If you're new to Mesos
See the getting started page for more information about downloading, building, and deploying Mesos.
If you'd like to get involved or you're looking for support
See our community page for more details.
Mesos Fetcher Cache Internals
It assumed that readers of this document are familiar with the contents of the Mesos fetcher user guide. The present document makes direct references to notions defined in the former.
Design goals for the initial fetcher cache prototype:
- Direct fetching: Provide the pre-existing fetcher functionality (as in Mesos 0.22 and before) when caching is not explicitly requested.
- Program isolation: Preserve the approach to employ an external “mesos-fetcher” program to handle all (potentially very lengthy or blocking) content download operations.
- Cache effect: Significant lessen overall time spent on fetching in case of repetition of requests for the same URI. This holds for both sequential and concurrent repetition. The latter is the case when concurrently launched tasks on the same agent require overlapping URI sets.
- Cache space limit: Use a user-specified directory for cache storage and maintain a user-specified physical storage space limit for it. Evict older cache files as needed to fetch new cache content.
- Fallback strategy: Whenever downloading to or from the cache fails for any reason, fetching into the sandbox should still succeed somehow if at all possible.
- Agent recovery: Support agent recovery.
For future releases, we foresee additional features:
- Automatic refreshing of cache content when a URI’s content has changed.
- Prefetching URIs for subsequent tasks. Prefetching can run in parallel with task execution.
How the fetcher cache works
In this section we look deeper into the implementation of design goals #1, #2, #3. The others are sufficiently covered in the user guide.
Fetcher process and mesos-fetcher
The fetcher mechanism consists of two separate entities:
- The fetcher process included in the agent program. There is exactly one instance of this per agent.
- The separate mesos-fetcher program. There is one invocation of this per fetch request from the agent to the fetcher process.
The fetcher process performs internal bookkeeping of what is in the cache and what is not. As needed, it invokes the mesos-fetcher program to download resources from URIs to the cache or directly to sandbox directories, and to copy resources from the cache to a sandbox directory.
All decision making “intelligence” is situated in the fetcher process and the mesos-fetcher program is a rather simple helper program. Except for cache files, there is no persistent state at all in the entire fetcher system. This greatly simplifies dealing with all the inherent intricacies and races involved in concurrent fetching with caching.
The mesos-fetcher program takes straight forward per-URI commands and executes these. It has three possible modes of operation for any given URI:
- Bypass the cache and fetch directly into the specified sandbox directory.
- Fetch into the cache and then copy the resulting cache file into the sandbox directory.
- Do not download anything. Copy (or extract) a resource from the cache into the sandbox directory.
Besides minor complications such as archive extraction and execution rights settings, this already sums up all it does.
Based on this setup, the main program flow in the fetcher process is concerned with assembling a list of parameters to the mesos-fetcher program that describe items to be fetched. This figure illustrates the high-level collaboration of the fetcher process with mesos-fetcher program runs. It also depicts the next level of detail of the fetcher process, which will be described in the following section.
Cache state representation and manipulation
The fetcher process uses a private instance of class Cache to represent what URIs are cached, where the respective cache files are, what stage of processing they are in, and so on.
The main data structure to hold all this information is a hashmap from URI/user combinations to Cache::Entry
objects, which each contain information about an individual cache file on disk. These objects are referenced by shared_ptr, because they can be addressed by multiple callbacks on behalf of concurrent fetch attempts while also being held in the hashmap.
A cache entry corresponds directly to a cache file on disk throughout the entire life time of the latter, including before and after its existence. It holds all pertinent state to inform about the phase and results of fetching the corresponding URI.
This figure illustrates the different states which a cache entry can be in.
While a cache entry is referenced it cannot be evicted by a the current or any other concurrent fetch attempt in order to make space for a download of a new cache file.
The two blue states are essentially the same: no cache file exists. The two green disk states on the right are also the same.
The figure only depicts what happens from the point of view of one isolated fetch run. Any given cache entry can be referenced simultaniously by another concurrent fetch run. It must not be evicted as long as it is referenced by any fetching activity. We implement this by reference counting. Every cache entry has a reference count field that gets incremented at the beginning of its use by a fetch run and decremented at its end. The latter must happen no matter whether the run has been successful or whether there has been an error. Increments happen when:
- A new cache entry is created. It is immediately referenced.
- An existing cache entry’s file download is going to be waited for.
- An existing cache entry has a resident cache file that is going to be retrieved.
Every increment is recorded in a list. At the very end of the fetch procedure, no matter what its outcome is, each entry in the list gets its reference count decremented.
(Currently, we are even leaving reference counts for cache entries for which we fall back to bypassing the cache untouched until the end of the fetch procedure. This may be unnecessary, but it is safe. It is also supposedly rare, because fallbacks only occur to mitigate unexpected error situations. A future version may optimize this behavior.)
The per-URI control flow
As menitoned above, the fetcher process' main control flow concerns sorting out what to do with each URI presented to it in a fetch request. An overview of the ensuing control flow for a given URI is depicted in this figure.
After going through this procedure for each URI, the fetcher process assembles the gathered list of per-URI actions into a JSON object (FetcherInfo
), which is passed to the mesos-fetcher program in an environment variable. The possible fetch actions for a URI are shown at the bottom of the flow chart. After they are determined, the fetcher process invokes mesos-fetcher.
The implementation is oriented at this control flow but its code structure cannot match it directly, because some of these branches must span multiple libprocess continuations. There are two layers of futures, one for each of these phases.
- Before making fetcher cache items,
- Wait for concurrent downloads for pre-existing cache entries.
- Wait for size fetching combined and then space reservation for new cache entries.
- After making fetcher cache items and running mesos-fetcher,
- Complete new cache items with success/failure, which as an important side-effect informs concurrent fetch runs' futures in phase 1/a.
The futures for phase 1 are not shared outside one fetch run. They exclusively guard asynchronous operations for the same fetch run. Their type parameter does not really matter. But each needs to correspond to one URI and eventual fetch item somehow. Multiple variants have been proposed for this. The complexity remains about the same.
The futures for phase 2 need to be part of the cache entries, because they are shared between concurrent fetch runs.
Some time between phase 1 and 2, the fallback strategy needs to be applied where indicated: when a future from phase 1 has failed for any reason, we fall back on bypassing the cache.
Besides, everything touched in 1/a and 1/b needs to be prevented from being cache-evicted until the end. One can in principle release cache entries right after they fail, but this requires more complexity and is harder to prove correct.
Cache eviction
The resources named “A” and “B” have been fetched with caching into sandbox 1 and 2 below. In the course of this, two cache entries have been created and two files have been downloaded into the cache and named “1” and “2”. (Cache file names have unique names that comprise serial numbers.)
The next figure illustrates the state after fetching a different cached URI into sandbox 3, which in this case requires evicting a cache-resident file and its entry. Cache eviction removes cache entries in the order of the least recently used cache entries. Steps if “A” was fetched before “B”:
- Remove the cache entry for “A” from the fetcher process' cache entry table. Its faded depiction is supposed to indicate this. This immediately makes it appear as if the URI has never been cached, even though the cache file is still around.
- Proceed with fetching “C”. This creates a new cache file, which has a different unique name. (The fetcher process remembers in its cache entry which file name belongs to which URI.)
The next figure then shows what happens if the first URI is fetched once again. Here we also assume the cache being so filled up that eviction is necessary and this time the entry and file for “B” are the victims.


An interface between R and the OSRM API. OSRM is a routing
service based on OpenStreetMap data. See <http://project-osrm.org/> for more
information. This package allows to compute distances (travel time and
kilometric distance) between points and travel time matrices.
| Version: |
3.1.1 |
| Depends: |
R (≥ 2.10) |
| Imports: |
jsonlite, RCurl, utils, stats, rgeos, methods, gepaf, raster, sp |
| Suggests: |
cartography (≥ 2.0.1) |
| Published: |
2018-03-12 |
| Author: |
Timothée Giraud [cre, aut],
Robin Cura [ctb],
Matthieu Viry [ctb] |
| Maintainer: |
Timothée Giraud <timothee.giraud at cnrs.fr> |
| BugReports: |
https://github.com/rCarto/osrm/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/rCarto/osrm |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| In views: |
WebTechnologies |
| CRAN checks: |
osrm results |


| emmeans::FAQs | FAQs for emmeans | HTML | source | R code | |
| emmeans::basics | Basics of EMMs | HTML | source | R code | |
| emmeans::comparisons | Comparisons and contrasts | HTML | source | R code | |
| emmeans::confidence-intervals | Confidence intervals and tests | HTML | source | R code | |
| emmeans::interactions | Interaction analysis in emmeans | HTML | source | R code | |
| emmeans::messy-data | Working with messy data | HTML | source | R code | |
| emmeans::models | Models supported by emmeans | HTML | source | ||
| emmeans::predictions | Prediction in emmeans | HTML | source | R code | |
| emmeans::sophisticated | Sophisticated models in emmeans | HTML | source | R code | |
| emmeans::transformations | Transformations and link functions | HTML | source | R code | |
| emmeans::utilities | Utilities and options | HTML | source | R code | |
| emmeans::vignette-topics | Index of vignette topics | HTML | source | ||
| emmeans::xplanations | Explanations supplement | HTML | source | R code | |
| emmeans::xtending | For developers: Extending emmeans | HTML | source | R code |


Creates a wrapper for the 'SuiteSparse' routines that execute the Takahashi equations. These equations compute the elements of the inverse of a sparse matrix at locations where the its Cholesky factor is structurally non-zero. The resulting matrix is known as a sparse inverse subset. Some helper functions are also implemented. Support for spam matrices is currently limited and will be implemented in the future. See Rue and Martino (2007) <doi:10.1016/j.jspi.2006.07.016> and Zammit-Mangion and Rougier (2018) <doi:10.1016/j.csda.2018.02.001> for the application of these equations to statistics.
| Version: | 0.1.3 |
| Depends: | R (≥ 3.1) |
| Imports: | Matrix, methods, Rcpp, spam |
| LinkingTo: | Rcpp |
| Suggests: | covr, testthat |
| Published: | 2018-08-23 |
| Author: | Andrew Zammit-Mangion [aut, cre], Timothy Davis [ctb], Patrick Amestoy [ctb], Iain Duff [ctb], John K. Reid [ctb] |
| Maintainer: | Andrew Zammit-Mangion <andrewzm at gmail.com> |
| License: | GPL (≥ 2.1) |
| NeedsCompilation: | yes |
| CRAN checks: | sparseinv results |
| Reference manual: | sparseinv.pdf |
| Package source: | sparseinv_0.1.3.tar.gz |
| Windows binaries: | r-devel: sparseinv_0.1.3.zip, r-release: sparseinv_0.1.3.zip, r-oldrel: sparseinv_0.1.3.zip |
| OS X binaries: | r-release: sparseinv_0.1.3.tgz, r-oldrel: sparseinv_0.1.3.tgz |
| Old sources: | sparseinv archive |
| Reverse imports: | FRK, IDE |
Please use the canonical form https://CRAN.R-project.org/package=sparseinv to link to this page.


