astroNN is developed on GitHub. You can download astroNN from its Github.
But the easiest way to install is via
pip: astroNN on Python PyPI
pip install astroNN
For latest version, you can clone the latest commit of astroNN from github
git clone --depth=1 https://github.com/henrysky/astroNN
and run the following command to install after you open a command line window in the package folder:
python setup.py install
Latest version of Anaconda is recommended, but generally the use of Anaconda is still highly recommended
Python 3.6 or above Tensorflow (the latest version is recommended) Tensorflow-Probability (the latest version is recommended) CUDA and CuDNN (optional) graphviz and pydot are required to plot the model architecture scikit-learn, tqdm, pandas, h5py and astroquery required for astroNN functions
Since Tensorflow and Tensorflow-Probability are rapidly developing packages and astroNN heavily depends on Tensorflow. The support policy of astroNN to these packages is only the last 2 official versions are supported (i.e. the latest and the previous version are included in test suite). Generally the latest version of Tensorflow, Tensorflow-Probability are recommended. The current supporting status (i.e. included in test suites) are
Tensorflow 2.5.x (correspond to Tensorflow-Probability 0.12.x) Tensorflow 2.4.x (correspond to Tensorflow-Probability 0.11.x)
Due to bugs in Tensorflow, 1.12.x: https://github.com/tensorflow/tensorflow/issues/22952, 1.14.x: https://github.com/tensorflow/tensorflow/issues/27543 or 2.5.x: https://github.com/tensorflow/tensorflow/pull/47957, you have to patch a few lines in order for astroNN to work proporly. You can patch Tensorflow by running the following code
from astroNN.config import tf_patch tf_patch()
You can also unpatch Tensorflow to undo changes made by astroNN by running the following code
from astroNN.config import tf_unpatch tf_unpatch()
For instruction on how to install Tensorflow, please refers to their official website Installing TensorFlow
Currently official Tensorflow python wheels do not compiled with AVX2 - sets of CPU instruction extensions that can speed up calculation on CPU. If you are using tensorflow which runs on CPU only or want to use latest CUDA/CuDNN , you should consider to download High Performance Tensorflow MacOS build for MacOS, Or High Performance Tensorflow Windows build for Windows.
Recommended system requirement:
64-bits operating system CPU which supports AVX2 (List of CPUs: https://en.wikipedia.org/wiki/Advanced_Vector_Extensions#CPUs_with_AVX2) 8GB RAM or above NVIDIA Graphics card (Optional, GTX 10 series or above) (If using NVIDIA GPU): At least 2GB VRAM on GPU
Multi-GPU, Intel/AMD graphics are not supported. Only Windows and Linux are officially supported by Tensorflow-GPU with compatible NVIDIA graphics
Using astroNN on Google Colab¶
To use the latest commit of astroNN on Google colab, you can copy and paste the following
!pip install tensorflow !pip install tensorflow_probability !pip install git+https://github.com/henrysky/astroNN.git
My hardware or software cannot meet the prerequisites, what should I do?¶
The hardware and software requirement is just an estimation. It is entirely possible to run astroNN without those requirement. But generally, python 3.6 or above (as Tensorflow only supports py36 or above) and mid-to-high end hardware.
Can I contribute to astroNN?¶
You can contact me (Henry: henrysky.leung [at] utoronto.ca) or refer to Contributor and Issue Reporting guide.
I have found a bug in astorNN¶
Please try to use the latest commit of astroNN. If the issue persists, please report to https://github.com/henrysky/astroNN/issues
I keep receiving warnings on APOGEE and Gaia environment variables¶
If you are not dealing with APOGEE or Gaia data, please ignore those warnings. If error raised to prevent you to use some of astroNN functionality, please report it as a bug to https://github.com/henrysky/astroNN/issues
If you don’t want those warnings to be shown again, go to astroNN’s configuration file and set
I have installed pydot and graphviz but still fail to plot the model¶
if you are encountering this issue, please uninstall both
graphviz and run the following command
pip install pydot conda install graphviz
Then if you are using Mac, run the following command
brew install graphviz
If you are using Windows, go to https://graphviz.gitlab.io/_pages/Download/Download_windows.html to download the Windows package and add the package to the PATH environment variable.
astroNN configuration file is located at
~/.astroNN/config.ini which contains a few astroNN settings.
