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Installation#

CAREamics is a deep-learning library and we therefore recommend having GPU support as training the algorithms on the CPU can be very slow.

We recommend using mamba (miniforge) to install all packages in a virtual environment. As an alternative, you can use conda (miniconda) with the same commands (replacing mamba by conda).

For macOS silicon-acceleration, please refer to the specific sections!

CAREamics library step-by-step#

This section install CAREamics for use in your own library or tool, via jupyter notebook or as scripts. For the napari plugin, refer to the next section.

  1. Open the terminal and type mamba to verify that mamba is available.

  2. Create a new environment:

    mamba create -n careamics python=3.10
mamba activate careamics

  3. Install PyTorch following the official instructions

    As an example, our test machine requires:

    mamba install pytorch torchvision pytorch-cuda=11.8 -c pytorch -c nvidia

  4. Verify that the GPU is available:

    python -c "import torch; print([torch.cuda.get_device_properties(i) for i in range(torch.cuda.device_count())])"

    This should show a list of available GPUs. If the list is empty, then you will need to change the pytorch and pytorch-cuda versions to match your hardware (linux and windows).

  5. Install CAREamics. We have several extra options (dev, examples, wandb and tensorboard). If you wish to run the example notebooks, we recommend the following:

    pip install "careamics[examples]"

These instructions were tested on a linux virtual machine (RedHat 8.6) with a NVIDIA A40-8Q GPU.

  1. Open the terminal and type mamba to verify that mamba is available.

  2. Create a new environment:

    mamba create -n careamics python=3.10
mamba activate careamics

  3. Install PyTorch following the official instructions

    As an example, our test machine requires:

    mamba install pytorch::pytorch torchvision -c pytorch

    ⚠ Note that this will probably not install silicon GPU acceleration. If you want GPU acceleration, please refer to the relevant section to ensure that you install packages for the correct platform.

  4. Install CAREamics. We have several extra options (dev, examples, wandb and tensorboard). If you wish to run the example notebooks, we recommend the following:

    pip install "careamics[examples]"

Extra dependencies#

CAREamics extra dependencies can be installed by specifying them in brackets. In the previous section we installed careamics[examples]. You can add other extra dependencies, for instance wandb by doing:

pip install "careamics[examples, wandb]"

Here is a list of the extra dependencies:

  • examples: Dependencies required to run the example notebooks.
  • wandb: Dependencies to use WandB as a logger.
  • tensorboard: Dependencies to use TensorBoard as a logger.
  • dev: Dependencies required to run all the tooling necessary to develop with CAREamics.

MacOS silicon GPU#

  1. Open the terminal and type conda to verify that conda is available.

  2. Create a new environment:

    CONDA_SUBDIR=osx-arm64 conda create -n careamics python=3.10
conda activate careamics
conda config --env --set subdir osx-arm64

  3. Install PyTorch following the official instructions

    As an example, our test machine requires:

    pip3 install torch torchvision

  4. Verify that GPU is available: 

    python -c "import torch; import platform; print((platform.processor() in ('arm', 'arm64') and torch.backends.mps.is_available()))"

    If this prints False, make sure that you do have an M1, M2 or M3 chip, and that the conda/mamba macOS-arm64 release was installed correctly.

  5. Install CAREamics. We have several extra options (dev, examples, wandb and tensorboard). If you wish to run the example notebooks, we recommend the following:

    pip install "careamics[examples]"
  1. Open the terminal and type mamba to verify that mamba is available.

  2. Create a new environment:

    mamba create -n careamics python=3.10 --platform osx-arm64
mamba activate careamics

  3. Install PyTorch following the official instructions

    As an example, our test machine requires:

    pip3 install torch torchvision

  4. Verify that GPU is available: 

    python -c "import torch; import platform; print((platform.processor() in ('arm', 'arm64') and torch.backends.mps.is_available()))"

    If this prints False, make sure that you do have an M1, M2 or M3 chip, and that the conda/mamba macOS-arm64 release was installed correctly.

  5. Install CAREamics. We have several extra options (dev, examples, wandb and tensorboard). If you wish to run the example notebooks, we recommend the following:

    pip install "careamics[examples]"

Quickstart#

Once you have installed CAREamics, the easiest way to get started is to look at the applications for full examples and the guides for in-depth tweaking.

CAREamics napari plugin#

  1. Open the terminal and type mamba to verify that mamba is available.

  2. Create a new environment:

    mamba create -n careamics python=3.10
mamba activate careamics

  3. Install PyTorch following the official instructions

    As an example, our test machine requires:

    mamba install pytorch torchvision pytorch-cuda=11.8 -c pytorch -c nvidia

  4. Verify that the GPU is available:

    python -c "import torch; print([torch.cuda.get_device_properties(i) for i in range(torch.cuda.device_count())])"

    This should show a list of available GPUs. If the list is empty, then you will need to change the pytorch and pytorch-cuda versions to match your hardware (linux and windows).

  5. Install CAREamics napari plugin and napari:

    pip install careamics-napari "napari[all]"

These instructions were tested on a linux virtual machine (RedHat 8.6) with a NVIDIA A40-8Q GPU.

  1. Open the terminal and type mamba to verify that mamba is available.

  2. Create a new environment:

    mamba create -n careamics python=3.10
mamba activate careamics

  3. Install PyTorch following the official instructions

    As an example, our test machine requires:

    pip3 install torch torchvision

    ⚠ Note that this will probably not install silicon GPU acceleration. If you want GPU acceleration, please refer to the relevant section to ensure that you install packages for the correct platform.

  4. Install CAREamics napari plugin and napari:

    pip install careamics-napari "napari[all]"

MacOS silicon GPU#

  1. Open the terminal and type conda to verify that conda is available.

  2. Create a new environment:

    CONDA_SUBDIR=osx-arm64 conda create -n careamics python=3.10
conda activate careamics
conda config --env --set subdir osx-arm64

  3. Install PyTorch following the official instructions

    As an example, our test machine requires:

    pip3 install torch torchvision

  4. Verify that GPU is available: 

    python -c "import torch; import platform; print((platform.processor() in ('arm', 'arm64') and torch.backends.mps.is_available()))"

    If this prints False, make sure that you do have an M1, M2 or M3 chip, and that the conda/mamba macOS-arm64 release was installed correctly.

  5. Install CAREamics napari plugin and napari:

    pip install careamics-napari "napari[all]"
  1. Open the terminal and type mamba to verify that mamba is available.

  2. Create a new environment:

    mamba create -n careamics python=3.10 --platform osx-arm64
mamba activate careamics

  3. Install PyTorch following the official instructions while specifying the platform. As an example, our test machine requires:

    pip3 install torch torchvision

  4. Verify that GPU is available: 

    python -c "import torch; import platform; print((platform.processor() in ('arm', 'arm64') and torch.backends.mps.is_available()))"

    If this prints False, make sure that you do have an M1, M2 or M3 chip, and that the conda/mamba macOS-arm64 release was installed correctly.

  5. Install CAREamics napari plugin and napari:

    pip install careamics-napari "napari[all]"