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Won Ryu
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Lucas Kerr
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Chad Altobelli
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Tanya Chaudhary
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Navid Boozarpour
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Sanket Swamy
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Lindsey Anderson
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Felix Majewski
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Brenda Wang
Officers
Stephanie Robinson
President
Sammy Marinkovich
Vice President
Ashley Nensel
Master of Ceremonies
Jazzy Cisneros
Master of Ceremonies
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Pledgemaster
Rachel Chan
Investment Manager
Edward Mu
Treasurer
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Sean Park
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Nathan Chao
Historian
Justin Yeung
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Professional Chair
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Professional Chair
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Fundraising Chair
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DSS Write Back Pipe DMA related Config parameters. More...
#include <csl_dssWbPipe.h>
Data Fields | |
| uint32_t | bufLowThreshold |
| uint32_t | bufHighThreshold |
| uint32_t | arbitration |
| uint32_t | frameCaptureRate |
| uint32_t | idleSize |
| uint32_t | idleNumber |
DSS Write Back Pipe DMA related Config parameters.
| uint32_t CSL_DssWbPipeDmaCfg::arbitration |
Determines the priority of the write back pipeline. For valid values see CSL_DssWbPipePriorityVal
| uint32_t CSL_DssWbPipeDmaCfg::bufHighThreshold |
Write back DMA buffer high threshold. Number of 128 bits defining the Threshold value
| uint32_t CSL_DssWbPipeDmaCfg::bufLowThreshold |
Write back DMA buffer low threshold. Number of 128 bits defining the threshold value
| uint32_t CSL_DssWbPipeDmaCfg::frameCaptureRate |
Frame capture rate of the write back pipeline. For valid values see CSL_DssWbPipeCaptRate
| uint32_t CSL_DssWbPipeDmaCfg::idleNumber |
Determines the number of idles between requests on the L3 interconnect. Used only in memory to memory mode. Range depends on idleSize parameter Range: 0-15 when idleSize is 0 0-120 when idleSize is 1
| uint32_t CSL_DssWbPipeDmaCfg::idleSize |
Determines if idleNumber corresponds to number of bursts or singles. Possible values are 0 or 1


HTTP Client
Introduction
Laravel provides an expressive, minimal API around the Guzzle HTTP client, allowing you to quickly make outgoing HTTP requests to communicate with other web applications. Laravel's wrapper around Guzzle is focused on its most common use cases and a wonderful developer experience.
Before getting started, you should ensure that you have installed the Guzzle package as a dependency of your application. By default, Laravel automatically includes this dependency. However, if you have previously removed the package, you may install it again via Composer:
composer require guzzlehttp/guzzle
Making Requests
To make requests, you may use the get
, post
, put
, patch
, and delete
methods provided by the Http
facade. First, let's examine how to make a basic GET
request to another URL:
use Illuminate\Support\Facades\Http;
$response = Http::get('http://example.com');
The get
method returns an instance of Illuminate\Http\Client\Response
, which provides a variety of methods that may be used to inspect the response:
$response->body() : string;
$response->json() : array|mixed;
$response->collect() : Illuminate\Support\Collection;
$response->status() : int;
$response->ok() : bool;
$response->successful() : bool;
$response->failed() : bool;
$response->serverError() : bool;
$response->clientError() : bool;
$response->header($header) : string;
$response->headers() : array;
The Illuminate\Http\Client\Response
object also implements the PHP ArrayAccess
interface, allowing you to access JSON response data directly on the response:
return Http::get('http://example.com/users/1')['name'];
Dumping Requests
If you would like to dump the outgoing request instance before it is sent and terminate the script's execution, you may add the dd
method to the beginning of your request definition:
return Http::dd()->get('http://example.com');
Request Data
Of course, it is common when making POST
, PUT
, and PATCH
requests to send additional data with your request, so these methods accept an array of data as their second argument. By default, data will be sent using the application/json
content type:
use Illuminate\Support\Facades\Http;
$response = Http::post('http://example.com/users', [
'name' => 'Steve',
'role' => 'Network Administrator',
]);
GET Request Query Parameters
When making GET
requests, you may either append a query string to the URL directly or pass an array of key / value pairs as the second argument to the get
method:
$response = Http::get('http://example.com/users', [
'name' => 'Taylor',
'page' => 1,
]);
Sending Form URL Encoded Requests
If you would like to send data using the application/x-www-form-urlencoded
