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Contains a command to show changes in parameters: diff.


usage: dvc params [-h] [-q | -v] {diff} ...

positional arguments:
    diff         Show changes in params between commits in the
                 DVC repository, or between a commit and the workspace.


In order to track parameters and hyperparameters associated to machine learning experiments in DVC projects, DVC provides a different type of dependencies: parameters. Parameters are defined using the the -p (--params) option of dvc run, using simple names like epochs, learning-rate, batch_size, etc.

In contrast to a regular dependency, a parameter is not a file (or directory). Instead, it consists of a parameter name (or key) to find inside a YAML 1.2, JSON, TOML, or Python parameters file. Multiple parameter dependencies can be specified from one or more parameters files.

The default parameters file name is params.yaml. Parameters should be organized as a tree hierarchy inside, as DVC will locate param names by their tree path. Parameters files have to be manually written, or generated, and these can be versioned directly with Git.

Supported parameter value types are: string, integer, float, and arrays. DVC itself does not ascribe any specific meaning for these values. They are user-defined, and serve as a way to generalize and parametrize an machine learning algorithms or data processing code.

DVC saves the param names and their latest values in the dvc.yaml file. These values will be compared to the ones in the params files to determine if the stage is invalidated upon pipeline reproduction.

Note that DVC does not pass the parameter values to stage commands. The associated command executed by dvc run or dvc repro will have to open and parse the parameters file by itself, and use the params specified with -p.

The parameters concept helps to define stage dependencies more granularly. A particular parameter or set of parameters will be required for the stage invalidation (see dvc status and dvc repro). Changes to other parts of the dependency file will not affect the stage. This prevents situations where several stages share a (configuration) file as a common dependency, and any change in this dependency invalidates all these stages and causes their reproduction unnecessarily.

dvc params diff is available to show changes in parameters, displaying the param names as well as their current and previous values.


  • -h, --help - prints the usage/help message, and exit.
  • -q, --quiet - do not write anything to standard output.
  • -v, --verbose - displays detailed tracing information.


First, let's create a simple parameters file in YAML format, using the default file name params.yaml:

lr: 0.0041

  epochs: 70
  layers: 9

  thresh: 0.98
  bow: 15000

Define a stage that depends on params lr, layers, and epochs from the params file above. Full paths should be used to specify layers and epochs from the train group:

$ dvc run -n train -d users.csv -o model.pkl \
          -p lr,train.epochs,train.layers \
          python train.py

Note that we could use the same parameter addressing with JSON, TOML, or Python parameters files.

The train.py script will have some code to parse the needed parameters. For example:

import yaml

with open("params.yaml", 'r') as fd:
    params = yaml.safe_load(fd)

lr = params['lr']
epochs = params['train']['epochs']
layers = params['train']['layers']

You can find that each parameter and it's value were saved to dvc.yaml. These values will be compared to the ones in the parameters files whenever dvc repro is used, to determine if dependency to the params file is invalidated:

    cmd: python train.py
      - users.csv
      - lr
      - train
      - model.pkl

Alternatively, the entire group of parameters train can be referenced, instead of specifying each of the group parameters separately:

$ dvc run -n train -d users.csv -o model.pkl \
          -p lr,train \
          python train.py

In the examples above, the default parameters file name params.yaml was used. This file name can be redefined with a prefix in the -p argument:

$ dvc run -n train -d logs/ -o users.csv \
          -p parse_params.yaml:threshold,classes_num \
          python train.py

Examples: Python parameters file

Consider this Python parameters file named params.py:

# All standard variable types are supported.
BOOL = True
INT = 5
FLOAT = 0.001
STR = 'abc'
DICT = {'a': 1, 'b': 2}
LIST = [1, 2, 3]
SET = {4, 5, 6}
TUPLE = (10, 100)
NONE = None

# DVC can retrieve class constants and variables defined in __init__
class TrainConfig:

    EPOCHS = 70

    def __init__(self):
        self.layers = 5
        self.layers = 9  # TrainConfig.layers param will be 9
        self.sum = 1 + 2  # Will NOT be found due to the expression
        bar = 3  # Will NOT be found since it's locally scoped

class TestConfig:

    TEST_DIR = 'path'
    METRICS = ['metric']

The following stage depends on params BOOL, INT, as well as TrainConfig's EPOCHS and layers:

$ dvc run -n train -d users.csv -o model.pkl \
          -p params.py:BOOL,INT,TrainConfig.EPOCHS,TrainConfig.layers \
          python train.py

Resulting dvc.yaml and dvc.lock files (notice the params list):

    cmd: python train.py
      - users.csv
      - BOOL
      - INT
      - TrainConfig.EPOCHS
      - TrainConfig.layers
      - model.pkl
  cmd: python train.py
    - path: users.csv
      md5: 23be4307b23dcd740763d5fc67993f11
    INT: 5
    BOOL: true
    TrainConfig.EPOCHS: 70
    TrainConfig.layers: 9
    - path: model.pkl
      md5: 1c06b4756f08203cc496e4061b1e7d67

Alternatively, the entire TestConfig params group (class) can be referenced (dictionaries are also supported), instead of the parameters in it:

$ dvc run -n train -d users.csv -o model.pkl \
          -p params.py:BOOL,INT,TestConfig \
          python train.py

Examples: Print all parameters

Following the previous example, we can use dvc params diff to list all of the param values available in the workspace:

$ dvc params diff
Path         Param           Old    New
params.yaml  lr              โ€”      0.0041
params.yaml  process.bow     โ€”      15000
params.yaml  process.thresh  โ€”      0.98
params.yaml  train.epochs    โ€”      70
params.yaml  train.layers    โ€”      9

This command shows the difference in parameters between the workspace and the last committed version of the params.yaml file. In our example there's no previous version, which is why all Old values are โ€”.


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