London
London is the capital city of England. It is the most populous city int the United Kingdom, with a metropolitan area of over 13 million inhabitants.
Standing on the River Themes, London has been a major settlement for two millennia, its history going back to its founding by the Romans, who named it Londinium.


| NEED DIRECTIONS? Click a street address of any meeting location and an interactive map, courtesy of Google Maps, will appear in this frame for easy reference. (A.A. does not endorse nor have any affiliation with Google Maps and is not responsible for their content.) |
| SPECIAL CODES: C
(CLOSED for Alcoholics & for those "with a desire
to stop drinking.") M (for Men Only) W (for Women Only) LGBT (Lesbian/Gay/Transgender/Bisexual) BS (Babysitting Available) * (Wheel Chair Access) ASL (Signed for Hearing Impaired) CF (Child Friendly) |
| TIME | NAME OF MEETING/ADDRESS | CODES | LOCATION |
|---|---|---|---|
| 07:15P | ALPINE GROUP Church 1460 Midway Dr | * | ALPINE |
| 07:30P | WE'VE COME A LONG WAY Church 545 E Naples St | C W * | CHULA VISTA |
| 07:30P | MONDAY NIGHT LIVE Church 6745 Amherst St | BS * | COLLEGE |
| 07:30P | IMPERIAL MEETING Club 564 Broadway St | C * | EL CENTRO |
| 07:30P | FLETCHER HILLS SMOKELESS Church 455 Church Way | FLETCHER HILLS | |
| 07:30P | ENCANTO GROUP BIG BOOK STUDY Club 6901 Central Ave | LEMON GROVE | |
| 07:30P | MONDAY NIGHT BIG BOOK STUDY Church 2660 Hardy Dr | Stamp only-court slips | LEMON GROVE |
| 07:30P | HOW IT IS NOW SPEAKERS Hospital 7850 Vista Hill Ave (rear) | LINDA VISTA | |
| 07:30P | MEDICINE WHEEL Church 3725 30th St | Native American * | NORTH PARK |
| 07:30P | BACON ST USUAL SUSPECTS Club 2229 Bacon St | * | OCEAN BEACH |
| 07:30P | MISSION POSSIBLE Church 9310 Dalehurst Rd | CF | SANTEE |
| 08:00P | PIONEER MENS Recv Ctr 270 C St | C * M | CHULA VISTA |
| 08:00P | MONDAY STEP STUDY Church-Youth Room 7250 Eckstrom Ave | * | CLAIREMONT |
| 08:00P | UP THE STREET BIG BOOK Club 1944 30th St | * | GOLDEN HILL |
| 08:00P | PRACTICAL SOLUTIONS Church-upstairs #205 3900 Cleveland Ave | LGBT | HILLCREST |
| 08:00P | MEN AND WOMENS MONDAY NIGHT Hosp Cafeteria 10666 North Torrey Pines Rd Free parking lot: https://goo.gl/maps/UA8wwjre6X82 | * | LA JOLLA |
| 08:00P | GRAB YOUR SOCKS Recv Hm 3317 Hancock St | LOMAL PORTAL | |
| 08:00P | MONDAY NIGHT YOUNG PEOPLES STEPS Church 4650 Mansfield St | NORMAL HEIGHTS | |
| 08:00P | RULE 62 GROUP Church 1370 Euclid Ave | OAK PARK | |
| 08:00P | KEEP YOURS SEATS Club-Upstairs 4861 Cass Street | PACIFIC BEACH | |
| 08:15P | NORTH CLAIREMONT Church-Hall 4271 Clairemont Mesa Blvd | * | CLAIREMONT |
| 09:00P | MISSION HILLS LATE NIGHT MONDAY Church 4044 Lark St |   | MISSION HILLS |


Workweek
1 Monday
2 Thuesday
3 Wednesday
4 Thursday
5 Friday
Weekend
6 Saturday
7 Sunday
REMINDERS
SPECIAL DATES


OpenAPI ClientLIb
API & Changelog Documentation
Code coverage report
Release build
Release build sourcemap


This package lets you access ACyRSA’s API using R. This will enable you to integrate ACyRSA’s data more easily into your R workflow.
At the moment acyRsa
is only available through GitHub usind devtools
.
devtools
# 1. In R install the package `devtools`.
install.packages("devtools")
# 2. Once `devtools` is installed, install `acyRsa`
library(devtools)
devtools::install_github("augustohassel/acyRsa")
Development of this software was driven by HasselPunk as part of an Open Source initiative.


Sudoku Solver
Based on Graham Hutton’s implementation.
> module SudokuSolver where
>
> import Data.List
> import Constants
>
> {- solve DIAGRAM
>
> ["43..",
> "123.",
> "..2.",
> "21.."]
>
> ||
> solve ::[[Char]] -> [[Char]]
> ||
> \/
>
> ["4312",
> "1234",
> "3421",
> "2143"]
>
> Break to subproblems!
>
>
> ["43..",
> "123.",
> "..2.",
> "21.."]
>
> ||
> choices :: [[Char]] -> [[[Char]]]
> ||
> \/
>
> [[[4],[3],[1,2,3,4],[1,2,3,4]],
> [[1],[2],[3],[1,2,3,4]],
> [[1,2,3,4],[1,2,3,4],[2],[1,2,3,4]],
> [[2],[1],[1,2,3,4][1,2,3,4]]
>
> ||
> collapse :: [[[Char]]] -> [[[Char]]]
> ||
> \/
>
> [["4311", ["4311", ["4311", ["4311", ["4311",
> "1231", "1231", "1231", "1231", "1231",
> "1121", "1121", "1121", "1121", "1121",
> "2111"], "2112"], "2113"], "2114"], "2121"],...
>
> ||
> filter valid :: [[[Char]]] -> [[[Char]]]
> ||
> \/
>
> [["4312",
> "1234",
> "3421",
> "2143"]]
>
> DIAGRAM SUMMARY:
> solve :: [[Char]] -> [[Char]]
> 1. choices :: [[Char]] -> [[[Char]]]
> 2. collapse :: [[[Char]]] -> [[[Char]]]
> 3. filter valid :: [[[Char]]] -> [[[Char]]]
> -}
>
> -- Problem 1: Is Char a choice or a value?
>
> type Sudoku = Matrix Value
> type Matrix a = [[a]]
>
> type Choice = [Char]
> type Value = Char
>
>
>
>
>
>
>
> -- Problem 2: Types don't really compose!
>
>
>
>
>
>
>
>
>
>
>
>
> {-
> DIAGRAM SUMMARY:
> solve :: Sudoku -> Sudoku
> 1. choices :: Sudoku -> Matrix Choice
> 2. collapse :: Matrix Choice -> [Matrix Value]
> 3. filter valid :: [Matrix Value] -> [Matrix Value]
> 4. ?? :: [Sudoku] -> Sudoku
> -}
>
>
>
> solve :: Sudoku -> [Sudoku]
> solve = filter valid . collapse . choices
>
>
>
> --------------------------------------
> choices :: Matrix Char -> Matrix [Char]
> --------------------------------------
> choices = map (map toChoices)
>
> toChoices :: Char -> [Char]
> toChoices '.' = values
> toChoices x = [x]
>
>
>
> --------------------------------------
> collapse :: Matrix Choice -> [Matrix Value]
> --------------------------------------
>
> {- collapse DIAGRAM
>
> [[[1,2],[3,4]],
> [[1],[2,3]] ]
>
> ||
> collapse ::[[[Char]]] -> [[[Char]]]
> ||
> \/
>
> [ [[1,3] [[1,3] [[1,4] [[1,4] [[2,3] [[2,3] [[2,4] [[2,4]
> ,[1,2]], [1,3]], [1,2]], [1,3]], [1,2]], [1,3]], [1,2]], [1,3]]]
>
> Break to subproblems!
>
> [[[1,2],[3,4]],
> [[1],[2,3]] ]
>
> ||
> map prod ::[[[Char]]] -> [[[Char]]]
> ||
> \/
>
> [[[1,3],[1,4],[2,3],[2,4]],
> [[1,2],[1,3]] ]
>
> ||
> prod ::[[[Char]]] -> [[[Char]]]
> ||
> \/
> [ [[1,3] [[1,3] [[1,4] [[1,4] [[2,3] [[2,3] [[2,4] [[2,4]
> ,[1,2]], [1,3]], [1,2]], [1,3]], [1,2]], [1,3]], [1,2]], [1,3]]]
>
> DIAGRAM SUMMARY:
> collapse :: Martix Choices -> [Matrix Value]
> 1. map prod :: [[[Char]]] -> [[[Char]]]
> 2. prod :: [[[Char]]] -> [[[Char]]]
>
> -}
>
> collapse = prod . map prod
>
> prod :: [[a]] -> [[a]]
> prod [] = [[]]
> prod (xs:xss) = [y:ys | y <- xs, ys <- prod xss]
>
>
>
> --------------------------------------
> valid :: Matrix Value -> Bool
> --------------------------------------
> valid xs = all nodups (rows xs)
> && all nodups (cols xs)
> && all nodups (boxs xs)
>
>
> nodups :: Eq a => [a] -> Bool
> nodups [] = True
> nodups (x:xs) = notElem x xs && nodups xs
>
>
> rows :: Matrix a -> [[a]]
> rows xs = xs
>
> cols :: [[a]] -> [[a]]
> cols = transpose
>
> boxs :: Matrix a -> [[a]]
> boxs = unpack . map cols . pack
> where
> pack = split . map split
> split = chop boxsize
> unpack = map concat . concat
>
> chop :: Int -> [a] -> [[a]]
> chop n [] = []
> chop n xs = take n xs : chop n (drop n xs)
>
> {-
> Example: if boxsize = 2, then we have
>
> [[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]]
>
> |
> pack
> |
> v
>
> [[[[1,2],[3,4]],[[5,6],[7,8]]],[[[9,10],[11,12]],[[13,14],[15,16]]]]
>
> |
> map cols
> |
> v
>
> [[[[1,2],[5,6]],[[3,4],[7,8]]],[[[9,10],[13,14]],[[11,12],[15,16]]]]
>
> |
> unpack
> |
> v
>
> [[1,2,5,6],[3,4,7,8],[9,10,13,14],[11,12,15,16]]
> -}
>
>
> -- Let's try it
>
> trysmall = solve sudokusmall
>
> trybig = undefined
>
> -- Wait! Let's see the choises first...
>
> choicesBig = choices sudoku
>
>
>
>
>
> -- Rows can be obsviously pruned!
> pruneRows = undefined
>
>
>
>
>
>
>
>
>
>
>
>
> -- Let's generalize pruning ...
> pruneAll :: Matrix Choice -> Matrix Choice
> pruneAll = pruneBy rows . pruneBy cols . pruneBy boxs
> where pruneBy f = f . map prune . f
>
> prune :: [[Char]] -> [[Char]]
> prune xs = map remove xs
> where singles :: [Char]
> singles = concat $ filter single xs
> remove :: [Char] -> [Char]
> remove [x] = [x]
> remove ys = filter (`notElem` singles) ys
>
> single :: [a] -> Bool
> single [_] = True
> single _ = False
>
>
> solve1 :: Sudoku -> [Sudoku]
> solve1 = filter valid . collapse . pruneAll . choices
>
> -- Oups! Too slow...
>
>
>
>
>
>
>
>
>
>
>
>
> -- Can we prune more?
>
>
>
>
>
> solve2 :: Sudoku -> [Sudoku]
> solve2 = filter valid . collapse . fix pruneAll . choices
>
> fix f x = if x == x' then x else fix f (f x)
> where x' = f x
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> -- Still too slow...
> -- The problem: it should backtrack:
> -- Bad choices are first fully expanded and then checked for validity
> -- Let's expand one choise at the time
>
>
>
>
> -- Some predicates
>
> complete :: Matrix Choice -> Bool
> complete = all (all single)
>
> void :: Matrix Choice -> Bool
> void = any (any null)
>
> safe :: Matrix Choice -> Bool
> safe cm = all consistent (rows cm) &&
> all consistent (cols cm) &&
> all consistent (boxs cm)
>
> consistent :: [Choice] -> Bool
> consistent = nodups . concat . filter single
>
> blocked :: Matrix Choice -> Bool
> blocked m = void m || not (safe m)
>
>
> -- Predicate based searching
>
>
> search :: Matrix Choice -> [Sudoku]
> search m
> | blocked m = []
> | complete m = collapse m
> | otherwise = [g | m' <- expand m
> , g <- search (pruneAll m')]
>
> -- Where expand only opens one choice at the time!
>
>
> expand :: Matrix Choice -> [Matrix Choice]
> expand m =
> [rows1 ++ [row1 ++ [c] : row2] ++ rows2 | c <- cs]
> where
> (rows1,row:rows2) = break (any (not . single)) m
> (row1,cs:row2) = break (not . single) row
>
>
> -- And finally a quick solution
>
> solve3 :: Sudoku -> [Sudoku]
> solve3 = search . pruneAll . choices