content type, you should call the asForm
method before making your request:
$response = Http::asForm()->post('http://example.com/users', [
'name' => 'Sara',
'role' => 'Privacy Consultant',
]);
Sending A Raw Request Body
You may use the withBody
method if you would like to provide a raw request body when making a request. The content type may be provided via the method's second argument:
$response = Http::withBody(
base64_encode($photo), 'image/jpeg'
)->post('http://example.com/photo');
Multi-Part Requests
If you would like to send files as multi-part requests, you should call the attach
method before making your request. This method accepts the name of the file and its contents. If needed, you may provide a third argument which will be considered the file's filename:
$response = Http::attach(
'attachment', file_get_contents('photo.jpg'), 'photo.jpg'
)->post('http://example.com/attachments');
Instead of passing the raw contents of a file, you may pass a stream resource:
$photo = fopen('photo.jpg', 'r');
$response = Http::attach(
'attachment', $photo, 'photo.jpg'
)->post('http://example.com/attachments');
Headers
Headers may be added to requests using the withHeaders
method. This withHeaders
method accepts an array of key / value pairs:
$response = Http::withHeaders([
'X-First' => 'foo',
'X-Second' => 'bar'
])->post('http://example.com/users', [
'name' => 'Taylor',
]);
You may use the accept
method to specify the content type that your application is expecting in response to your request:
$response = Http::accept('application/json')->get('http://example.com/users');
For convenience, you may use the acceptJson
method to quickly specify that your application expects the application/json
content type in response to your request:
$response = Http::acceptJson()->get('http://example.com/users');
Authentication
You may specify basic and digest authentication credentials using the withBasicAuth
and withDigestAuth
methods, respectively:
// Basic authentication...
$response =<EMAIL_ADDRESS>'secret')->post(...);
// Digest authentication...
$response =<EMAIL_ADDRESS>'secret')->post(...);
Bearer Tokens
If you would like to quickly add a bearer token to the request's Authorization
header, you may use the withToken
method:
$response = Http::withToken('token')->post(...);
Timeout
The timeout
method may be used to specify the maximum number of seconds to wait for a response:
$response = Http::timeout(3)->get(...);
If the given timeout is exceeded, an instance of Illuminate\Http\Client\ConnectionException
will be thrown.
Retries
If you would like HTTP client to automatically retry the request if a client or server error occurs, you may use the retry
method. The retry
method accepts two arguments: the maximum number of times the request should be attempted and the number of milliseconds that Laravel should wait in between attempts:
$response = Http::retry(3, 100)->post(...);
If all of the requests fail, an instance of Illuminate\Http\Client\RequestException
will be thrown.
Error Handling
Unlike Guzzle's default behavior, Laravel's HTTP client wrapper does not throw exceptions on client or server errors (400
and 500
level responses from servers). You may determine if one of these errors was returned using the successful
, clientError
, or serverError
methods:
// Determine if the status code is >= 200 and < 300...
$response->successful();
// Determine if the status code is >= 400...
$response->failed();
// Determine if the response has a 400 level status code...
$response->clientError();
// Determine if the response has a 500 level status code...
$response->serverError();
Throwing Exceptions
If you have a response instance and would like to throw an instance of Illuminate\Http\Client\RequestException
if the response status code indicates a client or server error, you may use the throw
method:
$response = Http::post(...);
// Throw an exception if a client or server error occurred...
$response->throw();
return $response['user']['id'];
The Illuminate\Http\Client\RequestException
instance has a public $response
property which will allow you to inspect the returned response.
The throw
method returns the response instance if no error occurred, allowing you to chain other operations onto the throw
method:
return Http::post(...)->throw()->json();
If you would like to perform some additional logic before the exception is thrown, you may pass a closure to the throw
method. The exception will be thrown automatically after the closure is invoked, so you do not need to re-throw the exception from within the closure:
return Http::post(...)->throw(function ($response, $e) {
//
})->json();
Guzzle Options
You may specify additional Guzzle request options using the withOptions
method. The withOptions
method accepts an array of key / value pairs:
$response = Http::withOptions([
'debug' => true,
])->get('http://example.com/users');
Concurrent Requests
Sometimes, you may wish to make multiple HTTP requests concurrently. In other words, you want several requests to be dispatched at the same time instead of issuing the requests sequentially. This can lead to substantial performance improvements when interacting with slow HTTP APIs.
