Tata October 2019 Model-Wise Sales Analysis – Nexon, Harrier, Tiago, Hexa
by Team GaadiWaadiDue to increased sales around Diwali, Tata Motors registered an improved sales performance by its models, with the Safari Storme and Bolt registering the highest growth
Diwali 2019 has brought some relief to most of the car manufacturers as the higher demand for new cars has led to improved sales. Last month, most mainstream carmakers, including Maruti Suzuki, Hyundai, Honda, Tata, etc, have posted healthy growth in sales. Tata Motors, which has a pretty long lineup, witnessed triple-digit growth in demand on MoM basis for Bolt and Safari. Even Tiago and Harrier posted healthy growth figures.
With the discontinuation of the Nano, the Tiago has been the most affordable car in the company’s lineup. It sold 5,460 units last month, which resulted into a 78 per growth on MoM basis. On YoY basis, however, it clocked a 27.7 per cent de-growth. Tigor, the compact sedan sibling, sold 1,152 copies, which is a 56.3 per cent improvement on MoM basis. On YoY basis, however, it experienced a de-growth of 60 per cent.
The Tata Bolt has got a bit too long in the tooth now and will be replaced by the Altroz. Last month, it sold just 24 units. However, this resulted into a whopping 100 per cent growth on MoM basis. Sales fell by 82.5 per cent on YoY basis. Zest, the Bolt’s compact sedan sibling, sold 443 units. This resulted into a 64.1 per cent increase on MoM basis and a 50 per cent drop on YoY basis.
Tata Nexon, the company’s Maruti Vitara Brezza and Hyundai Venue rival, has clocked a monthly sale of 4,438 units This has led to a healthy MoM growth of 56.2 per cent but a marginal 3.7 per cent drop on the YoY basis. The Safari Storme sold 165 copies, thereby clocking an amazing 108.9 per cent growth on MoM basis but a 74.3 de-growth as per YoY calculations.
The Tata Hexa sold 229 units in October 2019. This has resulted into a 54.7 per cent growth on MoM basis but a 69.3 per cent de-growth as per YoY calculations. The Harrier, which is its company’s latest offering, sold 1,258 units – a respectable 33.7 per cent growth from 941 units sold September 2019.
| Model | October 2019 Sales | October 2018 Sales |
| Tata Tiago | 5,460 | 7,549 |
| Tata Nexon | 4,438 | 4,608 |
| Tata Harrier | 1,258 | – |
| Tata Tigor | 1,152 | 2,927 |
| Tata Zest | 443 | 896 |
| Tata Hexa | 229 | 745 |
| Tata Safari Storme | 165 | 642 |
| Tata Bolt | 24 | 137 |
DataSource : AutoPunditz
YoY data for the Harrier isn’t available as the the SUV wasn’t available around this time last year. Going by these numbers, it’s pretty evident that the homegrown carmaker could be on the path to recovery after months of sales slowdown.
Currently, however, the biggest challenge will be to smoothly slow past the period of low demand around the year end. A factor that might help the company here could be the launch of the Tata Nexon EV and the Altroz. The electric Nexon will become the first-ever sub-4-metre SUV to offer an electric powertrain.
On the other hand, the Altroz will be the carmaker’s Maruti Baleno and Hyundai Elite i20 rival and will be the successor to the Bolt. It will be the first model to be underpinned by the ALFA modular architecture and the second model to be designed in accordance with the company’s IMPACT 2.0 design language.
Following the Altroz, Tata Motors will launch launch the Buzzard, essentially a 7-seater version of the Harrier. The upcoming flagship from the company will be powered by Kroyotec170, a more powerful version of the Harrier’s Fiat-sourced engine, and will be even available with an optional automatic transmission.
This company will release the same gearbox-motor combo even on the Harrier around the time BSVI emission norms come into effect. The Buzzard will be followed by a slew of other models like the Hornbill cross-hatch and Blackbird SUV.


#include <fileio.h>
Definition at line 55 of file fileio.h.
◆ Delete()
| bool tesseract::File::Delete |
( |
const char * |
pathname | ) |
|
|
static |
Definition at line 104 of file fileio.cpp.
108 WIN32_FIND_DATA data;
110 HANDLE handle = FindFirstFile(pattern, &data);
111 bool all_deleted =
true;
112 if (handle != INVALID_HANDLE_VALUE) {
113 for (; result; result = FindNextFile(handle, &data)) {
◆ DeleteMatchingFiles()
| bool tesseract::File::DeleteMatchingFiles |
( |
const char * |
pattern | ) |
|
|
static |
◆ JoinPath()
Definition at line 98 of file fileio.cpp.
99 tprintf(
"ERROR: Unable to delete file '%s$: %s\n", pathname,
◆ Open()
◆ OpenOrDie()
Definition at line 58 of file fileio.cpp.
60 tprintf(
"Unable to open '%s' for writing: %s\n",
61 filename.c_str(), strerror(errno));
64 fputs(str.c_str(), stream);
◆ Readable()
| bool tesseract::File::Readable |
( |
const std::string & |
filename | ) |
|
|
static |
Definition at line 80 of file fileio.cpp.
87 return (prefix.empty() || prefix[prefix.size() - 1] ==
'/')
◆ ReadFileToString()
Definition at line 89 of file fileio.cpp.
89 : prefix +
"/" + suffix;
93 #if !defined(_WIN32) || defined(__MINGW32__)
94 const int status = unlink(pathname);
96 const int status = _unlink(pathname);
◆ WriteStringToFileOrDie()
Definition at line 68 of file fileio.cpp.
69 FILE* stream = fopen(filename.c_str(),
"rb");
70 if (stream ==
nullptr) {
78 FILE* stream =
File::Open(filename.c_str(),
"rb");
The documentation for this class was generated from the following files:


[include file=includes/header.html] [include file=includes/sidebar.html]
[maincontent]
[include file=includes/footer.html]
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|
Simbody
3.5
|
TODO: not implemented yet. More...
Public Member Functions | |
| StateLimitedFriction () | |
| virtual | ~StateLimitedFriction () |
| virtual bool | disable (State &state) const =0 |
| Disable the friction constraints if they were enabled. More... | |
| virtual bool | enable (State &state) const =0 |
| Enable the friction constraints if they were disabled. More... | |
| virtual Real | getNormalForceMagnitude (const State &state) const =0 |
| Return the current value of the state-dependent normal force magnitude that limits this friction element. More... | |
| virtual Real | calcEffectiveCOF (const State &state, Real defaultTransitionSpeed, Real slipSpeed) const =0 |
| virtual Real | getSlipSpeed (const State &state) const =0 |
| virtual Vec3 | getPositionInfo (const State &state) const |
| TODO: kludge needed because we're misusing existing constraints. More... | |
| virtual void | setInstanceParameter (State &state, const Vec3 &pos) const |
| TODO: kludge to set instance parameters on internal constraints; this should be the same Vec3 you got from getPositionInfo(). More... | |
| void | setMyIndex (StateLimitedFrictionIndex fx) |
| StateLimitedFrictionIndex | getMyIndex () const |
TODO: not implemented yet.
|
inline |
|
inlinevirtual |
|
pure virtual |
Disable the friction constraints if they were enabled.
Return true if we actually had to disable something.
|
pure virtual |
Enable the friction constraints if they were disabled.
Return true if we actually had to enable something.
|
pure virtual |
Return the current value of the state-dependent normal force magnitude that limits this friction element.
|
pure virtual |
|
inlinevirtual |
TODO: kludge needed because we're misusing existing constraints.
This must be called while Stage::Position is valid.
|
inlinevirtual |
TODO: kludge to set instance parameters on internal constraints; this should be the same Vec3 you got from getPositionInfo().
|
inline |
|
inline |


