LIBRARY MANAGEMENT SYSTEM
SET REMAINDER
Nabin Jaiswal
B.Tech ECE
Final Year
141114132
Dashboard
Search Database
Set Reminder
History
Review
Log out
You can set a custom reminder here. Once you set a remainder, the system will send you an automated email to remind you before you meet the deadline to submit the books in library. In this way, you can avoid fines.
Send me Reminder before
days


TRY TO FLY THROUGH CIRCLES ASAP W = FORWARD A = LEFT D = RIGHT S = BACKWARD HOW TO: press E to reset currenct game press ESC to have mouse score reduces by 1 per sec CLICK TO PLAY
START THE GAME
Please press ESC to have mouse


{# The default project templates assumes a _base.html template to be extended. #} {% extends '_base.html' %} {% block css %} {# Call base template css block #} {{ super() }} {# Now add our stylesheet using static_url function #} {% endblock %} {# Uncomment the next line to use your skip the Tarbell app and use your own Javascript. #} {# {% block scripts %}{% endblock scripts %} #} {% block content %}
Grab a cup of coffee and tea and started on your new project. This page currently demonstrates basic features of Tarbell and the base template to help get you started. When you're ready to start building your project, edit example-project/templates/index.html
to replace this page.
Below, you'll find some simple Javascript examples and a demonstration of the read_file
helper that repeats project building documentation for your convenience.
The navbar, quote generator, current time, and spinner are all set up by
the default Javascript app associated with this project, which uses
RequireJS to automatically create an environment for app development.
Edit the default app in example-project/static/app.js
.
To disable the default Javascript behavior and do your own thing, add an empty {{ "{% block scripts %}{% endblock scripts %}" }}
to your index.html
template and start populating it with script tags.


5 Using the Erlang Generic Server Back-end
5.1 Introduction
The mapping of OMG IDL to the Erlang programming language when Erlang generic server is the back-end of choice is similar to the one used in the chapter 'OMG IDL Mapping'. The only difference is in the generated code, a client stub and server skeleton to an Erlang gen_server. Orber's User's Guide contain a more detailed description of IDL to Erlang mapping.
5.2 Compiling the Code
The ic:gen/2 function can be called from the command line as follows:
shell> erlc "+{be, erl_genserv}" MyFile.idl
5.3 Writing the Implementation File
For each IDL interface <interface name> defined in the IDL file :
- Create the corresponding Erlang file that will hold the Erlang implementation of the IDL definitions.
- Call the implementation file after the scope of the IDL interface, followed by the suffix _impl.
- Export the implementation functions.
For each function defined in the IDL interface :
- Implement an Erlang function that uses as arguments in the same order, as the input arguments described in the IDL file, and returns the value described in the interface.
- When using the function, follow the mapping described in chapter 2.
5.4 An Example
In this example, a file random.idl generates code for the Erlang gen_server back-end:
// Filename random.idl module rmod { interface random { // Generate a new random number double produce(); // Initialize random generator oneway void init(in long seed1, in long seed2, in long seed3); }; };
When the file "random.idl" is compiled (e.g., shell> erlc "+{be, erl_genserv}" random.idl) five files are produced; two for the top scope, two for the interface scope, and one for the module scope. The header files for top scope and interface are empty and not shown here. In this case, the stub/skeleton file rmod_random.erl is the most important. This module exports two kinds of operations:
- Administrative - used when, for example, creating and terminating the server.
- IDL dependent - operations defined in the IDL specification. In this case, produce and init.
Administrative Operations
To create a new server instance, one of the following functions should be used:
- oe_create/0/1/2 - create a new instance of the object. Accepts Env and RegName, in that order, as parameters. The former is passed uninterpreted to the initialization operation of the call-back module, while the latter must be as the gen_server parameter ServerName. If Env is left out, an empty list will be passed.
- oe_create_link/0/1/2 - similar to oe_create/0/1/2, but create a linked server.
- typeID/0 - returns the scooped id compliant with the OMG standard. In this case the string "IDL:rmod/random:1.0".
- stop/1 - asynchronously terminate the server. The required argument is the return value from any of the start functions.
IDL Dependent Operations
Operations can either be synchronous or asynchronous (i.e., oneway). These are, respectively, mapped to gen_server:call/2/3 and gen_server:cast/2. Consult the gen_server documentation for valid return values.
The IDL dependent operations in this example are listed below. The first argument must be the whatever the create operation returned.
- init(ServerReference, Seed1, Seed2, Seed3) - initialize the random number generator.
- produce(ServerReference) - generate a new random number.
If the compile option timeout is used a timeout must be added (e.g., produce(ServerReference, 5000)). For more information, see the gen_server documentation.
Implementation Module
The implementation module shall, unless the compile option impl is used, be named rmod_random_impl.erl. and could look like this:
-module('rmod_random_impl'). %% Mandatory gen_server operations -export([init/1, terminate/2, code_change/3]). %% Add if 'handle_info' compile option used -export([handle_info/2]). %% API defined in IDL specification -export([produce/1,init/4]). %% Mandatory operations init(Env) -> {ok, []}. terminate(From, Reason) -> ok. code_change(OldVsn, State, Extra) -> {ok, State}. %% Optional handle_info(Info, State) -> {noreply, NewState}. %% IDL specification produce(State) -> case catch random:uniform() of {'EXIT',_} -> {stop, normal, "random:uniform/0 - EXIT", State}; RUnif -> {reply, RUnif, State} end. init(State, S1, S2, S3) -> case catch random:seed(S1, S2, S3) of {'EXIT',_} -> {stop, normal, State}; _ -> {noreply, State} end.
Compile the code and run the example:
1> make:all(). Recompile: rmod_random Recompile: oe_random Recompile: rmod_random_impl up_to_date 2> {ok,R} = rmod_random:oe_create(). {ok,<0.30.0>} 3> rmod_random:init(R, 1, 2, 3). ok 4> rmod_random:produce(R). 1.97963e-4 5>


Welcome
[[${#request.userPrincipal.principal.fullName}]]
List of all supported vulnerabilities
Add new vulnerability
ID
CVE
Name
Description
Severity
Vulnerability ID
CVE
Name
Description
Severity
Edit
Delete
List of user's devices


