/docs/user-guide/basic-concepts/stage.md new file mode 100644 index 0000000000..e73027b460 --- /dev/null +++ b/content/docs/user-guide/basic-concepts/stage.md @@ -0,0 +1,8 @@ +--- +name: Stage +match: [stage, stages] +tooltip: >- + A stage represents individual data processes, including their input and + resulting output which can be combined to build detailed machine learning + pipelines. +---

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pre-release --- .../blog/2021-02-18-dvc-2-0-pre-release.md | 282 ++++++++++++++++++ .../images/2021-02-18/dvc-2-0-pre-release.png | Bin 0 -> 29488 bytes 2 files changed, 282 insertions(+) create mode 100644 content/blog/2021-02-18-dvc-2-0-pre-release.md create mode 100644 static/uploads/images/2021-02-18/dvc-2-0-pre-release.png diff --git a/content/blog/2021-02-18-dvc-2-0-pre-release.md b/content/blog/2021-02-18-dvc-2-0-pre-release.md new file mode 100644 index 0000000000..8492420434 --- /dev/null +++ b/content/blog/2021-02-18-dvc-2-0-pre-release.md @@ -0,0 +1,282 @@ +--- +title: DVC 2.0 Pre-Release +date: 2021-02-17 +description: | + Today, we're announcing DVC 2.0 pre-release. We'll share learnings from our + journey and how these will be reflected in the coming release. + +descriptionLong: | + The new release is a result of our learnings from our users. There are four + major features are coming: + + ๐Ÿ”— ML pipelines templating and iterative foreach stages + + ๐Ÿงช Lightweight ML experiments + + ๐Ÿ“ ML model checkpoints + + ๐Ÿ“ˆ Dvc-live - metrics logging + +picture: 2021-02-18/dvc-2-0-pre-release.png +pictureComment: DVC 2.0 Pre-Release +author: dmitry_petrov +commentsUrl: https://discuss.dvc.org/t/dvc-3-years-anniversary-and-1-0-pre-release/374 +tags: + - Release + - MLOps + - DataOps +--- + +## Install + +First things first. You can install the 1.0 pre-release from the master branch +in our repo (instruction [here](https://dvc.org/doc/install/pre-release)) or +through pip: + +```bash +pip install --upgrade --pre dvc +``` + +## ML pipelines parametrization and foreach stages + +After introducing the multi-stage pipeline file `dvc.yaml`, it was quickly +adopted among DVC users. The DVC team got tons of positive feedback from users +as well as feature requests. + +### Parameters + +The most requested feature was the ability to use parameters in the pipeline +file. So, you can pass the same seed value or filename to multiple stages in the +pipeline. + +```yaml +var: + train_matrix: train.pkl + test_matrix: test.pkl + seed: 20210215 + +... + +stages: + process: + cmd: python process.py \ + --seed ${seed} \ + --train ${train_matrix} \ + --test ${test_matrix} + outs: + - ${test_matrix} + - ${train_matrix} + + ... + + train: + cmd: python train.py ${train_matrix} --seed ${seed} + deps: + - ${train_matrix} +``` + +Also, it gives an ability to localize all important parameters in a single `var` +block and play with them. This is a natural thing to do for scenarios like NLP +or when hyperparameter optimization is happening not only in the model training +code but in the data processing as well. + +### Pipeline params from params.yaml + +It is quite common to define pipeline parameters in a config file or the +parameters file `params.yaml` instead of in the pipeline file `dvc.yaml`. +Parameters defined in `params.yaml` can also be used in `dvc.yaml`. + +Params.yaml: + +```yaml +models: + us: + thresh: 10 + filename: 'model-us.hdf5' +``` + +Pipeline file `dvc.yaml`: + +```yaml +stages: + build-us: + cmd: >- + python script.py + --out ${models.us.filename} + --thresh ${models.us.thresh} + outs: + - ${models.us.filename} +``` + +DVC properly tracks params dependencies for each stage