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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. MacOS users can also benefit from GPU-acceleration if they have the new chip generations (M1, M2, etc.).

Support is provided directly from PyTorch, and is still experimental for macOS.

Step-by-step#

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

  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 torchaudio -c pytorch

    ⚠ Note that accelerated-training is only available on macOS silicon.

  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.

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.