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,"$",return,break,continue,opr_atr,id,idt_int,c_c_brckt,o_c_brckt,c_brckt,comma,o_brckt,blk_for,blk_while,blk_els,blk_if,ct_true,ct_false,oprlr_lgeq,oprlr_and,oprlr_or,oprl_not,ct_int,ct_float,opr_dm,opr_pm,ct_string,vec_in,idt_char,idt_bool,idt_float,idt_string,idt_void,const | |
S,S → eps,,,,,,S → RETYPE id PARAM o_c_brckt ALL_INTER c_c_brckt S,,,,,,,,,,,,,,,,,,,,,,S → RETYPE id PARAM o_c_brckt ALL_INTER c_c_brckt S,S → RETYPE id PARAM o_c_brckt ALL_INTER c_c_brckt S,S → RETYPE id PARAM o_c_brckt ALL_INTER c_c_brckt S,S → RETYPE id PARAM o_c_brckt ALL_INTER c_c_brckt S,S → RETYPE id PARAM o_c_brckt ALL_INTER c_c_brckt S,S → RETYPE id PARAM o_c_brckt ALL_INTER c_c_brckt S | |
TYPE,,,,,,,TYPE → CONST_R TP,,,,,,,,,,,,,,,,,,,,,,TYPE → CONST_R TP,TYPE → CONST_R TP,TYPE → CONST_R TP,TYPE → CONST_R TP,,TYPE → CONST_R TP | |
CONST_R,,,,,,,CONST_R → eps,,,,,,,,,,,,,,,,,,,,,,CONST_R → eps,CONST_R → eps,CONST_R → eps,CONST_R → eps,,CONST_R → const | |
TP,,,,,,,TP → idt_int,,,,,,,,,,,,,,,,,,,,,,TP → idt_char,TP → idt_bool,TP → idt_flo |
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calculate_maxmin_seconds | clustering_time_seconds | elapsed_time_seconds | create_histogram_seconds | MAPE | MSE | R2 | testing_model_seconds | train_global_model_seconds | local_training_seconds | |
---|---|---|---|---|---|---|---|---|---|---|
7.84 | 11.58 | 83.12 | 1.47 | 0.0 | 0.0 | 0.0 | 1.38 | 6.31 | 35.21 | |
0.75 | 0.0 | 0.0 | 0.0 | 1421.4631446102694 | 0.05745143175937836 | -4.589788559515978 | 0.0 | 1.74 | 26.99 | |
0.75 | 0.0 | 0.0 | 0.0 | 109.32119364788844 | 0.010610696842972408 | -0.0005945533565028249 | 0.0 | 1.73 | 26.33 | |
0.73 | 0.0 | 0.0 | 0.0 | 308.9760879338995 | 0.07834509168613471 | 0.5166180593637022 | 0.0 | 1.77 | 26.4 |
We can make this file beautiful and searchable if this error is corrected: It looks like row 9 should actually have 9 columns, instead of 1 in line 8.
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CONTAINER,CPU%,MEMUSAGE/LIMIT,MEM%,NETI/O,BLOCKI/O,PIDS_JULIA,PIDS_DOCKER,TIMESTAMP | |
e3ee71cf7389204aa6f43be430f17a593b9eeb29d760ba4c1aa15c82dea5db3a,99.94%,131.9MiB/1.907GiB,6.75%,1.72MB/16.2kB,0B/0B,2,10,20:02:39 | |
78787469c6d1766a1ab237acdd382f7ec26ed8c1e660bc2fc990e22cbdd1eb4e,97.76%,143.5MiB/1.907GiB,7.35%,1.76MB/19.8kB,0B/0B,3,14,20:02:39 | |
632e3ff7798847792e2ff821d7688e29ae58b5a41eb250012491e7741226e952,99.73%,154.1MiB/1.907GiB,7.89%,3.76MB/23.9kB,0B/0B,4,14,20:02:39 | |
e3ee71cf7389204aa6f43be430f17a593b9eeb29d760ba4c1aa15c82dea5db3a,98.89%,164.6MiB/1.907GiB,8.43%,5.54MB/32.5kB,0B/0B,2,10,20:02:51 | |
78787469c6d1766a1ab237acdd382f7ec26ed8c1e660bc2fc990e22cbdd1eb4e,99.75%,167.6MiB/1.907GiB,8.58%,5.61MB/36.9kB,0B/0B,3,14,20:02:51 | |
632e3ff7798847792e2ff821d7688e29ae58b5a41eb250012491e7741226e952,100.22%,168.5MiB/1.907GiB,8.63%,5.61MB/35kB,0B/0B,4,14,20:02:51 | |
e3ee71cf7389204aa6f43be430f17a593b9eeb29d760ba4c1aa15c82dea5db3a,5.29%,172.2MiB/1.907GiB,8.82%,7.49MB/61.7kB,0B/0B,2,10,20:03:03 | |
78787469c6d1766a1ab237acdd382f7e |