starting from the +previous DVC version 1.0. See +[params option](https://dvc.org/doc/command-reference/run#for-displaying-and-comparing-data-science-experiments) +in `dvc run` for more details. + +### Iterating over params with foreach stages + +Iterating over params was a frequently requested feature. Now users can define +multiple similar stages by a single command. + +```yaml +stages: + build: + foreach: + gb: + thresh: 15 + filename: 'model-gb.hdf5' + us: + thresh: 10 + filename: 'model-us.hdf5' + do: + cmd: >- + python script.py --out ${item.filename} --thresh ${item.thresh} + outs: + - ${item.filename} +``` + +## Lightweight ML experiments + +DVC uses Git as a foundation for ML experiments. This solid foundation makes +each ML experiment reproducible and accessible from Git history. This Git-based +approach works very well for ML projects with mature ML models when only a few +new experiments per day are running. However, in more active development when +dozens or hundreds of experiments need to be run in a single day, Git creates +overhead - each experiment run requires additional Git commands +`git add/commit`, and comparing all experiments is difficult. + +We introduce lightweight experiments in DVC 2.0! This is the way of +auto-tracking without any overhead from ML engineers: + +```yaml +$ dvc exp run +... +Reproduced experiment(s): exp-0c0f7 +โ€ฆ +$ dvc exp run --params learning_rate=0.03 +Reproduced experiment(s): exp-bc5cd + + +$ dvc exp run --params learning_rate=0.025 +Reproduced experiment(s): exp-49181 + +$ vi train.py # edit code +$ dvc exp run --params learning_rate=0.025 +Reproduced experiment(s): exp-a814a +``` + +See all the runs: + +```yaml +$ dvc exp show --no-pager +``` + +Any of the runs can be easily obtained with code, data and models: + +```yaml +$ dvc apply exp-49181 Changes for experiment 'exp-0c0f7' have been applied to +your current workspace. +``` + +## Model checkpoints tracking + +ML model checkpoints are an essential part of deep learning. ML engineers prefer +to save the model files (or weights) at checkpoints during a training process +and return back when metrics start diverging or learning is not fast enough. + +The checkpoints create a different dynamic around ML modeling process and need a +special support from the toolset: + +1. Track and save model checkpoints (DVC outputs) periodically, not only the + final result or training epoch. +2. Save metrics corresponding to each of the checkpoints. +3. Reuse checkpoints - warm-start training with an existing model file, + corresponding code, dataset version and metrics. + +This new behaviour is supported in DVC 2.0. Now, DVC can version all your +checkpoints with corresponding code and data. It brings reproducibility of DL +processes to the next level - every checkpoint is reproducible. + +This is how you define checkpoints with live-metrics: + +``` + +``` + +Start and interrupt training process: + +``` + +``` + +Navigate in checkpoints: + +``` + +``` + +Get back to any version: + +``` + +``` + +Under the hood DVC uses Git to store the checkpoint meta-information. +Straight-forward implementation of checkpoints on top of Git should include +branches and auto-commits in the branches. This approach over-pollutes the +branch namespace very quickly. To avoid this issue, we introduced Git custom +references `exps` the same way as GitHub uses Git custom references `pulls` to +track pull requests. This is an interesting technical topic that deserves a +separate blog post. Please follow us if you are interested. + +## Metrics logging + +Continuously logging ML metrics is a very common practice in the ML world. +Instead of a simple command