10 June 1996
Just how fast is FastCGI? How does the performance of a FastCGI application compare with the performance of the same application implemented using a Web server API?
Of course, the answer is that it depends upon the application. A more complete answer is that FastCGI often wins by a significant margin, and seldom loses by very much.
Papers on computer system performance can be laden with complex graphs showing how this varies with that. Seldom do the graphs shed much light on why one system is faster than another. Advertising copy is often even less informative. An ad from one large Web server vendor says that its server "executes web applications up to five times faster than all other servers," but the ad gives little clue where the number "five" came from.
This paper is meant to convey an understanding of the primary factors that influence the performance of Web server applications and to show that architectural differences between FastCGI and server APIs often give an "unfair" performance advantage to FastCGI applications. We run a test that shows a FastCGI application running three times faster than the corresponding Web server API application. Under different conditions this factor might be larger or smaller. We show you what you'd need to measure to figure that out for the situation you face, rather than just saying "we're three times faster" and moving on.
This paper makes no attempt to prove that FastCGI is better than Web server APIs for every application. Web server APIs enable lightweight protocol extensions, such as Open Market's SecureLink extension, to be added to Web servers, as well as allowing other forms of server customization. But APIs are not well matched to mainstream applications such as personalized content or access to corporate databases, because of API drawbacks including high complexity, low security, and limited scalability. FastCGI shines when used for the vast majority of Web applications.
Since this paper is about performance we need to be clear on what "performance" is.
The standard way to measure performance in a request-response system like the Web is to measure peak request throughput subject to a response time constriaint. For instance, a Web server application might be capable of performing 20 requests per second while responding to 90% of the requests in less than 2 seconds.
Response time is a thorny thing to measure on the Web because client communications links to the Internet have widely varying bandwidth. If the client is slow to read the server's response, response time at both the client and the server will go up, and there's nothing the server can do about it. For the purposes of making repeatable measurements the client should have a high-bandwidth communications link to the server.
[Footnote: When designing a Web server application that will be accessed over slow (e.g. 14.4 or even 28.8 kilobit/second modem) channels, pay attention to the simultaneous connections bottleneck. Some servers are limited by design to only 100 or 200 simultaneous connections. If your application sends 50 kilobytes of data to a typical client that can read 2 kilobytes per second, then a request takes 25 seconds to complete. If your server is limited to 100 simultaneous connections, throughput is limited to just 4 requests per second.]
Response time is seldom an issue when load is light, but response times rise quickly as the system approaches a bottleneck on some limited resource. The three resources that typical systems run out of are network I/O, disk I/O, and processor time. If short response time is a goal, it is a good idea to stay at or below 50% load on each of these resources. For instance, if your disk subsystem is capable of delivering 200 I/Os per second, then try to run your application at 100 I/Os per second to avoid having the disk subsystem contribute to slow response times. Through careful management it is possible to succeed in running closer to the edge, but careful management is both difficult and expensive so few systems get it.
If a Web server application is local to the Web server machine, then its internal design has no impact on network I/O. Application design can have a big impact on usage of disk I/O and processor time.
It is a rare Web server application that doesn't run fast when all the information it needs is available in its memory. And if the application doesn't run fast under those conditions, the possible solutions are evident: Tune the processor-hungry parts of the application, install a faster processor, or change the application's functional specification so it doesn't need to do so much work.
The way to make information available in memory is by caching. A cache is an in-memory data structure that contains information that's been read from its permanent home on disk. When the application needs information, it consults the cache, and uses the information if it is there. Otherwise is reads the information from disk and places a copy in the cache. If the cache is full, the application discards some old information before adding the new. When the application needs to change cached information, it changes both the cache entry and the information on disk. That way, if the application crashes, no information is lost; the application just runs more slowly for awhile after restarting, because the cache doesn't improve performance when it is empty.
Caching can reduce both disk I/O and processor time, because reading information from disk uses more processor time than reading it from the cache. Because caching addresses both of the potential bottlenecks, it is the focal point of high-performance Web server application design. CGI applications couldn't perform in-memory caching, because they exited after processing just one request. Web server APIs promised to solve this problem. But how effective is the solution?
Today's most widely deployed Web server APIs are based on a pool-of-processes server model. The Web server consists of a parent process and a pool of child processes. Processes do not share memory. An incoming request is assigned to an idle child at random. The child runs the request to completion before accepting a new request. A typical server has 32 child processes, a large server has 100 or 200.
In-memory caching works very poorly in this server model because processes do not share memory and incoming requests are assigned to processes at random. For instance, to keep a frequently-used file available in memory the server must keep a file copy per child, which wastes memory. When the file is modified all the children need to be notified, which is complex (the APIs don't provide a way to do it).
FastCGI is designed to allow effective in-memory caching. Requests are routed from any child process to a FastCGI application server. The FastCGI application process maintains an in-memory cache.
In some cases a single FastCGI application server won't provide enough performance. FastCGI provides two solutions: session affinity and multi-threading.
With session affinity you run a pool of application processes and the Web server routes requests to individual processes based on any information contained in the request. For instance, the server can route according to the area of content that's been requested, or according to the user. The user might be identified by an application-specific session identifier, by the user ID contained in an Open Market Secure Link ticket, by the Basic Authentication user name, or whatever. Each process maintains its own cache, and session affinity ensures that each incoming request has access to the cache that will speed up processing the most.
With multi-threading you run an application process that is designed to handle several requests at the same time. The threads handling concurrent requests share process memory, so they all have access to the same cache. Multi-threaded programming is complex -- concurrency makes programs difficult to test and debug -- but with FastCGI you can write single threaded or multithreaded applications.
Many Web server applications perform database access. Existing databases contain a lot of valuable information; Web server applications allow companies to give wider access to the information.
Access to database management systems, even within a single machine, is via connection-oriented protocols. An application "logs in" to a database, creating a connection, then performs one or more accesses. Frequently, the cost of creating the database connection is several times the cost of accessing data over an established connection.
To a first approximation database connections are just another type of state to be cached in memory by an application, so the discussion of caching above applies to caching database connections.
But database connections are special in one respect: They are often the basis for database licensing. You pay the database vendor according to the number of concurrent connections the database system can sustain. A 100-connection license costs much more than a 5-connection license. It follows that caching a database connection per Web server child process is not just wasteful of system's hardware resources, it could break your software budget.
We designed a test application to illustrate performance issues. The application represents a class of applications that deliver personalized content. The test application is quite a bit simpler than any real application would be, but still illustrates the main performance issues. We implemented the application using both FastCGI and a current Web server API, and measured the performance of each.
The application is based on a user database and a set of content files. When a user requests a content file, the application performs substitutions in the file using information from the user database. The application then returns the modified content to the user.
Each request accomplishes the following:
Of course, it is fair game to perform caching to shortcut any of these steps.
Each user's database record (including password and attribute values) is approximately 100 bytes long. Each content file is 3,000 bytes long. Both database and content files are stored on disks attached to the server platform.
A typical user makes 10 file accesses with realistic think times (30-60 seconds) between accesses, then disappears for a long time.
The FastCGI application maintains a cache of recently-accessed attribute values from the database. When the cache misses the application reads from the database. Because only a small number of FastCGI application processes are needed, each process opens a database connection on startup and keeps it open.
The FastCGI application is configured as multiple application processes. This is desirable in order to get concurrent application processing during database reads and file reads. Requests are routed to these application processes using FastCGI session affinity keyed on the user id. This way all a user's requests after the first hit in the application's cache.
The API application does not maintain a cache; the API application has no way to share the cache among its processes, so the cache hit rate would be too low to make caching pay. The API application opens and closes a database connection on every request; keeping database connections open between requests would result in an unrealistically large number of database connections open at the same time, and very low utilization of each connection.
The test load is generated by 10 HTTP client processes. The processes represent disjoint sets of users. A process makes a request for a user, then a request for a different user, and so on until it is time for the first user to make another request.
For simplicity the 10 client processes run on the same machine as the Web server. This avoids the possibility that a network bottleneck will obscure the test results. The database system also runs on this machine, as specified in the application scenario.
Response time is not an issue under the test conditions. We just measure throughput.
The API Web server is in these tests is Netscape 1.1.
Here are the test results:
FastCGI 12.0 msec per request = 83 requests per second API 36.6 msec per request = 27 requests per second
Given the big architectural advantage that the FastCGI application enjoys over the API application, it is not surprising that the FastCGI application runs a lot faster. To gain a deeper understanding of these results we measured two more conditions:
API 16.0 msec per request = 61 requests per secondAnswer: Still not as fast as the FastCGI application.
FastCGI 20.1 msec per request = 50 requests per secondAnswer: A very substantial benefit, even though the database access is quite simple.
What these two extra experiments show is that if the API and FastCGI applications are implemented in exactly the same way -- caching database connections but not caching user profile data -- the API application is slightly faster. This is what you'd expect, since the FastCGI application has to pay the cost of inter-process communication not present in the API application.
In the real world the two applications would not be implemented in the same way. FastCGI's architectural advantage results in much higher performance -- a factor of 3 in this test. With a remote database or more expensive database access the factor would be higher. With more substantial processing of the content files the factor would be smaller.
Web servers with a multi-threaded internal structure (and APIs to match) are now starting to become more common. These servers don't have all of the disadvantages described in Section 3. Does this mean that FastCGI's performance advantages will disappear?
A superficial analysis says yes. An API-based application in a single-process, multi-threaded server can maintain caches and database connections the same way a FastCGI application can. The API-based application does not pay for inter-process communication, so the API-based application will be slightly faster than the FastCGI application.
A deeper analysis says no. Multi-threaded programming is complex, because concurrency makes programs much more difficult to test and debug. In the case of multi-threaded programming to Web server APIs, the normal problems with multi-threading are compounded by the lack of isolation between different applications and between the applications and the Web server. With FastCGI you can write programs in the familiar single-threaded style, get all the reliability and maintainability of process isolation, and still get very high performance. If you truly need multi-threading, you can write multi-threaded FastCGI and still isolate your multi-threaded application from other applications and from the server. In short, multi-threading makes Web server APIs unusable for practially all applications, reducing the choice to FastCGI versus CGI. The performance winner in that contest is obviously FastCGI.
Just how fast is FastCGI? The answer: very fast indeed. Not because it has some specially-greased path through the operating system, but because its design is well matched to the needs of most applications. We invite you to make FastCGI the fast, open foundation for your Web server applications.


Valid ALT
Addy Osmani tweeted the other day the following:
Tip: highlight images missing alt text with img:not([alt]) pic.twitter.com/zgjdE5iTU6
— Addy Osmani (@addyosmani) February 2, 2020
This left me thinking, this only provides insight into examples of when the `alt` attribute is missing altogther, but what about use cases for examples in React when the alt text is present and not containing any information. Yes, you are correct in saying this is a valid use case if the image is for decorative, but what about the cases where you are using a component that is setup to output the value based on a property passed in from another component. We can also provide insight into images that are on the page that have an alt attribute but does not contain a value. By adding a small helper in our CSS we can provide a user with a "warning" of images that have alt but no value, so they are able to quicky see if that's correct behaviour for a given image.
By providing a warning style, below you will see an orange border around images that have an alt attribute but no value we can quickly identify what is known as decorative images, and then see if they are all decorative images.
For example in a web application where a developer is using a utility function to display and image/graphic and assumes the alt text is being set based on them passing a value, or even if they are just passing an object of data from a API or other datasource. Example of a CMS, where the UI is built in React and you just pass the image data from the API into your component - the issue here is you are hoping the admin/editor added the alt text in the photo/media library tool.
See the example below, showing how the utility function is set up so the alt value defaults to a blank string. If an API key is missing this accounts for that and will not cause issues.
Code Example
// Simple Image Component
const Image = ({src, alt=""}) => <img src={src} alt={alt} />
// example payload from an API/Datasource
const imageObject = {
src: 'https://picsum.photos/id/233/200/200',
alt: '',
};
// Passing the payload straight to the component for rendering
<Image {...imageObject} />
CSS
// Error - missing alt
img:not([alt]) {
border: 5px solid rgb(255, 0, 0);
}
// Warning - Has alt attribute but no text value
img[alt=''] {
border: 5px solid rgb(255, 165, 0);
}


MinLengthValidationRule
public class MinLengthValidationRule : BaseValidationRule<String?>
Undocumented
-
Undocumented
Declaration
Swift
public let minLength: Int
-
Undocumented
Declaration
Swift
public init(message: String, minLength: Int)
-
Undocumented
Declaration
Swift
public override func validate(value: String?) -> Bool


"Find yourself before finding someone else."
Hello strong, beautiful women of Earth,
Welcome to the wonderful world of "FINDING SOMEONE BETTER".
Let's start off by saying that I am very glad you all made it to this page.

Here's what I can do to help.


Index
Rotation 5: 3D Printing and Scanning
Project Description:
The limitations for the Makerbot Replicator 5th Gen printers:
The size of the object that is being printed is limited to the size of the platform on the 3D printer
Needs to refill the printer ink carthridge
The details on an object can not be too refined because the printing materials are not able to detect small intricate details
The design rules for the Makerbot Replicatior 5th Gen printers:
Use softwares such as Fusion360 and makerbot to design your perfered object for 3D printing
Project Description:
For my second project, I had to combine two 3D files using the Meshmixer software. First, I had my friend scan my face, then I downloaded the pedestal template online on Thingiverse. I combined my head on the pedestal and smoothed out any features that were protruding. I transfered the overall design to Makerbot and exported it so it is ready to be 3D printed.
Project Description:
Maggie Corl and I decided to work together to 3D print something useful using additive manufacturing. We decided to print a web camera cover. A web camera cover is useful because the FBI won't be able to watch our every move when we are on our laptops. The design process was relatively easy and we used the Fusion360 and Makerbot software to make this all possible.
Project Description:
For the project that could not be made subtractively, I decided to make a ring or small bracelet. I used the Fusion360 and makerbot software. On Fusion360, I started off by following a video tutorial online. It was very frustrating to make the ring/bracelet and it took forever for me to properly align the support stand in the circles. In the end, I'm happy with the end result.
Files:


I have heard that a certain prelate in France had a very useful horse; but his brother, a soldier, badly wanted it, to use it in tournaments, and couldn't get it in any way. At last, with many prayers, he got his brother to lend the horse to him for three days. And, approaching one of the prelate chaplain's, his brother carefully began to seek out what sort of words he said most often while he was riding. Now that one sat and thought and then replied: "When my lord is a-horse he says his offices, and I do not know any word that he says more often than what he says in the beginning of every office, that is: 'O God, reach out to aid me." Then the soldier started to ride the horse and, frequently saying the aforesaid words, and as often as he said them he would prick the horse with his spurs and so within the three days he taught the horse so that, everytime he said: "O God, reach out to aid me," the horse, fearing the pricking of the spurs, would buck and bolt, and he could barely restrain it. A little later, when the prelate was riding his horse, his brother went along to see the country. But when the prelate said: "O God, reach out to aid me," the horse began to buck and bolt so that he nearly unseated his rider.  Then the prelate said with deep sorrow: "This horse used to have a smooth gait that was pleasant to me, but now - I know not how it befell him - I grieve that I have lost a good horse, but since it is so, please have it; for it is more fit for soldiers than for prelates." And so he got the horse that he had wished for.


Money Play. How should Red play 43?
pips Red=143 White=117
(CorrectAnswer= 0/0, ErrorScore= 0)
After the hit 13/10* should it be 10/6, 9/5 or 8/4 with the 4?
This could be a blitz but it could also be a prime vs. prime game so dismantling the prime with 9/5 is wrong. Equally well 10/6 doesn’t really improve Red’s position.
With 13 checkers in the zone this could be a blitz so although slightly unusual Red should start the next point with 8/4. if the blot is hit there are multiple return shots and if not it will be full steam ahead on the blitz.
This could be a blitz but it could also be a prime vs. prime game so dismantling the prime with 9/5 is wrong. Equally well 10/6 doesn’t really improve Red’s position.
With 13 checkers in the zone this could be a blitz so although slightly unusual Red should start the next point with 8/4. if the blot is hit there are multiple return shots and if not it will be full steam ahead on the blitz.
1. Cubeful 2-ply 13/10* 8/4 Eq.: +0.085 0.463 0.153 0.005 - 0.537 0.149 0.007 2. Cubeful 2-ply 13/10* 6/2 Eq.: -0.008 (-0.094) 0.432 0.131 0.004 - 0.568 0.147 0.007 3. Cubeful 2-ply 13/10*/6 Eq.: -0.014 (-0.099) 0.418 0.123 0.004 - 0.582 0.116 0.004 4. Cubeful 2-ply 13/10* 9/5 Eq.: -0.014 (-0.099) 0.419 0.128 0.004 - 0.581 0.120 0.005 5. Cubeful 0-ply 13/10* 5/1 Eq.: -0.411 (-0.496) 0.317 0.076 0.002 - 0.683 0.218 0.013


Class Ringmind.Moon
- java.lang.Object
-
- Ringmind.Particle
-
- Ringmind.Moon
-
- All Implemented Interfaces:
Ringmind.Alignable
- Direct Known Subclasses:
Ringmind.ResonantMoon
- Enclosing class:
- Ringmind
class Ringmind.Moon extends Ringmind.Particle implements Ringmind.Alignable
Class Moon- Author:
- Thomas Cann, Sam Hinson
-
-
Field Summary
Fields Modifier and Type Field Description (package private) int
c
(package private) float
GM
(package private) float
moonSizeScale
(package private) float
radius
-
Fields inherited from class Ringmind.Particle
acceleration, position, velocity
-
-
Constructor Summary
Constructors Constructor Description Moon()
Moon(float Gm, float radius, float orb_radius)
Class Constuctor - General Moon object with random angle.Moon(float Gm, float radius, float orb_radius, int c)
Class Constuctor - General Moon object with random angle.Moon(processing.core.PVector p, processing.core.PVector v)
Class Constuctor - Default Moon object with properties of Mima (loosely).
-
Method Summary
All Methods Instance Methods Concrete Methods Deprecated Methods Modifier and Type Method Description Ringmind.Particle
clone()
Clone Method - Return New Object with same properties.void
display()
Deprecated.processing.core.PVector
getAcceleration(Ringmind.RingmindSystem s)
Calculates the acceleration on this particle (based on its current position) (Does not override value of acceleration of particle)boolean
isAligned(Ringmind.Alignable other)
Returns a boolean of true when 2 alignable object are within angular thresholdfloat
kepler_omega(Ringmind.Moon m)
Method to calculate the Keplerian orbital angular frequency (using Kepler's 3rd law).float
moonAngle(Ringmind.Moon m)
Method to get the angle in degrees of the moonfloat
timeToAlignment(Ringmind.Alignable other)
Time taken for two Alignale objects to align.-
Methods inherited from class Ringmind.Particle
getAcceleration, set_getAcceleration, updatePosition, updateVelocity
-
-
-
-
Field Detail
-
GM
float GM
-
radius
float radius
-
c
int c
-
moonSizeScale
final float moonSizeScale
- See Also:
- Constant Field Values
-
-
Constructor Detail
-
Moon
Moon(float Gm, float radius, float orb_radius, int c)
Class Constuctor - General Moon object with random angle.
-
Moon
Moon(float Gm, float radius, float orb_radius)
Class Constuctor - General Moon object with random angle.
-
Moon
Moon(processing.core.PVector p, processing.core.PVector v)
Class Constuctor - Default Moon object with properties of Mima (loosely).
-
Moon
Moon()
-
-
Method Detail
-
display
@Deprecated public void display()
Deprecated.Display Method - Renders this object to screen displaying its position and colour.
-
getAcceleration
public processing.core.PVector getAcceleration(Ringmind.RingmindSystem s)
Calculates the acceleration on this particle (based on its current position) (Does not override value of acceleration of particle)- Parameters:
System
- Object- Returns:
- current acceleration of this moon due to rest of System [ms^(-2)] .
-
clone
public Ringmind.Particle clone()
Clone Method - Return New Object with same properties.- Specified by:
clone
in classRingmind.Particle
- Returns:
- particle object a deep copy of this.
-
isAligned
public boolean isAligned(Ringmind.Alignable other)
Returns a boolean of true when 2 alignable object are within angular threshold- Specified by:
isAligned
in interfaceRingmind.Alignable
- Parameters:
Object
- that implements Alignable.- Returns:
- Returns true when 2 alignable object are within angular threshold.
-
timeToAlignment
public float timeToAlignment(Ringmind.Alignable other)
Time taken for two Alignale objects to align.- Parameters:
Object
- that implements Alignable.- Returns:
- time taken for two Alignale objects to align.
-
kepler_omega
public float kepler_omega(Ringmind.Moon m)
Method to calculate the Keplerian orbital angular frequency (using Kepler's 3rd law).- Parameters:
r
- Radial position (semi-major axis) to calculate the period [m].- Returns:
- The angular frequency [radians/s].
-
moonAngle
public float moonAngle(Ringmind.Moon m)
Method to get the angle in degrees of the moon- Parameters:
m
- Moon Object.- Returns:
- [degrees].
-
-


import collections
import contextlib
import functools
import logging
import numbers
import sys
import threading
from datetime import timedelta
import torch
import torch.distributed as dist
from . import (
RpcBackendOptions,
WorkerInfo,
_cleanup_python_rpc_handler,
_delete_all_user_rrefs,
_destroy_rref_context,
_get_current_rpc_agent,
_invoke_remote_builtin,
_invoke_remote_python_udf,
_invoke_remote_torchscript,
_invoke_rpc_builtin,
_invoke_rpc_python_udf,
_invoke_rpc_torchscript,
_is_current_rpc_agent_set,
_reset_current_rpc_agent,
_set_and_start_rpc_agent,
_set_rpc_timeout,
backend_registry,
)
from .internal import (
PythonUDF,
RPCExecMode,
_internal_rpc_pickler,
_start_record_function,
)
logger = logging.getLogger(__name__)
# NB: Ignoring RRef leaks during shutdown. Without this, applications have to
# make sure there is no references to any RRef in the application code and
# Python GC has done its job to delete those RRefs. This is could result in bad
# debugging experiences especially when for large applications. Therefore, by
# default, we are going to ignore RRef leaks during shutdown. This is usually
# fine as shutdown means applications have done training and no longer care
# about states.
#
# To enable RRef leak checking, set this _ignore_rref_leak to False
_ignore_rref_leak = True
_default_pickler = _internal_rpc_pickler
@contextlib.contextmanager
def _use_rpc_pickler(rpc_pickler):
r"""
rpc_pickler: (.internal._InternalRPCPickler) Overrides the default RPC pickler
"""
global _default_pickler
_default_pickler = rpc_pickler
try:
yield
finally:
_default_pickler = _internal_rpc_pickler
def _require_initialized(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
if not _is_current_rpc_agent_set():
raise RuntimeError(
"RPC has not been initialized. Call "
"torch.distributed.rpc.init_rpc first."
)
return func(*args, **kwargs)
return wrapper
class WaitAllWorkersStates(object):
def __init__(self):
# Each `intent_worker_names` is an empty set at beginning.
# It's only used by leader worker. Leader worker is user-specified or
# elected as the first worker in a sorted worker name list.
# Whenever there is a worker showing shutdown intention to the leader, by
# calling `_wait_all_workers()`, the leader adds this worker's name to the set.
# The leader also adds itself's name to the set on calling
# `_wait_all_workers()`. We need this because, we confine `_wait_all_workers()`
# to be called only once, by examing if leader's name has been added to the set.
self.intent_worker_names = set()
# Once `intent_worker_names == _ALL_WORKER_NAMES`,
# we flip `_SHUTDOWN_PROCEED_SIGNAL` on the leader, and leader will send RPCs
# to follower workers to flip their `_SHUTDOWN_PROCEED_SIGNAL`s.
self.proceed_signal = threading.Event()
# States used by `def _wait_all_workers()`.
# `_ALL_WORKER_NAMES` is initialized on initiaizing RPC layer.
_ALL_WORKER_NAMES = None
_wait_all_workers_dict_lock = threading.Lock()
_wait_all_workers_sequence_id = 0
_wait_all_workers_sequence_id_to_states = collections.defaultdict(WaitAllWorkersStates)
def _on_leader_follower_report_shutdown_intent(sequence_id, worker_name):
assert (
worker_name in _ALL_WORKER_NAMES
), "{worker_name} is not expected by leader.".format(worker_name=worker_name)
intent_worker_names = _wait_all_workers_sequence_id_to_states[
sequence_id
].intent_worker_names
assert (
worker_name not in intent_worker_names
), "{worker_name} reported intent sequence id {sequence_id} twice. ".format(
worker_name=worker_name, sequence_id=sequence_id
)
intent_worker_names.add(worker_name)
if _ALL_WORKER_NAMES == intent_worker_names:
_set_proceed_shutdown_signal(sequence_id)
def _set_proceed_shutdown_signal(sequence_id):
proceed_signal = _wait_all_workers_sequence_id_to_states[sequence_id].proceed_signal
assert (
not proceed_signal.is_set()
), "Termination signal sequence id {} got set twice.".format(
sequence_id=sequence_id
)
proceed_signal.set()
@_require_initialized
def _wait_all_workers():
r"""
Block until all local and remote RPC processes reach this method and wait
for all outstanding work to complete. Every RPC process must call this
method before exit to perform a graceful shutdown. This should be used to
terminate the RPC framework, and there is no guarantee that the RPC
framework will work after this method returns.
"""
assert (
_ALL_WORKER_NAMES is not None
), "`_ALL_WORKER_NAMES` is not initialized for `def _wait_all_workers`."
leader_worker_name = sorted(_ALL_WORKER_NAMES)[0]
self_worker_name = _get_current_rpc_agent().get_worker_info().name
global _wait_all_workers_sequence_id
with _wait_all_workers_dict_lock:
sequence_id = _wait_all_workers_sequence_id
_wait_all_workers_sequence_id += 1
is_leader_worker = leader_worker_name == self_worker_name
# Phase 1: Followers send intents.
# All followers report intents to the leader.
if is_leader_worker:
_on_leader_follower_report_shutdown_intent(sequence_id, self_worker_name)
else:
rpc_sync(
leader_worker_name,
_on_leader_follower_report_shutdown_intent,
args=(sequence_id, self_worker_name,),
)
proceed_signal = _wait_all_workers_sequence_id_to_states[
sequence_id
].proceed_signal
proceed_signal.wait()
# Phase 2: Leader asks followers to proceed.
# Leader's signal is the first to be unblocked,
# after receiving all followers' intents.
if is_leader_worker:
# The leader sends out proceeed signals to all followers.
timeout = timedelta(seconds=5)
_set_rpc_timeout(timeout)
worker_name_to_response_future_dict = dict()
for follower_worker_name in _ALL_WORKER_NAMES - {leader_worker_name}:
fut = rpc_async(follower_worker_name, _set_proceed_shutdown_signal, args=(sequence_id,))
worker_name_to_response_future_dict[follower_worker_name] = fut
for follower_worker_name, fut in worker_name_to_response_future_dict.items():
try:
fut.wait()
except RuntimeError as ex:
logger.error(
"{worker_name} failed to respond to 'Shutdown Proceed.' request in {timeout}".format(
worker_name=follower_worker_name, timeout=timeout
)
)
[docs]@_require_initialized
def shutdown(graceful=True):
r"""
Perform a shutdown of the RPC agent, and then destroy the RPC agent. This
stops the local agent from accepting outstanding requests, and shuts
down the RPC framework by terminating all RPC threads. If ``graceful=True``,
this will block until all local and remote RPC processes reach this method
and wait for all outstanding work to complete. Otherwise, if
``graceful=False``, this is a local shutdown, and it does not wait for other
RPC processes to reach this method.
Arguments:
graceful (bool): Whether to do a graceful shutdown or not. If True,
this will 1) wait until there is no pending system
messages for ``UserRRefs`` and delete them; 2) block
until all local and remote RPC processes have reached
this method and wait for all outstanding work to
complete.
Example::
Make sure that ``MASTER_ADDRESS`` and ``MASTER_PORT`` are set properly
on both workers. Refer to :meth:`~torch.distributed.init_process_group`
API for more details. For example,
>>> export MASTER_ADDRESS=localhost
>>> export MASTER_port=5678
Then run the following code in two different processes:
>>> # On worker 0:
>>> import torch
>>> import torch.distributed.rpc as rpc
>>> rpc.init_rpc("worker0", rank=0, world_size=2)
>>> # do some work
>>> result = rpc.rpc_sync("worker1", torch.add, args=(torch.ones(1), 1))
>>> # ready to shutdown
>>> rpc.shutdown()
>>> # On worker 1:
>>> import torch.distributed.rpc as rpc
>>> rpc.init_rpc("worker1", rank=1, world_size=2)
>>> # wait for worker 0 to finish work, and then shutdown.
>>> rpc.shutdown()
"""
if graceful:
_wait_all_workers()
_delete_all_user_rrefs()
_get_current_rpc_agent().join()
try:
# This raises a `TORCH_CHECK()` exception on RRef leak detected.
_destroy_rref_context(_ignore_rref_leak)
finally:
_get_current_rpc_agent().shutdown()
# clean up python rpc handler in shutdown(), see comments in
# PythonRpcHandler::cleanup(), call it in python API because the
# cleanup() function has python dependency, it assumes python
# interpreter exists.
# No matter if RRef leak exception is raised, this clean-up code
# must run to avoid destruction segfault in Python 3.5.
_cleanup_python_rpc_handler()
_reset_current_rpc_agent()
# TODO: add a context manager to wrap _init_rpc_backend and shutdown
def _init_rpc_backend(
backend=backend_registry.BackendType.PROCESS_GROUP,
store=None,
name=None,
rank=-1,
world_size=-1,
rpc_backend_options=None,
):
if sys.version_info < (3, 0):
raise RuntimeError("RPC package does not support Python2.")
_validate_rpc_args(backend, store, name, rank, world_size, rpc_backend_options)
if _is_current_rpc_agent_set():
raise RuntimeError("RPC is already initialized")
# Initialize RPC.
rpc_agent = backend_registry.init_backend(
backend,
store=store,
name=name,
rank=rank,
world_size=world_size,
rpc_backend_options=rpc_backend_options,
)
worker_infos = rpc_agent.get_worker_infos()
global _ALL_WORKER_NAMES
_ALL_WORKER_NAMES = {worker_info.name for worker_info in worker_infos}
_set_and_start_rpc_agent(rpc_agent)
[docs]@_require_initialized
def get_worker_info(worker_name=None):
r"""
Get :class:`~torch.distributed.rpc.WorkerInfo` of a given worker name.
Use this :class:`~torch.distributed.rpc.WorkerInfo` to avoid passing an
expensive string on every invocation.
Arguments:
worker_name (str): the string name of a worker. If ``None``, return the
the id of the current worker. (default ``None``)
Returns:
:class:`~torch.distributed.rpc.WorkerInfo` instance for the given
``worker_name`` or :class:`~torch.distributed.rpc.WorkerInfo` of the
current worker if ``worker_name`` is ``None``.
"""
if worker_name:
return _get_current_rpc_agent().get_worker_info(worker_name)
else:
return _get_current_rpc_agent().get_worker_info()
def _to_worker_info(name_or_info):
if isinstance(name_or_info, WorkerInfo):
return name_or_info
elif isinstance(name_or_info, str):
return get_worker_info(name_or_info)
else:
raise ValueError("Cannot get WorkerInfo from name {}".format(name_or_info))
def _validate_rpc_args(backend, store, name, rank, world_size, rpc_backend_options):
type_mapping = {
backend: backend_registry.BackendType,
store: dist.Store,
name: str,
