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apply

Apply a model to data. The result will be saved as a MLEM object to output if provided. Otherwise, it will be printed to stdout.

Synopsis

usage: mlem apply [-p <path>] [--rev <commitish>] [-o <path>]
                  [-m <text>] [--dr <path>]
                  [--data-rev <commitish>] [-i] [--it <text>]
                  [-b <integer>] [--index] [-e] [--json] [-h]
                  model data

arguments:
  model            Path to model object
  data             Path to data object

Description

Applying a model to data means calling a model's method (e.g. predict) with all the data points in the dataset, and returning the output as a MLEM Object.

This command addresses a very common workflow, replacing the need to write a Python script to load models & datasets, apply the datasets on the models, and saving the results.

Models and Data, which represent MLEM objects, can be used directly through command line together to easily run inferences on entire datasets.

Options

  • -p <path>, --project <path> - Path to MLEM project [default: (none)]
  • --rev <commitish> - Repo revision to use [default: (none)]
  • -o <path>, --output <path> - Where to save model outputs
  • -m <text>, --method <text> - Which model method is to be applied [default: predict]
  • --dr <path>, --data-project <path> - Project with data
  • --data-rev <commitish> - Revision of data
  • -i, --import - Try to import data on-the-fly
  • --it <text>, --import-type <text> - Specify how to read data file for import. Available types: ['pandas', 'pickle', 'torch']
  • -b <integer>, --batch_size <integer> - Batch size for reading data in batches
  • --index / --no-index - Whether to index output in .mlem directory
  • -e, --external - Save result not in .mlem, but directly in project
  • --json - Output as json
  • -h, --help - Show this message and exit.

Examples

Apply a local MLEM model to a local MLEM dataset

$ mlem apply mymodel mydata --method predict --output myprediction

Apply a local MLEM model to a dataset imported from a local data file

$ mlem apply mymodel data.csv --method predict --import --import-type pandas[csv] --output myprediction

Apply a version of a remote model (from HEAD of main branch) to a version of a remote dataset (again, HEAD of main branch)

$ mlem apply rf --project https://github.com/iterative/example-mlem-get-started --rev main
                iris.csv --data-project https://github.com/iterative/example-mlem-get-started --data-rev main
                --method predict --output myprediction