Carousel of unwanted guests
by Sarita PatelA downtown Westbank resident is frustrated after an unwelcome guest approached his home in the early morning hours to check if his vehicle was unlocked.
The culprit was caught on the family's surveillance camera, which has been in place for the past two years, thanks to previous robberies, including a thief removing a garden hose and reel from the property.
The resident, who asked to remain anonymous, tells Castanet the man came onto his property in the Gossett and Brenda Lee Road area at 1:16 a.m. Sunday.
“That is quite the regular occurrence around here, so I just wanted the neighbourhood and whoever (lives in) downtown Westbank here to look out for that.”
The homeowner is fed up with the lack of respect for other people's property.
“We’ve had people overdose on the corner of Gossett and Brenda Lee.
The resident says he's spoken to RCMP about thefts in the neighbourhood, but nothing could be done because of insufficient evidence.
“I’ve been in this house for 14 years, and I’ve had my tire slashed, had my stereo in my car stolen, and three months later they stole the new stereo I put back in it.”
His son, who visits from Alberta, also notices things go missing when he’s in town to visit.
“It’s that kind of thing that’s happening down here, and it’s happening more… I just want people to know how brazen these guys are. As you can see in the second video, the guy walks out with my garden hose and reel in broad daylight.”
Arrests haven’t been made, but neighbours have been warned.
The newest video also shows the prowler walking across the street to another house to check their vehicle.


The Bristol shop selling wedding dresses for as little as £50 for brides on a budget
“It’s about creating an experience for everyone with no strings attached”
by Shashana BrownThere’s nothing more disheartening than finding the dress of your dreams that simply falls way outside of your budget.
And with the average price of a wedding dress effortlessly flowing into the thousands, preparing for your big day can be stressful – unless you know exactly where to look.
Which makes the hidden second-hand bridal shop, hidden above a Gloucester Road charity shop, a secret of many Bristol brides.
The ground floor of Tenovus is filled with everyday clothing, books and accessories but upstairs is another story.
The store sells dresses for brides on a budget with low costing designer brands such as Pronovias, Jen Jen House and Benjamin Roberts.
But as soon as you ascend to the upper-floor, you’re immediately blown away by rows of bridal dresses you can try on ‘till your heart’s content’.
View gallery
Shop manager Sheila Cleary says customers are often surprised and shocked by the huge variety and affordable prices.
She said: “We once had a shopper that was fuming because she saw the exact same dress she had recently purchased for £1,400 and the same identical dress was in our store for £200.”
While there is nothing better than spotting an inexpensive dress that fits you like a glove, this charity shop allow you to explore with ‘no strings attached’.
“It’s about creating an experience for everyone with no strings attached”
Mrs Cleary said: “Just pop in, you don’t need to make an appointment you can bring a couple of friends with you just try on anything - hopefully you’ll find one that suits and fits.
She says people can often feel under pressure when trying on different styles of dresses - especially price wise but at Tenovus, “there is nobody hassling you”.
She said: “Instead of people feeling embarrassed that they have got a second-hand wedding dress, they should be chuffed - now they have money to spend on other things.
“People want retro dresses these days - they don’t want great big meringue.”
Mrs Cleary who has worked in the charity industry for 28 years says Gloucester road has become very cosmopolitan.
She said: “The area has become very green with people now more aware of saving the planet - donating is a great start.
“We have had people calling to tell us they have wedding dresses from the 1980s, and we say that is absolutely fine.”
Mrs Cleary says dresses that are stained or ragged around the edges are sold even cheaper for Halloween or hen parties.

“I sold my wedding dress four years ago to be used for bride of Dracula.”
What was the experience like?
"As soon as I walked in asked to see the bridal section - the staff did not hesitate.
"I was led up the stairs and told to have fun, explore and take as much time as I needed - it was great," said Shashana.
For Rema who has plans to get married in the future, experimenting with different styles was just what she wanted.
She said: "I'd never tried a wedding dress on before, so it was definitely a fun experience and nothing like what I thought it would be.
“I've always said I only want a small wedding on the beach, but when I put on that dress I didn't want to take it off hahah!."
Ella said: “Whenever I think of a charity shop, wedding dresses do not spring to mind so to discover a whole floor of second-hand gowns was unbelievable.
“Tenovus offers a great way to save money on dresses without sacrificing style and I think it’s important to make brides aware that they’re other options, rather than spending a fortune.”
What shoppers can look forward to
In spring next year the store is set to welcome £70,000 worth of brand new stock including mother of the bride dresses and accessories.
As part of its bridal launch, shop assistant Lyn Lewis said: “We are hoping to have an opening with balloons and entertainment.”
For 67-year-old Mrs Cleary reusing a ‘pre-loved dress’ is not only great for the environment but also people on a low income or people having a second marriage.
She said: “I think people are realising that to pay £1,500 to £2,000 on wedding dresses is just crazy for one day – so it benefits them and the charity.
“It’s about creating an experience for everyone with no strings attached.”
For the latest news in and around Bristol, check back on Bristol Live's homepage


Please enable Javascript if you want to hear the evosynth as it uses the web audio API which is only accessible via Javascript.
play
stop
new sound


This command writes the current selection to a text file. It writes each byte as a pair of hex digits, each pair separated by a space. The number of pairs per line is determined by the Bytes per line option in the Export section of the System Options page of the Options dialog. The command is disabled if there is no selection.
This command brings up the standard Save As dialog which allows you to save the file using any valid file name. If the file already exists you are prompted to overwrite it.
Addresses and checksums are not written as in Export Intel Hex Records and the Motorola S record exports. If there is any chance of data corruption it is preferred to use the Export Motorola S3 Records which provides a checksum of each line, a record count record at the end of file to make sure that no complete lines were lost and (if importing into HexEdit again) a check that addresses are contiguous.


I'm soon to be a graduate of Brooklyn College at CUNY with a Bachelors of Science in Computer Science. I have working experience with Python, JavaScript, Java and SQL programming. I'm especially experienced with Python data science and machine learning libraries like Pandas and Scikit Learn as well as web frameworks like Django, Django REST, React.js and Dash. I'm especially interested in data driven web development.
View Resume
U.S. accumulated college debt reached $1.6 trillion in 2020. Students need to know what to expect and and machine learning can provide a forecast they can rely on. The College Debt Forecast app provides a sleek and user friendly interface, enabling users to get an accurate prediction of student debt with just a few clicks. I Built this app using python, Django REST Framework, and the Federal College Scorecard data API. I used Pandas and Numpy for data wrangling and analysis of a dataset containing student debt data for 10s of thousands of vocational schools and colleges. I leveraged Scikit Learn machine learning library to build a random forest regression model with 0.74 R-Squared score which was then integrated with the application backend. I built the application user interface using React.js, Material UI, and JavaScript and deployed the application using Heroku.
Jam, dance, learn, or laugh with friends. Use your Spotify premium account to listen to your favorite music and podcasts together. The app enables users to listen in sync. Guests can vote to skip, with hosts determining the number of votes required for a skip to take place. I built Listen Party using python and Django REST Framework for the backend API. I built the frontend logic and user interface, which consumes the Django API, using React.js and the Material-UI library. I leveraged Spotify's Web API to facilitate user authentication, to get playback data, and to affect playback-state changes. I used Spotify's Web Playback SDK to enable content streaming through the browser. I deployed Listen Party using Python Anywhere.
NYPD Complaints Navigator is a data visualization "dashboard" application, that enables users to filter and analyze data regarding civilian complaints against members of the NYPD, including 12,000 complaints and over 30,000 allegations. My project partner and I used Pandas and Numpy for data wrangling and analysis, the Plotly Graphing Library to generate interactive visualizations, and Google Geolocation API to populate our dataframes with geographic data for map visualizations. We used the Dash Components to build the user interface including interactive input and visualization toggling features. Finally, we styled the app using the Dash Bootstrap Components library and deployed NYPD Complaints Navigator using Heroku.
Get in touch!
E-mail Me


