Supported Applications

Description

an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning, and the flexible numerical computation core is used across many other scientific domains.

Usage

Usage Notes

Start tensorflow with:

source /programs/biogrids.shrc

python.tensorflow

TensorFlow is included with its own python3.7 distribution.

The python binary in the TensorFlow distribution has an alias called:

python.tensorflow.

Use this aliased python within the BioGrids environment to ensure inclusion of the TensorFlow libraries.

The TensorFlow python distribution also include scikit-learn, Keras, pandas, numpy and biopython.

Notes:

Illegal Instruction Error: TensorFlow uses the AVX CPU instruction set (among others). These are not supported on older CPUs. If you receive an "Illegal instruction" error message upon importing tensorflow, it is likely due to lack of the AVX instruction set. Contact help@biogrids.org for help resolving or working around this problem.

NumPy version 1.17.0 has an incompatibility with tensorflow. You will receive warnings about this when starting tensorflow. These can be safely ignored. See the NumPy release notes for details: https://docs.scipy.org/doc/numpy/release.html

Future Changes Shape-1 fields in dtypes won’t be collapsed to scalars in a future version Currently, a field specified as [(name, dtype, 1)] or "1type" is interpreted as a scalar field (i.e., the same as [(name, dtype)] or [(name, dtype, ()]). This now raises a FutureWarning; in a future version, it will be interpreted as a shape-(1,) field, i.e. the same as [(name, dtype, (1,))] or "(1,)type" (consistently with [(name, dtype, n)] / "ntype" with n>1, which is already equivalent to [(name, dtype, (n,)] / "(n,)type").

Installation

Primary Citation*

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, R. Jozefowicz, Y. Jia, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, M. Schuster, R. Monga, S. Moore, D. Murray, C. Olah, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng. 2016. TensorFlow: Large-scale machine learning on heterogeneous systems. CoRR.
(Note: Cite as: arXiv:1603.04467)

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  *Full citation information available through

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