Currently, the default configuration file should look like this
[Basics] magicnumber = -9999.0 multiprocessing_generator = False environmentvariablewarning = True [NeuralNet] custommodelpath = None cpufallback = False gpu_mem_ratio = True
magicnumber refers to the Magic Number which representing missing labels/data, default is -9999. Please do not change
this value if you rely on APOGEE data.
multiprocessing_generator refers to whether enable multiprocessing in astroNN data generator. Default is False
except on Linux and MacOS.
environmentvariablewarning refers to whether you will be warned about not setting APOGEE and Gaia environment variable.
custommodelpath refers to a list of custom models, path to the folder containing custom model (.py files),
multiple paths can be separated by
Default value is None meaning no additional path will be searched when loading model.
Or for example:
/users/astroNN/custom_models/;/local/some_other_custom_models/ if you have self defined model in those locations.
cpufallback refers to whether force to use CPU. No effect if you are using tensorflow instead of tensorflow-gpu
gpu_mem_ratio refers to GPU management. Set
True to dynamically allocate memory which is astroNN default or enter a float between 0 and 1
to set the maximum ratio of GPU memory to use or set
None to let Tensorflow pre-occupy all of available GPU memory
which is a designed default behavior from Tensorflow.
For whatever reason if you want to reset the configure file:
1 2 3 4
from astroNN.config import config_path # astroNN will reset the config file if the flag = 2 config_path(flag=2)
Folder Structure for astroNN, APOGEE, Gaia and LAMOST data¶
This code depends on environment variables and folders for APOGEE, Gaia and LAMOST data. The environment variables are
SDSS_LOCAL_SAS_MIRROR: top-level directory that will be used to (selectively) mirror the SDSS Science Archive Server (SAS)
GAIA_TOOLS_DATA: top-level directory under which the Gaia data will be stored.
LASMOT_DR5_DATA: top-level directory under which the LASMOST DR5 data will be stored.
How to set environment variable on different operating system: Guide here
$SDSS_LOCAL_SAS_MIRROR/ ├── dr14/ │ ├── apogee/spectro/redux/r8/stars/ │ │ ├── apo25m/ │ │ │ ├── 4102/ │ │ │ │ ├── apStar-r8-2M21353892+4229507.fits │ │ │ │ ├── apStar-r8-**********+*******.fits │ │ │ │ └── ****/ │ │ ├── apo1m/ │ │ │ ├── hip/ │ │ │ │ ├── apStar-r8-2M00003088+5933348.fits │ │ │ │ ├── apStar-r8-**********+*******.fits │ │ │ │ └── ***/ │ │ ├── l31c/l31c.2/ │ │ │ ├── allStar-l30e.2.fits │ │ │ ├── allVisit-l30e.2.fits │ │ │ ├── 4102/ │ │ │ │ ├── aspcapStar-r8-l30e.2-2M21353892+4229507.fits │ │ │ │ ├── aspcapStar-r8-l30e.2-**********+*******.fits │ │ │ │ └── ****/ │ │ │ └── Cannon/ │ │ │ └── allStarCannon-l31c.2.fits └── dr13/ └── *similar to dr14 above/* $GAIA_TOOLS_DATA/ └── Gaia/ ├── gdr1/tgas_source/fits/ │ ├── TgasSource_000-000-000.fits │ ├── TgasSource_000-000-001.fits │ └── ***.fits └── gdr2/gaia_source_with_rv/fits/ ├── GaiaSource_2851858288640_1584379458008952960.fits ├── GaiaSource_1584380076484244352_2200921635402776448.fits └── ***.fits $LASMOT_DR5_DATA/ └── DR5/ ├── LAMO5_2MS_AP9_SD14_UC4_PS1_AW_Carlin_M.fits ├── 20111024 │ ├── F5902 │ │ ├──spec-55859-F5902_sp01-001.fits.gz │ │ └── ****.fits.gz │ └── ***/ ├── 20111025 │ ├── B6001 │ │ ├──spec-55860-B6001_sp01-001.fits.gz │ │ └── ****.fits.gz │ └── ***/ └── ***/
A dedicated project folder is recommended to run astroNN, always run astroNN under the root of project folder. So that astroNN will always create folder for every neural network you run under the same place. Just as below