python-conda-environment-management
License: restricted — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution.
Summary
Establish and document a reproducible Python environment pinned to specific versions (Python 3.6.10, numpy 1.18.1) via r-miniconda, integrated with R via reticulate for cross-language interoperability in SERDA normalization workflows. This skill ensures that deep learning (keras), statistical analysis (pROC, caret), and numerical computation dependencies are locked and portable.
When to use
When setting up a multi-language data analysis pipeline that requires both R and Python components to coexist with exact version constraints, especially when deep learning (keras) and machine learning (caret, pROC) packages must share a common Python backend. Specifically applicable when reproducing SERDA normalization or similar R-Python hybrid workflows where version mismatches cause keras or numpy interoperability failures.
When NOT to use
- When using modern Python (≥3.8) or R versions; this skill is locked to legacy stacks (R 3.6.3, Python 3.6.10) and should not be applied to current-generation analyses.
- When R and Python components are deployed separately with no cross-language calls; conda environment management is overhead-heavy for monolingual pipelines.
- When a container (Docker/Singularity) is already the reproducibility vehicle; lock files are redundant if the image is versioned and archived.
Inputs
- R 3.6.3 installation with package management capability
- System access to conda/Anaconda repository for Python 3.6.10
- List of required R packages (reticulate, keras, pROC, caret) and Python dependencies (numpy 1.18.1)
Outputs
- Reproducible lock file (environment.yml or renv.lock) with version pinning
- Configured r-miniconda environment with Python 3.6.10 and numpy 1.18.1
- Verified R environment with reticulate bridging to Python backend
- py_config() and py_discover_config() output confirming interoperability
How to apply
- Install r-miniconda and configure a dedicated conda environment pinned to Python 3.6.10 (Anaconda distribution) with numpy 1.18.1. 2. In R 3.6.3, install reticulate 1.19 and configure it to point to the r-miniconda Python environment via py_config() and py_discover_config() verification. 3. Install keras 2.7.0 (which routes through the Python backend), pROC 1.17.0.1, and caret 6.0.86 in R, ensuring reticulate bridges all Python dependencies. 4. Build a lock file (environment.yml for conda or renv.lock for R) that captures the complete dependency graph including version pins and the r-miniconda path constraint. 5. Validate the environment by executing py_config() in R and confirming the numpy path and version match the conda environment specification.
Related tools
- reticulate (R–Python interoperability bridge; routes keras, numpy, and other Python packages into R environments) — https://rstudio.github.io/reticulate/index.html
- r-miniconda (Anaconda-based Python distribution manager; provides isolated conda environment with Python 3.6.10 and numpy 1.18.1)
- keras (Deep learning backend for SERDA normalization; accessed via reticulate from R)
- caret (Machine learning framework in R; integrates with keras via reticulate for model training and evaluation)
- pROC (ROC curve analysis and AUC computation in R; used for classifier performance evaluation in SERDA workflow)
- numpy (Numerical array backend; version 1.18.1 pinned for compatibility with Python 3.6.10 and keras 2.7.0)
Examples
# In R: Rscript -e "library(reticulate); py_config(); packageVersion('keras'); packageVersion('caret'); packageVersion('pROC')"
Evaluation signals
- py_config() output confirms libpython path points to r-miniconda environment and numpy_version is exactly 1.18.1
- py_discover_config() returns identical Python configuration to py_config(), indicating consistent discovery
- packageVersion() in R returns reticulate='1.19', keras='2.7.0', pROC='1.17.0.1', caret='6.0.86' without version conflicts
- Lock file (environment.yml or renv.lock) contains all pinned versions and can reproduce identical environment on clean system without version resolution errors
- SERDA runner.R executes without Python ImportError, version mismatch, or reticulate bridging failures when data_file is set and script is sourced
Limitations
- Python 3.6.10 and R 3.6.3 are end-of-life; security patches and dependency updates are unavailable. This skill is suitable only for reproducing archived analyses, not new development.
- Windows-specific configuration shown in README (x86_64-w64-mingw32 platform); Linux/macOS paths to r-miniconda may differ and require manual adjustment.
- No changelog documented in the repository, so breaking changes in upstream packages (numpy, keras) are not tracked; environment may degrade if r-miniconda or conda channels rebuild old package versions with incompatible dependencies.