Thankfully, you may accomplish this using the pool
method. The pool
method accepts a closure which receives an Illuminate\Http\Client\Pool
instance, allowing you to easily add requests to the request pool for dispatching:
use Illuminate\Http\Client\Pool;
use Illuminate\Support\Facades\Http;
$responses = Http::pool(fn (Pool $pool) => [
$pool->get('http://localhost/first'),
$pool->get('http://localhost/second'),
$pool->get('http://localhost/third'),
]);
return $responses[0]->ok() &&
$responses[1]->ok() &&
$responses[2]->ok();
As you can see, each response instance can be accessed based on the order it was added to the pool. If you wish, you can name the requests using the as
method, which allows you to access the corresponding responses by name:
use Illuminate\Http\Client\Pool;
use Illuminate\Support\Facades\Http;
$responses = Http::pool(fn (Pool $pool) => [
$pool->as('first')->get('http://localhost/first'),
$pool->as('second')->get('http://localhost/second'),
$pool->as('third')->get('http://localhost/third'),
]);
return $responses['first']->ok();
Testing
Many Laravel services provide functionality to help you easily and expressively write tests, and Laravel's HTTP wrapper is no exception. The Http
facade's fake
method allows you to instruct the HTTP client to return stubbed / dummy responses when requests are made.
Faking Responses
For example, to instruct the HTTP client to return empty, 200
status code responses for every request, you may call the fake
method with no arguments:
use Illuminate\Support\Facades\Http;
Http::fake();
$response = Http::post(...);
{note} When faking requests, HTTP client middleware are not executed. You should define expectations for faked responses as if these middleware have run correctly.
Faking Specific URLs
Alternatively, you may pass an array to the fake
method. The array's keys should represent URL patterns that you wish to fake and their associated responses. The *
character may be used as a wildcard character. Any requests made to URLs that have not been faked will actually be executed. You may use the Http
facade's response
method to construct stub / fake responses for these endpoints:
Http::fake([
// Stub a JSON response for GitHub endpoints...
'github.com/*' => Http::response(['foo' => 'bar'], 200, $headers),
// Stub a string response for Google endpoints...
'google.com/*' => Http::response('Hello World', 200, $headers),
]);
If you would like to specify a fallback URL pattern that will stub all unmatched URLs, you may use a single *
character:
Http::fake([
// Stub a JSON response for GitHub endpoints...
'github.com/*' => Http::response(['foo' => 'bar'], 200, ['Headers']),
// Stub a string response for all other endpoints...
'*' => Http::response('Hello World', 200, ['Headers']),
]);
Faking Response Sequences
Sometimes you may need to specify that a single URL should return a series of fake responses in a specific order. You may accomplish this using the Http::sequence
method to build the responses:
Http::fake([
// Stub a series of responses for GitHub endpoints...
'github.com/*' => Http::sequence()
->push('Hello World', 200)
->push(['foo' => 'bar'], 200)
->pushStatus(404),
]);
When all of the responses in a response sequence have been consumed, any further requests will cause the response sequence to throw an exception. If you would like to specify a default response that should be returned when a sequence is empty, you may use the whenEmpty
method:
Http::fake([
// Stub a series of responses for GitHub endpoints...
'github.com/*' => Http::sequence()
->push('Hello World', 200)
->push(['foo' => 'bar'], 200)
->whenEmpty(Http::response()),
]);
If you would like to fake a sequence of responses but do not need to specify a specific URL pattern that should be faked, you may use the Http::fakeSequence
method:
Http::fakeSequence()
->push('Hello World', 200)
->whenEmpty(Http::response());
Fake Callback
If you require more complicated logic to determine what responses to return for certain endpoints, you may pass a closure to the fake
method. This closure will receive an instance of Illuminate\Http\Client\Request
and should return a response instance. Within your closure, you may perform whatever logic is necessary to determine what type of response to return:
Http::fake(function ($request) {
return Http::response('Hello World', 200);
});
Inspecting Requests
When faking responses, you may occasionally wish to inspect the requests the client receives in order to make sure your application is sending the correct data or headers. You may accomplish this by calling the Http::assertSent
method after calling Http::fake
.
The assertSent
method accepts a closure which will receive an Illuminate\Http\Client\Request
instance and should return a boolean value indicating if the request matches your expectations. In order for the test to pass, at least one request must have been issued matching the given expectations:
use Illuminate\Http\Client\Request;
use Illuminate\Support\Facades\Http;
Http::fake();
Http::withHeaders([
'X-First' => 'foo',
])->post('http://example.com/users', [
'name' => 'Taylor',
'role' => 'Developer',
]);
Http::assertSent(function (Request $request) {
return $request->hasHeader('X-First', 'foo') &&
$request->url() == 'http://example.com/users' &&
$request['name'] == 'Taylor' &&
$request['role'] == 'Developer';
});
If needed, you may assert that a specific request was not sent using the assertNotSent
method:
use Illuminate\Http\Client\Request;
use Illuminate\Support\Facades\Http;
Http::fake();
Http::post('http://example.com/users', [
'name' => 'Taylor',
'role' => 'Developer',
]);
Http::assertNotSent(function (Request $request) {
return $request->url() === 'http://example.com/posts';
});
Or, you may use the assertNothingSent
method to assert that no requests were sent during the test:
Http::fake();
Http::assertNothingSent();


For the ham hocks, soak the ham hocks overnight in a large saucepan of cold water. Drain the next day to remove any excess salt (this applies with an unsmoked ham too), keeping the ham in the pan.