Academic Integrity Policies for CIS 351
You are acting with academic integrity to the extent that you do your academic work
honestly and ethically. In particular, you are acting with academic integrity if you are
taking full credit for your own work, and giving full credit to others who have helped
you, or whose work you have incorporated into your own
representing your own work honestly and accurately
cooperating with other students, on academic exercises, only where specifically
authorized
Computer science is a collaborative discipline. Whether in a professional or an educational setting, teams can accomplish more than individuals. However, in CIS351 and many programming courses in the EECS department may require that you work individually to better serve or assess your learning.
Although it is common practice to adapt code examples found on the web,
You should never copy code from other students.
Your peers are not considered an authorized source.
You should not simply re-use code as the solution to an assignment. Like academic writing, your code can incorporate the ideas of others but should reflect your original approach to the problem.
In this course, to recieve gardes, you need to submit the Academic Integrity Pldege within 2 week of the semester.
In CIS351, many activities will permit or require collaboration like labs and in-class activities. Collaboration may be an explicit learning goal of such activities. When you turn in collaborative work, even as a lab solution, you DO NOT need to credit the help or peer that you seeked help from.
HOWEVER, In this course, Homework, i.e. programming assignments are designed to: (1) help you learn the materials; (2) help you understand what you have and have not learned; and (3) encourage you to do the work that is necessary for learning to occur. Unambiguously, the best way for you to achieve the second objective is for you to complete programming assignments entirely on your OWN, occasionally asking questions of a general nature (i.e., about the topics you need to understand in order to complete assignment but not specifics about the assignment itself).
You may discuss HW, i.e. programming assignments, in an abstract way, with other students who are currently enrolled in your class, but you should limit the amount of collaboration and you should keep a careful record of it (so that you can assess yourself).
You must not discuss the programming assignments with students who are not currently enrolled in your course, except for the designated TAs or lab assistants.
You also must not discuss programming assignments with tutors of any kind – paid or volunteer, local or remote. Finally, you must not submit (even before the due date/time) another student’s work as your own.
In other words, the code you submit must be written/typed entirely by you and you alone.
Failure to comply with these policies is a violation of the academic honesty.
Though you are entitled to discuss the programming assignments with other people, you must be courteous. Most importantly, students are not required, or even encouraged, to provide assistance. So, if asked, you should feel free to decline such a request and, if asking, you must not take offense if such a request is denied.
For Homework, the following policies will be adopted regarding how much cooperation is permitted in CIS351:
A. Students are to work on their HW without consulting with anyone that is not explicitly approved by the instructor. You can always ask for help from TA, grader and the instructor.
B. Students are allowed to discuss the meaning of HW programs or problems together but are expected to work totally independent of anyone that is not explicitly approved by the instructor when working on the solutions to a program or problem and when doing their coding.
C. In some cases, the instructor may distribute a written policy (possibly as a part of their syllabus) that specifies how much cooperation and joint work is permitted on their HW.
Policy A is the default policy that will be used unless the instructor specifies otherwise in writing (e.g., in their syllabus or in assignment instructions) indicating that one of the other policies will be followed. Violation of this policy will result in reporting for academic integrity violations. Students are to clarify with their instructors any questions they may have about how much cooperation and joint work is permitted on HW.
Cheating on an exam includes, but is not limited to:
taking answers from another student’s exam or paper or allowing another student to
take answers from your exam or paper
the use of notes or other materials not allowed or approved by the instructor
collaboration on a take-home or online exam unless the collaboration is approved by
the instructor
allowing another student to take a take-home or on-line exam for you and
representing the work on the exam as your own
copying materials from a computer file during an exam when the instructor has not
designated the use of computer files
talking to other students during a test
Keeping your files/documents unsecured
Altering or destroying file or any evidence
Whether working in a group or individually, at times you may seek assistance from a variety of sources, including professors, mentors, grader, TAs, classmates, and other students.
Assistance with programming exercises may serve one of three purposes: To help you get started, to correct a syntax error, or to debug code that does not work as intended. In all cases, those providing assistance should ask questions instead of providing answers. Never write code for another student or show them your solutions. Moving away from the computer, for example to draw on a whiteboard, will help to emphasize concepts. Whether giving or receiving such assistance, your goal should be learning. Remember that responsibility for completing the assignment belongs to the learner.
Web sites such as stackoverflow.com provide assistance to both professionals and learners. Feel free to google for error reasons and technical challenges in stackoverflow as these sources may suggest approaches or help you debug your code. However, in using such sites, again, your goal should be learning.
Others' intellectual contributions to your work must be explicitly acknowledged, e.g., by a comment in your code. Such acknowledgments credit others for their contributions, and also allow readers of your code to consult the resources you used. People should be thanked by name. If you are using any book resources, you need to credit the book too.
It’s also fine to use any code provided by this semester’s 6.031 staff (in class, readings, or problem sets), without need for attribution. Staff-provided code may not be publicly shared without permission.
Example1: Here is an example of how to credit sources in your HW programs. When citing within the code, the citation information could be placed as a comment at the begining of your code, as shows below:
***************************************************************************************/
* Acknowledgements:
* Title: GraphicsDrawer source code
* Author: Smith, J
* Date: 2011
* Code version: 2.0
* Availability: http://www.graphicsdrawer.comM
*
***************************************************************************************/
Example2: Here is an example of how to credit the help you have taken from your TA.
***************************************************************************************/
* Acknowledgements:
* I have taken help for calculateArea method from TA XXX
*
***************************************************************************************/
Please remember that in cases involving cheating or plagiarism, a person who knowingly assists another person in cheating or plagiarism is considered as guilty as the person receiving help. The person whose work is copied is also considered responsible for violating academic integrity principles. Using solutions or code from a problem assigned in a previous semester without the explicit permission of the instructor is also considered to be cheating or plagiarism. It is each student's responsibility to place protection on their files containing assignments, avoid leaving their computer account open in the labs when they are not present, protect current and old printouts of their code, and take other reasonable precautions to ensure these files are not accessible to another student.
Take special care to protect your files, directories, and systems appropriately, and be sure to discard printouts so they cannot be retrieved by others (e.g., do not discard printouts in public recycling or garbage bins until after the assignment due date is passed).
A student who becomes aware of cheating is strongly encouraged to report this matter. It is important that the students and instructors work together to achieve an academic environment that is fair to all students.
The following constitutes cheating on a HW in CIS351:
Failure to give credit for help received. All help received must be credited, whether
the help is from the instructor, a TA, a grader or another student in class.
Failure to work individually. Unless otherwise specified by the instructor,
HWa are to be done individually. This means that you may talk to
classmates and other students about a HW, but you must do all
the work yourself from beginning to end. Two or more students may sit down together
and discuss an assignment/lab/project as they are working on it, but each student
must do his/her own work in completing the assignment. Note that if you talk to other
students about the HW or work with someone on the HW, that activity
should be included in the “credit” section described in the previous bullet.
Submitting the same work in more than one course without prior permission of both
instructors.
Failure to keep your workspace private so that others are able to access your files.
Sharing your passwords and logins with others can create academic situations under
which all parties are liable. Use only your computer, your logins, and your SU
account.
You may never copy or share computer files with any one, unless your instructor has
provided written instructions that explicitly spell out when sharing or copying is
allowed. All parties involved will be liable for academic dishonesty if files are shared
or copied.
Copying or sharing code or work that is yours or from another source, unless explicitly
(in writing) allowed by your instructor. You may never copy or share code or work that
is yours or from another source, unless your instructor has provided written
instructions that explicitly spell out when copying is permissible. For example,
you cannot copy all or part of someone else’s program code
you cannot copy all or part of someone else’s written documentation
you cannot copy classes or methods that someone else has created
you cannot share your code with someone else at any time, under any circumstances
The instructor will follow the procedures provided in detail in the current University poclicy document posted here. Read this information very carefully! It is the policy of this school to file a report to the Center for Learning and Student Success for every incident of academic dishonesty.
Acknowledgements
For the policies outlined in this document, we have drawn upon ideas of Academic Honesty Policy in the Computer Science Grinnell College, Whitman College, University of Arizona, MIT, Rutgers University well as the ACM Code of Ethics and Professional Practice.


5/13 email
The COVID-19 Flexible Grading Policy was designed to provide students with maximum flexibility during the ongoing and challenging transition of all courses to distance learning.
For some, the Credit/No Credit policy can seem complicated, so we thought we would highlight key take-aways of the policy:
The CR/NC option is available to ALL Undergraduate and Graduate students for ALL CUNY courses; no student may be denied the option of a CR/NC grade in any course.
The CR/NC grades have no impact on the GPA.
A faculty member does not award a CR/NC grade to the student.
CR/NC is a choice students make in CUNYfirst after they have seen the letter grades that they have earned.
Students have up to 20 business days after the University’s final grade submission deadline.
No student with a CR grade in a course will be prevented from taking a subsequent course (even those that require a minimum grade to advance).
Letter grades of A, B, C, D qualify for conversion to CR and credit is awarded.
The letter grade of F qualifies for conversion to NC and no credit is awarded.
The WU grade (unofficial withdrawal) DOES NOT qualify for conversion to NC. Therefore, for the Spring 2020 semester only, the F grade might be the more compassionate option.
10.There are 3 grading options for students to consider if they are NOT doing well in a course:
a. The W - a withdrawal from the course by the appropriate deadline (which may have financial aid implications).
b. The INC - a contract with the faculty, initiated by the student, to complete course requirements by the Fall 2020.
c. The CR/NC option.
Faculty advising are strongly recommended to advise students on the implications of selecting a CR/NC option.
For further explanation, here are a few scenarios to explain the impact of the Flexible Grading Policy:
If a student receives an F grade, the student can opt into the CR/NC policy. The F grade can be converted to NC, with no impact on GPA. In fact, the assignment of CR/NC on grades A through F does not have any GPA impact whatsoever.
If a student has missed the final exam, or a few assignments, and was earning a passing letter grade, you may consider giving the student an INC for the course. The student will have one more semester to complete the required work.
If a student receives a WU grade, this grade has the same weight on the GPA as an F - but cannot be converted to NC. The student could potentially appeal this WU grade through the process set out by the Course and Standing Committee. The process for the student is as follows: The student would be required to visit the Registrar for verification, gather signatures on the appeal form, provide documentation supporting the appeal, and a letter requesting the change of the grade from WU to W. The appeal package is then reviewed by the committee who approves or denies the appeal.
We must consider compassionate grading for all students, as their successful completion of the semester and their subsequent progression is our priority. We ask that you are especially mindful of the impact of your grading this term.
Respectfully,
Your Faculty Senate Committee on Course and Standing
Nota bene: The above was passed unanimously during the May 6th, 2020, meeting and is being distributed after consultation with the Faculty Senate Executive Committee.


Commands: create car NAME MAKE MILEAGE FAULTS output garage output car CARNUMBER print CARNUMER check out CARNUMBER bill CARNUMBER bill all (The statements with CAPITALS are your specific variables)


Mysterious Wizard:
You don't have 200 vampire hearts yet.
I'll trade you something for the dead's brains though.
Trade the dead's brains.
Continue hunting.
Stop hunting.


H4 inside nested article
Paragraph inside nested article -span text-
This is a paragraph that contains several span tags. I'd like to style these span tags across my entire site at once, to establish a default site-wide format for them. The most efficient selector for this would be the element selector, as it will allow me to establish a base-line format for the span tag with a low degree of specificity. This means that the formatting will be controlled throughout the entire site with a single selector, and it will be easy to overwrite in specific instances where I want the styling to change.
In this example I want to target specific class attribute values. Note how the styling is applied regardless of element type. Class selectors are also more specific than element selectors, so they allow us to overwrite the more general styles of element selectors.
They also allow us to target very specific locations or instances by simply applying a class attribute. This is a great way to write styles for specific purposes with a single selector and then apply it whenever needed by adding the appropriate class attribute.
Care should be taken when using class selectors as overuse of them can bloat code and create stylesheets that are hard to maintain.
In this example I want to target specific ID attribute values. Note how the styling is applied regardless of element type. ID selectors are extremely specific, and will overwrite most selector types.
Many designers avoid using ID selectors and discourage their use in projects due to their highly specific nature. ID selector styling is very hard to overwrite later, and generic interface styling can fail if dropped into a region being controlled through ID selectors. Still, ID selectors can play an important role in styling sites. You simply need to develop clear rules for when they are appropriate to use and how they might impact future styles.
Descendent selectors allow us to be much more precise in terms of where styling is applied. Using them allows us to style elements a certain way when found inside a specific element type and another way if found elsewhere. This allows us to create discreet areas of styling in an efficient manner. It also makes it easy to create self-contained elements, as class and ID attributes can be used to identify specific UI elements that are styled with a combination of descendent selectors.
Paragraph inside section
Paragraph inside section -span text-
Paragraph inside nested article -span text-
This paragraph contains an anchor


