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| To ensure consistency and control of python libraries in the lab, a custom python3 (anaconda) distribution for internal freesurfer developers is installed at `/space/freesurfer/python/linux`. To make sure calls to `python` or `python3` point to this version, add the following to your PATH. '''''Note:''' the examples on this page assume you're using bash as your shell.'' | To facilitate straightforward and reproducible python development in the lab, a custom python3 (anaconda) distribution for internal developers is installed at '''/space/freesurfer/python/linux'''. To use this version, make sure all calls to `python` or `python3` point to this install by adding the following to your PATH. |
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| Keep in mind there's also a `python3` link in `/usr/pubsw/bin`, so it's important that `/space/freesurfer/python/linux/bin` comes before it in your PATH. | Keep in mind there's also a `python3` link in `/usr/pubsw/bin`, so it's important that `/space/freesurfer/python/linux/bin` comes before that in your PATH. |
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| The freesurfer python library, which contains general FS utilities and various submodules like deepsurfer, gems, and samseg, is developed in the repository under `repo/python/freesurfer`. In order to utilize this library during development, you must point your `PYTHONPATH` at it: | The freesurfer python library, which contains general utilities and various submodules like deepsurfer, gems, and samseg, is developed in the repository under '''repo/python/freesurfer'''. In order to utilize this library during development, you must point your `PYTHONPATH` at it: |
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| This allows you to import your working version of the freesurfer library in any script or python interpreter. You can test this by calling `python` then running: | This allows you to import your working version of the freesurfer library in any script or python interpreter. You can test it by opening a python interpreter and running: |
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| }}} | }}} |
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| === Tensorflow GPU === | === Tensorflow === |
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| By default, the cpu-tensorflow package is installed, but if you're on a machine with cuda installed, you can use the gpu-enabled tensorflow by adding to the following search paths: | By default, the GPU version of tensorflow is installed. You can still make tensorflow use the CPU with this version, but if your machine does not have cuda installed (like `oribi`), then you unfortunately won't be able to import tensorflow at all. In this case, you must activate the `tensorflow-cpu` conda environment by running: |
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| export PYTHONPATH=/space/freesurfer/python/linux/tensorflow-gpu:$PYTHONPATH export LD_LIBRARY_PATH=/usr/pubsw/packages/CUDA/lib64:$LD_LIBRARY_PATH |
source activate tensorflow-cpu |
FreeSurfer Python Development
To facilitate straightforward and reproducible python development in the lab, a custom python3 (anaconda) distribution for internal developers is installed at /space/freesurfer/python/linux. To use this version, make sure all calls to python or python3 point to this install by adding the following to your PATH.
export PATH=/space/freesurfer/python/linux/bin:$PATH
Keep in mind there's also a python3 link in /usr/pubsw/bin, so it's important that /space/freesurfer/python/linux/bin comes before that in your PATH.
Developing and Using the FreeSurfer Python Library
The freesurfer python library, which contains general utilities and various submodules like deepsurfer, gems, and samseg, is developed in the repository under repo/python/freesurfer. In order to utilize this library during development, you must point your PYTHONPATH at it:
export PYTHONPATH=/path/to/your/freesurfer-repository/python:$PYTHONPATH
This allows you to import your working version of the freesurfer library in any script or python interpreter. You can test it by opening a python interpreter and running:
import freesurfer as fs
status = fs.run('echo hello!')
Tensorflow
By default, the GPU version of tensorflow is installed. You can still make tensorflow use the CPU with this version, but if your machine does not have cuda installed (like oribi), then you unfortunately won't be able to import tensorflow at all. In this case, you must activate the tensorflow-cpu conda environment by running:
source activate tensorflow-cpu