Stud the onion with the cloves, then add to the pan with the cider and peppercorns. Add enough cold water to cover the ham, cover with a lid and bring to the boil. Once boiling, reduce the heat to a simmer and cook for 2 hours until the ham is tender to the point of a knife, topping up with boiling water to cover the ham again if necessary.
Meanwhile, to make the chutney. Heat a frying pan over a medium heat. Once hot, add the chillies, stirring continuously for a few minutes until they smell fragrant and delicious. Try not to burn them or they will turn from delicious to bitter! Transfer to a bowl and cover with boiling water from the kettle (at least 100ml/3½fl oz).
Add the sugar to a small saucepan and melt with the tamarind pulp. Stir in the vinegar, brandy, spices and raisins and let them simmer very gently over a very low heat.
Meanwhile check the chillies. When soft, use a stick blender to blend them to a smooth paste. Add the chillies to the rest of the chutney and simmer gently over a low heat for 10 minutes, seasoning generously with salt.
Preheat the oven to 200C/180C Fan/Gas 6.
Lift the hocks out of the pan onto a wire rack sat in a roasting tin (reserve the cooking liquor). When cool enough to handle, remove the skin of the ham (not the fat) using a sharp knife. Discard the skin (or save it to make crackling) and lightly score the fat into diamonds.
Pour 200ml/7fl oz of the ham cooking liquor into the roasting tin (so the meat doesn’t dry out as it roasts), and roast the hocks on a rack for 35 minutes until the fat is golden and glazed. Remove from the oven and allow to rest.
Meanwhile, for the celeriac mash, steam or boil the potato and celeriac together for about 15 minutes, until tender. Then, using plenty of elbow grease, roughly mash everything together, stirring in the butter and crème fraîche as you go. If you prefer super-smooth mash, use an electric whisk. Season to taste with salt and pepper.
For the salad, mix all the ingredients except the celeriac, apple and parsley, together in the bottom of a serving dish. Toss the celeriac and apple in the dressing with the parsley.
To serve, pull the ham off the bone in large chunks and serve with the celeriac mash, salad and chutney.


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FROM:Dr. Tony Williams (Atlantis mining Co.) (C.E.O) A.M.C. (LTD) 34-45 MACOLAR ACCRA GHANA.  Please dear, I hope I do not offend you for taking your time to read this mail. Am looking for a partner who will be my sells representative abroad (gold and diamond) please let me know if you have any buyer or agent who will be selling the (gold and diamond) whenever I ship. When replying, please let me know your % percentage in order to help me locate the sellers. However, I will be more than happy to have you as my partner to represent in selling (gold and diamond). The full details, will be furnish to you as soon as you reply. Please include your fax, phone I am expecting your positive response soon through above email fax and phone. Yours truly, Dr. Tony Williams (C.E.O)


This is HTML Table and CSS Practice
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Course
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Description
Schedule
MISY350
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Web Design
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ECON308
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MWF


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From our base in London, we offer self-contained, end-to-end learning across multiple digital platforms, supplemented by more traditional means such as print and instructor-led training. Design Lab produces innovative learning solutions that remain true to the client’s corporate brand and identity.
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BAM offers early bird tickets for Perodua Malaysia Master 2020
KUALA LUMPUR, Nov 24 —The Badminton Association of Malaysia (BAM) is offering Perodua Malaysia Masters (PMM) 2020 early bird tickets at 15 per cent discount from tomorrow to Dec 10 and 10 per cent off from Dec 11 to 25.
According to a statement issued by BAM, children aged 4-18 could enjoy 50 per cent discount for the Upper Tier category.
There are 50 limited ‘Super Fan’ tickets for badminton enthusiasts seeking a better experience at PMM 2020, which will be held from Jan 7-12 at the Axiata Arena, Kuala Lumpur Sports City (KLSC).
Holders of the ‘Super Fan’ passes will gain access to court side seats and receive perks such as the official tournament souvenir book, an official poster signed by Malaysia’s national players and exclusive hospitality at Axiata Arena.
