# Installation

:::{important}
Currently, this installation method is for installing the plugin via the command line, rather than through *napari*'s Plugin Manager. Below is the step-by-step guide.
:::


## A. Pre-installation

### 1. Install Python

Before installing the plugin, please make sure you have `Python >=3.9,<3.11`

- New to `Python`? We recommend installing `Python 3.10` through the [official Python website](https://www.python.org/downloads/). This will include the `pip` package manager, which is required to install the plugin.

- If you are unsure if you have `Python` installed or which version you may have, you can check by running one of the following commands in your terminal or powershell:
    ```python
    python --version
    ```

  - If the above does not work, try:
    ```python
    python3 --version
    ```

  - To specifically check for `Python 3.10`
    ```python
    python3.10 --version
    ``` 
  - Example output:
    ```python
    Python 3.10.11
    ```

### 2. Create a Virtual Environment

Next we will create a new `Python` environment to install the plugin. This will help avoid conflicts with other packages you may have installed by creating an isolated environment for the plugin to live in.

:::{tip}
In general, it is good practice to choose a name for your environment that is related to either the project you are working on or the software you are installing. In this case, we use `venv-allen-segmenter-ml` where `venv` stands for "virtual environment".
:::

Navigate to where you want to create a new environment (e.g. `Documents`), run the following command in your terminal or powershell:

- To create a new environment:

  ```python
  python3.10 -m venv venv-allen-segmenter-ml
  ```

- To activate the environment:

  ```python
  source venv-allen-segmenter-ml/bin/activate
  ```

#### Confirm Virtual Environment is Activated

To confirm that the virtual environment has been successfully activated, you can follow these steps:

- Check that the prompt includes the name of your virtual environment `venv-allen-segmenter-ml`

  - In general it should look something like this:

    ```python
    (venv-allen-segmenter-ml) $
    ```

  - If on a Windows machine:

    ```python
    (venv-allen-segmenter-ml) PS C:\Users\Administrator\Documents>
    ```

- Run the following command to verify `Python 3.10` is being used within the virtual environment:

  ```python
  python --version
  ```

______________________________________________________________________

## B. Main installation

### 1. Install the plugin and *napari*

To install the latest version of the plugin together with *napari* in a single command:

```python
pip install allencell-segmenter-ml
```

:::{note}
Installing might take several minutes. Please wait until the CLI return to normal:
  ```bash
  (venv-allen-segmenter-ml) PS C:\Users\Aministrator\Documents>
  ```
:::

### 2. Install pyqt5

Lastly, we need to install `pyqt5`, a library to run the user interface (UI). Run the following command in your terminal or powershell:

```python
pip install "napari[pyqt5]"
```

______________________________________________________________________

## C. Post-Installation

:::{note}
This section is specifically for users with at least one NVIDIA GPU installed on their machine.

- Not sure if you have an NVIDIA GPU?
  : - You can check by running `nvidia-smi` (see below for instruction)
- **If you do not have an NVIDIA GPU system, you can skip this section.**
:::

After installing the plugin, you need to install a [PyTorch](https://pytorch.org/get-started/locally/) version that is compatible with your system. PyTorch is a deep learning library that is used to train and run the models in the plugin. However, we understand that everyone manages CUDA drivers and PyTorch versions differently depending on their system and use cases, and we want to respect those decisions because CUDA drivers can be a pain.

Required Package:

- `torch` (PyTorch) 2.0 or later

### 1. Checking CUDA Version

To check your CUDA version, you can run the following command in your terminal or powershell:

```
nvidia-smi
```

As an example, the output will look similar to the table below:

- In this case, my `CUDA Version` is `11.8`:

```
PS C:\Users\Administrator> nvidia-smi
Fri Sep 13 03:22:15 2024
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 522.06       Driver Version: 522.06       CUDA Version: 11.8     |
|-------------------------------+----------------------+----------------------+
| GPU  Name            TCC/WDDM | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla T4           TCC   | 00000000:00:1E.0 Off |                    0 |
| N/A   27C    P8     9W /  70W |      0MiB / 15360MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+
```

### 2. PyTorch installation

To Install PyTorch, please visit the [PyTorch website](https://pytorch.org/get-started/locally/) and select the appropriate installation options for your system.

:::{figure} images/PyTorch_installation_guide.png
PyTorch Installation for Windows, MacOS, and Linux
:::

- For instance, if I am using:

  - Windows workstation
  - pip package manager
  - Python (3.10)
  - CUDA 11.8

- Then the command for me according to the interactive guide from PyTorch would be:

  ```python
  pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
  ```

If the installation is successful, let's test just to be sure that your GPU is detected by PyTorch. Run the following command in your terminal or powershell:

```python
python -c "import torch; print(torch.cuda.is_available())"
```

You should see `True` if your GPU is detected (see below). If you see `False`, then PyTorch is not detecting your GPU. You may need to reinstall PyTorch or check your CUDA drivers. Double check that your virtual environment is activated (`venv-allen-segmenter-ml`).

```python
(venv-allen-segmenter-ml) PS C:\Users\Administrator\Documents> python -c "import torch; print(torch.cuda.is_available())"
```

- If it returns `True`, then you have successfully installed the plugin and PyTorch 🎉.

**You are now ready to use the plugin!**

______________________________________________________________________