line output with the metrics values many ML +engineers prefer visuals and plots. These plots can be organized in a "database" +of ML experiments to keep track of a project. There are many special solutions +for metrics collecting and experiment tracking such as sacred, mlflow, weight +and biases, neptune.ai or other. + +With DVC 2.0 we are releasing new open-source library +[DVC-Live](https://github.com/iterative/dvclive) that provide functionality for +tracking model metrics and organizing metrics in simple text files in a way that +DVC can visualize the metrics with navigation in Git histroy. So, DVC can show +you a metrics difference between current model and a model in `master` or any +other branch. + +This approach is similar to the other metrics tracking tools with the difference +that Git becomes a "database" or of ML experiments. + +In your code: + +``` + +``` + +Result: + +``` + +``` + +Visualize: + +``` + +``` + +Navigate in Git history: + +``` + +``` + +## Thank you! + +I'd like to thank all of you DVC community members for the feedback that we are +constantly getting. This feedback helps us build new functionalities in DVC and +make it more stable. + +Please be in touch with us on [Twitter](https://twitter.com/DVCorg) and our +[Discord channel](https://dvc.org/chat). diff --git a/static/uploads/images/2021-02-18/dvc-2-0-pre-release.png b/static/uploads/images/2021-02-18/dvc-2-0-pre-release.png new file mode 100644 index 0000000000000000000000000000000000000000..e4c0d3562494d66d2fc57954aca06117118613a1 GIT binary patch literal 29488 zcmbrlWmr_*7dL!{l$I7LX^`#?k?sy922r|8ngOIFM7jh70qK&C89+cKMd=zqI;C^w zIo==N>w2FL|L1&|i`ldGI;;2EzrBgm(@`bDe~1qN0FklC z2>_UYo|cg^5AS2NsVPV8x4-VnXf!(7vFhW;A^;{{Soo2nW-!X3($nbOft!+tVUk@~ z(~+AJ^i2zU^?+ewdzXu^NqoC)M02Z4pr8=HW5}027Y}#;h9;MgdMDU`tE)>;;~R&H zTVrEy-+GO-F5UN?S}C29{5%{D!yPGc?F^O8@Nq*6eNDGch4K0p4AAbsuTT45!=* z=NMsO;ncL04wu(rVxqzBHRm46{N)4P-CZ16Rkydd#-c*m+1V*h1>W9X7e+?$@o$Tj z6m>-eK7IN$s-+na;IE~vIjE`mPC@?I&@ewge`j=edecJPZ?#j*j;B_L3ZndNtHDo+$j*)}GbX z`T6swjI5NTlsK0#uZ_59c}2wwTbs+v%afCn(b18O&5ccceR&00dkHaaF)>LoxwrDN 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zzKtg|#hitTCsS%9){Y`ekMz$77AEvgnE&XL*XXIlz&xK2_KAk$E^d$-iHI<>Pgip@ z&kVJQmTw3VHzU(=4oI3{bYYh6I@AGtJ_i`l@RO7c3sed?d_8y_lTAlu$pkhL;o90NZme2O0%&CTTKOJDJA*Kj;(*#LWCIc}Aw+Fok{C&luf*o8 z z2{Ajc<|c*xa0vOO=b@#131pM<&s1a zBrz~Kph9csfF}_xmc@@L0w7$1uaKm_tNfp21ZAWlZu8Yzp}8NFGpv-jA{<661mwZe zt1Iy#K(_uC?~dr7OyP}v=mwct7~U-P3KL8Y71%7`!OGG@Zw?uQiB}z0o&Xud%HBPD z=5^DYtR*V0ccc+beUy)W{Q0zcX;Mg5pTfCd;^PnLK;^G!#qOy@1*pkfE_LM%g;fg- zq%fb29>M%??Gfrum65Ydd?fNr2x)QYV6VT$%n6%A!~P^Tl<}_cE;ErskL`yHtZ5PR zDf4)wIQ#1Y^@np}De*h**)>1?>YG3!7?Tvv%P2`nU5%SS!DESF9}m7~lIy zv%VlKkCnT^{^Lut0k-8cN2FQXcAc8~O^I!-1o~C@ai$rwy?!!Z>@Ps^T0r@$XU~?? zEOEx6{ literal 0 HcmV?d00001 From be79555e48ab8570ee575e7d997f41f5ccebd204 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Thu, 18 Feb 2021 01:42:09 -0600 Subject: [PATCH 23/40] Update content/blog/2021-02-18-dvc-2-0-pre-release.md --- content/blog/2021-02-18-dvc-2-0-pre-release.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/blog/2021-02-18-dvc-2-0-pre-release.md b/content/blog/2021-02-18-dvc-2-0-pre-release.md index 8492420434..6cbd0c6bf1 100644 --- a/content/blog/2021-02-18-dvc-2-0-pre-release.md +++ b/content/blog/2021-02-18-dvc-2-0-pre-release.md @@ -50,7 +50,7 @@ file. So, you can pass the same seed value or filename to multiple stages in the pipeline. ```yaml -var: +vars: train_matrix: train.pkl test_matrix: test.pkl seed: 20210215 From f84dac4ed82bb8f71eb70e16339757f6fd0151e7 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Thu, 18 Feb 2021 01:42:38 -0600 Subject: [PATCH 24/40] Update content/blog/2021-02-18-dvc-2-0-pre-release.md --- content/blog/2021-02-18-dvc-2-0-pre-release.