{HEADER}
Worksheet of employee for a period
Employee
{RABOTNIK}
From
To
Job done per employee and operation
Order name
{ORDERS}
From
To
Job done per order and operation
From
To
Job done per team, order, operation
Team
{BRIGADA}
Order
{ORDERS}
From
To
Payroll for salary
Month
Exclude these operations
{OPERA}
{FOOTER}


| | | Upgrade | |
Quick Edit:
DStarRepeater |
ircDDBGateway |
TimeServer |
MMDVMHost |
DMR GW |
YSF GW |
P25 GW |
NXDN GW
Full Edit:
DMR GW |
PiStar-Remote |
WiFi |
BM API |
DAPNET API |
System Cron |
RSSI Dat
Tools:
CSS Tool |
SSH Access


Island of PathiramanalKerala is truly the land of twisted trails to the world who have just discovered it. We are talking about the Island of Paathiramanal, which is located in the Vembanad Lake. It is one of the many fascinating islands in Kerala. We cannot stop talking about the number of green sceneries you will come across and you will have everything you ever wanted to see: rare birds, swaying coconut palms, whispering waters and whatnot. You will love to come back here once you go visit this place. It is actually a great break from the tiring work you do! So get to see the thick forests, paddy fields, green farmlands on this island. Ideal for: Stressful Professionals who like to take a break from their usual work schedule. Location: 31 km drive from Kottayam. |
|
NadukaniIf you are a patron of ethereally beautiful places, then you need to go visit Nadukani. Another popular hill station near Kottayam, this is a major picnic spot. It offers wonderful views and major landscapes are covered with dense forests. So, you get to see a lot of greenery. Apart from that, Nadukani emerged as one of those new offbeat places which every college-going kid wanted to go and all the couple who wanted to seek some refuge. Ideal for: Family, Youth, and Couples Location: You can take taxis or buses to go to Nadukani from Kottayam. If you have a self-drive vehicle, then that would be great too! |


about
I am a filmmaker and live event producer with a history in web design and IT. I have worked in film and corporate production for fifteen years, for the last five years I have worked in the Bay Area with a focus on corporate communications for healthcare and tech companies throughout the valley. This has included everything from small one man band productions to large broadcast commercials, large scale live product announcements, and trade shows. A number of these projects have included internal live quarterly sales, earnings, and town hall meetings, over the last 11 months all of these projects have shifted to virtual online events and remote productions.
 Lately that has meant homelab server development for digital independence as well as open source live streaming tools for ARM Linux computers.


Competitive Cup Stacking
Tallest Stacking
Fastest Stacking
Competitive Stapling
Fastest Stapling
Weight Stapling
Competitive Cup Stacking
In this new Olympic sport, atheletes will compete in two categories.
Tallest Stacked Cup Monument
Fastest Stack


16.8 Geometry Management
In all the examples so far, we have
made each widget visible by calling method pack on
the widget. This is representative of real-life
Tkinter usage. However, two other layout managers
exist and are sometimes useful. This section covers all three layout
managers provided by the Tkinter module.
Never mix geometry managers for the same container widget: all
children of each given container widget must be handled by the same
geometry manager, or very strange effects (including
Tkinter going into infinite loops) may result.
16.8.1 The Packer
Calling method pack
on a widget delegates widget geometry management to a simple and
flexible layout manager component called the
Packer. The Packer sizes and
positions each widget within a container (parent) widget, according
to each widget's space needs (including options
padx and pady). Each widget
w supplies the following
Packer-related methods.
Delegates geometry management to the
packer. pack_options may include:
- expand
-
When true, w expands to fill any space not
otherwise used in w's
parent.
- fill
-
Determines whether w fills any extra space
allocated to it by the packer, or keeps its own minimal dimensions:
NONE (default), X (fill only
horizontally), Y (fill only vertically), or
BOTH (fill both horizontally and vertically).
- side
-
Determines which side of the parent w
packs against: TOP (default),
BOTTOM, LEFT, or
RIGHT. To avoid confusion, don't
mix different values for option side= in widgets
that are children of the same container. When more than one child
requests the same side (for example TOP), the rule
is first come, first served: the first child packs at the top, the
second child packs second from the top, and so on.
The packer forgets about w.
w remains alive but invisible, and you may
show w again later (by calling
w.pack again, or
perhaps w.grid or
w.place).
Returns a dictionary with the current
pack_options of
w.
16.8.2 The Gridder
Calling method grid
on a widget delegates widget geometry management to a specialized
layout manager component called the Gridder. The
Gridder sizes and positions each widget into cells
of a table (grid) within a container (parent) widget. Each widget
w supplies the following
Gridder-related methods.
Delegates geometry management to the
gridder. grid_options may include:
- column
-
The column to put w in; default
0 (leftmost column).
- columnspan
-
How many columns w occupies; default
1.
- ipadx, ipady
-
How many pixels to pad w, horizontally and
vertically, inside w's
borders.
- padx, pady
-
How many pixels to pad w, horizontally and
vertically, outside w's
borders.
- row
-
The row to put w in; default the first row
that is still empty.
- rowspan
-
How many rows w occupies; default
1.
- sticky
-
What to do if the cell is larger than w.
By default, with sticky='',
w is centered in its cell.
sticky may be the string concatenation of zero or
more of N, E,
S, W, NE,
NW, SE, and
SW, compass directions indicating the sides and
corners of the cell to which w sticks. For
example, sticky=N means that
w sticks to the cell's
top and is centered horizontally, while sticky=N+S
means that w expands vertically to fill
the cell and is centered horizontally.
For example:
import Tkinter
root = Tkinter.Tk( )
for r in range(3):
for c in range(4):
Tkinter.Label(root, text='R%s/C%s'%(r,c),
borderwidth=1 ).grid(row=r,column=c)
root.mainloop( ) displays 12 labels arrayed in a 3 x 4 grid.
The gridder forgets about w.
w remains alive but invisible, and you may
show w again later (by calling
w.grid again, or
perhaps w.pack or
w.place).
Returns a dictionary with the current
grid_options of
w.
16.8.3 The Placer
Calling method place
on a widget explicitly handles widget geometry management, thanks to
a simple layout manager component called the
Placer. The Placer sizes and
positions each widget w within a container
(parent) widget exactly as w explicitly
requires. Other layout managers are usually preferable, but the
Placer can help you implement custom layout
managers. Each widget w supplies the
following Placer-related methods.
Delegates geometry management to the
placer. place_options may include:
- anchor
-
The exact spot of w other options refer
to: may be N, E,
S, W, NE,
NW, SE, or
SW, compass directions indicating the corners and
sides of w; default is
NW (the upper left corner of
w)
- bordermode
-
INSIDE (the default) to indicate that other
options refer to the parent's inside (ignoring the
parent's border); OUTSIDE
otherwise
- height, width
-
Height and width in pixels
- relheight, relwidth
-
Height and width as a float between 0.0 and
1.0, as a fraction of the height and width of the
parent widget
- relx, rely
-
Horizontal and vertical offset as a float between
0.0 and 1.0, as a fraction of
the height and width of the parent widget
- x, y
-
Horizontal and vertical offset in pixels
The placer forgets about
w. w remains
alive but invisible, and you may show w
again later (by calling
w.place again, or
perhaps w.pack or
w.grid).
Returns a dictionary with the current
place_options of
w.
|