All online ticket purchases can be made on the My Ticket Asia website https://myticket.asia/ starting from 12 noon (MYT) tomorrow, while physical tickets can be purchased at the counter throughout the tournament period.
The Perodua Malaysia Masters 2020, the first event on the HSBC World Tour Super 500 calendar next year, offers US$400,000 (RM1.66 million) in prize money. — Bernama


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Dynamic Event Calendar For Bootstrap 4 Example
A minimal clean calendar component for Bootstrap 4 that dynamically displays events on a month view.


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TorchScript¶
TorchScript is a way to create serializable and optimizable models from PyTorch code. Any TorchScript program can be saved from a Python process and loaded in a process where there is no Python dependency.
We provide tools to incrementally transition a model from a pure Python program to a TorchScript program that can be run independently from Python, such as in a standalone C++ program. This makes it possible to train models in PyTorch using familiar tools in Python and then export the model via TorchScript to a production environment where Python programs may be disadvantageous for performance and multi-threading reasons.
For a gentle introduction to TorchScript, see the Introduction to TorchScript tutorial.
For an end-to-end example of converting a PyTorch model to TorchScript and running it in C++, see the Loading a PyTorch Model in C++ tutorial.
Creating TorchScript Code¶
-
torch.jit.
script
(obj)[source]¶ Scripting a function or
nn.Module
will inspect the source code, compile it as TorchScript code using the TorchScript compiler, and return aScriptModule
orScriptFunction
. TorchScript itself is a subset of the Python language, so not all features in Python work, but we provide enough functionality to compute on tensors and do control-dependent operations. For a complete guide, see the TorchScript Language Reference.torch.jit.script
can be used as a function for modules and functions, and as a<EMAIL_ADDRESS>for TorchScript Classes and functions.- Parameters
obj (callable, class, or
nn.Module
) – Thenn.Module
, function, or class type to compile.- Returns
If
obj
isnn.Module
,script
returns aScriptModule
object. The returnedScriptModule
will have the same set of sub-modules and parameters as the originalnn.Module
. Ifobj
is a standalone function, aScriptFunction
will be returned.
- Scripting a function
The
@torch.jit.script
decorator will construct aScriptFunction
by compiling the body of the function.Example (scripting a function):
import torch @torch.jit.script def foo(x, y): if x.max() > y.max(): r = x else: r = y return r print(type(foo)) # torch.jit.ScriptFuncion # See the compiled graph as Python code print(foo.code) # Call the function using the TorchScript interpreter foo(torch.ones(2, 2), torch.ones(2, 2))
- Scripting an nn.Module
Scripting an
nn.Module
by default will compile theforward
method and recursively compile any methods, submodules, and functions called byforward
. If ann.Module
only uses features supported in TorchScript, no changes to the original module code should be necessary.script
will constructScriptModule
that has copies of the attributes, parameters, and methods of the original module.Example (scripting a simple module with a Parameter):
import torch class MyModule(torch.nn.Module): def __init__(self, N, M): super(MyModule, self).__init__() # This parameter will be copied to the new ScriptModule self.weight = torch.nn.Parameter(torch.rand(N, M)) # When this submodule is used, it will be compiled self.linear = torch.nn.Linear(N, M) def forward(self, input): output = self.weight.mv(input) # This calls the `forward` method of the `nn.Linear` module, which will # cause the `self.linear` submodule to be compiled to a `ScriptModule` here output = self.linear(output) return output scripted_module = torch.jit.script(MyModule(2, 3))
Example (scripting a module with traced submodules):
import torch import torch.nn as nn import torch.nn.functional as F class MyModule(nn.Module): def __init__(self): super(MyModule, self).__init__() # torch.jit.trace produces a ScriptModule's conv1 and conv2 self.conv1 = torch.jit.trace(nn.Conv2d(1, 20, 5), torch.rand(1, 1, 16, 16)) self.conv2 = torch.jit.trace(nn.Conv2d(20, 20, 5), torch.rand(1, 20, 16, 16)) def forward(self, input): input = F.relu(self.conv1(input)) input = F.relu(self.conv2(input)) return input scripted_module = torch.jit.script(MyModule())
To compile a method other than
forward
(and recursively compile anything it calls), add<EMAIL_ADDRESS>decorator to the method. To opt out of compilation<EMAIL_ADDRESS><EMAIL_ADDRESS>.Example (an exported and ignored method in a module):
import torch import torch.nn as nn class MyModule(nn.Module): def __init__(self): super(MyModule, self).__init__() @torch.jit.export def some_entry_point(self, input): return input + 10 @torch.jit.ignore def python_only_fn(self, input): # This function won't be compiled, so any # Python APIs can be used import pdb pdb.set_trace() def forward(self, input): if self.training: self.python_only_fn(input) return input * 99 scripted_module = torch.jit.script(MyModule()) print(scripted_module.some_entry_point(torch.randn(2, 2))) print(scripted_module(torch.randn(2, 2)))
-
torch.jit.
