Ansible: Up and Running by Lorin Hochstein

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By Lorin Hochstein

One of several configuration administration instruments to be had, Ansible has a few precise advantages—it’s minimum in nature, you don’t have to set up whatever in your nodes, and it has a simple studying curve. This sensible consultant indicates you the way to be effective with this instrument fast, no matter if you’re a developer deploying code to creation or a procedure administrator trying to find a greater automation solution.

Author Lorin Hochstein exhibits you ways to jot down playbooks (Ansible’s configuration administration scripts), deal with distant servers, and discover the tool’s genuine energy: integrated declarative modules.

You’ll notice that Ansible has the performance you wish and the simplicity you desire.

• know the way Ansible differs from different configuration administration systems
• Use the YAML dossier layout to put in writing your individual playbooks
• examine Ansible’s aid for variables and facts
• paintings with a whole instance to install a non-trivial applicationUse roles to simplify and reuse playbooks
• Make playbooks run quicker with ssh multiplexing, pipelining, and parallelism
• installation purposes to Amazon EC2 and different cloud platforms
• Use Ansible to create Docker photos and install Docker packing containers

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We speculate that this is due to the communication overhead between the GPU and the CPU during the iterations of the dose calculations for each PB. The maximum speedup of ∼127 was achieved for the highest energy and the largest field size. 9 28 G. Kalantzis et al. Fig. 10 Speedup factors as a function of energy and number of PBs 4 Conclusions A GPU-based PB algorithm for proton dose calculations in Matlab was presented. A maximum speedup of ∼127 was achieved. Future directions of the current work include extension of our method for dose calculation in heterogeneous phantoms.

Figures 3 and 4 illustrate a set of weighted Bragg peaks and their superposition creating a SOBP respectively. 22 G. Kalantzis et al. Fig. 3 A set of weighted pristine Bragg peaks of different energies Fig. 3 Lateral Profiles of Pencil Beams Protons are assumed to emanate axial symmetrically from the finite-sized source with a specified mean residual range and with no angular emittance initially. For simplicity we assumed scattering only in the patient, which is increasing the radial emittance at the depth of the point of interest.

A lot of recent researches of semi-supervised multiclass learning attempt to addressing those limitations. The related algorithms can be roughly divide into three categories. 1. Low-density separation. Low-density separation methods try to place boundaries in regions where there are few sample instance. One of the most commonly used algorithm is the transductive support vector machine(TSVM), TSVM attempt to labeling the unlabeled data with the decision boundary has maximal margin over all of the data, while SVM for supervised learning seeks a decision boundary with max margin between different classes.

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