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mlflow: Interface to 'MLflow'

R interface to 'MLflow', open source platform for the complete machine learning life cycle, see <https://mlflow.org/>. This package supports installing 'MLflow', tracking experiments, creating and running projects, and saving and serving models.

Version: 2.17.2
Depends: R (≥ 3.3.0)
Imports: base64enc, forge, fs, git2r, glue, httpuv, httr, ini, jsonlite, openssl, processx, purrr, rlang (≥ 0.2.0), swagger, tibble (≥ 2.0.0), withr, yaml, zeallot
Suggests: carrier, covr, h2o, keras, lintr, sparklyr, stringi, testthat (≥ 2.0.0), reticulate, xgboost
Published: 2024-10-31
DOI: 10.32614/CRAN.package.mlflow
Author: Matei Zaharia [aut, cre], Javier Luraschi [aut], Kevin Kuo ORCID iD [aut], RStudio [cph]
Maintainer: Matei Zaharia <matei at databricks.com>
BugReports: https://github.com/mlflow/mlflow/issues
License: Apache License 2.0
URL: https://github.com/mlflow/mlflow
NeedsCompilation: no
Materials: README
CRAN checks: mlflow results

Documentation:

Reference manual: mlflow.pdf

Downloads:

Package source: mlflow_2.17.2.tar.gz
Windows binaries: r-devel: mlflow_2.17.2.zip, r-release: mlflow_2.17.2.zip, r-oldrel: mlflow_2.17.2.zip
macOS binaries: r-release (arm64): mlflow_2.17.2.tgz, r-oldrel (arm64): mlflow_2.17.2.tgz, r-release (x86_64): mlflow_2.17.2.tgz, r-oldrel (x86_64): mlflow_2.17.2.tgz
Old sources: mlflow archive

Reverse dependencies:

Reverse suggests: mlr3tuning

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.