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Update doc and envs
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constantinpape committed Dec 4, 2024
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52 changes: 41 additions & 11 deletions doc/start_page.md
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Expand Up @@ -14,24 +14,53 @@ especially throught the [domain adaptation](domain-adaptation) functionality.
SynapseNet offers a [napari plugin](napari-plugin), [command line interface](command-line-interface), and [python library](python-library).
Please cite our [bioRxiv preprint](TODO) if you use it in your research.

**The rest of the documentation will be updated in the next days!**

## Requirements & Installation

- Requirements: Tested on Linux but should work on Mac/Windows.
- GPU needed to use 3d segmentation networks
- Installation via conda and local pip install
- GPU support
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.
Furthermore, SynapseNet requires a GPU for segmentation of 3D volumes.

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.

To install it you should follow these steps:
- First, download the SynapseNet repository via
```bash
git clone https://github.com/computational-cell-analytics/synapse-net
```
- Then, enter the `synapse-net` folder:
```bash
cd synapse-net
```
- Now you can install the environment for SynapseNet with `conda` from the environment file we proved:
```bash
conda env create -f environment.yaml
```
- You will need to confirm this step. It will take a while. Afterwards you can activate the environment:
```bash
conda activate synapse-net
```
- Finally, install SynapseNet itself into the environment:
```bash
pip install -e .
```

Now you can use all SynapseNet features. From now on, just activate the environment via
```
conda activate synapse-net
```
to use them.

> Note: If you use `mamba` instead of conda just replace `conda` in the commands above with `mamba`.
> Note: We also provide an environment for a CPU version of SynapseNet. You can install it by replacing `environment.yaml` with `environment_cpu.yaml` in the respective command above. This version can be used for 2D vesicle segmentation, but it does not work for 3D segmentation.
> Note: If you have issues with the CUDA version then install a PyTorch that matches your nvidia drivers. See [pytorch.org](https://pytorch.org/) for details.
- Make sure conda or mamba is installed.
- If you don't have a conda installation yet we recommend [micromamba](https://mamba.readthedocs.io/en/latest/installation/micromamba-installation.html)
- Create the environment with all required dependencies: `mamba env create -f environment.yaml`
- Activate the environment: `mamba activate synaptic-reconstruction`
- Install the package: `pip install -e .`

## Napari Plugin

lorem ipsum
**The rest of the documentation will be updated in the next days!**


## Command Line Functionality

Expand All @@ -40,6 +69,7 @@ lorem ipsum
- vesicles / spheres
- objects


## Python Library

- segmentation functions
Expand Down
15 changes: 10 additions & 5 deletions environment.yaml
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@@ -1,17 +1,22 @@
channels:
- pytorch
- nvidia
- conda-forge
name:
synaptic-reconstruction
synapse-net
dependencies:
- python-elf
- bioimageio.core
- kornia
- magicgui
- napari
- pip
- pyqt
- magicgui
- python-elf
- pytorch
- bioimageio.core
- kornia
- pytorch-cuda=12.4
- tensorboard
- torch_em
- torchvision
- trimesh
- pip:
- napari-skimage-regionprops
18 changes: 18 additions & 0 deletions environment_cpu.yaml
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@@ -0,0 +1,18 @@
channels:
- conda-forge
name:
synapse-net
dependencies:
- bioimageio.core
- kornia
- magicgui
- napari
- pip
- pyqt
- python-elf
- pytorch
- tensorboard
- torch_em
- trimesh
- pip:
- napari-skimage-regionprops
1 change: 0 additions & 1 deletion plot_distances.sh

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