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/blog/2021-02-18-dvc-2-0-pre-release.md b/content/blog/2021-02-18-dvc-2-0-pre-release.md index 6cbd0c6bf1..bfa9dcdaa7 100644 --- a/content/blog/2021-02-18-dvc-2-0-pre-release.md +++ b/content/blog/2021-02-18-dvc-2-0-pre-release.md @@ -75,10 +75,10 @@ stages: - ${train_matrix} ``` -Also, it gives an ability to localize all important parameters in a single `var` -block and play with them. This is a natural thing to do for scenarios like NLP -or when hyperparameter optimization is happening not only in the model training -code but in the data processing as well. +Also, it gives an ability to localize all important parameters in a single +`vars` block, and play with them. This is a natural thing to do for scenarios +like NLP or when hyperparameter optimization is happening not only in the model +training code but in the data processing as well. ### Pipeline params from params.yaml From 25b13fefd45313f057c37be91e353ea97909e522 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Thu, 18 Feb 2021 01:44:24 -0600 Subject: [PATCH 25/40] Update content/blog/2021-02-18-dvc-2-0-pre-release.md --- content/blog/2021-02-18-dvc-2-0-pre-release.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/blog/2021-02-18-dvc-2-0-pre-release.md b/content/blog/2021-02-18-dvc-2-0-pre-release.md index bfa9dcdaa7..61a15eb26c 100644 --- a/content/blog/2021-02-18-dvc-2-0-pre-release.md +++ b/content/blog/2021-02-18-dvc-2-0-pre-release.md @@ -148,7 +148,7 @@ overhead - each experiment run requires additional Git commands We introduce lightweight experiments in DVC 2.0! This is the way of auto-tracking without any overhead from ML engineers: -```yaml +```dvc $ dvc exp run ... Reproduced experiment(s): exp-0c0f7 From ed3e96f070886bd790bf45ca84b5a15c3a7df0fb Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Thu, 18 Feb 2021 01:44:53 -0600 Subject: [PATCH 26/40] Update content/blog/2021-02-18-dvc-2-0-pre-release.md --- content/blog/2021-02-18-dvc-2-0-pre-release.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/blog/2021-02-18-dvc-2-0-pre-release.md b/content/blog/2021-02-18-dvc-2-0-pre-release.md index 61a15eb26c..d5aa8be7a6 100644 --- a/content/blog/2021-02-18-dvc-2-0-pre-release.md +++ b/content/blog/2021-02-18-dvc-2-0-pre-release.md @@ -167,7 +167,7 @@ Reproduced experiment(s): exp-a814a See all the runs: -```yaml +```dvc $ dvc exp show --no-pager ``` From 37886627a7f52be61899e5ee8b0473effba14b03 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Thu, 18 Feb 2021 01:45:19 -0600 Subject: [PATCH 27/40] Update content/blog/2021-02-18-dvc-2-0-pre-release.md --- content/blog/2021-02-18-dvc-2-0-pre-release.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/blog/2021-02-18-dvc-2-0-pre-release.md b/content/blog/2021-02-18-dvc-2-0-pre-release.md index d5aa8be7a6..94fd319c85 100644 --- a/content/blog/2021-02-18-dvc-2-0-pre-release.md +++ b/content/blog/2021-02-18-dvc-2-0-pre-release.md @@ -173,7 +173,7 @@ $ dvc exp show --no-pager Any of the runs can be easily obtained with code, data and models: -```yaml +```dvc $ dvc apply exp-49181 Changes for experiment 'exp-0c0f7' have been applied to your current workspace. ``` From 8db86650f54c13970e1c4a2eb53bd23c75e7a489 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Thu, 18 Feb 2021 01:46:11 -0600 Subject: [PATCH 28/40] Update content/blog/2021-02-18-dvc-2-0-pre-release.md --- content/blog/2021-02-18-dvc-2-0-pre-release.