- keras 2.7.0 with Python 3.6 has known compatibility issues with modern TensorFlow backends; recommend validating model training before production use.
Evidence
- [other] SERDA normalization requires R 3.6.3 with packages reticulate 1.19, keras 2.7.0, pROC 1.17.0.1, and caret 6.0.86, integrated with Python 3.6.10 (via r-miniconda) and numpy 1.18.1.: "SERDA normalization requires R 3.6.3 with packages reticulate 1.19, keras 2.7.0, pROC 1.17.0.1, and caret 6.0.86, integrated with Python 3.6.10 (via r-miniconda) and numpy 1.18.1."
- [other] Define the dependency graph and version pinning constraints, noting that reticulate must route to Python 3.6.10 via r-miniconda.: "Define the dependency graph and version pinning constraints, noting that reticulate must route to Python 3.6.10 via r-miniconda."
- [other] Export the environment specification as a reproducible lock file (either renv.lock for R or environment.yml for conda/pip).: "Export the environment specification as a reproducible lock file (either renv.lock for R or environment.yml for conda/pip)."
- [readme] R version 3.6.3 (2020-02-29) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 10 x64 (build 19043): "R version 3.6.3 (2020-02-29)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)"
- [readme] version: 3.6.10 |Anaconda, Inc.| (default, May 7 2020, 19:46:08) [MSC v.1916 64 bit (AMD64)]: "version: 3.6.10 |Anaconda, Inc.| (default, May 7 2020, 19:46:08) [MSC v.1916 64 bit (AMD64)]"
- [readme] pythonhome: C:/Users/pcname/AppData/Local/r-miniconda/envs/r-reticulate numpy_version: 1.18.1: "pythonhome: C:/Users/pcname/AppData/Local/r-miniconda/envs/r-reticulate numpy_version: 1.18.1"
1---2name: python-conda-environment-management3description: Use when when setting up a multi-language data analysis pipeline that requires both R and Python components to coexist with exact version constraints, especially when deep learning (keras) and machine learning (caret, pROC) packages must share a common Python backend.4license: CC-BY-4.05---67# python-conda-environment-management89> **License: restricted** — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution. <!-- asb-license-banner -->10## Summary1112Establish and document a reproducible Python environment pinned to specific versions (Python 3.6.10, numpy 1.18.1) via r-miniconda, integrated with R via reticulate for cross-language interoperability in SERDA normalization workflows. This skill ensures that deep learning (keras), statistical analysis (pROC, caret), and numerical computation dependencies are locked and portable.1314## When to use1516When setting up a multi-language data analysis pipeline that requires both R and Python components to coexist with exact version constraints, especially when deep learning (keras) and machine learning (caret, pROC) packages must share a common Python backend. Specifically applicable when reproducing SERDA normalization or similar R-Python hybrid workflows where version mismatches cause keras or numpy interoperability failures.1718## When NOT to use1920- When using modern Python (≥3.8) or R versions; this skill is locked to legacy stacks (R 3.6.3, Python 3.6.10) and should not be applied to current-generation analyses.21- When R and Python components are deployed separately with no cross-language calls; conda environment management is overhead-heavy for monolingual pipelines.22- When a container (Docker/Singularity) is already the reproducibility vehicle; lock files are redundant if the image is versioned and archived.2324## Inputs2526- R 3.6.3 installation with package management capability27- System access to conda/Anaconda repository for Python 3.6.1028- List of required R packages (reticulate, keras, pROC, caret) and Python dependencies (numpy 1.18.1)2930## Outputs3132- Reproducible lock file (environment.yml or renv.lock) with version pinning33- Configured r-miniconda environment with Python 3.6.10 and numpy 1.18.134- Verified R environment with reticulate bridging to Python backend35- py_config() and py_discover_config() output confirming interoperability3637## How to apply38391. Install r-miniconda and configure a dedicated conda environment pinned to Python 3.6.10 (Anaconda distribution) with numpy 1.18.1. 2. In R 3.6.3, install reticulate 1.19 and configure it to point to the r-miniconda Python environment via py_config() and py_discover_config() verification. 