The Maestros is an online multiplayer RTS (real-time strategy) game set in a humorous fantasy world where you control a commander and his squadron of wildy cuddly and mildly vicious minions. You can play as either the dieselpunk Knights of Teutonia with their clunky, transforming robots or as the futuristic Alchemists of Regalis with their adorable, mutating monsters.
You amass minions by killing monster NPCs around the map.
Unique Commanders to Fit Any Playstyle
Each commander has special abilities that will help you fight alongside your minions.
Click one to learn more!
Amass Your Army...
Kill monster NPCs to add to your army of minions
Bigger monsters give you more minions
Monsters spawn continuously
...and Transform!
Direct your minions to transform pads scattered across the map to power them up. Each minion can turn into a variety of forms!

FAQ
What is this madness?
Basically, The Maestros is what happens when an FPS momma adopts a weeee little RTS baby and raises it as if it were its own child. You are absolutely controlling a fast-paced, top-down strategy game à la Starcraft, but sometimes you end up with "BOOM HEADSHOT" moments.
It looks like a lot of time has gone into The Maestros. What’s the story here?
Oh, why thank you for noticing, *bats eyelashes* But in all seriousness, this game has been an enormous labor of love for many contributors over numerous years. The Maestros began life as a student project at the University of Southern California. It was majorly inspired by Warcraft 3, and elements of MOBAs as well. From about Sept. 2013 to May 2014, the core of the game was built by a team of students and hobbyists all pitching in what we could and building a fun “first alpha” that we eventually put in front of the public. At the conclusion of the class, most of our core team graduated from their various schools and/or went on to get jobs.  It took almost four more years, but now you can play it for yourself!
Can I play on my 12 year old Idaho Potato of a Computer?
Short answer - probably not. Optimization is usually the last thing that happens in game development. We made some performance improvements though, and you just might be able to squeeze some games out of a pineapple using our “low” graphics settings. We recommend a 1 mbps internet connection, and a modern (<5 year old) nVidia or AMD graphics card.


WebAssembly enables load-time and run-time (dlopen
) dynamic linking in the
MVP by having multiple instantiated modules
share functions, linear memories,
tables and constants
using module imports and exports. In
particular, since all (non-local) state that a module can access can be imported
and exported and thus shared between separate modules’ instances, toolchains
have the building blocks to implement dynamic loaders.
Since the manner in which modules are loaded and instantiated is defined by the host environment (e.g., the JavaScript API), dynamic linking requires use of host-specific functionality to link two modules. At a minimum, the host environment must provide a way to dynamically instantiate modules while connecting exports to imports.
The simplest load-time dynamic linking scheme between modules A and B can be achieved by having module A export functions, tables and memories that are imported by B. A C++ toolchain can expose this functionality by using the same function attributes currently used to export/import symbols from native DSOs/DLLs:
#ifdef _WIN32
# define EXPORT __declspec(dllexport)
# define IMPORT __declspec(dllimport)
#else
# define EXPORT __attribute__ ((visibility ("default")))
# define IMPORT __attribute__ ((visibility ("default")))
#endif
typedef void (**PF)();
IMPORT PF imp();
EXPORT void exp() { (*imp())(); }
This code would, at a minimum, generate a WebAssembly module with imports for:
imp
imp
and exports for:
exp
A more realistic module using libc would have more imports including:
i32
global import for the offset in linear memory to place
global data segments and later use as a constant
base address when loading and storing from globalsi32
global import for the offset into the indirect function
table at which to place the modules’ indirectly called functions and later
compute their indices for address-ofOne extra detail is what to use as the module name for
imports (since WebAssembly has a two-level namespace). One option is to have a
single default module name for all C/C++ imports/exports (which then allows the
toolchain to put implementation-internal names in a separate namespace, avoiding
the need for __
-prefix conventions).
To implement run-time dynamic linking (e.g., dlopen
and dlsym
):
dlopen
would compile and instantiate a new module, storing the compiled
instance in a host-environment table, returning the index to the caller.dlsym
would be given this index, pull the instance out of the table,
search the instances’s exports, append the found function to the function
table (using host-defined functionality in the MVP, but directly from
WebAssembly code in the
future ) and return the
table index of the appended element to the caller.Note that the representation of a C function-pointer in WebAssembly is an index
into a function table, so the above scheme lines up perfectly with the
function-pointer return value of dlsym
.
More complicated dynamic linking functionality (e.g., interposition, weak
symbols, etc) can be simulated efficiently by assigning a function table
index to each weak/mutable symbol, calling the symbol via call_indirect
on that
index, and mutating the underlying element as needed.
After the MVP, we would like to standardize a single ABI per source language, allowing for WebAssembly libraries to interface with each other regardless of compiler. Specifying an ABI requires that all ABI-related future features (like SIMD, multiple return values and exception handling) have been implemented. While it is highly recommended for compilers targeting WebAssembly to adhere to the specified ABI for interoperability, WebAssembly runtimes will be ABI agnostic, so it will be possible to use a non-standard ABI for specialized purposes.


