Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Orchestrate: Infrastructure for Enabling Parallelism during
  Hyperparameter Optimization   Two key factors dominate the development of effective production grade
machine learning models. First, it requires a local software implementation and
iteration process. Second, it requires distributed infrastructure to
efficiently conduct training and hyperparameter optimization. While modern
machine learning frameworks are very effective at the former, practitioners are
often left building ad hoc frameworks for the latter. We present SigOpt
Orchestrate, a library for such simultaneous training in a cloud environment.
We describe the motivating factors and resulting design of this library,
feedback from initial testing, and future goals.

19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
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The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc