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Pipelines Files (dvc.yaml)

You can construct data science or machine learning pipelines by defining individual stages in one or more dvc.yaml files (or pipelines files). Stages form a pipeline when they connect with each other (forming a dependency graph, see dvc dag). Refer to Get Started: Data Pipelines.

Note that a helper command, dvc stage, is available to create and list stages.

dvc.yaml files can be versioned with Git.

These files use the YAML 1.2 file format, and a human-friendly schema explained below. We encourage you to get familiar with it so you may modify, write, or generate stages and pipelines on your own.

Note that we use GNU/Linux in most of our examples.


The stages list contains a list of user-defined stages. Here's a simple one named transpose:

    cmd: ./trans.r rows.txt > columns.txt
      - rows.txt
      - columns.txt

The most important part of a stage it's the terminal command(s) it executes (cmd field). This is what DVC runs when the stage is reproduced (see dvc repro).

If a command reads input files, these (or their directory locations) can be defined as dependencies (deps). DVC will check whether they have 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.

Parameter dependencies

Parameters are a special type of stage dependency. They consist of a name/value pair to find in a YAML, JSON, TOML, or Python parameters file (params.yaml by default). Example:

    cmd: bin/cleanup raw.txt clean.txt
      - raw.txt
      - threshold
      - passes
      - clean.txt

This allows several stages to depend on values of a shared structured file (which can be versioned directly with Git). See also dvc params diff.

Metrics and Plots outputs

Like common outputs, metrics and plots files are produced by the stage cmd. However, their purpose is different. Typically they contain metadata to evaluate pipeline processes. Example:

    cmd: python train.py
      - features.csv
      - model.pt
      - accuracy.txt:
          cache: false
      - auc.json:
          cache: false

cache: false is typical here, since they're small enough for Git to version directly.

The commands in dvc metrics and dvc plots help you display and compare metrics and plots.


New in DVC 2.0

dvc.yaml supports a templating format to insert values from different sources in the YAML structure itself. These sources can be parameters files, or vars defined in dvc.yaml instead.

Note that this parameterization feature is only supported via manual edition of dvc.yaml and incompatible with dvc run.

Let's say we have params.yaml (default params file) with the following contents:

    threshold: 10
    filename: 'model-us.hdf5'

Those values can be used anywhere in dvc.yaml with the ${} substitution expression:

    cmd: >-
      python train.py
      --thresh ${models.us.threshold}
      --out ${models.us.filename}
      - ${models.us.filename}:
          cache: true

DVC will track simple param values (numbers, strings, etc.) used in ${} (they will be listed by dvc params diff).

Alternatively, values for substitution can be listed as top-level vars like this:

  - models:
        threshold: 10
  - desc: 'Reusable description'

    desc: ${desc}
    cmd: python train.py --thresh ${models.us.threshold}

Note that values from vars are not tracked like parameters.

To load additional params files, list them in the top vars, in the desired order, e.g.:

Params file paths will be evaluated based on wdir, if one given.

  - params.json
  - myvar: 'value'
  - config/myapp.yaml

(ℹ️) Note that the default params.yaml file is always loaded first, if present.

It's also possible to specify what to include from additional params files, with a : colon:

  - params.json:clean,feats

    cmd: ${feats.exec}
      - ${clean.filename}
      - ${feats.dirname}

Stage-specific values are also supported, with inner vars. You may also load additional params files locally. For example:

      - params.json:build
      - model:
          filename: 'model-us.hdf5'
    cmd: python train.py ${build.epochs} --out ${model.filename}
      - ${model.filename}

DVC merges values from params files and vars in each scope when possible. For example, {"grp": {"a": 1}} merges with {"grp": {"b": 2}}, but not with {"grp": {"a": 7}}.

⚠️ Known limitations of local vars:

  • wdir cannot use values from local vars, as DVC uses the working directory first (to load any values from params files listed in vars).
  • foreach is also incompatible with local vars at the moment.

The substitution expression supports these forms:

${param} # Simple
${param.key} # Nested values through . (period)
${param.list[0]} # List elements via index in [] (square brackets)

To use the expression literally in dvc.yaml (so DVC does not replace it for a value), escape it with a backslash, e.g. \${....

foreach stages

New in DVC 2.0

You can define more than one stage in a single dvc.yaml entry with the following syntax. A foreach element accepts a list or dictionary with values to iterate on, while do contains the regular stage fields (cmd, outs, etc.). Here's a simple example:

    foreach: # List of simple values
      - raw1
      - labels1
      - raw2
      cmd: clean.py "${item}"
        - ${item}.cln