Things fancy cameras cannot help with: setting your 𝑓-stop too low so you don’t get everyone in focus.


| Topanol CA-SF General Information
-
| Synonyms: |
1,1,3-Tris-(2-methyl-4-hydroxy-5-tert-butyl-phenyl)-butane; 4-{1,3-bis[5-(tert-butyl)-4-hydroxy-2-methylphenyl]butyl}-2-(tert-butyl)-5-methylphenol |
| Molecular Formula: |
C37H52O3 |
| CAS Registry No.: |
1843-03-4 |
| Chemical Category: |
Others |
Usage and Performance Characteristics
-
| Manufacturer: |
Seal Sands Chemicals Ltd. |
| Used for Polymeric Materials: |
PVC |
Physical Properties
-
| Specific Gravity: |
0.995 (25°C) |
| Aggregate State at 25°C: |
Crystalline powder |
| Vapor Density, relative to H2: |
>1 |
Optical Properties
-
| Color: |
White to white-off |
Hazard-Related Data
-
| OSHA Hazard Class: |
Not hazardous: 29CFR 1910.1200 (USA); not dangerous 67/548/EEC, point 4 |
| Flash Point Temperature, °C: |
107 |
| HMIS Personal protection: |
Safety goggles, protective gloves, lab. coat |
Environmental Parameters
-
| Octanol-Water Partition Coeff., log Kow: |
12.7 |
| Theoretical Oxygen Demand, ThOD, g/g: |
2.85 |
Animal Toxicity
-
| Rat Oral, LD50, mg/kg: |
>5,000 |
Plasticizers Database (2nd Electronic Edition) Copyright © 2004 by ChemTec Publishing |


Watson Text to Speech Service
Your email
Reason for contacting
Questions
Admiration
Can I get your number?
Message
Send a copy to yourself