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Free Int'l call to mobile
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GO BACK
: string matching, regex, regexp, substring, string, String.prototype, startsWith, endsWith, includes, indexOf, toLowerCase, RegExp, match, search, test, exec, RegExp.prototype.exec, pattern, consuming, input string, alternation, parsing, character sets, named character sets, repetition, period metacharacter, escaping, true wildcard, grouping, lazy matches, greedy matches, backreferences, replacing groups, function replacements, anchoring, word boundry matching, lookaheads, dynamic constructions
Definitions
- provide sophisticated string matching functionality
- The provides basic substring matching and replacing methods: , , , , and
- To construct a regular expression:
const re1 = /going/;
- Or another way use the :
const re2 =
RegExp("going");
Searching with Regular Expressions
- The following matches all words three letters or longer (case
insensitive):
/\w{3,}/ig
- Use returns an array of matches:
input.match(re);
- returns the index of the first match:
input.search(re);
- returns if at least one match exists:
re.test(input);
- is an of the matches:
re.exec(input)
Input Consumption
- A is a pattern for input strings
Alternation
- The vertical bar is a regex that signals
alternation
- The signifies and
- Angle brackets are not regex megacharacters
Parsing and Limitations
- means to completely break something down into its component parts
- are capable of parsing languages only
- are extremely limited when it comes to matching
Character Sets
- provide a compact way to represent alternation of a
- can be combined with to represent
multiple characters
Named Character Sets
\d
-- a single digit
\D
-- not a digit
\s
-- tabs, spaces, and vertical tabs
\S
-- not tabs, spaces, and vertical tabs
\w
-- word "character" with no dash or periods
\W
-- not a word "character" with dash or period
Repetition
- metacharacters allow you to specify how many times something
matches
- The following the character group signals that the should match :
/[0-9]+/
- The repetition metacharacters are that modify
Repetition Modifiers
- Exactly n:
/\d{5}/
matches only five-digit numbers (such as a
zip code)
- At least n:
/\d{5,}/
matches five-digit numbers or longer
- At least n, at most m:
/\d{2,5}/
matches numbers that are
at least 2 and at most 5 digits
- Zero or one. Equivalent to :
/[a-z]\d?/i
matches letter followed by an optional digit
- Zero or more (called a Klene star or Klene closure):
/[a-z]\d*/i
matches a letter followed by an optional multiple digit number
- One or more:
/[a-z]\d+/i
matches a letter followed by a
required number, possibly containing multiple digits
Miscellaneous
- The period is special character that means match anything (except new
lines)
- Any special character can be escaped with a backslash:
\( \. \*
- matches everything (whitespace or non-whitespace including new lines)
Backreferences
- Grouping enables using:
\1\2
Grouping
- allows us to construct , which can be
treated as a single unit
- are specified using
parenthesis:
(<subexpression>)
- are specified
using:
(?:<subexpression>)
Lazy Matches, Greedy Matches
- By default, regular expressions are , meaning they will match as much
as possible before stopping
- Add a after repetition metacharacters to make the
expression :
/(.*?)/
Anchoring
- The anchor matches the beginning of the line
- The anchor matches the end of the line
- Use the multiline option to handle new lines:
/\w+$/mg
- The include: and
Lookaheads
- -- like and metacharacters -- don't consume input
Code


1 Naucratis was in the past the only trading port in Egypt. Whoever came to any other mouth of the Nile had to swear that he had not come intentionally, and had then to take his ship and sail to the Canobic mouth; or if he could not sail against contrary winds, he had to carry his cargo in barges around the Delta until he came to Naucratis. In such esteem was Naucratis held.


Definition at line 67 of file types.h.
Definition at line 79 of file types.h.
Definition at line 76 of file types.h.
Definition at line 70 of file types.h.
Definition at line 73 of file types.h.


[#include "/admin/common/commons.html"]
[#include "/admin/common/nav.html"]
模板列表
取消
更新
[#include "/admin/common/footer.html"] [#include "/admin/common/aside.html"]


You can customise the color of the signature as follows. $("#signature2").jSignature({color:"#f00"});
Clear Export Import Data to Image Import Data to Canvas


Proin gravida nibh vel velit auctor aliquet. Aenean sollicitudin, lorem quis bibendum auctor, nisi elit consequat ipsum, nec sagittis sem nibh id elit.
"Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
"It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout.
There are many variations of passages of Lorem Ipsum available, but the majority have suffered alteration in some.
Copyright © 2014.Company name All rights reserved.More Templates æ¨¡æ¿ä¹‹å®¶ - Collect from ç½‘é¡µæ¨¡æ¿


Questions in S12-Assignment-Solution:
What is your final accuracy?
Share the Github link to your ResNet-Tiny-ImageNet code. All the logs must be visible.
Describe the contents of the JSON file in detail. You need to explain each element in detail.
Share the link to your Github file where you have calculated the best K clusters for your 50 dog dataset.
Share the link to your 50 Dog Images Folder on GitHub
Share the link to your JSON file on GitHub
Github Link: https://github.com/satyajitghana/TSAI-DeepVision-EVA4.0/blob/master/12_TinyImageNet/TinyImageNet.ipynb
Final Accuracy
Test: 53.66
Train: 63.22
Images: https://github.com/satyajitghana/TSAI-DeepVision-EVA4.0/tree/master/12_TinyImageNet/Doggos/images
(includes the explanation of json file)
KMeans run on Doggo: https://github.com/satyajitghana/TSAI-DeepVision-EVA4.0/blob/master/12_TinyImageNet/Doggos/Doggo_KMeans.ipynb
Donno Annotations: https://github.com/satyajitghana/TSAI-DeepVision-EVA4.0/blob/master/12_TinyImageNet/Doggos/annotations.json
Annotation
KMeans


Congratulations {email}, thank you for registering with our site!
Visit the site
Thanks for using Wintellect Demo,
Talk Python


Contains functions designed to facilitate the loading and transformation of 'Agricultural Production Systems Simulator' output files <https://www.apsim.info>. Input meteorological data (also known as "weather" or "met") files can also be generated from user supplied data.
| Version: | 0.9.2 |
| Imports: | data.table (≥ 1.9.4), lubridate (≥ 1.3.3), stringr (≥ 0.6.2), plyr (≥ 1.8.1), sirad, methods, utils, RSQLite |
| Published: | 2017-07-24 |
| Author: | Justin Fainges |
| Maintainer: | Justin Fainges <Justin.Fainges at csiro.au> |
| License: | GPL-3 |
| URL: | https://www.apsim.info |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | APSIM results |
| Reference manual: | APSIM.pdf |
| Package source: | APSIM_0.9.2.tar.gz |
| Windows binaries: | r-devel: APSIM_0.9.2.zip, r-release: APSIM_0.9.2.zip, r-oldrel: APSIM_0.9.2.zip |
| OS X binaries: | r-release: APSIM_0.9.2.tgz, r-oldrel: APSIM_0.9.2.tgz |
| Old sources: | APSIM archive |
| Reverse imports: | nasapower |
Please use the canonical form https://CRAN.R-project.org/package=APSIM to link to this page.


Background¶
This section outlines the system being simulated as well as some theoretical background of learning mechanisms.
The system being simulated¶
Figure 1 illustrates the system being simulated.
The system consists of a subject (or organism, individual, or agent) interacting with a world (or environment).
The world displays stimuli that the subject responds to. As a consequence of that response, the world displays the next stimulus, which the subject responds to. This continues, and the subject learns about the world in each step. The learning is modelled with a learning mechanism that uses a set of memory state variables to represent the subject’s memory of the past stimuli.
The subject has a pre-defined set of behaviors \(\mathscr{B}\) - the behavior repertoire. It also has an output function (decision function) that determines which of these behaviors to respond with. In addition, it has state transition functions that update memory state variables and other internal states.
The world, which often is defined by an experiment, has a pre-defined set of stimulus elements - the stimulus repertoire. It also has an output function that determines which of these stimuli to expose to the subject. In addition, it has state transition functions that update the state of the world. A stimulus \(S\) consists either of one single stimulus element \(S=\{E\}\) in the stimulus repertoire, or a set of two or more simultaneous elements \(S=\{E_1,E_2,\dotsc\}\). In the latter case, the stimulus is called a compound stimulus.
The behavior of the subject influences both the internal state of the subject and the internal state of the world. Also, the stimulus that the world generates depends on the subject’s behavior (being the response to the last generated stimulus), and the behavior that the subject responds with depends on the stimulus that it responds to.
The system operates in the discrete time steps \(S_{1}\rightarrow B_{1}\rightarrow S_{2}\rightarrow B_{2}\rightarrow S_{3}\rightarrow\dotsc\), in other words, the process starts with the world producing a stimulus \(S_1\) at time step 1, and the subject responds with behavior \(B_1\) at time step 2, etc. It is this process that is being simulated in Learning Simulator. The Learning Simulator scripting language specifies all the properties of the subject, the world and the simulation.
Technically, the world is a dynamical system whose state transition function depends on the subject’s last behavior, and outputs a stimulus. In the same way, the subject is a dynamical system whose state transition function depends on the last stimulus, and outputs a behavior. Thus, the system being simulated consists of two interacting dynamical systems.
The world¶
The world receives a behavior \(B\) from the subject and responds with a stimulus \(S\) that may or may not depend on \(B\). A description of the world specifies which stimulus is generated in each step and how it depends on the behavior and internal state variables of the world. In the Learning Simulator scripting language, this is done one or several @phase blocks of the script. It basically consists of a number of labelled lines, where each line specifies which stimulus to expose the subject to, and which line to go to in the next step.
The subject¶
When the subject interacts with the world, it learns about it. This learning, in other words, exactly how and which memory state variables are updated, is specified by a learning mechanism. The subject’s response to an observed stimulus is controlled by a decision function. This section describes the different learning mechanisms as well as the decision function.
The mechanisms¶
The subject is assumed to have an initial (genetic) value \(u_E\) for each stimulus element \(E\). The subject is also assumed to have a cost \(c_B\) associated with each behavior \(B\) in its behavior repertoire. These \(u\)- and \(c\)-values do not change during the simulation.
When the subject observes a stimulus \(S\) it makes a decision about which behavior \(B\) (in the behavior repertoire) to respond with. This is done by the decision function; see below.
The possible responses to a given stimulus \(S\) may be restricted to a subset of the subject’s behavior repertoire \(\mathscr{B}\). For example, if a stimulus involves a pressable lever, the subject can respond with the behavior “press lever”, but for a stimulus that does not involve a lever, the subject cannot respond with “press lever”. We use the notation \(\mathscr{B}(E)\) for the possible responses to the stimulus element \(E\), and \(\mathscr{B}(S)=\cup_{E\in S}\mathscr{B}(E)\) for \(S=\{E_1,E_2,\dotsc\}\). In the scripting language, such restrictions are specified in the parameter response_requirements.
After observing the next stimulus \(S'\) (being a consequence of the decision to respond with \(B\)), the subject “learns” by updating an internal state variable \(v\) indexed by stimulus-response pairs (so that \(v_{E\rightarrow B}\) is the value associated with the response \(B\) to stimulus element \(E\)). Some mechanisms also use a state variable \(w\) indexed by stimulus elements (so that \(w_E\) is the value associated with stimulus element \(E\)). The \(w\)-values are usually called conditional reinforcement values. The table below shows the available learning mechanisms, their memory state variables, and how they are updated after the subject has experienced the steps
where \(S=\{E_1, E_2, \dotsc\}\) and \(S=\{E'_1, E'_2, \dotsc\}\). In the table the following notation is used:
and
Mechanism |
Abbrev. |
Memory states |
Memory updates |
|
|---|---|---|---|---|
SR |
\(v_{E\to B}\) |
\(\Delta v_{E\to B}=\alpha_{E\to B}(u_{S'}-v_{S\to B}-c_B)\) |
\(\forall E\in S\) |
|
QL |
\(v_{E\to B}\) |
\(\Delta v_{E\to B}=\alpha_{E\to B}\big(u_{S'}-v_{S\to B}-c_B+\gamma\max\limits_{B'}v_{S'\to B'}\big)\) |
\(\forall E\in S\) |
|
Expected SARSA 5 |
ES |
\(v_{E\to B}\) |
\(\Delta v_{E\to B}=\alpha_{E\to B}\big(u_{S'}-v_{S\to B}-c_B+\gamma E(v_{S'\to B'})\big)\) |
\(\forall E\in S\) |
Actor-critic 6 |
AC |
\(v_{E\to B}\)
\(w_E\)
|
\(\Delta v_{E\to B}=\alpha_{E\to B}\beta_{E\to B}\big(1-\Pr(S\to B)\big)\delta\)
\(\Delta w_E=\alpha_E \delta\)
where \(\delta = u_{S'}+\gamma w_{S'}-w_{S}-c_B\)
|
\(\forall E\in S\) |
A-learning (Guided associative learning) 7 |
A, GA |
\(v_{E\to B}\)
\(w_E\)
|
\(\Delta v_{E\to B}=\alpha_{E\to B}(u_{S'}+\gamma w_{S'}-v_{S\to B})\)
\(\Delta w_{E}=\alpha_E (u_{S'}+\gamma w_{S'}-c_{B}-w_{S})\)
|
\(\forall E\in S\) |
Rescorla-Wagner |
RW |
\(v_{E_1\to E_2}\) |
Only implemented for single-element stimuli \(S=\{E\}\).
\(\Delta v_{E\to E'} = \alpha_{E\to E'}(\lambda_{E'}-v_{E\to E'})\)
\(\Delta v_{E\to X} = -\alpha_{E\to X} v_{E\to X} \; \forall X\ne E'\)
|
The decision function¶
When the subject is exposed to a stimulus \(S=\{E_1,E_2,\dotsc\}\), it makes a decision about which behavior in the
behavior repertoire to respond with. This is done by associating each possible behavior \(B_i\)
with a probability \(\Pr(S\rightarrow B_i)\) and then choosing a response by sampling from
this probability distribution. In
Learning Simulator
a version of the so called soft-max rule is used to compute the probabilities:
where the parameters \(\beta_{E\to B}\) control the extent to which the decision is governed by the \(v\)-values: Smaller \(\beta\) means more exploration in the reponse decision. The parameter \(\mu\) may be used for genetic predisposition, for example to increase/decrease the probability for a particular response to a particular stimulus element.
References
- 1
Bush, R. R., & Mosteller, F. (1951). A mathematical model for simple learning. Psychological review, 58(5), 313.
- 2
Rescorla, R. A., & Wagner, A. R. (1972), A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and non-reinforcement. In A. H. Black & W. F. Prokasy (Eds.), Classical conditioning II. Current research and theory* (pp 64-99). New York: Appleton- Century- Crofts.
- 3
Watkins, C. J. C. H. (1989). Learning from delayed rewards.
- 4
Watkins, C. J., & Dayan, P. (1992). Q-learning. Machine learning, 8(3-4), 279-292.
- 5
Van Seijen, H., Van Hasselt, H., Whiteson, S., & Wiering, M. (2009, March). A theoretical and empirical analysis of Expected Sarsa. In 2009 ieee symposium on adaptive dynamic programming and reinforcement learning (pp. 177-184). IEEE.
- 6
Witten, I. H. (1977), An adaptive optimal controller for discrete-time Markov environments, Inf. Control, vol. 34, pp. 286-295, .
- 7
Ghirlanda, S., Lind, J., & Enquist, M. (2020). A-learning: A new formulation of associative learning theory. Psychonomic Bulletin & Review, 1-29.


