# Installation

Installing nabu itself is quite easy. To install the latest release:
```
pip install nabu
```

To install the current development version
```bash
pip install git+https://gitlab.esrf.fr/tomotools/nabu.git
```

## Dependencies

The most complicated part, when it comes to install nabu, is the installation of its optional dependencies:
  - Computations acceleration (GPU/manycore CPU): `pycuda`, `scikit-cuda`
  - Image processing and fitting utils: `scikit-image`





## Frequently asked questions

### Does nabu need a GPU ?

No. Nabu can reconstruct a full volume without needing a GPU.

That being said:
  - The overall process will likely be slow
  - The FBP steps still requires [pyopencl](https://pypi.org/project/pyopencl), and OpenCL ICD/implementation installed on the host

Technically speaking, the reference implementation for each individual step (flat-field, filtering, etc) is in Python/numpy.

That's why **a GPU is recommended (not required)** if you want to use nabu.

### Which OS/python version are supported ?

  - Nabu is only tested on Linux. As it is primarily designed for running on a compute cluster, **there is no plan to support Windows or MacOS**. Pull requests for fixes on these platforms will still be welcome.

  - Nabu supports Python >= 3.5, though at this point many of its dependencies will probably require python >= 3.6 or 3.7.


### What about conda ?

Currently there is no conda recipe for nabu. There might be in the future.


## Installing nabu from (almost) scratch, using pip

In this section, it is assumed that you know the basics about [python virtual environment](https://packaging.python.org/en/latest/tutorials/installing-packages/#creating-virtual-environments), unix commands, git, etc.

First create a new virtual environment, here named `nabu`:

```bash
python3 -m venv nabu
source nabu/bin/activate
```

```{note}
If `venv` is not installed on your system, ask your system administrator to install it or consider using [get-pip.py](https://bootstrap.pypa.io/get-pip.py)
```


Make sure the newly created venv has up-to-date basic tools:

```bash
pip install --upgrade pip setuptools wheel Cython

```

Then install the basic scientific software stack

```bash
pip install cycler certifi
pip install numpy scipy h5py matplotlib lxml fabio
pip install silx
```

```{note}
The installation commands have to be run in this order, otherwise problems might pop up (eg. pip unable to guess matplotlib version to install if cycler/certify are not already installed)
```

**If you have a power9 processor**, you can get python wheels from [silx wheelhouse](http://www.silx.org/pub/wheelhouse).
It's optional but will make the above command much faster.

```bash
# only if you have a power9 CPU, use this command instead
pip install --trusted-host www.silx.org --find-links http://www.silx.org/pub/wheelhouse/ --no-index numpy scipy h5py matplotlib lxml fabio
```

Now let's install pycuda. Here is an example procedure:

```bash
# pycuda installation script fragment
PYCUDA_BUILDDIR="/tmp/pycuda_$(whoami)"
PYCUDA_REPO_URL="https://github.com/inducer/pycuda"
git clone "$PYCUDA_REPO_URL" "$PYCUDA_BUILDDIR"
cd "$PYCUDA_BUILDDIR"
git submodule update --init
rm -f siteconf.py
# curand might have different versions across machines. Disable it.
python configure.py --no-cuda-enable-curand
# pycuda wants old versions of numpy for build. Here we use the available numpy version.
pip install --no-build-isolation .
cd -
```

```{note}
If you encounter a compilation-related issue at this stage, it can have different causes:
  - outdated boost library
  - outdated cuda toolkit and/or nvidia driver

In any case, the problem comes from the operating system, and there is little this tutorial can do to help.

Solutions can be:
  - Upgrade OS components (nvidia-cuda-toolkit, nvidia-driver, ...)
  - Use [environment modules](https://modules.readthedocs.io/en/latest) if available on your system
  - Use conda to get recent software (though it won't solve outdated drivers problems)

```

Now install scikit-cuda:

```bash
# scikit-cuda does not have recent release on pypi. Use the repo version
pip install git+https://github.com/lebedov/scikit-cuda
```

Optionally, install [pycudwt](https://pypi.org/project/pycudwt) (if you want to use the GPU Fourier-Wavelets rings removal on sinogram)

```bash
# Provide the compute capability of the GPU in the form "86" (for 8.6).
# See https://en.wikipedia.org/wiki/CUDA#GPUs_supported
PYCUDWT_CC=86 pip install --no-build-isolation --no-cache-dir "pycudwt"
```



Finally, install nabu:

```bash
pip install nabu
```

Done! Remember to activate the virtual environment (`source path/to/nabu/bin/activate`) when you log again.