Hyper Parameters¶
There are 3 primary hyper parameters :
- DiscreteHyperParameter (DiscreteHP)
- UniformContinuousHyperParameter (UniformHP)
- NormalContinuousHyperParameter (NormalHP)
As their names suggest, each hyper paramter takes specific parameters or value ranges.
-
Discrete Hyper Parmeters takes lists of any python data type that can be serialized.
- This includes numpy values, and even numpy arrays
-
Continuous Hyper Parameters take ranges of values
- Uniform Continuous Hyper Parameters will sample uniformly between the min and max values
- Normal Continuous Hyper Parameters will sample from the normal distribution with the provided mean and standard deviation
A special composite hyper parameter, called HyperParameterList
is used to provide a convenient interface to several
hyper parameters at once, and is used internally by the training algorithm.
Class Information¶
AbstractHyperParameter¶
pyshac.config.hyperparameters.AbstractHyperParameter(name, values, seed)
Abstract Hyper Parameter that defines the methods that all hyperparameters need to supply
Arguments:
- name (str): Name of the hyper parameter
- values (List, None): A list of values (must all be pickle-able and hashable) values or None. If None, it is assumed to be a continuous value generator.
Raises:
- ValueError: If the
name
is not specified.
AbstractHyperParameter methods¶
decode¶
decode(x)
Abstract method that defines how the parameter is decoded so that the model can be properly trained.
Arguments:
- x (int | float): an encoded value that needs to be decoded.
Raises:
- NotImplementedError: Must be overridden by the subclass.
Returns:
a decoded value for the encoded input x
.
encode¶
encode(x)
Abstract method that defines how the parameter is encoded so that the model can properly be trained.
Arguments:
- x (int | float | str): a single value that needs to be encoded.
Raises:
- NotImplementedError: Must be overridden by the subclass.
Returns:
an encoded representation of the value of x
.
get_config¶
get_config()
Creates the config of the class with all of its values.
Returns:
a dictionary with the config of the class.
load_from_config¶
load_from_config(config)
Utilizes the provided config to instantiate a new instance of the class with the same arguments.
Arguments:
- config (dict): A dictionary having keys as the argument names and values as the values specified to the class using its constructor.
Returns:
A new instance of this class with the correct arguments.
sample¶
sample()
Abstract method that defines how parameters are sampled.
Raises:
- NotImplementedError: Must be overridden by the subclass.
Returns:
a singular value sampled from possible values.
set_seed¶
set_seed(seed)
Sets the random seed of the local RNG.
Arguments:
- seed (int | None): Random seed value.
AbstractContinuousHyperParameter¶
pyshac.config.hyperparameters.AbstractContinuousHyperParameter(name, val1, val2, log_encode=False, seed=None)
An abstract hyper parameter that represents a parameter that can take a range of values from a certain distribution.
Arguments:
- name (str): Name of the parameter.
- val1 (float): A symbolic value that is used by subclasses.
- val2 (float): A symbolic value that is used by subclasses.
- log_encode (bool): Determines whether the encoding must be in natural log-space or not.
Raises:
- NotImplementedError: If
sample()
is called.
AbstractContinuousHyperParameter methods¶
decode¶
decode(x)
Decodes the floating point value into normal space if log_space
was set in
the constructor, else returns its original value.
Arguments:
- x (float): a single encoded sample.
Returns:
float.
encode¶
encode(x)
Encodes the floating point value into log space if log_space
was set in
the constructor, else returns its original value.
Arguments:
- x (float): a single sample.
Returns:
float.
get_config¶
get_config()
Creates the config of the class with all of its values.
Returns:
a dictionary with the config of the class.
load_from_config¶
load_from_config(config)
Utilizes the provided config to instantiate a new instance of the class with the same arguments.
Arguments:
- config (dict): A dictionary having keys as the argument names and values as the values specified to the class using its constructor.
Returns:
A new instance of this class with the correct arguments.
sample¶
sample()
Abstract method that must be redefined by base classes.
Returns:
a float value.
set_seed¶
set_seed(seed)
Sets the random seed of the local RNG.
Arguments:
- seed (int | None): Random seed value.
AbstractMultiContinuousHyperParameter¶
pyshac.config.hyperparameters.AbstractMultiContinuousHyperParameter(name, val1, val2, log_encode=False, sample_count=1, seed=None)
An abstract hyper parameter that represents a parameter that can take a range of values from a certain distribution, sampled multiple times.
Arguments:
- name (str): Name of the parameter.
- val1 (float): A symbolic value that is used by subclasses.
- val2 (float): A symbolic value that is used by subclasses.
- log_encode (bool): Determines whether the encoding must be in natural log-space or not.
- sample_count (int): Number of samples that are required from this hyper parameter.
Raises:
- NotImplementedError: If
sample()
is called. - ValueErroe: If sample count is less than 1.
AbstractMultiContinuousHyperParameter methods¶
decode¶
decode(x)
Decodes a list of floating point values into normal space if log_space
was set in the constructor, else returns its original value.
Arguments:
- x (float): a list of encoded samples.
Returns:
list of floats.
encode¶
encode(x)
Encodes a list of floating point values into log space if log_space
was set in the constructor, else returns its original value.
Arguments:
- x (float): a list of samples.
Returns:
list of floats.
get_config¶
get_config()
Creates the config of the class with all of its values.
Returns:
a dictionary with the config of the class.
load_from_config¶
load_from_config(config)
Utilizes the provided config to instantiate a new instance of the class with the same arguments.
Arguments:
- config (dict): A dictionary having keys as the argument names and values as the values specified to the class using its constructor.
Returns:
A new instance of this class with the correct arguments.
sample¶
sample()
Abstract method that must be redefined by base classes.
Returns:
a float value.
set_seed¶
set_seed(seed)
Sets the random seed of the local RNG.
Arguments:
- seed (int | None): Random seed value.
DiscreteHyperParameter¶
pyshac.config.hyperparameters.DiscreteHyperParameter(name, values, seed=None)
Discrete Hyper Parameter that defines a set of discrete values that it can take.
Arguments:
- name (str): Name of the hyper parameter.
- values (list): A list of values (must all be pickle-able and hashable) values or None.
Raises:
- ValueError: If the
name
is not specified. .
Raises:
- ValueError: If the
name
is not specified.
DiscreteHyperParameter methods¶
decode¶
decode(x)
Decodes a single encoded integer into its original value.
Args:
- x (int): an integer encoded value.
Returns:
(int | float | str) representing the actual decoded value.
encode¶
encode(x)
Encodes a single value into an integer index.
Arguments:
- x (int | float | str): A value sampled from its possible values.
Returns:
int value representing its encoded index.
get_config¶
get_config()
Creates the config of the class with all of its values.
Returns:
a dictionary with the config of the class.
load_from_config¶
load_from_config(config)
Utilizes the provided config to instantiate a new instance of the class with the same arguments.
Arguments:
- config (dict): A dictionary having keys as the argument names and values as the values specified to the class using its constructor.
Returns:
A new instance of this class with the correct arguments.
sample¶
sample()
Samples a single value from its set of discrete values.
Returns:
a single value from its list of possible values.
set_seed¶
set_seed(seed)
Sets the random seed of the local RNG.
Arguments:
- seed (int | None): Random seed value.
UniformContinuousHyperParameter¶
pyshac.config.hyperparameters.UniformContinuousHyperParameter(name, min_value, max_value, log_encode=False, seed=None)
A hyper parameter that represents a parameter that can take a range of values from a uniform distribution.
Arguments:
- name (str): Name of the parameter.
- min_value (float): The minimum value (inclusive) that the uniform distribution can take.
- max_value (float): The maximum value (exclusive) that the uniform distribution can take.
- log_encode (bool): Determines whether the encoding must be in natural log-space or not.
UniformContinuousHyperParameter methods¶
decode¶
decode(x)
Decodes the floating point value into normal space if log_space
was set in
the constructor, else returns its original value.
Arguments:
- x (float): a single encoded sample.
Returns:
float.
encode¶
encode(x)
Encodes the floating point value into log space if log_space
was set in
the constructor, else returns its original value.
Arguments:
- x (float): a single sample.
Returns:
float.
get_config¶
get_config()
Creates the config of the class with all of its values.
Returns:
a dictionary with the config of the class.
load_from_config¶
load_from_config(config)
Utilizes the provided config to instantiate a new instance of the class with the same arguments.
Arguments:
- config (dict): A dictionary having keys as the argument names and values as the values specified to the class using its constructor.
Returns:
A new instance of this class with the correct arguments.
sample¶
sample()
Samples uniformly from the range [min_value, max_value).
Returns:
float.
set_seed¶
set_seed(seed)
Sets the random seed of the local RNG.
Arguments:
- seed (int | None): Random seed value.
NormalContinuousHyperParameter¶
pyshac.config.hyperparameters.NormalContinuousHyperParameter(name, mean, std, seed=None)
A hyper parameter that represents a parameter that can take a range of values from a normal distribution.
Arguments:
- name (str): Name of the parameter.
- mean (float): The mean of the normal distribution.
- std (float): The standard deviation of the normal distribution.
NormalContinuousHyperParameter methods¶
decode¶
decode(x)
Decodes the floating point value into normal space if log_space
was set in
the constructor, else returns its original value.
Arguments:
- x (float): a single encoded sample.
Returns:
float.
encode¶
encode(x)
Encodes the floating point value into log space if log_space
was set in
the constructor, else returns its original value.
Arguments:
- x (float): a single sample.
Returns:
float.
get_config¶
get_config()
Creates the config of the class with all of its values.
Returns:
a dictionary with the config of the class.
load_from_config¶
load_from_config(config)
Utilizes the provided config to instantiate a new instance of the class with the same arguments.
Arguments:
- config (dict): A dictionary having keys as the argument names and values as the values specified to the class using its constructor.
Returns:
A new instance of this class with the correct arguments.
sample¶
sample()
Samples from the normal distribution with a mean and standard deviation as specified in the constructor.
Returns:
float.
set_seed¶
set_seed(seed)
Sets the random seed of the local RNG.
Arguments:
- seed (int | None): Random seed value.
MultiDiscreteHyperParameter¶
pyshac.config.hyperparameters.MultiDiscreteHyperParameter(name, values, sample_count=1, seed=None)
Discrete Hyper Parameter that defines a set of discrete values that it can take, and acts upon a list of samples.
Arguments:
- name (str): Name of the hyper parameter.
- values (list): A list of values (must all be pickle-able and hashable) values or None.
- sample_count (int): Number of samples that are required from this hyper parameter.
Raises:
- ValueError: If the
name
is not specified or ifsample_count
is less than 1. .
Raises:
- ValueError: If the
name
is not specified or ifsample_count
is less than 1.
MultiDiscreteHyperParameter methods¶
decode¶
decode(x)
Decodes a list of encoded integers into their original value.
Args:
- x (int): a list of integer encoded values.
Returns:
list of (int | float | str) representing the actual decoded values.
encode¶
encode(x)
Encodes a list of values into a list of the corresponding integer index.
Arguments:
- x (int | float | str): A list of values sampled from its possible values.
Returns:
list of int values representing their encoded index.
get_config¶
get_config()
Creates the config of the class with all of its values.
Returns:
a dictionary with the config of the class.
load_from_config¶
load_from_config(config)
Utilizes the provided config to instantiate a new instance of the class with the same arguments.
Arguments:
- config (dict): A dictionary having keys as the argument names and values as the values specified to the class using its constructor.
Returns:
A new instance of this class with the correct arguments.
sample¶
sample()
Samples a number of values from its set of discrete values.
Returns:
a list of values from its set of possible values.
set_seed¶
set_seed(seed)
Sets the random seed of the local RNG.
Arguments:
- seed (int | None): Random seed value.
MultiUniformContinuousHyperParameter¶
pyshac.config.hyperparameters.MultiUniformContinuousHyperParameter(name, min_value, max_value, log_encode=False, sample_count=1, seed=None)
A hyper parameter that represents a parameter that can take a range of values from a uniform distribution, sampled multiple times.
Arguments:
- name (str): Name of the parameter.
- min_value (float): The minimum value (inclusive) that the uniform distribution can take.
- max_value (float): The maximum value (exclusive) that the uniform distribution can take.
- log_encode (bool): Determines whether the encoding must be in natural log-space or not.
- sample_count (int): Number of samples that are required from this hyper parameter.
Raises:
- ValueErroe: If sample count is less than 1.
MultiUniformContinuousHyperParameter methods¶
decode¶
decode(x)
Decodes a list of floating point values into normal space if log_space
was set in the constructor, else returns its original value.
Arguments:
- x (float): a list of encoded samples.
Returns:
list of floats.
encode¶
encode(x)
Encodes a list of floating point values into log space if log_space
was set in the constructor, else returns its original value.
Arguments:
- x (float): a list of samples.
Returns:
list of floats.
get_config¶
get_config()
Creates the config of the class with all of its values.
Returns:
a dictionary with the config of the class.
load_from_config¶
load_from_config(config)
Utilizes the provided config to instantiate a new instance of the class with the same arguments.
Arguments:
- config (dict): A dictionary having keys as the argument names and values as the values specified to the class using its constructor.
Returns:
A new instance of this class with the correct arguments.
sample¶
sample()
Samples uniformly from the range [min_value, max_value).
Returns:
list of floats.
set_seed¶
set_seed(seed)
Sets the random seed of the local RNG.
Arguments:
- seed (int | None): Random seed value.
MultiDiscreteHyperParameter¶
pyshac.config.hyperparameters.MultiDiscreteHyperParameter(name, values, sample_count=1, seed=None)
Discrete Hyper Parameter that defines a set of discrete values that it can take, and acts upon a list of samples.
Arguments:
- name (str): Name of the hyper parameter.
- values (list): A list of values (must all be pickle-able and hashable) values or None.
- sample_count (int): Number of samples that are required from this hyper parameter.
Raises:
- ValueError: If the
name
is not specified or ifsample_count
is less than 1. .
Raises:
- ValueError: If the
name
is not specified or ifsample_count
is less than 1.
MultiDiscreteHyperParameter methods¶
decode¶
decode(x)
Decodes a list of encoded integers into their original value.
Args:
- x (int): a list of integer encoded values.
Returns:
list of (int | float | str) representing the actual decoded values.
encode¶
encode(x)
Encodes a list of values into a list of the corresponding integer index.
Arguments:
- x (int | float | str): A list of values sampled from its possible values.
Returns:
list of int values representing their encoded index.
get_config¶
get_config()
Creates the config of the class with all of its values.
Returns:
a dictionary with the config of the class.
load_from_config¶
load_from_config(config)
Utilizes the provided config to instantiate a new instance of the class with the same arguments.
Arguments:
- config (dict): A dictionary having keys as the argument names and values as the values specified to the class using its constructor.
Returns:
A new instance of this class with the correct arguments.
sample¶
sample()
Samples a number of values from its set of discrete values.
Returns:
a list of values from its set of possible values.
set_seed¶
set_seed(seed)
Sets the random seed of the local RNG.
Arguments:
- seed (int | None): Random seed value.
HyperParameterList¶
pyshac.config.hyperparameters.HyperParameterList(hyper_parameter_list=None, seed=None)
A composite hyper parameter, that encloses a list of hyper parameters (either discrete or continuous) and provides utility methods for efficient handling by the engine.
Arguments:
- hyper_parameter_list (list(AnstractHyperParameter) | None): A list of hyper parameters or None (which initializes this with 0 elements).
HyperParameterList methods¶
add_hyper_parameter¶
add_hyper_parameter(parameter)
Adds a single hyper parameter (discrete or continuous) to the list of hyper parameters managed by this HyperParameterList.
Arguments:
- parameter (AbstractHyperParameter): a subclass of AbstractHyperParameter, which will be embedded into this composite class.
Raises:
- ValueError: If the passed parameter is
None
, or the name already exists in the list of managed parameters.
remove_hyper_parameter¶
remove_hyper_parameter(parameter)
Removes a single hyper parameter (discrete or continuous) from the list of hyper parameters managed by this HyperParameterList.
Arguments:
- parameter (AbstractHyperParameter, str): A string name or a subclass of AbstractHyperParameter which needs to be removed.
Raises:
- ValueError: If the passed parameter is
None
.
sample¶
sample()
Samples all of its component parameters and returns a list of the samples.
Returns:
list of sampled parameters.
encode¶
encode(x)
Encodes a list of sampled hyper parameters.
Arguments:
- x (list | np.ndarray): A python list or numpy array of samples from the list of hyper parameters.
Raises:
- ValueError: If a numpy array of more than 1 dimension is provided.
Returns:
ndarray(float).
decode¶
decode(x)
Decodes a list of sampled hyper parameters.
Arguments:
x (list(int | float)): a list of encoded integer or floating point values that are to be decoded.
Returns:
list of decoded samples.
get_config¶
get_config()
Creates the config of the class with all of its values.
Returns:
an ordered dictionary with the config of the class.
load_from_config¶
load_from_config(config)
get_parameter_names¶
get_parameter_names()
Gets a list of all the parameter names managed by this class.
Returns:
a list(str) with the names of the parameters.
set_seed¶
set_seed(seed)
Sets the seed of all the parameters held by the container.
Arguments:
- seed (int | None): Seed value for the random state.
get_parameter¶
get_parameter(name)
Utility method to get the hyper parameter class by its name.
Arguments:
- name (str): Name of the class or its alias.
Raises:
- ValueError: If the class with the provided name does not exists in the set of available parameters.
Returns:
The hyper parameter class.
set_custom_parameter_class¶
set_custom_parameter_class(cls)
Utility function to dynamically add a custom hyper parameter to the set of available hyper parameters.
Arguments:
- cls (cls): A class which extends
AbstractHyperParameter
in some way and implements the abstract methods.