trace
(func, example_inputs, optimize=None, check_trace=True, check_inputs=None, check_tolerance=1e-5)[source]¶ Trace a function and return an executable or
ScriptFunction
that will be optimized using just-in-time compilation. Tracing is ideal for code that operates only onTensor
s and lists, dictionaries, and tuples ofTensor
s.Using
torch.jit.trace
andtorch.jit.trace_module
, you can turn an existing module or Python function into a TorchScriptScriptFunction
orScriptModule
. You must provide example inputs, and we run the function, recording the operations performed on all the tensors.The resulting recording of a standalone function produces
ScriptFunction
.The resulting recording of
forward
function ofnn.Module
ornn.Module
producesScriptModule
.
This module also contains any parameters that the original module had as well.
Warning
Tracing only correctly records functions and modules which are not data dependent (e.g., do not have conditionals on data in tensors) and do not have any untracked external dependencies (e.g., perform input/output or access global variables). Tracing only records operations done when the given function is run on the given tensors. Therefore, the returned
ScriptModule
will always run the same traced graph on any input. This has some important implications when your module is expected to run different sets of operations, depending on the input and/or the module state. For example,Tracing will not record any control-flow like if-statements or loops. When this control-flow is constant across your module, this is fine and it often inlines the control-flow decisions. But sometimes the control-flow is actually part of the model itself. For instance, a recurrent network is a loop over the (possibly dynamic) length of an input sequence.
In the returned
ScriptModule
, operations that have different behaviors intraining
andeval
modes will always behave as if it is in the mode it was in during tracing, no matter which mode theScriptModule
is in.
In cases like these, tracing would not be appropriate and
scripting
is a better choice. If you trace such models, you may silently get incorrect results on subsequent invocations of the model. The tracer will try to emit warnings when doing something that may cause an incorrect trace to be produced.- Parameters
func (callable or torch.nn.Module) – A Python function or
torch.nn.Module
that will be run withexample_inputs
. arguments and returns tofunc
must be tensors or (possibly nested) tuples that contain tensors. When a module is passed totorch.jit.trace
, only theforward
method is run and traced (seetorch.jit.trace
for details).example_inputs (tuple) – A tuple of example inputs that will be passed to the function while tracing. The resulting trace can be run with inputs of different types and shapes assuming the traced operations support those types and shapes.
example_inputs
may also be a single Tensor in which case it is automatically wrapped in a tuple.
- Keyword Arguments
check_trace (bool, optional) – Check if the same inputs run through traced code produce the same outputs. Default:
True
. You might want to disable this if, for example, your network contains non- deterministic ops or if you are sure that the network is correct despite a checker failure.check_inputs (list of tuples, optional) – A list of tuples of input arguments that should be used to check the trace against what is expected. Each tuple is equivalent to a set of input arguments that would be specified in
example_inputs
. For best results, pass in a set of checking inputs representative of the space of shapes and types of inputs you expect the network to see. If not specified, the originalexample_inputs
are used for checkingcheck_tolerance (float, optional) – Floating-point comparison tolerance to use in the checker procedure. This can be used to relax the checker strictness in the event that results diverge numerically for a known reason, such as operator fusion.
- Returns
If
callable
isnn.Module
orforward
ofnn.Module
,trace
returns aScriptModule
object with a singleforward
method containing the traced code. The returnedScriptModule
will have the same set of sub-modules and parameters as the originalnn.Module
. Ifcallable
is a standalone function,trace
returnsScriptFunction
Example (tracing a function):
import torch def foo(x, y): return 2 * x + y # Run `foo` with the provided inputs and record the tensor operations traced_foo = torch.jit.trace(foo, (torch.rand(3), torch.rand(3))) # `traced_foo` can now be run with the TorchScript interpreter or saved # and loaded in a Python-free environment
Example (tracing an existing module):
import torch import torch.nn as nn class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv = nn.Conv2d(1, 1, 3) def forward(self, x): return self.conv(x) n = Net() example_weight = torch.rand(1, 1, 3, 3) example_forward_input = torch.rand(1, 1, 3, 3) # Trace a specific method and construct `ScriptModule` with # a single `forward` method module = torch.jit.trace(n.forward, example_forward_input) # Trace a module (implicitly traces `forward`) and construct a # `ScriptModule` with a single `forward` method module = torch.jit.trace(n, example_forward_input)
-
torch.jit.
