Installation#

You can try DeLTA out without installing anything on Google Colab.

You can also install DeLTA on your computer or your cluster easily with conda or pip, as described on this page.

If you don’t know anything about conda, you might find the following dropdown helpful:

Hardware requirements#

DeLTA runs much faster on a GPU. To do so you will need an nvidia gpu with a cuda compute capability of 3.5 or higher and an up-to-date gpu driver. If no compatible GPU/driver is found on the system, tensorflow will run on CPU.

For more information see the anaconda tensorflow guide and the tensorflow guide.

If you do not have access to a compatible GPU, DeLTA will run fine on CPU but will be slower. Computing clusters and cloud computing platforms are also an alternative that has been working well for us.

Prerequisites#

Windows (native)

On Windows, you will need c++ and .NET support for Visual studio.

Finally, on Windows, to be able to use the ffmpeg-python module and save mp4 output files you will need to download ffmpeg.exe and place it in your environment’s binaries directory. Otherwise ffmpeg-python will issue a cryptic message about not finding the file specified when trying to save.

Windows (WSL/WSL2)

If you want to use DeLTA on Windows, it might be easier to install it inside the WSL (Windows Subsystem for Linux) than natively.

The WSL can be seen as a Linux emulator running inside Windows. If you already have it installed, you can already continue with the Linux instructions. Otherwise, install it, and then follow the Linux instructions.

You can from now on follow the Linux instructions, starting with this tab.

Linux

We recommend to install DeLTA with conda, it makes installation much easier than with pip. If you already have it, you are all set, if you don’t, the easiest way to install it is through the Mambaforge distribution. Just type these three lines in the terminal:

$ sudo apt install wget
$ wget https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh
$ bash Mambaforge-Linux-x86_64.sh

Mac OS

TODO

Installation#

If you don’t think that you will ever need to modify DeLTA’s own code, choose the “for users” option. Otherwise, choose the “for devs” option.

Overall, conda is recommended in both cases because even though DeLTA is pure Python, its dependencies are not (typically, tensorflow and CUDA), and it is difficult to install them properly with pip.

Conda (for users)

DeLTA is available as a conda package on conda-forge. To install it, simply run in your system’s shell:

(base)$ conda create -n delta_env -c conda-forge delta2

This command creates a new environment named delta_env and installs DeLTA inside. To use DeLTA, you just need to activate the environment with conda activate delta_env, and you will be able to import delta.

Attention

Note that the conda-forge package name is delta2 but the python package to import is just delta.

You can also complete the environment with various packages, such as the elastic-deform library to do some advanced data augmentation for training:

(base)$ conda activate delta_env
(delta_env)$ pip install elastic-deform

Note

For now DeLTA is only available on conda-forge for Linux and MacOSX because the conda-forge Windows version of tensorflow is not up to date. You can however install from the cloned git on Windows.

Pip (for users)

DeLTA is available as a pip package on Pypi.

If you want to be able to use your GPU, you need to install the CUDA toolkit and cuDNN before installing DeLTA with pip, as explained on tensorflow’s website.

Tip

You might want to create a virtual environment first. You can use your favorite environment manager to do so.

Then, just run the following command to install DeLTA:

$ pip install delta2

You can now import delta.

Attention

Note that the pip package name is delta2 but the python package to import is just delta.

Conda (for devs)

You can clone our git repository and then use the delta_dev.yml file to install the environment:

(base)$ git clone https://gitlab.com/delta-microscopy/DeLTA
(base)$ cd DeLTA
(base)$ conda env create -f delta_dev.yml

After activating the environment, you can complete it with various packages, such as the elastic-training library to do some advanced data augmentation for training:

(base)$ conda activate delta_env
(delta_env)$ pip install elastic-deform

Pip (for devs)

If you want to be able to use your GPU, you need to install the CUDA toolkit and cuDNN before installing DeLTA with pip, as explained on tensorflow’s website.

Tip

You might want to create a virtual environment first. You can use your favorite environment manager to do so.

Then you should be able to install the rest of the dependencies and DeLTA by the following command, run inside DeLTA’s directory:

$ pip install -e .

Note

The -e installs DeLTA in “editable” mode, meaning that any change you make to DeLTA’s source code will be taken into account next time you import delta.

Check installation#

You can check what libraries have been installed with:

(base)$ conda activate delta_env
(delta_env)$ conda list

or with pip:

$ pip list

To check that Tensorflow is able to detect the GPU, please run the following in the python interpreter:

>>> import tensorflow as tf
>>> tf.config.list_physical_devices()

Your GPU should appear in the list.

Import DeLTA#

You should be all set. The following line in a python interpreter should work from anywhere on your system (it will issue a warning about not finding elastic-deform if you didn’t install it):

>>> import delta

Tip

If python can’t find DeLTA even though you are inside the DeLTA environment, it might be because you installed DeLTA’s dependencies but not DeLTA itself. You can do so either with pip install -e . or with conda develop, as explained above, in the “(for devs)” tabs.

Installation#

Hardware requirements#

Prerequisites#

Installation#

Check installation#

Import DeLTA#

Troubleshooting#