Metadata-Version: 2.1
Name: bioimageio.core
Version: 0.6.3
Summary: Python functionality for the bioimage model zoo
Home-page: https://github.com/bioimage-io/core-bioimage-io-python
Author: Bioimage Team
License: UNKNOWN
Project-URL: Bug Reports, https://github.com/bioimage-io/core-bioimage-io-python/issues
Project-URL: Source, https://github.com/bioimage-io/core-bioimage-io-python
Description: # core-bioimage-io-python
        
        Python specific core utilities for bioimage.io resources (in particular models).
        
        ## Installation
        
        ### Via Mamba/Conda
        
        The `bioimageio.core` package can be installed from conda-forge via
        
        ```console
        mamba install -c conda-forge bioimageio.core
        ```
        
        If you do not install any additional deep learning libraries, you will only be able to use general convenience
        functionality, but not any functionality for model prediction.
        To install additional deep learning libraries use:
        
        * Pytorch/Torchscript:
        
          CPU installation (if you don't have an nvidia graphics card):
        
          ```console
          mamba install -c pytorch -c conda-forge bioimageio.core pytorch torchvision cpuonly
          ```
        
          GPU installation (for cuda 11.6, please choose the appropriate cuda version for your system):
        
          ```console
          mamba install -c pytorch -c nvidia -c conda-forge bioimageio.core pytorch torchvision pytorch-cuda=11.8
          ```
        
          Note that the pytorch installation instructions may change in the future. For the latest instructions please refer to [pytorch.org](https://pytorch.org/).
        
        * Tensorflow
        
          Currently only CPU version supported
        
          ```console
          mamba install -c conda-forge bioimageio.core tensorflow
          ```
        
        * ONNXRuntime
        
          Currently only cpu version supported
        
          ```console
          mamba install -c conda-forge bioimageio.core onnxruntime
          ```
        
        ### Via pip
        
        The package is also available via pip
        (e.g. with recommended extras `onnx` and `pytorch`):
        
        ```console
        pip install bioimageio.core[onnx,pytorch]
        ```
        
        ### Set up Development Environment
        
        To set up a development conda environment run the following commands:
        
        ```console
        mamba env create -f dev/env.yaml
        mamba activate core
        pip install -e . --no-deps
        ```
        
        There are different environment files available that only install tensorflow or pytorch as dependencies.
        
        ## 💻 Use the Command Line Interface
        
        `bioimageio.core` installs a command line interface (CLI) for testing models and other functionality.
        You can list all the available commands via:
        
        ```console
        bioimageio
        ```
        
        Check that a model adheres to the model spec:
        
        ```console
        bioimageio validate <MODEL>
        ```
        
        Test a model (including prediction for the test input):
        
        ```console
        bioimageio test-model <MODEL>
        ```
        
        Run prediction for an image stored on disc:
        
        ```console
        bioimageio predict-image <MODEL> --inputs <INPUT> --outputs <OUTPUT>
        ```
        
        Run prediction for multiple images stored on disc:
        
        ```console
        bioimagei predict-images -m <MODEL> -i <INPUT_PATTERN> - o <OUTPUT_FOLDER>
        ```
        
        `<INPUT_PATTERN>` is a `glob` pattern to select the desired images, e.g. `/path/to/my/images/*.tif`.
        
        ## 🐍 Use in Python
        
        `bioimageio.core` is a python package that implements prediction with bioimageio models
        including standardized pre- and postprocessing operations.
        These models are described by---and can be loaded with---the bioimageio.spec package.
        
        In addition bioimageio.core provides functionality to convert model weight formats.
        
        To get an overview of this functionality, check out these example notebooks:
        
        * [model creation/loading with bioimageio.spec](https://github.com/bioimage-io/spec-bioimage-io/blob/main/example_use/load_model_and_create_your_own.ipynb)
        
        and the [developer documentation](https://bioimage-io.github.io/core-bioimage-io-python/bioimageio/core.html).
        
        ## Model Specification
        
        The model specification and its validation tools can be found at <https://github.com/bioimage-io/spec-bioimage-io>.
        
        ## Changelog
        
        ### 0.6.3
        
        * Fix [#386](https://github.com/bioimage-io/core-bioimage-io-python/issues/386)
        * (in model inference testing) stop assuming model inputs are tileable
        
        ### 0.6.2
        
        * Fix [#384](https://github.com/bioimage-io/core-bioimage-io-python/issues/384)
        
        ### 0.6.1
        
        * Fix [#378](https://github.com/bioimage-io/core-bioimage-io-python/pull/378) (with [#379](https://github.com/bioimage-io/core-bioimage-io-python/pull/379))*
        
        ### 0.6.0
        
        * add compatibility with new bioimageio.spec 0.5 (0.5.2post1)
        * improve interfaces
        
        ### 0.5.10
        
        * [Fix critical bug in predict with tiling](https://github.com/bioimage-io/core-bioimage-io-python/pull/359)
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Description-Content-Type: text/markdown
Provides-Extra: pytorch
Provides-Extra: tensorflow
Provides-Extra: onnx
Provides-Extra: dev