trace_module
(mod, inputs, optimize=None, check_trace=True, check_inputs=None, check_tolerance=1e-5)[source]¶ Trace a module and return an executable
ScriptModule
that will be optimized using just-in-time compilation. When a module is passed totorch.jit.trace
, only theforward
method is run and traced. Withtrace_module
, you can specify a dictionary of method names to example inputs to trace (see theexample_inputs
) argument below.See
torch.jit.trace
for more information on tracing.- Parameters
mod (torch.nn.Module) – A
torch.nn.Module
containing methods whose names are specified inexample_inputs
. The given methods will be compiled as a part of a single ScriptModule.example_inputs (dict) – A dict containing sample inputs indexed by method names in
mod
. The inputs will be passed to methods whose names correspond to inputs’ keys while tracing.{ 'forward' : example_forward_input, 'method2': example_method2_input}
- Keyword Arguments
check_trace (
bool
, optional) – Check if the same inputs run through traced code produce the same outputs. Default:True
. You might want to disable this if, for example, your network contains non- deterministic ops or if you are sure that the network is correct despite a checker failure.check_inputs (list of dicts, optional) – A list of dicts of input arguments that should be used to check the trace against what is expected. Each tuple is equivalent to a set of input arguments that would be specified in
example_inputs
. For best results, pass in a set of checking inputs representative of the space of shapes and types of inputs you expect the network to see. If not specified, the originalexample_inputs
are used for checkingcheck_tolerance (float, optional) – Floating-point comparison tolerance to use in the checker procedure. This can be used to relax the checker strictness in the event that results diverge numerically for a known reason, such as operator fusion.
- Returns
A
ScriptModule
object with a singleforward
method containing the traced code. Whenfunc
is atorch.nn.Module
, the returnedScriptModule
will have the same set of sub-modules and parameters asfunc
.
Example (tracing a module with multiple methods):
import torch import torch.nn as nn class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv = nn.Conv2d(1, 1, 3) def forward(self, x): return self.conv(x) def weighted_kernel_sum(self, weight): return weight * self.conv.weight n = Net() example_weight = torch.rand(1, 1, 3, 3) example_forward_input = torch.rand(1, 1, 3, 3) # Trace a specific method and construct `ScriptModule` with # a single `forward` method module = torch.jit.trace(n.forward, example_forward_input) # Trace a module (implicitly traces `forward`) and construct a # `ScriptModule` with a single `forward` method module = torch.jit.trace(n, example_forward_input) # Trace specific methods on a module (specified in `inputs`), constructs # a `ScriptModule` with `forward` and `weighted_kernel_sum` methods inputs = {'forward' : example_forward_input, 'weighted_kernel_sum' : example_weight} module = torch.jit.trace_module(n, inputs)
-
class
torch.jit.
ScriptModule
[source]¶ ScriptModule``s wrap a C++ ``torch::jit::Module
.ScriptModule``s contain methods, attributes, parameters, and constants. These can be accessed the same as on a normal ``nn.Module
.-
property
code
¶ Returns a pretty-printed representation (as valid Python syntax) of the internal graph for the
forward
method. See Inspecting Code for details.
-
property
graph
¶ Returns a string representation of the internal graph for the
forward
method. See Interpreting Graphs for details.
-
property
inlined_graph
¶ Returns a string representation of the internal graph for the
forward
method. This graph will be preprocessed to inline all function and method calls. See Interpreting Graphs for details.
-
save
(f, _extra_files=ExtraFilesMap{})¶ See
torch.jit.save
for details.
-
property
-
class
torch.jit.
ScriptFunction
¶ Functionally equivalent to a
ScriptModule
, but represents a single function and does not have any attributes or Parameters.
-
torch.jit.
save
(m, f, _extra_files=ExtraFilesMap{})[source]¶ Save an offline version of this module for use in a separate process. The saved module serializes all of the methods, submodules, parameters, and attributes of this module. It can be loaded into the C++ API using
torch::jit::load(filename)
or into the Python API withtorch.jit.load
.To be able to save a module, it must not make any calls to native Python functions. This means that all submodules must be subclasses of
ScriptModule
as well.Danger
All modules, no matter their device, are always loaded onto the CPU during loading. This is different from
torch.load()
’s semantics and may change in the future.- Parameters
m – A
ScriptModule
to save.f – A file-like object (has to implement write and flush) or a string containing a file name.
_extra_files – Map from filename to contents which will be stored as part of ‘f’.