rank: numbers.Integral,
world_size: numbers.Integral,
rpc_backend_options: RpcBackendOptions,
}
for arg, arg_type in type_mapping.items():
if not isinstance(arg, arg_type):
raise RuntimeError(
"Argument {} must be of type {} but got type {}".format(
arg, arg_type, type(arg)
)
)
[docs]@_require_initialized
def remote(to, func, args=None, kwargs=None):
r"""
Make a remote call to run ``func`` on worker ``to`` and return an
:class:`~torch.distributed.rpc.RRef` to the result value immediately.
Worker ``to`` will be the owner of the returned
:class:`~torch.distributed.rpc.RRef`, and the worker calling ``remote`` is
a user. The owner manages the global reference count of its
:class:`~torch.distributed.rpc.RRef`, and the owner
:class:`~torch.distributed.rpc.RRef` is only destructed when globally there
are no living references to it.
Arguments:
to (str or WorkerInfo): id or name of the destination worker.
func (callable): a callable function, such as Python callables, builtin
operators (e.g. :meth:`~torch.add`) and annotated
TorchScript functions.
args (tuple): the argument tuple for the ``func`` invocation.
kwargs (dict): is a dictionary of keyword arguments for the ``func``
invocation.
Returns:
A user :class:`~torch.distributed.rpc.RRef` instance to the result
value. Use the blocking API :meth:`torch.distributed.rpc.RRef.to_here`
to retrieve the result value locally.
.. warning ::
Using GPU tensors as arguments or return values of ``func`` is not
supported since we don't support sending GPU tensors over the wire. You
need to explicitly copy GPU tensors to CPU before using them as
arguments or return values of ``func``.
.. warning ::
The ``remote`` API does not copy storages of argument tensors until
sending them over the wire, which could be done by a different thread
depending on the RPC backend type. The caller should make sure that the
contents of those tensors stay intact until the returned RRef is
confirmed by the owner, which can be checked using the
:meth:`torch.distributed.rpc.RRef.confirmed_by_owner` API.
Example::
Make sure that ``MASTER_ADDRESS`` and ``MASTER_PORT`` are set properly
on both workers. Refer to :meth:`~torch.distributed.init_process_group`
API for more details. For example,
>>> export MASTER_ADDRESS=localhost
>>> export MASTER_port=5678
Then run the following code in two different processes:
>>> # On worker 0:
>>> import torch
>>> import torch.distributed.rpc as rpc
>>> rpc.init_rpc("worker0", rank=0, world_size=2)
>>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3))
>>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1))
>>> x = rref1.to_here() + rref2.to_here()
>>> rpc.shutdown()
>>> # On worker 1:
>>> import torch.distributed.rpc as rpc
>>> rpc.init_rpc("worker1", rank=1, world_size=2)
>>> rpc.shutdown()
Below is an example of running a TorchScript function using RPC.
>>> # On both workers:
>>> @torch.jit.script
>>> def my_script_add(t1, t2):
>>> return torch.add(t1, t2)
>>> # On worker 0:
>>> import torch.distributed.rpc as rpc
>>> rpc.init_rpc("worker0", rank=0, world_size=2)
>>> rref = rpc.remote("worker1", my_script_add, args=(torch.ones(2), 3))
>>> rref.to_here()
>>> rpc.shutdown()
>>> # On worker 1:
>>> import torch.distributed.rpc as rpc
>>> rpc.init_rpc("worker1", rank=1, world_size=2)
>>> rpc.shutdown()
"""
qualified_name = torch.jit._find_builtin(func)
dst_worker_info = _to_worker_info(to)
# If profiling is enabled, kick off the timer and retrieve back a
# RecordFunction instance.
rf = None
if torch.autograd._profiler_enabled():
rf = _start_record_function(
RPCExecMode.REMOTE,
str(qualified_name) if qualified_name is not None else func.__qualname__,
get_worker_info().name,
dst_worker_info.name,
)
args = args if args else ()
kwargs = kwargs if kwargs else {}
if qualified_name is not None:
return _invoke_remote_builtin(dst_worker_info, qualified_name, rf, *args, **kwargs)
elif isinstance(func, torch.jit.ScriptFunction):
return _invoke_remote_torchscript(
dst_worker_info.name, torch._jit_internal._qualified_name(func), *args, **kwargs
)
else:
(pickled_python_udf, tensors) = _default_pickler.serialize(
PythonUDF(func, args, kwargs)
)
return _invoke_remote_python_udf(dst_worker_info, pickled_python_udf, tensors, rf)
def _invoke_rpc(to, func, rpc_type, args=None, kwargs=None):
if not callable(func):
raise TypeError("function should be callable.")
qualified_name = torch.jit._find_builtin(func)
dst_worker_info = _to_worker_info(to)
# If profiling is enabled, kick off the timer and retrieve back a
# RecordFunction instance.
rf = None
if torch.autograd._profiler_enabled():
rf = _start_record_function(
rpc_type,
str(qualified_name) if qualified_name is not None else func.__qualname__,
get_worker_info().name,
dst_worker_info.name,
)
args = args if args else ()
kwargs = kwargs if kwargs else {}
if qualified_name is not None:
fut = _invoke_rpc_builtin(dst_worker_info, qualified_name, rf, *args, **kwargs)
elif isinstance(func, torch.jit.ScriptFunction):
fut = _invoke_rpc_torchscript(dst_worker_info.name, func, args, kwargs)
else:
(pickled_python_udf, tensors) = _default_pickler.serialize(
PythonUDF(func, args, kwargs)
)
fut = _invoke_rpc_python_udf(dst_worker_info, pickled_python_udf, tensors, rf)
return fut
[docs]@_require_initialized
def rpc_sync(to, func, args=None, kwargs=None):
r"""
Make a blocking RPC call to run function ``func`` on worker ``to``. RPC
messages are sent and received in parallel to execution of Python code. This
method is thread-safe.
Arguments:
to (str or WorkerInfo): id or name of the destination worker.
func (callable): a callable function, such as Python callables, builtin
operators (e.g. :meth:`~torch.add`) and annotated
TorchScript functions.
args (tuple): the argument tuple for the ``func`` invocation.
kwargs (dict): is a dictionary of keyword arguments for the ``func``
invocation.
Returns:
Returns the result of running ``func`` with ``args`` and ``kwargs``.
.. warning ::
Using GPU tensors as arguments or return values of ``func`` is not
supported since we don't support sending GPU tensors over the wire. You
need to explicitly copy GPU tensors to CPU before using them as
arguments or return values of ``func``.
Example::
Make sure that ``MASTER_ADDRESS`` and ``MASTER_PORT`` are set properly
on both workers. Refer to :meth:`~torch.distributed.init_process_group`
API for more details. For example,
>>> export MASTER_ADDRESS=localhost
>>> export MASTER_port=5678
Then run the following code in two different processes:
>>> # On worker 0:
>>> import torch
>>> import torch.distributed.rpc as rpc
>>> rpc.init_rpc("worker0", rank=0, world_size=2)
>>> ret = rpc.rpc_sync("worker1", torch.add, args=(torch.ones(2), 3))
>>> rpc.shutdown()
>>> # On worker 1:
>>> import torch.distributed.rpc as rpc
>>> rpc.init_rpc("worker1", rank=1, world_size=2)
>>> rpc.shutdown()
Below is an example of running a TorchScript function using RPC.
>>> # On both workers:
>>> @torch.jit.script
>>> def my_script_add(t1, t2):
>>> return torch.add(t1, t2)
>>> # On worker 0:
>>> import torch.distributed.rpc as rpc
>>> rpc.init_rpc("worker0", rank=0, world_size=2)
>>> ret = rpc.rpc_sync("worker1", my_script_add, args=(torch.ones(2), 3))
>>> rpc.shutdown()
>>> # On worker 1:
>>> import torch.distributed.rpc as rpc
>>> rpc.init_rpc("worker1", rank=1, world_size=2)
>>> rpc.shutdown()
"""
fut = _invoke_rpc(to, func, RPCExecMode.SYNC, args, kwargs)
return fut.wait()
[docs]@_require_initialized
def rpc_async(to, func, args=None, kwargs=None):
r"""
Make a non-blocking RPC call to run function ``func`` on worker ``to``. RPC
messages are sent and received in parallel to execution of Python code. This
method is thread-safe. This method will immediately return a Future that can
be awaited on.
Arguments:
to (str or WorkerInfo): id or name of the destination worker.
func (callable): a callable function, such as Python callables, builtin
operators (e.g. :meth:`~torch.add`) and annotated
TorchScript functions.
args (tuple): the argument tuple for the ``func`` invocation.
kwargs (dict): is a dictionary of keyword arguments for the ``func``
invocation.
Returns:
Returns a Future object that can be waited
on. When completed, the return value of ``func`` on ``args`` and
``kwargs`` can be retrieved from the Future object.
.. warning ::
Using GPU tensors as arguments or return values of ``func`` is not
supported since we don't support sending GPU tensors over the wire. You
need to explicitly copy GPU tensors to CPU before using them as
arguments or return values of ``func``.
.. warning ::
The ``rpc_async`` API does not copy storages of argument tensors until
sending them over the wire, which could be done by a different thread
depending on the RPC backend type. The caller should make sure that the
contents of those tensors stay intact until the returned Future
completes.
Example::
Make sure that ``MASTER_ADDRESS`` and ``MASTER_PORT`` are set properly
on both workers. Refer to :meth:`~torch.distributed.init_process_group`
API for more details. For example,
>>> export MASTER_ADDRESS=localhost
>>> export MASTER_port=5678
Then run the following code in two different processes:
>>> # On worker 0:
>>> import torch
>>> import torch.distributed.rpc as rpc
>>> rpc.init_rpc("worker0", rank=0, world_size=2)
>>> fut1 = rpc.rpc_async("worker1", torch.add, args=(torch.ones(2), 3))
>>> fut2 = rpc.rpc_async("worker1", min, args=(1, 2))
>>> result = fut1.wait() + fut2.wait()
>>> rpc.shutdown()
>>> # On worker 1:
>>> import torch.distributed.rpc as rpc
>>> rpc.init_rpc("worker1", rank=1, world_size=2)
>>> rpc.shutdown()
Below is an example of running a TorchScript function using RPC.
>>> # On both workers:
>>> @torch.jit.script
>>> def my_script_add(t1, t2):
>>> return torch.add(t1, t2)
>>> # On worker 0:
>>> import torch.distributed.rpc as rpc
>>> rpc.init_rpc("worker0", rank=0, world_size=2)
>>> fut = rpc.rpc_async("worker1", my_script_add, args=(torch.ones(2), 3))
>>> ret = fut.wait()
>>> rpc.shutdown()
>>> # On worker 1:
>>> import torch.distributed.rpc as rpc
>>> rpc.init_rpc("worker1", rank=1, world_size=2)
>>> rpc.shutdown()
"""
return _invoke_rpc(to, func, RPCExecMode.ASYNC, args, kwargs)


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|
RTM for Unity3D
Agora Realtime Messaging for Unity3d
|
#import <Foundation/Foundation.h>
Go to the source code of this file.
Classes | |
| protocol | <AgoraRtmCallDelegate> |
| The AgoraRtmCallDelegate protocol enables Agora RTM call callback event notifications to your app. More... | |
Typedefs | |
| typedef void(^ | AgoraRtmLocalInvitationSendBlock) (AgoraRtmInvitationApiCallErrorCode errorCode) |
| Returns the result of the [sendLocalInvitation]([AgoraRtmCallKit sendLocalInvitation:completion:]) method call. More... | |
| typedef void(^ | AgoraRtmRemoteInvitationAcceptBlock) (AgoraRtmInvitationApiCallErrorCode errorCode) |
| Returns the result of the [acceptRemoteInvitation]([AgoraRtmCallKit acceptRemoteInvitation:completion:]) method call. More... | |
| typedef void(^ | AgoraRtmRemoteInvitationRefuseBlock) (AgoraRtmInvitationApiCallErrorCode errorCode) |
| Returns the result of the [refuseRemoteInvitation]([AgoraRtmCallKit refuseRemoteInvitation:completion:]) method call. More... | |
| typedef void(^ | AgoraRtmLocalInvitationCancelBlock) (AgoraRtmInvitationApiCallErrorCode errorCode) |
| Returns the result of the [cancelLocalInvitation]([AgoraRtmCallKit cancelLocalInvitation:completion:]) method call. More... | |
Functions | |
| typedef | NS_ENUM (NSInteger, AgoraRtmLocalInvitationState) |
| RETURNED TO THE CALLER. More... | |
| typedef | NS_ENUM (NSInteger, AgoraRtmRemoteInvitationState) |
| RETURNED TO THE CALLEE. More... | |
| typedef | NS_ENUM (NSInteger, AgoraRtmLocalInvitationErrorCode) |
| RETURNED TO THE CALLER. More... | |
| typedef | NS_ENUM (NSInteger, AgoraRtmRemoteInvitationErrorCode) |
| RETURNED TO THE CALLEE. More... | |
| typedef | NS_ENUM (NSInteger, AgoraRtmInvitationApiCallErrorCode) |
| Error codes of the call invitation methods. More... | |
Variables | |
| __attribute__((visibility("default"))) @interface AgoraRtmLocalInvitation NSString * | content |
| The caller's call invitation object. More... | |
| NSString * | response |
| The callee's reponse to the incoming call invitation. More... | |
| NSString * | channelId |
| The channel ID. More... | |
| AgoraRtmLocalInvitationState | state |
| The state of the outgoing call invitation. More... | |
| instancetype _Nonnull | initWithCalleeId |
| Creates an [AgoraRtmLocalInvitation](AgoraAgoraRtmLocalInvitation) instance. More... | |
| __attribute__((visibility("default"))) @interface AgoraRtmCallKit id< AgoraRtmCallDelegate > | callDelegate |
| An AgoraRtmKit instance. More... | |
| void | sendLocalInvitation |
| Allows the caller to send a call invitation to the callee. More... | |
| void | cancelLocalInvitation |
| Allows the caller to cancel a call invitation. More... | |
| void | acceptRemoteInvitation |
| Allows the callee to accept a call invitation. More... | |
| void | refuseRemoteInvitation |
| Allows the callee to decline a call invitation. More... | |
| typedef void(^ AgoraRtmLocalInvitationCancelBlock) (AgoraRtmInvitationApiCallErrorCode errorCode) |
Returns the result of the [cancelLocalInvitation]([AgoraRtmCallKit cancelLocalInvitation:completion:]) method call.
See AgoraRtmInvitationApiCallErrorCode for the error codes.
| typedef void(^ AgoraRtmLocalInvitationSendBlock) (AgoraRtmInvitationApiCallErrorCode errorCode) |
Returns the result of the [sendLocalInvitation]([AgoraRtmCallKit sendLocalInvitation:completion:]) method call.
See AgoraRtmInvitationApiCallErrorCode for the error codes.
| typedef void(^ AgoraRtmRemoteInvitationAcceptBlock) (AgoraRtmInvitationApiCallErrorCode errorCode) |
Returns the result of the [acceptRemoteInvitation]([AgoraRtmCallKit acceptRemoteInvitation:completion:]) method call.
See AgoraRtmInvitationApiCallErrorCode for the error codes.
| typedef void(^ AgoraRtmRemoteInvitationRefuseBlock) (AgoraRtmInvitationApiCallErrorCode errorCode) |
Returns the result of the [refuseRemoteInvitation]([AgoraRtmCallKit refuseRemoteInvitation:completion:]) method call.
See AgoraRtmInvitationApiCallErrorCode for the error codes.
| typedef NS_ENUM | ( | NSInteger | , |
| AgoraRtmInvitationApiCallErrorCode | |||
| ) |
Error codes of the call invitation methods.
0: The method call succeeds.
1: The method call fails. Invalid argument.
2: The method call fails. The call invitation has not started.
3: The method call fails. The call invitation has ended.
4: The method call fails. The call invitation is already accepted.
5: The method call fails. The call invitation is already sent.
| typedef NS_ENUM | ( | NSInteger | , |
| AgoraRtmLocalInvitationErrorCode | |||
| ) |
RETURNED TO THE CALLER.
Error codes of an outgoing call invitation.
0: RETURNED TO THE CALLER. The outgoing call invitation succeeds.
1: RETURNED TO THE CALLER. The callee is offline.
The SDK:
2: RETURNED TO THE CALLER. The callee is online but has not ACKed to the call invitation 30 seconds since it is sent.
3: RETURNED TO THE CALLER. SAVED FOR FUTURE USE. The call invitation expires 60 seconds since it is sent, if the callee ACKs to the call invitation but neither the caller or callee takes any further action (cancel, accpet, or decline it).
4: RETURNED TO THE CALLER. The caller is not logged in.
| typedef NS_ENUM | ( | NSInteger | , |
| AgoraRtmLocalInvitationState | |||
| ) |
RETURNED TO THE CALLER.
States of an outgoing call invitation.
0: RETURNED TO THE CALLER. The initial state of a call invitation (idle).
1: FOR INTERNAL USE ONLY.
2: RETURNED TO THE CALLER. The call invitation is received by the callee.
3: RETURNED TO THE CALLER. The call invitation is accepted by the callee.
4: RETURNED TO THE CALLER. The call invitation is declined by the callee.
5: RETURNED TO THE CALLER. You have canceled the call invitation.
6: RETURNED TO THE CALLER. The life cycle of the outgoing call invitation ends in failure.
| typedef NS_ENUM | ( | NSInteger | , |
| AgoraRtmRemoteInvitationErrorCode | |||
| ) |
RETURNED TO THE CALLEE.
Error codes of an incoming call invitation.
0: RETURNED TO THE CALLEE. The incoming call invitation succeeds.
1: RETURNED TO THE CALLEE. The call invitation received by the callee fails: the callee is not online.
2: RETURNED TO THE CALLEE. The call invitation received by callee fails: the acceptance of the call invitation fails.
3: RETURNED TO THE CALLEE. The call invitation received by the callee fails: the call invitation expires 60 seconds since it is sent, if the callee ACKs to the call invitation but neither the caller or callee takes any further action (cancel, accpet, or decline it).
| typedef NS_ENUM | ( | NSInteger | , |
| AgoraRtmRemoteInvitationState | |||
| ) |
RETURNED TO THE CALLEE.
States of an incoming call invitation.
0: RETURNED TO THE CALLEE. The initial state of a call invitation (idle).
1: RETURNED TO THE CALLEE. A call invitation from a remote caller is received.
2: FOR INTERNAL USE ONLY.
3: RETURNED TO THE CALLEE. You have declined the call invitation.
4: RETURNED TO THE CALLEE. You have accepted the call invitation.
5: RETURNED TO THE CALLEE. The call invitation is canceled by the remote caller.
6: RETURNED TO THE CALLEE. The life cycle of the incoming call invitation ends in failure.
| void acceptRemoteInvitation |
Allows the callee to accept a call invitation.
| remoteInvitation | An AgoraRtmRemoteInvitation object. |
| completion | An AgoraRtmRemoteInvitationAcceptBlock object. |
AgoraRtmInvitationApiCallErrorOk
state and the [localInvitationAccepted]([AgoraRtmCallDelegate rtmCallKit:localInvitationAccepted:withResponse:]) callback.
|
readwritenonatomicweak |
An AgoraRtmKit instance.
AgoraRtmCallDelegate enables Agora RTM call callback event notifications to your app.
| void cancelLocalInvitation |
Allows the caller to cancel a call invitation.
| localInvitation | An AgoraRtmLocalInvitation object. |
| completion | An AgoraRtmLocalInvitationCancelBlock object. |
AgoraRtmInvitationApiCallErrorOk
state and the [localInvitationCanceled]([AgoraRtmCallDelegate rtmCallKit:localInvitationCanceled:]) callback.
|
readwritenonatomiccopy |
The channel ID.
NOTE**
To intercommunicate with the legacy Agora Signaling SDK, you MUST set the channel ID. However, even if the callee successfully accepts the call invitation, the Agora RTM SDK does not join the channel of the specified channel ID.
|
readwritenonatomiccopy |
The caller's call invitation object.
The callee's call invitation object.
User ID of the callee. The call invitation content set by the caller.
User ID of the caller. The call invitation content set by the caller.
| instancetype _Nonnull initWithCalleeId |
Creates an [AgoraRtmLocalInvitation](AgoraAgoraRtmLocalInvitation) instance.
| calleeId | uid of the callee. |
| void refuseRemoteInvitation |
Allows the callee to decline a call invitation.
| remoteInvitation | An AgoraRtmRemoteInvitation object. |
| completion | An AgoraRtmRemoteInvitationRefuseBlock object. |
AgoraRtmInvitationApiCallErrorOk
state and the [localInvitationRefused]([AgoraRtmCallDelegate rtmCallKit:localInvitationRefused:withResponse:]) callback.
|
readwritenonatomiccopy |
The callee's reponse to the incoming call invitation.
The callee's reponse to the call invitation.
| void sendLocalInvitation |
Allows the caller to send a call invitation to the callee.
| localInvitation | An AgoraRtmLocalInvitation object. |
| completion | An AgoraRtmLocalInvitationSendBlock object, which indicates the result of sending a call invitation to a callee. See AgoraRtmInvitationApiCallErrorCode for the error codes. |
AgoraRtmInvitationApiCallErrorOk
state and the [localInvitationReceivedByPeer]([AgoraRtmCallDelegate rtmCallKit:localInvitationReceivedByPeer:]) callback.Failure: The caller receives the AgoraRtmLocalInvitationSendBlock object with an error code. See AgoraRtmInvitationApiCallErrorCode for the error codes.
|
readnonatomicassign |
The state of the outgoing call invitation.
The state of the incoming call invitation.
See AgoraRtmLocalInvitationState.
See AgoraRtmRemoteInvitationState.