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jMail is an Open source, easy to use and free email management system. Open source being its key feature, that means its source code is available directly and thus there is no concern for privacy breach. Developed by two amatuer devs in their college days, 'jMail' is open to suggestions and corrections. The best part of being open source is, one can easily get the source code and customize it based on one's individual skills and creativity. jMail was meant to be just another mailing system but it has potential of becoming much more and that depends upon the developer who's working on it.


Francisco Granados
114 Harding Street
Kitchener ON N2E 4L1
Cell Phone<PHONE_NUMBER>
<EMAIL_ADDRESS>Website: http://www.fgranados.ca
OBJECTIVE: To obtain a full time position where I can utilize my skills and expand my capabilities.
Proficient in C,C++,C# and Java programming languages
Proficient in JavaScript scripting language
Skilled in the creation/use of manual and automated test methods
Understanding of the full system development life cycle
Strong object oriented programming skills
Exceptional ability to learn new things at a fast rate
Outstanding work ethic in all tasks
Proficient in written and spoken English and Spanish
Created several T-SQL stored procedure, views, functions and triggers.
EDUCATION
Software Engineering Technology 2011 - 2014
Institute of Conestoga College, Kitchener, On
EMPLOYMENT EXPERIENCE
Junior Web Developer 2014 - Present
Codesyne Integrated Technology
Cambridge, Ontario
Contribute new features to existing web application written in python
Use postgres to create databases for new projects
Make CSS/HTML changes when necessary
Dynamically manipulate elements using jQuery when specified by the client
Intern Web Developer 2014
Design and Logic
Thunder bay, Ontario
Maintain and integrate new features to existing infrastructure
Develop PHP and ASP.Net applications
Integrate mobile applications with social media A.P.I’S (Twitter and LinkedIn)
Perform regression tests on system when new features are integrated
References available upon request


--- layout: api title: "v1.6.3 API: Mobile" categories: api version: v1.6.3 permalink: /api/v1.6.3/mobile ---
Mapbox.js is optimized for mobile devices and small screens by default. There are, however, best practices to make sure your map always looks its best.
Modern mobile browsers now support scaling of webpages by leveraging the meta
tag viewport
. This enlarges the window making your map look better on a
mobile device.  Having a 'tall' map can cause the user
to get stuck on the map while scrolling. Another way around this is to disable
dragging
for mobile devices: map.dragging.disable();
Having the ability to use retina tiles when the device supports them is easy.
When creating the map, use the detectRetina
to verify if retina is available
and retinaVersion
to use a tilelayer which is designed for retina screens.
var map = L.mapbox.map('map', 'your.mapid', {
tileLayer: {
detectRetina: true,
retinaVersion: 'your.mapid'
}
}).setView([40, -74.50], 9);
Some Mapbox maps support switching to retina scale automatically: if you're using
one of these maps, you can simply set detectRetina
and the higher-scale
tiles will be used when retina is detected.
var map = L.mapbox.map('map', 'your.mapid', {
tileLayer: {
detectRetina: true
}
}).setView([40, -74.50], 9);


