Managing Dependencies

JFrog ML supports a variety of Python frameworks to manage model dependencies.

Supported Python Versions

When building and managing your Python projects, different tools have varying levels of support for Python versions. Below is a summary of the supported Python versions for each tool:

Note: Support for Python version 3.9 ends on January 31 2026.

Using Poetry with JFrogML

Model Directory Structure

frogml_based_model/
├── main/
├──── pyproject.toml
├──── poetry.lock
├── tests/

Both files pyproject.toml and poetry.lock will be used by Poetry while executing the poetry install command.

Example Project Setup

pyproject.toml

[tool.poetry]
name = "example-project"
version = "0.1.0"
description = "Example project for production and development"
authors = ["Your Name <you@example.com>"]

[tool.poetry.dependencies]
python = ">=3.11,<3.12"
scipy = "^1.7"
scikit-learn = "^0.24"
catboost = "^1.0"

[tool.poetry.dev-dependencies]
frogml-cli = "*"

[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"

Using Conda with JFrogML

Model Directory Structure

frogml_based_model/
├── main/
├──── conda.yml
├──── ...
├── tests/

The conda.yml file can be placed at the root level alongside main or within it—both structures work equally well.

Example Project Setup

To get started, here’s a basic conda.yml setup:

conda.yml

name: example_conda_model
channels:
  - defaults
  - conda-forge
dependencies:
  - python=3.11
  - scipy
  - scikit-learn
  - catboost
  - pip:
      - # additional pip dependencies

.frogmlignore file

Occasionally, we may want to exclude a file from the JFrog ML build but keep it in the repository with the model code. In such cases, we should add the .frogmlignore file to the root directory of our project.

In the file, we define the patterns to match files to exclude from the model build. For example, suppose we have the following file structure:

.frogmlignore
main/
    __init__.py
    model.py
    README.md
tests/
    test_model.py
research/
    paper_a.pdf
    paper_b.pdf

If we want to exclude the entire research directory and the README.md file from the build, our .frogmlignore file may contain:

research
README.md

Incorporating Python Dependencies from .whl Files

JFrog ML facilitates the use of Python dependencies packaged as .whl files through requirements.txt and conda.yaml for managing dependencies. It is important to note that Poetry's support for dependencies from .whl files is limited.

1. Preparing Your .whl Files

First, ensure your .whl file(s) are either uploaded with your model code or fetched from external storage. For instructions on uploading additional dependencies, refer to the FrogML CLI documentation (frogml models build --help). Below is an example directory structure for your model, where main is uploaded by default and the dep directory, containing the pandas dependency in a .whl file, is included via the --dependency-required-folders dep option in the FrogML command.

The wheel file has to be uploaded as part of an additional dependencies folder, and not as part of the main model folder.

Model Build Container Directory

/frogml/model_dir/
.├── main                   # Main directory containing core code
│   ├── __init__.py        # An empty file that indicates this directory is a Python package
│   ├── model.py           # Defines the Credit Risk Model
│   └── conda.yaml         # Conda environment configuration
├── dep                    # Additional dependency directory added with --dependency-required-folders
│   └── pandas-2.2.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
├── tests                  # Empty directory reserved for future test
│   └── ...                # Future tests
└── 

2. Configuring Dependency Management Files

Conda

Include the .whl file in your conda.yaml as follows:

conda.yaml

name: test_model
channels:
  - defaults
  - conda-forge
dependencies:
  - python=3.11
  - pip:
    - "/frogml/model_dir/dep/pandas-2.2.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl"

Poetry

pyproject.toml

[tool.poetry]
name = "example-project"
version = "0.1.0"
description = "Example project for production and development"
authors = ["Your Name <you@example.com>"]

[tool.poetry.dependencies]
python = "^3.10"
scipy = "^1.7"
scikit-learn = "^0.24"
catboost = "^1.0"
pandas = { path = "dep/pandas-2.2.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl" }

[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"

Requirements.txt

Directly reference the .whl file path relative to the requirements file location:

requirements.txt

# requirements file located in main model folder
./../deps/wheel_test-0.1-py3-none-any.whl

# requirements file located in model dir
./deps/wheel_test-0.1-py3-none-any.whl

3. Using the Dependency in Your Code

Once the dependency is properly configured, you can import and use it in your Python code as usual:

Python

import pandas as pd