My name is Manar Abdelkarim ,I Made this game for level 201 lab 2 and 3
Here the game start
Please read the paragraph carefully and quickly, the game will start after 10 seconds (: !My name is Manar Abdelkarim ,I am 25 years old I born In Riyadh-Saudi Arabia and currently live in Amman- jordan.
II studied CIS at Princess Noura University and graduated in 2019. I like to watch anime, my favorite anime is
Attack on
Titans and the worst anime is One Peace. My favorite food is Shish Barak and the second favorite is Mansaf.
TOP TEN Anime
Attack on Titans
The promised neverland
Hunter X Hunter
Naruto
Demon Slayer
The deadly seven sins
The tower of god
My hero academy
Fullmetal Alchemist
Fairy Tail


Signal Nos. 1, 2 in more areas as Tropical Storm Ramon approaches
Tropical Storm Ramon (Kalmaegi) also becomes slightly stronger on Sunday evening, November 17
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Let's educate everyone, everybody together.
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@* * Copyright 2021 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import uk.gov.hmrc.traderservices.views.components.FooterLinks @import uk.gov.hmrc.hmrcfrontend.views.viewmodels.language._ @import uk.gov.hmrc.traderservices.controllers.routes @import uk.gov.hmrc.hmrcfrontend.views.html.helpers.HmrcReportTechnicalIssueHelper @import uk.gov.hmrc.hmrcfrontend.views.html.helpers.HmrcTrackingConsentSnippet @import views.html.helper.CSPNonce @this( govukLayout2: GovukLayout2, govukPhaseBanner: GovukPhaseBanner, govukBackLink: GovukBackLink, siteHeader: components.siteHeader, phaseBanner: components.phaseBanner, languageSelection: components.languageSelection, link: components.link, researchBanner: components.researchBanner, appConfig: uk.gov.hmrc.traderservices.wiring.AppConfig, hmrcReportTechnicalIssueHelper: HmrcReportTechnicalIssueHelper, hmrcTrackingConsentSnippet: HmrcTrackingConsentSnippet ) @( pageTitle: Option[String] = None, timeout: Boolean = true, js: Option[Html] = None, backLink: Option[String] = None, refresh: Int = 0, mainClasses: Option[String] = Some("govuk-main-wrapper--auto-spacing"), mainContentClasses: String = "govuk-grid-column-two-thirds", hasPrintReceipt: Boolean = false )(contentBlock: Html)(implicit request: Request[_], messages: Messages) @head = { @hmrcTrackingConsentSnippet(nonce = CSPNonce.get) @if(hasPrintReceipt) { } else { } @if(refresh > 0) { } @if(timeout) { } } @scripts = { } @beforeContentBlock = { @researchBanner() @phaseBanner("beta") @languageSelection( appConfig.languageMap, appConfig.routeToSwitchLanguage ) @if(backLink.isDefined) { @govukBackLink(BackLink(href = backLink.get, content = Text(messages("site.back")), attributes = Map("id" -> "back-link"))) } } @content = {