[page:Material] →
[name]
Material rendered with custom shaders. A shader is a small program written in [link:https://www.opengl.org/documentation/glsl/ GLSL] to run on the GPU. You may want to use a custom shader if you need to:
- implement an effect not included with any of the built-in [page:Material materials]
- combine many objects into a single [page:Geometry] or [page:BufferGeometry] in order to improve performance
There are the following notes to bear in mind when using a *ShaderMaterial*:
- A *ShaderMaterial* will only be rendered properly by [page:WebGLRenderer], since the GLSL code in the *vertexShader* and *fragmentShader* properties must be compiled and run on the GPU using WebGL.
- As of THREE r72, directly assigning attributes in a *ShaderMaterial* is no longer supported. A [page:BufferGeometry] instance (instead of a [page:Geometry] instance) must be used instead, using [page:BufferAttribute] instances to define custom attributes.
Example
var material = new THREE.ShaderMaterial( {
uniforms: {
time: { value: 1.0 },
resolution: { value: new THREE.Vector2() }
},
attributes: {
vertexOpacity: { value: [] }
},
vertexShader: document.getElementById( 'vertexShader' ).textContent,
fragmentShader: document.getElementById( 'fragmentShader' ).textContent
} );
Vertex shaders and fragment shaders
You can specify two different types of shaders for each material:
- The *vertex shader* runs first; it receives *attributes*, calculates/manipulates the position of each individual vertex, and passes additional data (*varying*s) to the fragment shader.
- The *fragment shader* runs second; it sets the color of each individual "fragment" (pixel) rendered to the screen.
There are three types of variables in shaders: uniforms, attributes, and varyings:
- *Uniforms* are variables that have the same value for all vertices---lighting, fog, and shadow maps are examples of data that would be stored in uniforms. Uniforms can be accessed by both the vertex shader and the fragment shader.
- *Attributes* are variables associated with each vertex---for instance, the vertex position, face normal, and vertex color are all examples of data that would be stored in attributes. Attributes can only be accessed within the vertex shader.
- *Varyings* are variables that are passed from the vertex shader to the fragment shader. For each fragment, the value of each varying will be smoothly interpolated from the values of adjacent vertices.
Note that within the shader itself, uniforms and attributes act like constants; you can only modify their values by passing different values to the buffers from your JavaScript code.
Built-in attributes and uniforms
[page:WebGLRenderer] provides many attributes and uniforms to shaders by default; definitions of these variables are prepended to your *fragmentShader* and *vertexShader* code by [page:WebGLProgram] when the shader is compiled; you don't need to declare them yourself. These variables are described in [page:WebGLProgram].
Some of these uniforms or attributes (e.g. those pertaining lighting, fog, etc.) require properties to be set on the material in order for [page:WebGLRenderer] to copy the appropriate values to the GPU---make sure to set these flags if you want to use these features in your own shader.
If you don't want [page:WebGLProgram] to add anything to your shader code, you can use [page:RawShaderMaterial] instead of this class.
Custom attributes and uniforms
Both custom attributes and uniforms must be declared in your GLSL shader code (within *vertexShader* and/or *fragmentShader*). Custom uniforms must be defined in both the *uniforms* property of your *ShaderMaterial*, whereas any custom attributes must be defined via [page:BufferAttribute] instances. Note that *varying*s only need to be declared within the shader code (not within the material).
To declare a custom attribute, please reference the [page:BufferGeometry] page for an overview, and the [page:BufferAttribute] page for a detailed look at the *BufferAttribute* API.
When creating your attributes, each typed array that you create to hold your attribute's data must be a multiple of your data type's size. For example, if your attribute is a [page:Vector3 THREE.Vector3] type, and you have 3000 vertices in your [page:BufferGeometry], your typed array value must be created with a length of 3000 * 3, or 9000 (one value per-component). A table of each data type's size is shown below for reference:
Attribute sizes
| GLSL type |
JavaScript type |
Size |
| float |
[page:Number] |
1 |
| vec2 |
[page:Vector2 THREE.Vector2] |
2 |
| vec3 |
[page:Vector3 THREE.Vector3] |
3 |
| vec3 |
[page:Color THREE.Color] |
3 |
| vec4 |
[page:Vector4 THREE.Vector4] |
4 |
Note that attribute buffers are not refreshed automatically when their values change. To update custom attributes, set the *needsUpdate* flag to true on the [page:BufferAttribute] of the geometry (see [page:BufferGeometry] for further details).
To declare a custom uniform, use the *uniforms* property:
uniforms: {
time: { value: 1.0 },
resolution: { value: new THREE.Vector2() }
}
Each uniform must have a *value* property. The type of the value must correspond to the type of the uniform variable in the GLSL code as specified for the primitive GLSL types in the table below. Uniform structures and arrays are also supported. GLSL arrays of primitive type must either be specified as an array of the corresponding THREE objects or as a flat array containing the data of all the objects. In other words; GLSL primitives in arrays must not be represented by arrays. This rule does not apply transitively. An array of *vec2* arrays, each with a length of five vectors, must be an array of arrays, of either five *THREE.Vector2* objects or ten *number*s.
Uniform types
| GLSL type |
JavaScript type |
| int |
[page:Number] |
| float |
[page:Number] |
| bool |
[page:Boolean] |
| bool |
[page:Number] |
| vec2 |
[page:Vector2 THREE.Vector2] |
| vec2 |
[page:Float32Array Float32Array] (*) |
| vec2 |
[page:Array Array] (*) |
| vec3 |
[page:Vector3 THREE.Vector3] |
| vec3 |
[page:Color THREE.Color] |
| vec3 |
[page:Float32Array Float32Array] (*) |
| vec3 |
[page:Array Array] (*) |
| vec4 |
[page:Vector4 THREE.Vector4] |
| vec4 |
[page:Quaternion THREE.Quaternion] |
| vec4 |
[page:Float32Array Float32Array] (*) |
| vec4 |
[page:Array Array] (*) |
| mat2 |
[page:Float32Array Float32Array] (*) |
| mat2 |
[page:Array Array] (*) |
| mat3 |
[page:Matrix3 THREE.Matrix3] |
| mat3 |
[page:Float32Array Float32Array] (*) |
| mat3 |
[page:Array Array] (*) |
| mat4 |
[page:Matrix3 THREE.Matrix4] |
| mat4 |
[page:Float32Array Float32Array] (*) |
| mat4 |
[page:Array Array] (*) |
| ivec2, bvec2 |
[page:Float32Array Float32Array] (*) |
| ivec2, bvec2 |
[page:Array Array] (*) |
| ivec3, bvec3 |
[page:Int32Array Int32Array] (*) |
| ivec3, bvec3 |
[page:Array Array] (*) |
| ivec4, bvec4 |
[page:Int32Array Int32Array] (*) |
| ivec4, bvec4 |
[page:Array Array] (*) |
| sampler2D |
[page:Texture THREE.Texture] |
| sampler2D |
[page:WebGLRenderTarget THREE.WebGLRenderTarget] |
| samplerCube |
[page:CubeTexture THREE.CubeTexture] |
| samplerCube |
[page:WebGLRenderTargetCube THREE.WebGLRenderTargetCube] |
(*) Same for an (innermost) array (dimension) of the same GLSL type, containing the components of all vectors or matrices in the array.
Constructor
[name]( [page:Object parameters] )
parameters -- An object containing various parameters setting up shaders and their uniforms.
shading — Define shading type. Default is THREE.SmoothShading.
fog — Define whether the material color is affected by global fog settings. Default is true.
wireframe — render geometry as wireframe. Default is false.
wireframeLinewidth — Line thickness. Default is 1.
vertexColors — Define how the vertices gets colored. Default is THREE.NoColors.
skinning — Define whether the material uses skinning. Default is false.
morphTargets — Define whether the material uses morphTargets. Default is false.
Properties
See the base [page:Material] class for common properties.
[property:Object uniforms]
Object specifying the uniforms to be passed to the shader code; keys are uniform names, values are definitions of the form
{ value: 1.0 }
where *value* is the value of the uniform. Names must match the name of the uniform, as defined in the GLSL code. Note that uniforms are refreshed on every frame, so updating the value of the uniform will immediately update the value available to the GLSL code.
[property:Object defines]
Defines custom constants using *#define* directives within the GLSL code for both the vertex shader and the fragment shader; each key/value pair yields another directive:
defines: {
FOO: 15,
BAR: true
}
yields the lines
#define FOO 15
#define BAR true
in the GLSL code.
[property:String vertexShader]
Vertex shader GLSL code. This is the actual code for the shader. In the example above, the *vertexShader* and *fragmentShader* code is extracted from the DOM; it could be passed as a string directly or loaded via AJAX instead.
[property:String fragmentShader]
Fragment shader GLSL code. This is the actual code for the shader. In the example above, the *vertexShader* and *fragmentShader* code is extracted from the DOM; it could be passed as a string directly or loaded via AJAX instead.
[property:Number shading]
Define shading type, which determines whether normals are smoothed between vertices; possible values are [page:Materials THREE.SmoothShading] or [page:Materials THREE.FlatShading]. Default is THREE.SmoothShading.
[property:Number linewidth]
Controls line thickness; only effective if the material is attached to a [page:Line]. Default is 1.
Due to limitations in the
ANGLE layer, on Windows platforms linewidth will always be 1 regardless of the set value.
[property:Boolean wireframe]
Render geometry as wireframe (using GL_LINES instead of GL_TRIANGLES). Default is false (i.e. render as flat polygons).
[property:Number wireframeLinewidth]
Controls wireframe thickness; only effective if the material is attached to a [page:Mesh] and *wireframe* is true. Default is 1.
Due to limitations in the
ANGLE layer, on Windows platforms linewidth will always be 1 regardless of the set value.
[property:Boolean fog]
Define whether the material color is affected by global fog settings; true to pass fog uniforms to the shader. Default is false.
[property:Boolean lights]
Defines whether this material uses lighting; true to pass uniform data related to lighting to this shader. Default is false.
[property:Boolean clipping]
Defines whether this material supports clipping; true to let the renderer pass the clippingPlanes uniform. Default is false.
[property:Number vertexColors]
Define how the vertices are colored, by defining how the *colors* attribute gets populated. Possible values are [page:Materials THREE.NoColors], [page:Materials THREE.FaceColors] and [page:Materials THREE.VertexColors]. Default is THREE.NoColors.
[property:Boolean skinning]
Define whether the material uses skinning; true to pass skinning attributes to the shader. Default is false.
[property:Boolean morphTargets]
Defines whether the material uses morphTargets; true morphTarget attributes to this shader
[property:boolean morphNormals]
Defines whether the material uses morphNormals. Set as true to pass morphNormal attributes from the [page:Geometry]
to the shader. Default is *false*.
[property:WebGLProgram program]
The compiled shader program associated with this material, generated by [page:WebGLRenderer]. You should not need to access this property.
Methods
[method:ShaderMaterial clone]() [page:ShaderMaterial this]
Generates a shallow copy of this material. Note that the vertexShader and fragmentShader are copied by reference, as are the definitions of the *attributes*; this means that clones of the material will share the same compiled [page:WebGLProgram]. However, the *uniforms* are copied by value, which allows you to have different sets of uniforms for different copies of the material.
Source
[link:https://github.com/mrdoob/three.js/blob/master/src/[path].js src/[path].js]