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/blog/2021-02-18-dvc-2-0-pre-release.md b/content/blog/2021-02-18-dvc-2-0-pre-release.md index 94fd319c85..07cff90edd 100644 --- a/content/blog/2021-02-18-dvc-2-0-pre-release.md +++ b/content/blog/2021-02-18-dvc-2-0-pre-release.md @@ -29,7 +29,7 @@ tags: ## Install -First things first. You can install the 1.0 pre-release from the master branch +First things first. You can install the 2.0 pre-release from the master branch in our repo (instruction [here](https://dvc.org/doc/install/pre-release)) or through pip: From 9503aa0bc3ecf5a6a89c0e19a6b0371b1ec1407f Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Thu, 18 Feb 2021 01:47:29 -0600 Subject: [PATCH 29/40] Update content/blog/2021-02-18-dvc-2-0-pre-release.md --- content/blog/2021-02-18-dvc-2-0-pre-release.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/blog/2021-02-18-dvc-2-0-pre-release.md b/content/blog/2021-02-18-dvc-2-0-pre-release.md index 07cff90edd..cef2193f59 100644 --- a/content/blog/2021-02-18-dvc-2-0-pre-release.md +++ b/content/blog/2021-02-18-dvc-2-0-pre-release.md @@ -33,8 +33,8 @@ First things first. You can install the 2.0 pre-release from the master branch in our repo (instruction [here](https://dvc.org/doc/install/pre-release)) or through pip: -```bash -pip install --upgrade --pre dvc +```dvc +$ pip install --upgrade --pre dvc ``` ## ML pipelines parametrization and foreach stages From cd27812ac7f77c8c2f0cf32f4c317079c824c7ee Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Thu, 18 Feb 2021 01:51:13 -0600 Subject: [PATCH 30/40] Update content/blog/2021-02-18-dvc-2-0-pre-release.md --- content/blog/2021-02-18-dvc-2-0-pre-release.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/content/blog/2021-02-18-dvc-2-0-pre-release.md b/content/blog/2021-02-18-dvc-2-0-pre-release.md index cef2193f59..8ca4f3b591 100644 --- a/content/blog/2021-02-18-dvc-2-0-pre-release.md +++ b/content/blog/2021-02-18-dvc-2-0-pre-release.md @@ -86,9 +86,8 @@ It is quite common to define pipeline parameters in a config file or the parameters file `params.yaml` instead of in the pipeline file `dvc.yaml`. Parameters defined in `params.yaml` can also be used in `dvc.yaml`. -Params.yaml: - ```yaml +# params.yaml models: us: thresh: 10 From 47b85cbcd6f2327f6b2a63af6c2201fdfb0135fd Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Thu, 18 Feb 2021 01:51:21 -0600 Subject: [PATCH 31/40] Update content/blog/2021-02-18-dvc-2-0-pre-release.md --- content/blog/2021-02-18-dvc-2-0-pre-release.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/content/blog/2021-02-18-dvc-2-0-pre-release.md b/content/blog/2021-02-18-dvc-2-0-pre-release.md index 8ca4f3b591..9b30cc18a2 100644 --- a/content/blog/2021-02-18-dvc-2-0-pre-release.md +++ b/content/blog/2021-02-18-dvc-2-0-pre-release.md @@ -94,9 +94,8 @@ models: filename: 'model-us.hdf5' ``` -Pipeline file `dvc.yaml`: - ```yaml +# dvc.yaml stages: build-us: cmd: >- From 26fda1dd40dbc72d1154c7ab0864af480f89e57c Mon Sep 17 00:00:00 2001 From: Dmitry Petrov Date: Thu, 18 Feb 2021 01:19:46 -0800 Subject: [PATCH 32/40] feedback & experiments --- .../blog/2021-02-18-dvc-2-0-pre-release.md | 138 +++++++++++++----- 1 file changed, 104 insertions(+), 34 deletions(-) diff --git a/content/blog/2021-02-18-dvc-2-0-pre-release.md b/content/blog/2021-02-18-dvc-2-0-pre-release.md index 9b30cc18a2..84e445fd5d 100644 --- a/content/blog/2021-02-18-dvc-2-0-pre-release.md +++ b/content/blog/2021-02-18-dvc-2-0-pre-release.md @@ -2,20 +2,20 @@ title: DVC 2.0 Pre-Release date: 2021-02-17 description: | - Today, we're announcing DVC 2.0 pre-release. We'll share learnings from our + Today, we're announcing DVC 2.0 pre-release. We'll share lessons from our journey and how these will be reflected in the coming release. descriptionLong: | - The new