.. autoclass:: MLFlowHandler

:members:

NVTX Handlers

.. automodule:: monai.handlers.nvtx_handlers

To install optional features such as handling the NIfTI files using

[Nibabel](https://nipy.org/nibabel/), or building workflows using [Pytorch

Ignite](https://pytorch.org/ignite/), please follow the instructions:

- [Installing the recommended dependencies](#installing-the-recommended-dependencies)

The installation commands bellow usually end up installing CPU variant of PyTorch. To install GPU-enabled PyTorch:

1. Install the latest NVIDIA driver.

1. Check [PyTorch Official Guide](https://pytorch.org/get-started/locally/) for the recommended CUDA versions. For Pip package, the user needs to download the CUDA manually, install it on the system, and ensure CUDA_PATH is set properly.

1. Continue to follow the guide and install PyTorch.

1. Install MONAI using one the ways described below.

To install the [current milestone release](https://pypi.org/project/monai/):

pip install monai

To install the [weekly preview release](https://pypi.org/project/monai-weekly/):

pip install monai-weekly

The weekly build is released to PyPI every Sunday with a pre-release build number `dev[%y%U]`.

To report any issues on the weekly preview, please include the version and commit information:

python -c "import monai; print(monai.__version__); print(monai.__commit_id__)"

To address this issue, please uninstall both packages, and retry the installation.

The packages installed using `pip install` could be removed by:

pip uninstall -y monai

pip uninstall -y monai-weekly

To install the [current milestone release](https://anaconda.org/conda-forge/monai):

conda install -c conda-forge monai

(_If you have installed the

default prefers the milestone release_.)

codebase is under active development, you may want to install MONAI from GitHub

for the latest features:

pip install git+https://github.com/Project-MONAI/MONAI#egg=monai

or, to build with MONAI C++/CUDA extensions:

BUILD_MONAI=1 pip install git+https://github.com/Project-MONAI/MONAI#egg=monai

To build the extensions, if the system environment already has a version of Pytorch installed,

`--no-build-isolation` might be preferred:

BUILD_MONAI=1 pip install --no-build-isolation git+https://github.com/Project-MONAI/MONAI#egg=monai

this command will download and install the current `dev` branch of [MONAI from

GitHub](https://github.com/Project-MONAI/MONAI).

This documentation website by default shows the information for the latest version.

To install an editable version of MONAI, it is recommended to clone the codebase directly:

git clone https://github.com/Project-MONAI/MONAI.git

You can install it by running:

cd MONAI/

python setup.py develop

or, to build with MONAI C++/CUDA extensions and install:

cd MONAI/

BUILD_MONAI=1 python setup.py develop

To uninstall the package please run:

cd MONAI/

python setup.py develop --uninstall

./runtests.sh --clean

and the codebase is ready to use (without the additional features of MONAI C++/CUDA extensions).

You can verify the installation by:

python -c "import monai; monai.config.print_config()"

If the installation is successful, this command will print out the MONAI version information, and this confirms the core

modules of MONAI are ready-to-use.

The MONAI version string shows the current status of your local installation. For example:

Make sure you have installed the NVIDIA driver and Docker 19.03+ for your Linux distribution.

Note that you do not need to install the CUDA toolkit on the host, but the driver needs to be installed.

Please find out more information on [nvidia-docker](https://github.com/NVIDIA/nvidia-docker).

Assuming that you have the Nvidia driver and Docker 19.03+ installed, running the following command will

download and start a container with the latest version of MONAI. The latest `dev` branch of MONAI from GitHub

is included in the image.

docker run --gpus all --rm -ti --ipc=host projectmonai/monai:latest

You can also run a milestone release docker image by specifying the image tag, for example:

By default, the installation steps will only download and install the minimal requirements of MONAI.

Optional dependencies can be installed using [the extras syntax](https://packaging.python.org/tutorials/installing-packages/#installing-setuptools-extras) to support additional features.

For example, to install MONAI with Nibabel and Scikit-image support:

git clone https://github.com/Project-MONAI/MONAI.git

cd MONAI/

pip install -e '.[nibabel,skimage]'

Alternatively, to install all optional dependencies:

git clone https://github.com/Project-MONAI/MONAI.git

cd MONAI/

pip install -e '.[all]'

To install all optional dependencies with `pip` based on MONAI development environment settings:

git clone https://github.com/Project-MONAI/MONAI.git

cd MONAI/

To install all optional dependencies with `conda` based on MONAI development environment settings (`environment-dev.yml`;

this will install PyTorch as well as `pytorch-cuda`, please follow https://pytorch.org/get-started/locally/#start-locally for more details about installing PyTorch):

git clone https://github.com/Project-MONAI/MONAI.git

cd MONAI/

Since MONAI v0.2.0, the extras syntax such as `pip install 'monai[nibabel]'` is available via PyPI.

- The options are

which correspond to `nibabel`, `scikit-image`, `pillow`, `tensorboard`,

- `pip install 'monai[all]'` installs all the optional dependencies.

from .checkpoint_loader import CheckpointLoader

from .checkpoint_saver import CheckpointSaver

from .classification_saver import ClassificationSaver

from .confusion_matrix import ConfusionMatrix

from .decollate_batch import DecollateBatch

from .earlystop_handler import EarlyStopHandler