{% load static %}
The CodaLab competitions platform and its new (beta) version Codabench are powerful open source frameworks for running scientific competitions and benchmarks, which involve result or code submission. You can either participate in an existing competition (or benchmark) or host one.
Most competitions hosted on such platforms are machine
learning (data science) competitions, but they are NOT
limited to this application domain. They can accommodate
any problem for which a solution can be provided in the
form of a zip archive containing a number of files to be
evaluated quantitatively by a scoring program (provided
by the organizers). The scoring program must return a
numeric score, which is displayed on a leaderboard where
the performances of participants are compared.
Codalab is more geared towards organizing limited-time
competitive events (challenges) and Codabench
is designed for collaborative benchmarking of
algorithms against sets of tasks (or vice-versa)
over a long period of time. Codabench is more flexible
than Codalab and fully backward compatible (old Codalab
challenge bundles can be uploaded to Codabench). But is
is newer and still in a beta version.
Codalab was created in 2013 as a joint venture between
Microsoft and Stanford University. Originally the vision
was to create an ecosystem for conducting computational
research in a more efficient, reproducible, and
collaborative manner, combining worksheets and
competitions. Worksheets capture complex research
pipelines in a reproducible way and create "executable
papers".
In 2014, ChaLearn joined to
co-develop Codalab competitions. Since 2015, University
Paris-Saclay is community lead of Codalab competitions,
under the leadership of Isabelle Guyon,
professor of artificial intelligence. Codalab is
administered by LISN staff,
under the direction of Anne-Catherine
Letournel.
Since 2019 we are developing a new version of Codalab competitions called Codabench, to organize both competitions and benchmarks.
Codalab is used actively in research. In 2020/2021, 600
new challenges were launched. Recent popular challenges
organized with Codalab include the AAAI
2021 Covid-19 fake news detection (over 1000
participants), the COVID-19
infection percentage estimation, the COVID-19
retweet prediction challenge, the ECCV
2020 ChaLearn LAP Fair face recognition challenge,
the 2020 DriveML
Huawei Autonomous Vehicle Challenge. Recurrent
challenges attracting hundreds of participants each year
include the SemEval challenge
series in natural language processing and
the ChaLearn LAP
challenge series in computer vision.
High profile challenges include the 2
million Euro prize of the EU, organized by the See.4C
consortium, the CIKM
AnalytiCup 2017, which attracted 493 participants,
MSCOCO
(633 participants), the ChaLearn
AutoML challenge 2017 (687 participants), and the
Liver
Tumor Segmentation Challenge (over 4000
participants).
Since 2016, Codalab offers the possibility of organizing machine learning challenges with code submission. The simplest machine learning challenges require only the submission of results, which are compared to a solution (or key) by a scoring program. Result submission challenges are less computationally expensive than code submission challenges. However, they offer less possibilities. In particular, code submission allows conducting fair benchmarks by executing submitted code in the same condition for all participants.
Codalab has been providing free resources for challenge
organizers. New since version 1.5: organizers can hook
up their own compute workers to the backend of Codalab
to redirect the code submissions, enabling growth to big
data competitions running at the expense of the
organizers. For very special dedicated projects, Codalab
can be customized since it is an open source project.
Codalab is used in education. For example, a class on Artificial Neural Networks and Deep Learning 2021 with 400 students had homework on Codalab. We implemented a software called Chagrade to help grading homework using Codalab competitions.
Mars 2022: The new Codalab infrastructure is stable. We have migrated the storage over a distributed Minio (4 physical servers, each with 12 disks of 16 TB) spread over 2 buildings for robustness, and added 10 more GPUs to the existing 10 previous ones in the backend. A lot of horsepower to support Industry-strenght challenge. Thanks for the sponsorship of région Ile-de-France, ANR, Université Paris-Saclay, CNRS, INRIA, and ChaLearn. The new server is faster, runs in Python3, and features a renewed organizer interface.
December 2021: Codabench (beta) is announced at NeurIPS 2021.
August 2020: Codalab exceeds 50,000 users, 1000 competitions (over 400 last year), and ~600 submissions per day!
July 2020: We launched a new Learning to Run a Power Network competition, in collaboration with ChaLearn and RTE. We have a robustness and an adaptability track. This is an NeurIPS 2020 competition.
May 2020: We released a new application to help instructors use challenges in the classroom and grade them called Chagrade.
April 2020: The NeurIPS AutoDL challenge ended. But the series of challenges on Automated Deep Learning, in collaboration with ChaLearn, Google Zurich, and 4Paradigm continues with AutoSeries and AutoGraph.
June 2019: We organized a data science bootcamp at Data Science Africa 2019 in the form of a challenge to detect Malaria parasites in microscope images.
May 2019: We launched the first Learning to Run a Power Network competition, in collaboration with ChaLearn and RTE. This is an IJCNN competition.
October 2018: We are preparing a challenge on Automatic Deep Learning (AutoDL) challenging participants to design code eliminating the need of human expertise to choose the architecture and hyper-parameters of deep neural networks. The challenge is co-organized and sponsored by Google. The protocol will be tested on Codalab in October.
September 2018: The LAL and CERN are organizing a challenge to reconstruct particle trajectories in high energy physics detectors. After the success of the first phase with result submission only, a second phase with code submission will be run on Codalab. TrackML is an officially selected challenge of the NIPS 2018 conference.
August 2018: Codalab is proud to host the third challenge on Automatic Machine Learning: Lifelong Machine Learning with drift. AutoML3 is an officially selected challenge of the NIPS 2018 conference.
February 2018: 2 million Euro Big Data EU prize powered by Codalab.
February 2018: Isabelle Guyon presents Codalab at the newly formed Institute of Convergence DataIA
January 2018: Paris-Saclay master students create challenges for L2 students.
January 2018: Paris-Saclay instructors create reinforcement learning homework.
December 2017: Codalab exceeds 10000 users with 480 competitions (145 public)
December 2017: Codalab presented at the Challenges in Machine Learning workshop [slides].
November 2017: Explore the new features: scale up your code submission competition with your own compute workers (full privacy, dockers); organize RL challenges and hook up simulators providing data on demand (with your own "ingestion program"); use the ChaLab wizard to create competitions in minutes.