release is a result of our learnings from our users. There are four + The new release is a result of our learning from our users. There are four major features are coming: - ๐Ÿ”— ML pipelines templating and iterative foreach stages + ๐Ÿ”— ML pipeline templating and iterative foreach stages ๐Ÿงช Lightweight ML experiments ๐Ÿ“ ML model checkpoints - ๐Ÿ“ˆ Dvc-live - metrics logging + ๐Ÿ“ˆ Dvc-live - new open-source library for metrics logging picture: 2021-02-18/dvc-2-0-pre-release.png pictureComment: DVC 2.0 Pre-Release @@ -37,17 +37,17 @@ through pip: $ pip install --upgrade --pre dvc ``` -## ML pipelines parametrization and foreach stages +## ML pipelines parameterization and foreach stages After introducing the multi-stage pipeline file `dvc.yaml`, it was quickly -adopted among DVC users. The DVC team got tons of positive feedback from users +adopted among our users. The DVC team got tons of positive feedback from them, as well as feature requests. -### Parameters +### Pipeline parameters from `vars` -The most requested feature was the ability to use parameters in the pipeline -file. So, you can pass the same seed value or filename to multiple stages in the -pipeline. +The most requested feature was the ability to use parameters in `dvc.yaml`. For +example. So, you can pass the same seed value or filename to multiple stages in +the pipeline. ```yaml vars: @@ -80,11 +80,12 @@ Also, it gives an ability to localize all important parameters in a single like NLP or when hyperparameter optimization is happening not only in the model training code but in the data processing as well. -### Pipeline params from params.yaml +### Pipeline parameters from params files -It is quite common to define pipeline parameters in a config file or the -parameters file `params.yaml` instead of in the pipeline file `dvc.yaml`. -Parameters defined in `params.yaml` can also be used in `dvc.yaml`. +It is quite common to define pipeline parameters in a config file or a +parameters file (like `params.yaml`) instead of in the pipeline file `dvc.yaml` +itself. These parameters defined in `params.yaml` can also be used in +`dvc.yaml`. ```yaml # params.yaml @@ -107,14 +108,14 @@ stages: ``` DVC properly tracks params dependencies for each stage starting from the -previous DVC version 1.0. See -[params option](https://dvc.org/doc/command-reference/run#for-displaying-and-comparing-data-science-experiments) -in `dvc run` for more details. +previous DVC version 1.0. See the +[`--params` option](/doc/command-reference/run#for-displaying-and-comparing-data-science-experiments) +of `dvc run` for more details. ### Iterating over params with foreach stages Iterating over params was a frequently requested feature. Now users can define -multiple similar stages by a single command. +multiple similar stages with a templetized command. ```yaml stages: @@ -144,36 +145,105 @@ overhead - each experiment run requires additional Git commands `git add/commit`, and comparing all experiments is difficult. We introduce lightweight experiments in DVC 2.0! This is the way of -auto-tracking without any overhead from ML engineers: +auto-tracking without any overhead from ML engineers. + +โš ๏ธ Note, ML experiment is an experimental feature in the coming release. It +means the commands might change a bit even after the release. + +Run an ML experiment with a new hyperparameter from `params.yaml`: ```dvc -$ dvc exp run -... -Reproduced experiment(s): exp-0c0f7 -โ€ฆ -$ dvc exp run --params learning_rate=0.03 -Reproduced experiment(s): exp-bc5cd +$ dvc exp run --set-param featurize.max_features=3000 +Reproduced experiment(s): exp-bb55c +Experiment results have been applied to your