Difference between revisions of "Infrastructure/software/frameworks"
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= Background = | = Background = | ||
| − | General programming | + | General programming environments for so-called neural or deep learning (DL) |
are a prerequisite to much current NLP research, somewhat analogous to | are a prerequisite to much current NLP research, somewhat analogous to | ||
compilers or core libraries (like Boost or ICU). | compilers or core libraries (like Boost or ICU). | ||
These frameworks are not in principle discipline-specific and will often | These frameworks are not in principle discipline-specific and will often | ||
be difficult to install in user space (i.e. without administrator privileges). | be difficult to install in user space (i.e. without administrator privileges). | ||
| − | + | Thus, general DL support should be provided at the system level (not on a | |
| + | per-project basis), and NLPL will seek to work with the system administrators | ||
| + | to make sure that (a) relevant frameworks are available (in all relevant | ||
| + | versions, both for CPU and GPU usage) and (b) that system-wide installations | ||
| + | provide at least some degree of parallelism between the systems available | ||
| + | to NLPL users. | ||
| + | This is no small ambition :-). | ||
= Taito = | = Taito = | ||
Revision as of 09:51, 6 March 2018
Background
General programming environments for so-called neural or deep learning (DL) are a prerequisite to much current NLP research, somewhat analogous to compilers or core libraries (like Boost or ICU). These frameworks are not in principle discipline-specific and will often be difficult to install in user space (i.e. without administrator privileges). Thus, general DL support should be provided at the system level (not on a per-project basis), and NLPL will seek to work with the system administrators to make sure that (a) relevant frameworks are available (in all relevant versions, both for CPU and GPU usage) and (b) that system-wide installations provide at least some degree of parallelism between the systems available to NLPL users. This is no small ambition :-).
Taito
DyNet 2.0 and 2.0.1, apparently both for CPU and GPU nodes
TensorFlow many versions, from 0.11 through 1.5.0; seemingly only installed for GPU nodes, though page provides instructions for user-level installation of CPU-only version.
ML Bundle Python 2.x and 3.x installations with multiple DL frameworks included, notably MxNet, PyTorch, Theano, and TensorFlow. No version information available for individual DL frameworks, and version scheme for the bundle at large seems to follow Python versions. Seemingly only available on GPU nodes. Potentially troubling announcement that “packages in the Mlpython environments are updated periodically.”