Preparation time
less than 30 mins
Cooking time
over 2 hours
Serves
Serves 4
Jacob's Ladder, also called short rib, is a tasty cut of beef, here slow-cooked in red wine for a rich juicy steak supper.
For the braised beef preheat the oven to 160C/325F/Gas 3.
Heat a large lidded casserole pot and add the oil. Once hot, add the beef pieces, two at a time, and fry all over to brown them. Remove from the pan and set aside.
Add the pancetta and cook until crisp. Add the whole baby shallots and stir until they have colour on them.
Add the garlic, rosemary and thyme and cook for two more minutes.
Put all the beef back into the pan and add the red wine and heat until the volume of liquid has reduced by half. Add the tomatoes and jus and then cook in the preheated oven with the lid on for 2-3 hours, or until the meat is cooked to your liking.
For the mash, place the potatoes in a pan of cold water and bring to the boil. Cook for 15-20 minutes, drain and then put them back into the pan warm.
Keep on the heat for a minute and mash the potato. Add the butter and cream and mash until smooth.
To cook the baby carrots, place all the ingredients in a pan with 500ml/18fl oz water and cook for 4-6 minutes, or until the carrots are cooked.
When you are ready to serve, remove the casserole dish from the oven, take out the beef pieces and set aside. Heat the casserole on the hob with the lid off and reduce the liquid down in volume by half to a syrupy consistency.
While the sauce is reducing, in a separate pan heat the butter and, once melted and hot, add the wild mushrooms and cook for 2-3 minutes and then sprinkle over the chopped parsley.
Add the mushrooms into the reduced sauce.
Serve the Jacob’s Ladder beef pieces with the mushroom sauce, mashed potato and baby carrots.