workspace. -$ dvc exp run --params learning_rate=0.025 -Reproduced experiment(s): exp-49181 +$ dvc exp diff +Path Metric Value Change +scores.json auc 0.57462 0.0072197 -$ vi train.py # edit code -$ dvc exp run --params learning_rate=0.025 -Reproduced experiment(s): exp-a814a +Path Param Value Change +params.yaml featurize.max_features 3000 1500 ``` +More experiments: + +```dvc +$ dvc exp run --set-param featurize.max_features=4000 +Reproduced experiment(s): exp-9bf22 +Experiment results have been applied to your workspace. + +$ dvc exp run --set-param featurize.max_features=5000 +Reproduced experiment(s): exp-63ee0 +Experiment results have been applied to your workspace. + +$ dvc exp run --set-param featurize.max_features=5000 \ + --set-param featurize.ngrams=3 +Reproduced experiment(s): exp-80655 +Experiment results have been applied to your workspace. +``` + +In the examples above, hyperparamteres were changed automaticaly by option +`--set-param`. User can make this changes manualy by modifying the file. The +same way _any code or data files can be changed_ and `dvc exp run` will capture +the changes. + See all the runs: ```dvc -$ dvc exp show --no-pager +$ dvc exp show --no-pager --no-timestamp \ + --include-params featurize.max_features,featurize.ngrams +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ Experiment โ”ƒ auc โ”ƒ featurize.max_features โ”ƒ featurize.ngrams โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ workspace โ”‚ 0.56359 โ”‚ 5000 โ”‚ 3 โ”‚ +โ”‚ master โ”‚ 0.5674 โ”‚ 1500 โ”‚ 2 โ”‚ +โ”‚ โ”œโ”€โ”€ exp-80655 โ”‚ 0.56359 โ”‚ 5000 โ”‚ 3 โ”‚ +โ”‚ โ”œโ”€โ”€ exp-63ee0 โ”‚ 0.5515 โ”‚ 5000 โ”‚ 2 โ”‚ +โ”‚ โ”œโ”€โ”€ exp-9bf22 โ”‚ 0.56448 โ”‚ 4000 โ”‚ 2 โ”‚ +โ”‚ โ””โ”€โ”€ exp-bb55c โ”‚ 0.57462 โ”‚ 3000 โ”‚ 2 โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` + +No artificial branches, only custome references `exps` (do not worry if you +don't understand this part - it is an implementation detail): + +```dvc +$ git branch +* master + +$ git show-ref +5649f62d845fdc29e28ea6f7672dd729d3946940 refs/exps/exec/EXEC_APPLY +5649f62d845fdc29e28ea6f7672dd729d3946940 refs/exps/exec/EXEC_BRANCH +5649f62d845fdc29e28ea6f7672dd729d3946940 refs/exps/71/67904d89e116f28daf7a6e4c0878268117c893/exp-80655 +f16e7b7c804cf52d91d1d11850c15963fb2a8d7b refs/exps/97/d69af70c6fb4bc59aefb9a87437dcd28b3bde4/exp-63ee0 +0566d42cddb3a8c4eb533f31027f0febccbbc2dd refs/exps/91/94265d5acd847e1c439dd859aa74b1fc3d73ad/exp-bb55c +9bb067559583990a8c5d499d7435c35a7c9417b7 refs/exps/49/5c835cd36772123e82e812d96eabcce320f7ec/exp-9bf22 +``` + +The best experiment can be promoted to the workspace and commited to Git. + +```dvc +$ dvc exp apply exp-bb55c +$ git add . +$ git commit -m 'optimize max feature size' +``` + +Alternatively, an experiment can be promoted to a branch (`big_fr_size` branch +in this case): + +```dvc +$ dvc exp branch exp-80655 big_fr_size +Git branch 'big_fr_size' has been created from experiment 'exp-c695f'. +To switch to the new branch run: + + git checkout big_fr_size ``` -Any of the runs can be easily obtained with code, data and models: +Remove all the experiments that were not used: ```dvc -$ dvc apply exp-49181 Changes for experiment 'exp-0c0f7' have been applied to -your current workspace. +$ dvc exp gc --workspace --force ``` ## Model checkpoints tracking From a2310da7f1aaa8fbe636fe3db98b243a0a0ac635 Mon Sep 17 00:00:00 2001 From: Dmitry Petrov Date: Thu, 18 Feb 2021 03:52:12 -0800 Subject: [PATCH 33/40] live metrics --- .../blog/2021-02-18-dvc-2-0-pre-release.md | 91 +++++++++++++++++- .../images/2021-02-18/dvclive-html.png | Bin 0 -> 224767 bytes 2 files changed, 86 insertions(+), 5 deletions(-) create mode 