Helper command to create or update stages in
usage: dvc stage add [-h] [-q | -v] -n <name> [-f] [-d <path>] [-p [<filename>:]<params_list>] [-o <filename>] [-O <filename>] [-c <filename>] [--external] [--outs-persist <filename>] [--outs-persist-no-cache <filename>] [-m <path>] [-M <path>] [--plots <path>] [--plots-no-cache <path>] [-w <path>] [--always-changed] [--desc <text>] command positional arguments: command Command to execute
Writes stage definitions to
dvc.yaml (in the current working directory). To
update an existing stage, overwrite it with the
A stage name is required and can be provided using the
Most of the other options help with defining different kinds of
dependencies and outputs for the stage. The
remaining terminal input provided to
dvc stage add after any options/flags
will become the required
-- flags sent after the
command become part of the command itself and
are ignored by
dvc stage add.
See the guide on defining pipeline stages for more details.
By specifying lists of dependencies (
-d option) and/or
-O options) for each stage, we can create a
dependency 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 the full pipeline.
$ 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
Typically, scripts to run (or possibly a directory containing the source code) are included among the specified
-ddependencies. 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 addchecks 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
Entire directories produced by the stage can be tracked as outputs by DVC, which generates a single
.direntry in the cache (refer to Structure of cache directory for more info.)
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-persistis 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 without having to execute it again.
--params option) are a special type of
key/value dependencies. Multiple params can be specified from within one or more
structured files (
params.yaml by default). This allows tracking experimental
hyperparameters easily in ML.
Special types of output files, metrics (
-M options) and plots (
--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.
--name <stage>(required) - specify a name for the stage generated by this command (e.g.
-n train). Stage names can only contain letters, numbers, dash
--deps <path>- 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.
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.
--outs <path>- 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
--outs-no-cache <path>- the same as
-oexcept 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 <path>- declare output file or directory that will not be removed when
dvc reprostarts (but it can still be modified, overwritten, or even deleted by the stage command(s)).
--outs-persist-no-cache <path>- the same as
-outs-persistexcept that outputs are not tracked by DVC (same as with
--params [<path>:]<params_list>- specify one or more parameter dependencies from a structured file
./params.yamlby default). This is done by sending a comma separated list (
params_list) as argument, e.g.
-p learning_rate,epochs. A custom params file can be defined with a prefix, e.g.
-p params.json:threshold. Or use the prefix alone with
:to use all the parameters found in that file, e.g.
--metrics <path>- specify a metrics file produced by this stage. This option behaves like
-obut registers the file in a
metricsfield inside the
dvc.yamlstage. Metrics are usually small, human readable files (JSON, TOML, or YAML) with scalar numbers or other simple information that describes a model (or any other data artifact). See
dvc metricsto learn more about metrics.
--metrics-no-cache <path>- the same as
-mexcept that DVC does not track the metrics file (same as with
-Oabove). 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 <path>- specify a plots file or directory produced by this stage. This option behaves like
-obut registers the file or directory in a
plotsfield inside the
dvc.yamlstage. Plots outputs are either data series stored in tabular (CSV or TSV) or hierarchical (JSON or YAML) files, or image (JPEG, GIF, PNG, or SVG) files. See Visualizing Plots to learn more about plots.
--plots-no-cache <path>- the same as
--plotsexcept that DVC does not track the plots file (same as with
-Mabove). This may be desirable with plots, if they are small enough to be tracked with Git directly.
--wdir <path>- specifies a working directory for the
commandto run in (uses the
dvc.yaml). Dependency and output files (including metrics and plots) should be specified relative to this directory. It's used by
dvc reproto change the working directory before executing the
--force- overwrite an existing stage in
dvc.yamlfile without asking for confirmation.
--always-changed- always consider this stage as changed (sets the
dvc.yaml). As a result DVC will always execute it when reproducing the pipeline.
--external- allow writing outputs outside of the DVC repository. See Managing External Data.
--desc <text>- user description of the stage (optional). This doesn't
affect any DVC operations.
--help- prints the usage/help message, and exit.
--quiet- do not write anything to standard output. Exit with 0 if no problems arise, otherwise 1.
--verbose- displays detailed tracing information.
Let's create a stage (that counts the number of lines in a
$ 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 . ├── dvc.yaml └── test.txt
This results in the following stage entry in
stages: count: cmd: 'cat test.txt | wc -l > lines' deps: - test.txt outs: - lines
lines file in the workspace as the stage is not run yet. It'll be
created and tracked whenever
dvc repro is run.
The following stage runs a Python script that trains an ML model on the training
20180226 is a seed value):
$ 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
--force) option is
needed. Let's update the seed for the
$ dvc stage add -n train --force \ -d train_model.p -d matrix-train.p -o model.p \ python train_model.py 18494003 model.p
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
$ cd more_stages/ $ dvc stage add -n process_data \ -d data.in \ -o result.out \ ./my_script.sh data.in result.out $ tree .. . ├── dvc.yaml ├── dvc.lock ├── file1 ├── ... └── more_stages/ ├── data.in └── dvc.yaml
DVC pipelines 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
$ dvc stage add -n extract \ -d Posts.xml.zip \ -o data/Posts.xml \ unzip Posts.xml.zip -d data/
Note that the last
-dapplies 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 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 +---------+ | extract | +---------+ * * * +---------+ | parse | +---------+
We can use
dvc repro to execute this pipeline to get the outputs.
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
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 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 can use the
dvc.api.params_show() to parse the parameters:
import dvc.api params = dvc.api.params_show() seed = params['seed'] lr = params['train']['lr'] epochs = params['train']['epochs']
We use ruamel.yaml which supports YAML 1.2 (unlike the more popular PyYAML).
You can also use templating to parse parameters directly from
into the stage.
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.