3. Install keras 2.7.0 (which routes through the Python backend), pROC 1.17.0.1, and caret 6.0.86 in R, ensuring reticulate bridges all Python dependencies. 4. Build a lock file (environment.yml for conda or renv.lock for R) that captures the complete dependency graph including version pins and the r-miniconda path constraint. 5. Validate the environment by executing py_config() in R and confirming the numpy path and version match the conda environment specification.4041## Related tools4243- **reticulate** (R–Python interoperability bridge; routes keras, numpy, and other Python packages into R environments) — https://rstudio.github.io/reticulate/index.html44- **r-miniconda** (Anaconda-based Python distribution manager; provides isolated conda environment with Python 3.6.10 and numpy 1.18.1)45- **keras** (Deep learning backend for SERDA normalization; accessed via reticulate from R)46- **caret** (Machine learning framework in R; integrates with keras via reticulate for model training and evaluation)47- **pROC** (ROC curve analysis and AUC computation in R; used for classifier performance evaluation in SERDA workflow)48- **numpy** (Numerical array backend; version 1.18.1 pinned for compatibility with Python 3.6.10 and keras 2.7.0)4950## Examples5152```53# In R: Rscript -e "library(reticulate); py_config(); packageVersion('keras'); packageVersion('caret'); packageVersion('pROC')"54```5556## Evaluation signals5758- py_config() output confirms libpython path points to r-miniconda environment and numpy_version is exactly 1.18.159- py_discover_config() returns identical Python configuration to py_config(), indicating consistent discovery60- packageVersion() in R returns reticulate='1.19', keras='2.7.0', pROC='1.17.0.1', caret='6.0.86' without version conflicts61- Lock file (environment.yml or renv.lock) contains all pinned versions and can reproduce identical environment on clean system without version resolution errors62- SERDA runner.R executes without Python ImportError, version mismatch, or reticulate bridging failures when data_file is set and script is sourced6364## Limitations6566- Python 3.6.10 and R 3.6.3 are end-of-life; security patches and dependency updates are unavailable. This skill is suitable only for reproducing archived analyses, not new development.67- Windows-specific configuration shown in README (x86_64-w64-mingw32 platform); Linux/macOS paths to r-miniconda may differ and require manual adjustment.68- No changelog documented in the repository, so breaking changes in upstream packages (numpy, keras) are not tracked; environment may degrade if r-miniconda or conda channels rebuild old package versions with incompatible dependencies.69- keras 2.7.0 with Python 3.6 has known compatibility issues with modern TensorFlow backends; recommend validating model training before production use.7071## Evidence7273- [other] SERDA normalization requires R 3.6.3 with packages reticulate 1.19, keras 2.7.0, pROC 1.17.0.1, and caret 6.0.86, integrated with Python 3.6.10 (via r-miniconda) and numpy 1.18.1.: "SERDA normalization requires R 3.6.3 with packages reticulate 1.19, keras 2.7.0, pROC 1.17.0.1, and caret 6.0.86, integrated with Python 3.6.10 (via r-miniconda) and numpy 1.18.1."74- [other] Define the dependency graph and version pinning constraints, noting that reticulate must route to Python 3.6.10 via r-miniconda.: "Define the dependency graph and version pinning constraints, noting that reticulate must route to Python 3.6.10 via r-miniconda."75- [other] Export the environment specification as a reproducible lock file (either renv.lock for R or environment.yml for conda/pip).: "Export the environment specification as a reproducible lock file (either renv.lock for R or environment.yml for conda/pip)."76- [readme] R version 3.6.3 (2020-02-29) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 10 x64 (build 19043): "R version 3.6.3 (2020-02-29)77Platform: x86_64-w64-mingw32/x64 (64-bit)78Running under: Windows 10 x64 (build 19043)"79- [readme] version: 3.6.10 |Anaconda, Inc.| (default, May 7 2020, 19:46:08) [MSC v.1916 64 bit (AMD64)]: "version: 3.6.10 |Anaconda, Inc.| (default, May 7 2020, 19:46:08) [MSC v.1916 64 bit (AMD64)]"80- [readme] pythonhome: C:/Users/pcname/AppData/Local/r-miniconda/envs/r-reticulate numpy_version: 1.18.1: "pythonhome: C:/Users/pcname/AppData/Local/r-miniconda/envs/r-reticulate numpy_version: 1.18.1"
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