100644 static/uploads/images/2021-02-18/dvclive-html.png diff --git a/content/blog/2021-02-18-dvc-2-0-pre-release.md b/content/blog/2021-02-18-dvc-2-0-pre-release.md index 84e445fd5d..0eb9d123fd 100644 --- a/content/blog/2021-02-18-dvc-2-0-pre-release.md +++ b/content/blog/2021-02-18-dvc-2-0-pre-release.md @@ -316,28 +316,109 @@ other branch. This approach is similar to the other metrics tracking tools with the difference that Git becomes a "database" or of ML experiments. -In your code: +### Generate metrics file -``` +Install the library: +```dvc +$ pip install dvclive ``` -Result: +Instrument your code: + +```python +import dvclive +from dvclive.keras import DvcLiveCallback + +dvclive.init("logs") #, summarize=True) + +... + +model.fit(... + # Set up DVC-Live callback: + callbacks=[ DvcLiveCallback() ] + ) ``` +During the training you will see the metrics files that are continiously +populated each epoches: + +```dvc +$ ls logs/ +accuracy.tsv loss.tsv val_accuracy.tsv val_loss.tsv + +$ head logs/accuracy.tsv +timestamp step accuracy +1613645582716 0 0.7360000014305115 +1613645585478 1 0.8349999785423279 +1613645587322 2 0.8830000162124634 +1613645589125 3 0.9049999713897705 +1613645590891 4 0.9070000052452087 +1613645592681 5 0.9279999732971191 +1613645594490 6 0.9430000185966492 +1613645596232 7 0.9369999766349792 +1613645598034 8 0.9430000185966492 ``` -Visualize: +In addition to the continious metrics files you will see the summary metrics +file and html file with the same file prefix. The summary file conteins the +result of the latest epoch: +```dvc +$ cat logs.json | python -m json.tool +{ + "step": 41, + "loss": 0.015958430245518684, + "accuracy": 0.9950000047683716, + "val_loss": 13.705962181091309, + "val_accuracy": 0.5149999856948853 +} ``` +The html file contains all the visuals for continious metrics as well as the +summary metrics in a single page: + +![](/uploads/images/2021-02-18/dvclive-html.png) + +Note, the HTML and the summary metrics files are generating automatically for +each. So, you can monitor model performance in realtime. + +### Navigation with the metrics file + +DVC repository is NOT required to use the live metrics functionality from the +above. It works independently from DVC. + +DVC repository become usefule when the metrics and plots are commited in your +Git repository and you need navigation around the metrics. + +```dvc +$ dvc metrics diff +???? ``` -Navigate in Git history: +The same with the plots: +```dvc +$ dvc plots diff .... +???? ``` +Another nice thing about the live metrics - they work across ML experiments and +checkpoints if properly set up in dvc stages. To set up live metrics you need to +specify the metrics directory in `live` section of a stage: + +```yaml +stages: + train: + cmd: python train.py + live: + logs: + cache: false + summary: true + report: true + deps: + - data ``` ## Thank you! diff --git a/static/uploads/images/2021-02-18/dvclive-html.png b/static/uploads/images/2021-02-18/dvclive-html.png new file mode 100644 index 0000000000000000000000000000000000000000..eba95dcd2f559bc289d01aa441a043d2bcd13c9d GIT binary patch literal 224767 zcmeFZXIN9));1iPAV{$w3er@RBA~R;1A;W^y;tdk-g}Xvh*&^+lU_sbRS=LCq(dko zO$nie009EuVxM!K<36AJ?C;n2<9#2lE6F0O%r)m6bBuf3_gHzMsw_)RN=pg?fym|M zq}4$n;$RSnK$_$N@Qqp=j~57Z$;wtrN>yG;ib>Vg$;#Hj5(JWa5wAn6squw2-9TO5 zCPX#o`8?rIEd>(J&|u9D89DrmED_H$&1Gq*4V$9OpXfYsDCS_Ym)x+tyb(~5`LQIr zk@aO?%A4S_<367=A-A=D@YxA!Xn6>fIo|(X0j&xJ?XrYdKJQhXn5uTaUP!Ex#^pWB* zvHKLWg{!lq6I7&Mxf4j{fQ$#ktQc!Vb( zL~?`uauf^5hCEU*iu?OTChcxJ?rjfvnO|f>1Gy* zfLOJ1q7<}$X5aG{BA%dp$m~hI{plmKK#1VucQka_tGrcRmkpy9?IiPGk~PRv(LHWf 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