|
KlusterKite
0.0.0
A framework to create scalable and redundant services based on awesome Akka.Net project.
|
Description of single published local service More...
Public Member Functions | |
| bool | Equals (ServiceDescription other) |
Equals operator for
| |
| override bool | Equals (object obj) |
| Indicates whether this instance and a specified object are equal. More... | |
| override int | GetHashCode () |
| Returns the hash code for this instance. More... | |
Static Public Member Functions | |
| static bool | operator!= (ServiceDescription left, ServiceDescription right) |
Not equals operator for
| |
| static bool | operator== (ServiceDescription left, ServiceDescription right) |
Equals operator for
| |
Properties | |
| int | ListeningPort [get, set] |
| Gets or sets the port, where web service is listening connections More... | |
| string | LocalHostName [get, set] |
| Gets or sets local hostname that proxy should path More... | |
| string | PublicHostName [get, set] |
| Gets or sets public host name of service More... | |
| string | Route [get, set] |
| Gets or sets route (aka directory) path to service More... | |
Description of single published local service
Definition at line 20 of file ServiceDescription.cs.
| bool KlusterKite.Web.Client.Messages.ServiceDescription.Equals | ( | ServiceDescription | other | ) |
Equals operator for
| other | The object to compare |
Definition at line 80 of file ServiceDescription.cs.
| override bool KlusterKite.Web.Client.Messages.ServiceDescription.Equals | ( | object | obj | ) |
Indicates whether this instance and a specified object are equal.
| obj | The object to compare with the current instance. |
Definition at line 92 of file ServiceDescription.cs.
| override int KlusterKite.Web.Client.Messages.ServiceDescription.GetHashCode | ( | ) |
Returns the hash code for this instance.
Definition at line 109 of file ServiceDescription.cs.
|
static |
Not equals operator for
| left | Left object |
| right | Right object |
Definition at line 59 of file ServiceDescription.cs.
|
static |
Equals operator for
| left | Left object |
| right | Right object |
Definition at line 70 of file ServiceDescription.cs.
|
getset |
Gets or sets the port, where web service is listening connections
Definition at line 26 of file ServiceDescription.cs.
|
getset |
Gets or sets local hostname that proxy should path
This should be used to support virtual hosting inside single node
Definition at line 35 of file ServiceDescription.cs.
|
getset |
Gets or sets public host name of service
It doesn't supposed (but is not prohibited) that this should be real public service hostname. It's just used to distinguish services with identical url paths to be correctly published on frontend web servers. Real expected hostname should be configured in NginxConfigurator or similar publisher
Definition at line 45 of file ServiceDescription.cs.
|
getset |
Gets or sets route (aka directory) path to service
Definition at line 51 of file ServiceDescription.cs.


+++ draft = false date = "2009-11-30T11:57:54-02:00" title = "Odd thing in Django: the date filter takes PHP as reference instead of Python" tags = ["rants", "Django"] created = "2009-11-30T11:48:52-02:00" modified = "2009-11-30T11:58:06-02:00" permaLink = "http://play.pixelblaster.ro/blog/archive/2009/11/30/odd-thing-in-django-the-date-filter-takes-php-as-reference-instead-of-python" author = "Tiberiu Ichim" aliases = [ "/blog/archive/2009/11/30/odd-thing-in-django-the-date-filter-takes-php-as-reference-instead-of-python" ] +++
I wonder what possible explanation there is for the behaviour of the date template filter.
Uses the same format as PHP's date() function (http://php.net/date) with some custom extensions.
I understand where Django comes from, but I think this sort of things should be more aligned with the rest of the Python world.