Upon dvc repro, each item in the list is expanded into its own stage by substituting its value in expression ${item}. The item's value is appended to each stage name after a @. The final stages generated by the foreach syntax are saved to dvc.lock:

schema: '2.0'
    cmd: clean.py "labels1"
      - path: labels1.cln
    cmd: clean.py "raw1"
      - path: raw1.cln
    cmd: clean.py "raw2"
      - path: raw2.cln

For lists containing complex values (e.g. dictionaries), the substitution expression can use the ${item.key} form. Stage names will be appended with a zero-based index. For example:

      - epochs: 3
        thresh: 10
      - epochs: 10
        thresh: 15
      cmd: python train.py ${item.epochs} ${item.thresh}
# dvc.lock
schema: '2.0'
    cmd: python train.py 3 10
    cmd: python train.py 10 15

DVC can also iterate on a dictionary given directly to foreach, resulting in two substitution expressions being available: ${key} and ${item}. The former is used for the stage names:

        epochs: 3
        thresh: 10
        epochs: 10
        thresh: 15
      cmd: python train.py '${key}' ${item.epochs} ${item.thresh}
        - model-${key}.hdfs
# dvc.lock
schema: '2.0'
    cmd: python train.py 'uk' 3 10
      - path: model-uk.hdfs
        md5: 17b3d1efc339b416c4b5615b1ce1b97e
  build@us: ...

Importantly, dictionaries from parameters files can be used in foreach stages as well:

    foreach: ${myobject} # From params.yaml
      cmd: ./script.py ${key} ${item.prop1}
        - ${item.prop2}

Note that this feature is not compatible with templating at the moment.

Stage entries

These are the fields that are accepted in each stage:

cmd(Required) One or more commands executed by the stage (may contain either a single value or a list). Commands are executed sequentially until all are finished or until one of them fails (see dvc repro).
wdirWorking directory for the stage command to run in (relative to the file's location). Any paths in other fields are also based on this. It defaults to . (the file's location).
depsList of dependency paths of this stage (relative to wdir).
outsList of output paths of this stage (relative to wdir). These can contain certain optional subfields.
paramsList of parameter dependency keys (field names) to track from params.yaml (in wdir). The list may also contain other parameters file names, with a sub-list of the param names to track in them.
metricsList of metrics files, and optionally, whether or not this metrics file is cached (true by default). See the --metrics-no-cache (-M) option of dvc run.
plotsList of plot metrics, 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.
frozenWhether or not this stage is frozen from reproduction
always_changedWhether 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.
live(Optional) DVCLive configuration field

dvc.yaml files also support # comments.

Note that we maintain a dvc.yaml schema that can be used by editors like VSCode or PyCharm to enable automatic syntax validation and auto-completion.

See also How to Merge Conflicts.

Output subfields

These include a subset of the fields in .dvc file output entries.

cacheWhether or not this file or directory is cached (true by default). See the --no-commit option of dvc add.
remote(Optional) name of the remote to use for pushing/fetching.
persistWhether the output file/dir should remain in place while dvc repro runs (false by default: outputs are deleted when dvc repro starts
checkpoint(Optional) Set to true to let DVC know that this output is associated with in-code checkpoints. These outputs are reverted to their last cached version at dvc exp run and also persist during the stage execution.
desc(Optional) user description for this output. This doesn't affect any DVC operations.

⚠️ Note that using the checkpoint field in dvc.yaml is not compatible with dvc repro.

dvc.lock file

⚠️ Avoid editing these files. DVC will create and update them for you.

To record the state of your pipeline(s) and help track its outputs, DVC will maintain a dvc.lock file for each dvc.yaml. Their purposes include:

  • Allow DVC to detect when stage definitions, or their dependencies have changed. Such conditions invalidate stages, requiring their reproduction (see dvc status).
  • Tracking of intermediate and final outputs of a pipeline — similar to .dvc files.
  • Needed for several DVC commands to operate, such as dvc checkout or dvc get.

Here's an example:

schema: '2.0'
    cmd: jupyter nbconvert --execute featurize.ipynb
      - path: data/clean
        md5: d8b874c5fa18c32b2d67f73606a1be60
        levels.no: 5
      - path: features
        md5: 2119f7661d49546288b73b5730d76485
        size: 154683
      - path: performance.json
        md5: ea46c1139d771bfeba7942d1fbb5981e
        size: 975
      - path: logs.csv
        md5: f99aac37e383b422adc76f5f1fb45004
        size: 695947

Stages are listed again in dvc.lock, in order to know if their definitions change in dvc.yaml.

Regular dependency entries and all forms of output entries (including metrics and plots files) are also listed (per stage) in dvc.lock, including a content hash field (md5, etag, or checksum).

Full parameter dependencies (both key and value) are listed too (under params), under each parameters file name. templated dvc.yaml files, the actual values are written to dvc.lock (no ${} expression). As for foreach stages, individual stages are expanded (no foreach structures are preserved).


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