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calculate_maxmin_seconds | clustering_time_seconds | elapsed_time_seconds | create_histogram_seconds | MAPE | MSE | R2 | testing_model_seconds | train_global_model_seconds | local_training_seconds | |
---|---|---|---|---|---|---|---|---|---|---|
27.14 | 46.71 | 295.36 | 4.28 | 0.0 | 0.0 | 0.0 | 3.51 | 25.41 | 147.68 | |
3.0 | 0.0 | 0.0 | 0.0 | 906.1322576445252 | 0.03985202433514388 | -2.7316023184920333 | 0.0 | 7.09 | 114.31 | |
2.83 | 0.0 | 0.0 | 0.0 | 211.9095391060491 | 0.0100523133137159 | -0.003768696467918442 | 0.0 | 6.88 | 114.72 | |
2.99 | 0.0 | 0.0 | 0.0 | 288.82755659490664 | 0.07181148572017818 | 0.3468755504692067 | 0.0 | 7.39 | 112.45 |
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FROM julialang/julia:v0.4.7 | |
MAINTAINER Naelson Douglas | |
RUN wget https://julialang-s3.julialang.org/bin/linux/x64/0.6/julia-0.6.4-linux-x86_64.tar.gz | |
RUN tar -xvf julia.tar | |
RUN rm julia.tar | |
RUN mv julia* julia | |
RUN mv julia ~/julia | |
RUN export PATH=$PATH:~/julia/bin |
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Tıtulo do Plano de Trabalho: Projeto e Implementação de um protótipo para a paralelização automática do processamento de imagens utilizando a Computação em Nuvem | |
Resumo | |
A crescente demanda de armazenamento de dados de imagens digitais atingiu o ordem de grandeza de terabytes. Aplicações que envolvem grande quantidade de dados de imagens requerem a utilização de infraestruturas distribuídas para aumentar a capacidade de armazenamento e processar os dados paralelamente. No contexto dessas aplicações, destaca-se o processamento de imagens de sensoriamento remoto que também utilizando a computação em nuvem como infraestrutura de execução. Os serviços ofertados pela computação em nuvem utilizam tecnologias de virtualização para gerir automaticamente as infraestruturas físicas de processamento e de rede. Entretanto, as interfaces de programação disponibilizadas pela computação em nuvem não são de fácil utilização para os desenvolvedores de aplicações que envolvem grande quantidade de imagens. Esse projeto tem co |
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{"maxmimtime":{"(3, 4)":{"3":66.194437756},"(1, 2)":{"1":63.759599742},"(7, 8)":{"7":63.506341063},"(2, 3)":{"2":65.410765664},"(5, 6)":{"5":65.680099962},"(6, 7)":{"6":65.384873117},"(8, 9)":{"8":65.848615738},"(4, 5)":{"4":66.13077886}}} |
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oprp = [+|-] | |
oprm = [/|*] | |
oprln = [not] | |
oprlr_eq = [=|!=] | |
oprlr_eq = [<|>] | |
oprlr_lgt_e = [<=|>=] | |
comma = [,] | |
o_bracket = [\[] | |
c_bracket = [\]] |
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{ | |
"EOF" : -1, | |
"TYPE_VALUE": 1, | |
"TYPE_INT": 2, | |
"TYPE_CHAR": 3, | |
"TYPE_FLOAT": 4, | |
"TYPE_VEC": 5, | |
"SMCL": 6, | |
"EPS": 7, | |
"ID": 8, |
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function producer() | |
ch = Channel{Any}(1) | |
@async begin | |
for i=1:5 | |
put!(ch,i) | |
end | |
close(ch) | |
end | |
return ch |