The packages jgame and jgame.platform contain all the classes you need to import to write your own game.
The main classes are jgame.platform.JGEngine (the whole game) and jgame.JGObject (a game object). Sublass these to write your own game. It's handy to have your JGObject classes as inner classes of your JGengine. jgame.JGTimer is used for creating timers in your games. JGImage is an interface defining the cross-platform image methods that are used by JGame but can be used stand-alone as well.
StdGame is a game framework that handles a standard game state machine (startlevel, life lost, game over, etc). It is built as a layer on top of JGEngine.
AppConfig (JRE only) is a stand-alone class that can be used for game configuration. It is used by the JRE version of StdGame for handling keyboard settings. Highscores is a helper class that can be used to implement highscores. It is also used by the JRE version of StdGame.
JGame should also come with a user manual (the file MANUAL). It is distributed under the revised BSD license (see the file LICENSE). More information can be found on the website, http://www.13thmonkey.org/~boris/jgame/


About Us
We are Team 33 of Google SPS. Our team includes four members, Josh Chan, Joymaneet Kaur, Lily Ou, and Jeremy Tajonera, and our project advisor, Kevin Workman.
We are Team 33 of Google SPS. Our team includes four members, Josh Chan, Joymaneet Kaur, Lily Ou, and Jeremy Tajonera, and our project advisor, Kevin Workman.


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Our mission is to help people who engage groups in process. Here's how the cards fit into that.
Easily map out the processing tools and flow you'll use to accomplish your goals. Jot down the activity you're planning on each card. Erase or reorder — flexibility on demand.
When a new need presents itself during a facilitation, choose from your remaining cards to react accordingly. Give your group the power to help steer the ship.
Have an hour left that you weren't expecting? Did your ideation activity fall flat on its face? No worries! Shuffle the deck. Choose another card.
Sharing a common language and a comprehensive set of tools, you can seamlessly work alongside other facilitators. Whether it's your first time teaming up or the thousandth.
We’re happy to help! We offer easy-peasy, no-questions rebates on all multi-deck orders placed directly with us.
Get a co-facilitator (or two, or five) to order with you. Every extra deck gets you another chunk of cash back. Slide this bar and bask in the savings.
For orders of more than 9 decks, contact us for bulk pricing discounts.
Place an order for more than one deck of Facilitator Cards.
Send an email Meg & Sam, say hi (optional), ask for the rebate.
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--- extends: site.j2.html default_block: main title: Lobbycloud - Search description: Listing page for Lobbycloud - Library of influences on public policy. --- {% block content %}
243 matches, 12 documents, 2 discussions #JURI #ITRE #IMCO #GDPR
Furthermore it should be made clear that the theoretical possibility to identify an individual is in itself not sufficient for considering an individual as identifiable.
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To determine whether a person is specifically individually identifiable, account should be taken of all the means likely reasonably to be used by the controller (or by the recipient of the data in case of disclosures) to identify the specific individual.
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Draft DIGITALEUROPE amendments 3 days ago, 45 Pages 2 discussions #JURI #ITRE
Ensuring the data is secure during the process of anonymisation (since at this stage it remains personal data) is necessary. But since this type of processing will aim to ensure the data can no longer be related to any identified or identifiable person, any further requirements under this Regulation would only pose unnecessary burdens to competent authorities and businesses without effectively advancing the protection of privacy.
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Anonymous data are those data altered in such a way that the specific identity of the data subject is no longer identifiable.
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BT’s amendments to the proposed General Data Protection Regulation
6 days ago, 12 Pages 1 discussion #IMCO


VGMERGE
Section: Maintenance Commands (8)Updated: LVM TOOLS 2.02.95(2)-RHEL6 (2012-10-16)
Index Return to Main Contents
NAME
vgmerge - merge two volume groupsSYNOPSIS
vgmerge [-A|--autobackup y|n] [-d|--debug] [-h|-?|--help] [-l|--list] [-t|--test] [-v|--verbose] DestinationVolumeGroupName SourceVolumeGroupNameDESCRIPTION
vgmerge merges two existing volume groups. The inactive SourceVolumeGroupName will be merged into the DestinationVolumeGroupName if physical extent sizes are equal and physical and logical volume summaries of both volume groups fit into DestinationVolumeGroupName's limits.OPTIONS
See lvm for common options. -l, --list Display merged DestinationVolumeGroupName like "vgdisplay -v".- -t, --test
- Do a test run WITHOUT making any real changes.
Examples
"vgmerge -v databases my_vg" merges the inactive volume group named "my_vg" into the active or inactive volume group named "databases" giving verbose runtime information.SEE ALSO
lvm(8), vgcreate(8), vgextend(8), vgreduce(8)
Index
This document was created by man2html, using the manual pages.
Time: 05:34:31 GMT, December 24, 2015


Try to play with controls (buttons and inputs) and navigate through sections — you'll notice how they don't lose their state. It happens due to the fact that view segments are updated only when the corresponding route segments are changed, and not on every location change.
Also, check the data resolving features and how they are handled in asynchronous manner.
The source code is located at
https://github.com/artch/angular-route-segment/tree/master/example.
Feel free to browse and learn the page sources here.


...in the neck, back, and shoulder regions. It can also lead to poor circulation.
...for reduced chances of muscle injuries. Do it at least once every hour.
...to keep your spine, bones and joints in the correct alignment.
Sitting at a desk (or worse, a bed or a couch) for long periods of time affects your posture, leading to back and neck pain.
The Posture Check extension allows you to input the frequency of reminders (example, remind every hour).
You can visit our website by clicking on the logo on the extension, placed above where you input the time.
 You can disable the extension in the Extensions manager.
Pretty irresistible, if you ask us.


10 stunning photographs of the Sea of Galilee
From sunup to sundown, this historic freshwater lake in northern Israel is a shimmering jewel.
by Abigail Klein LeichmanThe Sea of Galilee is a must-see on any trip to Israel. It’s the shimmering jewel of the green, mountainous Galilee region in the north.
Technically this “sea” is a lake, fed by underground springs and by the Jordan River. Since biblical times, it’s variously been called Lake Kinneret, Lake Tiberias and Sea of Genneseret.
Whatever you call it, this body of water is special to Christians. Here, according to the Gospels, Jesus performed miracles and recruited four local fishermen as his first disciples.
The historic cities of Tiberias, Tabgha and Capernaum (Kfar Nahum) are on its shores. Several kibbutzim – including the very first one, Degania (“Cornflower”) – were established on its banks in modern times.
The Sea of Galilee is the only natural freshwater lake in Israel and the lowest freshwater lake in the world, at 209 meters below sea level.
Its circumference measures approximately 53 kilometers (33 miles). Its length is about 21 km (13 miles) and its width is 13 km (8 miles).
Although the Sea of Galilee is also Israel’s largest freshwater reservoir, these days the level is quite low, and it must be supplemented by Mediterranean desalination plants.
The cool Kinneret waters are surrounded by both sandy and rocky beaches, and many historic, natural, archeological, recreational and religious attractions that bring in visitors from all over the world.
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It’s fiery, but Israelis love it. And it’s now in your grocery store
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What you’ll see at the Israeli innovation museum in Jaffa
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Cross-cultural jam session leads to a musical wakeup call
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