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Merge pull request #92 from computational-cell-analytics/update-install
Update installation instructions
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.github/workflows/run_tests.yaml

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@@ -27,7 +27,7 @@ jobs:
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- name: Setup micromamba
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uses: mamba-org/setup-micromamba@v1
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with:
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environment-file: environment_cpu.yaml
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environment-file: ${{ runner.os == 'Windows' && 'environment_cpu_win.yaml' || 'environment.yaml' }}
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create-args: >-
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python=${{ matrix.python-version }}
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doc/start_page.md

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## Requirements & Installation
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SynapseNet was developed and tested on Linux. It should be possible to install and use it on Mac or Windows, but we have not tested this.
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SynapseNet was developed and tested on Linux. It is possible to install and use it on Mac or Windows, but we have not extensively tested this.
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Furthermore, SynapseNet requires a GPU for segmentation of 3D volumes.
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You need a [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) or [mamba](https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html) installation. Follow the instruction at the respective links if you have installed neither. We assume you have `conda` for the rest of the instructions. After installing it, you can use the `conda` command.
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```bash
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conda env create -f environment.yaml
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```
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- If you are using Windows then you have to use a different environment file for the installation: Use `environment_gpu_win.yaml` if you have a GPU and `environment_cpu_win.yaml` without a GPU.
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- You will need to confirm this step. It will take a while. Afterwards you can activate the environment:
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```bash
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conda activate synapse-net

environment.yaml

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channels:
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- pytorch
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- nvidia
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- conda-forge
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name:
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synapse-net
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- pyqt
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- python-elf
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- pytorch
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- pytorch-cuda=12.4
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- tensorboard
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- torch_em
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- torchvision

environment_cpu.yaml renamed to environment_cpu_win.yaml

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channels:
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- pytorch
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- conda-forge
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name:
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synapse-net
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dependencies:
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- cpuonly
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- bioimageio.core
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- kornia
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# This pin is necessary because later nifty versions have import errors on windows.
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- nifty =1.2.1=*_4
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- magicgui
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- napari
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- protobuf <5
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- pip
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- pyqt
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- python-elf
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- pytorch
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- torchvision
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- tensorboard
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- torch_em
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- trimesh

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