Build
At the end of this chapter you will be able to:
-
Define build
-
Define build templates
-
How to make service use builds for building container images a.k.a source-to -url
Prerequisite
The following checks ensures that each chapter exercises are done with right environment settings.
-
Make sure to be connected to minishift docker daemon and using right openshift client.
eval $(minishift docker-env) && eval $(minishift oc-env)
-
Make sure right OpenShift and kubernetes client are used.
# oc v3.11.0+0cbc58b kubernetes v1.11.0+d4cacc0
oc version
(OR)
# kubernetes v1.11+
kubectl version
-
Make sure right maven version is used (only for Java exercises)
# right maven version Apache Maven 3.5.x or above
./mvnw --version
-
Make sure to be on
knativetutorial
OpenShift project/kubernetes namespace
# verify to return knativetutorial
oc project -q
If you are not on knativetutorial
project, use the command oc project knativetutorial
to change to knativetutorial
project
|
Its required that you have container registry account which is needed during the exercises to push the built container images. You can get one for free at Docker Hub |
As the Knative build and build template spec needs some customizations,such as creating a secret with your container registry credentials; we need to run following commands to create the needed kubernetes and knative resources with all your customizations. All the generated files will be placed in $TUTORIAL_HOME/04-build/knative
.
Generate Docker Secret
A docker kubernetes secret is required for the build to authenticate with the Docker registry so that it can push the built container image to the registry.
Navigate to the tutorial chapter’s templates
folder:
cd $TUTORIAL_HOME/04-build/knative/templates
jsonnet --ext-str user='<your-docker-repo-userid>' \
--ext-str password='<your-docker-repo-user-password>' \
docker-secret.jsonnet \
| yq r - | tee ../docker-secret.yaml
(e.g.)
jsonnet --ext-str user='demo' \
--ext-str password='demopassword' \
docker-secret.jsonnet \
| yq r - | tee ../docker-secret.yaml
The above command will generate a kubernetes secret as shown below:
apiVersion: v1
kind: Secret
metadata:
annotations:
build.knative.dev/docker-0: https://index.docker.io/v1/ (1)
name: basic-user-pass
stringData:
password: demopassword (2)
username: demo (3)
type: kubernetes.io/basic-auth
| 1 | The username and password that are specified as part of this secret will be used for docker container registry available at https://index.docker.io/v1/ |
| 2 | The username to use when authenticating with https://index.docker.io/v1/ |
| 3 | The password to use when authenticating with https://index.docker.io/v1/ |
For more details on why we need to do this please check here.
Generate Knative build spec
The knative build spec is used to create and run the knative builds. You can find more details on Knative build here.
jsonnet --ext-str contextDir='<the build context directory>' \
--ext-str image='<your-docker-image-name>' \
docker-build.jsonnet \
| yq r - | tee ../docker-build.yaml
(e.g.)
jsonnet --ext-str contextDir='java/springboot' \
--ext-str image='docker.io/demo/event-greeter:0.0.1' \
docker-build.jsonnet\
| yq r - | tee ../docker-build.yaml
The above command will generate a knative build specification as shown below:
apiVersion: build.knative.dev/v1alpha1
kind: Build
metadata:
name: docker-build
spec:
serviceAccountName: build-bot
source:
git:
revision: master
url: https://github.com/redhat-developer-demos/knative-tutorial-event-greeter.git (1)
steps: (2)
- args:
- --context=/workspace/java/springboot (3)
- --dockerfile=/workspace/java/springboot/Dockerfile (4)
- --destination=docker.io/demo/event-greeter:0.0.1 (5)
env:
- name: DOCKER_CONFIG
value: /builder/home/.docker
image: gcr.io/kaniko-project/executor
name: docker-push
volumeMounts:
- mountPath: /builder/home/.m2
name: m2-cache
- mountPath: /cache
name: kaniko-cache
workingDir: /workspace/java/springboot
timeout: 20m
volumes:
- name: m2-cache
persistentVolumeClaim:
claimName: m2-cache
- name: kaniko-cache
persistentVolumeClaim:
claimName: kaniko-cache
| 1 | The Git project repository which will be built as part of this build |
| 2 | Each build can have one or more steps, in this case its just one step |
| 3 | Set from containerDir parameter — the sub directory relative to the root of the project from where the build will be run. In this example it is the java/springboot — |
| 4 | The Dockerfile is assumed to be in the root of the context directory |
| 5 | Set from image parameter — the fully qualified container image that will be pushed to the registry — |
Generate Knative service
Run the following command to create the knative service that will use the image created by the docker-build
.
jsonnet --ext-str image='<your-docker-image-name>' \
service.jsonnet\
| yq r - | tee ../service-build.yaml
(e.g)
jsonnet --ext-str image='docker.io/demo/event-greeter:0.0.1' \
service.jsonnet \
| yq r - | tee ../service-build.yaml
apiVersion: serving.knative.dev/v1alpha1
kind: Service
metadata:
name: event-greeter
spec:
runLatest:
configuration:
revisionTemplate:
metadata:
labels:
app: event-greeter
spec:
container:
image: docker.io/demo/event-greeter:0.0.1 (1)
| 1 | Set from image parameter — the fully qualified container image that will be pushed to the registry as part of the build — |
Apply pre-requisite resources
Navigate to the tutorial chapter’s knative
folder:
cd $TUTORIAL_HOME/04-build/knative
Before we create the build and service, we need to create a few kubernetes resources that will be used by the build.
Run the following commands to create the pre-requisite resources:
oc apply -n knativetutorial -f docker-secret.yaml (1)
oc apply -n knativetutorial -f build-sa.yaml (2)
oc apply -n knativetutorial -f m2-pvc.yaml (3)
oc apply -n knativetutorial -f kaniko-pvc.yaml (4)
(OR)
kubectl apply -n knativetutorial -f docker-secret.yaml
kubectl apply -n knativetutorial -f build-sa.yaml
kubectl apply -n knativetutorial -f m2-pvc.yaml
kubectl apply -n knativetutorial -f kaniko-pvc.yaml
| 1 | The kubernetes secret called basic-auth-pass , check here for more details |
| 2 | The kubernetes service account called build-bot that wll run the build. Only build-bot as access to the secret basic-auth-pass . Check here for more details |
| 3 | the kubernetes persistence volume claim used to cache maven artifacts, to make subsequent builds faster |
| 4 | the kubernetes persistence volume claim that will be used to cache docker base images to make subsequent Kaniko docker builds faster |
Create build
oc apply -n knativetutorial -f docker-build.yaml
(OR)
kubectl apply -n knativetutorial -f docker-build.yaml
Watching logs
The Knative build creates an init container for each build step. The init containers will be named like build-step-<step-name>
e.g. if the build step is named "foo" then there will be an init container named "build-step-foo" added to the build pod.
The following command can be used to watch the log of a specific container within a pod,
oc logs -n knativetutorial <pod-name> -c <build-init-container-name>
(OR)
kubectl logs -n knativetutorial <pod-name> -c <build-init-container-name>
(e.g.)
To watch the container logs of container docker-push
within the build pod called docker-build-pod-2a271e
:
oc logs -n knativetutorial docker-build-pod-2a271e -c build-step-docker-push
(OR)
kubectl logs -n knativetutorial docker-build-pod-2a271e -c build-step-docker-push
(e.g.) the following command lists all the init container names of the pod
(OR)
|
Checking build status
A successful build will have the container image pushed to the container registry, going by the examples above you should the image `event-greeter:0.0.1 ` in your container registry.
You can check the status of build as shown below:
$ oc -n knativetutorial get pods
(OR)
$ kubectl -n knativetutorial get pods
The above command should return an output as shown below:
NAME READY STATUS RESTARTS AGE
docker-build-pod-ecc235 0/1 Completed 0 10m
With this you should also be able to do docker pull docker.io/demo/event-greeter:0.0.1
successfully.
Deploy service using Build
With a successful build you are ready to deploy the service, run the following commands to deploy the service:
oc apply -n knativetutorial -f service-build.yaml
(OR)
kubectl apply -n knativetutorial -f service-build.yaml
After successful deployment of the service we should see a kubernetes deployment called event-greeter-00001-deployment
available in the OpenShift dashboard:
Invoke service
INGRESSGATEWAY=istio-ingressgateway
IP_ADDRESS="$(minishift ip):$(kubectl get svc $INGRESSGATEWAY --namespace istio-system --output 'jsonpath={.spec.ports[?(@.port==80)].nodePort}')"
curl -H "Host: event-greeter.knativetutorial.example.com" $IP_ADDRESS \
-H "Content-Type: application/json" \
-d '{"message": "test message"}'
(OR)
http POST $IP_ADDRESS \
'Host: event-greeter.knativetutorial.example.com' \
message="test message"
The above command should return an response like:
{
"message":"test message",
"host":"Event greeter => 'event-greeter-00001-deployment-7446845c4c-f5r79' : 2 ",
"time":"22:10:44"
}
| the actual response’s host and time properties will be based on your environment |
Cleanup
Before proceeding next chapter let us delete the build and service objects that were created.
oc -n knativetutorial delete services.serving.knative.dev event-greeter
oc -n knativetutorial delete builds.build.knative.dev docker-build
(OR)
kubectl -n knativetutorial delete services.serving.knative.dev event-greeter
kubectl -n knativetutorial delete builds.build.knative.dev docker-build


If a customized configuration file is not uploaded prior to running the QC function, the default thresholds and units listed below will be used for the four QC flag tests.
The QC function runs each data point in the input file through the following four ‘flag’ tests.
| Flag Test | Description |
|---|---|
| Unrealistic values (‘Gross range’) | Entries are flagged if values are above or below upper and lower limits |
| Spikes | Entries are flagged if adjacent points change by more than ‘x’ amount |
| Rate of change (RoC) | Entries are flagged if the RoC exceeds a given threshold (e.g., ≥ 3 st dev within 25 hrs) |
| Flat line | Entries are flagged if a certain number of consecutive measurements are within a certain amount of each other (e.g., >10 consecutive temperature measurements are within 0.01 degrees C of one another) |
The ‘flag’ tests were selected based on the following references:
U.S. Integrated Ocean Observing System, 2017. Manual for the Use of Real-Time Oceanographic Data Quality Control Flags, Version 1.1. 43 pp. https://cdn.ioos.noaa.gov/media/2017/12/QARTOD-Data-Flags-Manual_Final_version1.1.pdf
Wagner, R.J., Boulger, R.W., Oblinger, C.J., Smith, B.A., 2006. Guidelines and Standard Procedures for Continuous Water-quality Monitors: Station Operation, Record Computation, and Data Reporting. U.S. Geological Survey Techniques and Methods 1-D3, 51 pp. þ 8 attachments. http://pubs.water.usgs.gov/tm1d3.
R assigns the following flags to each data point:
P = Pass
S = Suspect
F = Fail
X = No Data or Not Applicable (NA)
The default thresholds for temperature are based on data from cool, freshwater, wadeable, medium to high gradient streams in the eastern US. Default thresholds for the hydrologic parameters, turbidity and chlorophyll-a are set very high since values vary widely depending on site-specific factors such as stream size. Once you have one or more years of continuous data at a site, we strongly encourage you to evaluate the performance of the QC test thresholds for each parameter and customize the thresholds as needed by editing the configuration file. For more information, see the ‘Customize QC flag test thresholds’ tab.
Units are an important consideration as well. The default configuration puts hydrologic parameters (like water level) in feet, which is generally preferred for streams among RMN partners. However, with lakes, more people prefer meters. If units are changed from feet to meters but there is not a corresponding change in thresholds, the QC tests lose relevance.
Configuration files can be downloaded here:
Default, with hydrologic parameters in feet
Customized, with hydrologic parameters in meters
This table shows the default QC test thresholds for each parameter as of 06/17/2021.
| Parameter | Units | Flag | Gross | Spike | Rate of Change | Flat Line |
|---|---|---|---|---|---|---|
| Temperature, Water | deg C | Fail | >30 or <-2 | ≥1.5 (±) | >30 consecutive measurements within 0.01 units of one another | |
| Suspect | >25 or <-0.1 | ≥1.0 (±) | ≥3 stdev within 25 hrs | >20 consecutive measurements within 0.01 units of one another | ||
| Temperature, Air | deg C | Fail | >38 or <-25 | ≥10 (±) | >20 consecutive measurements within 0.01 units of one another | |
| Suspect | >35 or <-23 | ≥8 (±) | ≥3 stdev within 25 hrs | >15 consecutive measurements within 0.01 units of one another | ||
| Sensor Depth or Water Level | feet | Fail | > 10^5 or < -1 | ≥5 (±) | >60 consecutive measurements within 0 units of one another | |
| Suspect | > 1000 or < 1 | ≥3 (±) | ≥3 stdev within 25 hrs | >20 consecutive measurements within 0 units of one another | ||
| Discharge | ft3/s | Fail | > 10^5 or < -1 | ≥10^4 (±) | > 60 consecutive measurements within 0.01 units of one another | |
| Suspect | > 1000 or < 1 | ≥10^3 (±) | ≥3 stdev within 25 hrs | > 30 consecutive measurements within 0.01 units of one another | ||
| Dissolved oxygen | mg/L | Fail | > 20 or < 1 | >= 10 (±) | > 60 consecutive measurements within 0.01 units of one another | |
| Suspect | > 18 or < 2 | >= 5 (±) | ≥3 stdev within 25 hrs | > 30 consecutive measurements within 0.01 units of one another | ||
| Conductivity | uS/cm | Fail | > 1500 or < 10 | >= 10 (±) | > 60 consecutive measurements within 0.01 units of one another | |
| Suspect | >1200 or < 20 | >= 5 (±) | ≥3 stdev within 25 hrs | > 30 consecutive measurements within 0.01 units of one another | ||
| pH | SU | Fail | > 12 or < 3 | >= 10 (±) | > 60 consecutive measurements within 0.01 units of one another | |
| Suspect | > 11 or < 4 | >= 5 (±) | ≥3 stdev within 25 hrs | > 30 consecutive measurements within 0.01 units of one another | ||
| Turbidity | NTU | Fail | > 10^5 or < -1 | ≥10^4 (±) | > 60 consecutive measurements within 0.01 units of one another | |
| Suspect | > 1000 or < -1 | ≥10^3 (±) | ≥3 stdev within 25 hrs | > 30 consecutive measurements within 0.01 units of one another | ||
| Pressure, Water | psi | Fail | >17 or <13 | ≥0.7 (±) | >15 consecutive measurements within 0.001 units of one another | |
| Suspect | >16.8 or <13.5 | ≥0.5 (±) | ≥3 stdev within 25 hrs | >10 consecutive measurements within 0.001 units of one another | ||
| Barometric Pressure, Air | psi | Fail | >15 or <13 | ≥0.25 (±) | >15 consecutive measurements within 0.001 units of one another | |
| Suspect | >14.8 or <13 | ≥0.15 (±) | ≥3 stdev within 25 hrs | >10 consecutive measurements within 0.001 units of one another | ||
| Chlorophyll a | g/cm3 | Fail | > 10^5 or < -1 | ≥10^4 (±) | > 60 consecutive measurements within 0.01 units of one another | |
| Suspect | > 1000 or < -1 | ≥10^3 (±) | ≥3 stdev within 25 hrs | > 30 consecutive measurements within 0.01 units of one another |


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