Warning
If you are using Python 2,
torch.jit.save
does NOT supportStringIO.StringIO
as a valid file-like object. This is because the write method should return the number of bytes written;StringIO.write()
does not do this.Please use something like
io.BytesIO
instead.Example:
import torch import io class MyModule(torch.nn.Module): def forward(self, x): return x + 10 m = torch.jit.script(MyModule()) # Save to file torch.jit.save(m, 'scriptmodule.pt') # This line is equivalent to the previous m.save("scriptmodule.pt") # Save to io.BytesIO buffer buffer = io.BytesIO() torch.jit.save(m, buffer) # Save with extra files extra_files = torch._C.ExtraFilesMap() extra_files['foo.txt'] = 'bar' torch.jit.save(m, 'scriptmodule.pt', _extra_files=extra_files)
-
torch.jit.
load
(f, map_location=None, _extra_files=ExtraFilesMap{})[source]¶ Load a
ScriptModule
orScriptFunction
previously saved withtorch.jit.save
All previously saved modules, no matter their device, are first loaded onto CPU, and then are moved to the devices they were saved from. If this fails (e.g. because the run time system doesn’t have certain devices), an exception is raised.
- Parameters
f – a file-like object (has to implement read, readline, tell, and seek), or a string containing a file name
map_location (string or torch.device) – A simplified version of
map_location
intorch.save
used to dynamically remap storages to an alternative set of devices._extra_files (dictionary of filename to content) – The extra filenames given in the map would be loaded and their content would be stored in the provided map.
- Returns
A
ScriptModule
object.
Example:
import torch import io torch.jit.load('scriptmodule.pt') # Load ScriptModule from io.BytesIO object with open('scriptmodule.pt', 'rb') as f: buffer = io.BytesIO(f.read()) # Load all tensors to the original device torch.jit.load(buffer) # Load all tensors onto CPU, using a device buffer.seek(0) torch.jit.load(buffer, map_location=torch.device('cpu')) # Load all tensors onto CPU, using a string buffer.seek(0) torch.jit.load(buffer, map_location='cpu') # Load with extra files. extra_files = torch._C.ExtraFilesMap() extra_files['foo.txt'] = 'bar' torch.jit.load('scriptmodule.pt', _extra_files=extra_files) print(extra_files['foo.txt'])
-
torch.jit.
ignore
(drop=False, **kwargs)[source]¶ This decorator indicates to the compiler that a function or method should be ignored and left as a Python function. This allows you to leave code in your model that is not yet TorchScript compatible. If called from TorchScript, ignored functions will dispatch the call to the Python interpreter. Models with ignored functions cannot be exported; use
@torch.jit.unused
instead.Example (using
@torch.jit.ignore
on a method):import torch import torch.nn as nn class MyModule(nn.Module): @torch.jit.ignore def debugger(self, x): import pdb pdb.set_trace() def forward(self, x): x += 10 # The compiler would normally try to compile `debugger`, # but since it is `@ignore`d, it will be left as a call # to Python self.debugger(x) return x m = torch.jit.script(MyModule()) # Error! The call `debugger` cannot be saved since it calls into Python m.save("m.pt")
Example (using
@torch.jit.ignore(drop=True)
on a method):import torch import torch.nn as nn class MyModule(nn.Module): @torch.jit.ignore(drop=True) def training_method(self, x): import pdb pdb.set_trace() def forward(self, x): if self.training: self.training_method(x) return x m = torch.jit.script(MyModule()) # This is OK since `training_method` is not saved, the call is replaced # with a `raise`. m.save("m.pt")
-
torch.jit.
unused
(fn)[source]¶ This decorator indicates to the compiler that a function or method should be ignored and replaced with the raising of an exception. This allows you to leave code in your model that is not yet TorchScript compatible and still export your model.
Example (using
@torch.jit.unused
on a method):import torch import torch.nn as nn class MyModule(nn.Module): def __init__(self, use_memory_efficent): super(MyModule, self).__init__() self.use_memory_efficent = use_memory_efficent @torch.jit.unused def memory_efficient(self, x): import pdb pdb.set_trace() return x + 10 def forward(self, x): # Use not-yet-scriptable memory efficient mode if self.use_memory_efficient: return self.memory_efficient(x) else: return x + 10 m = torch.jit.script(MyModule(use_memory_efficent=False)) m.save("m.pt") m = torch.jit.script(MyModule(use_memory_efficient=True)) # exception raised m(torch.rand(100))
Mixing Tracing and Scripting¶
In many cases either tracing or scripting is an easier approach for converting a model to TorchScript. Tracing and scripting can be composed to suit the particular requirements of a part of a model.
Scripted functions can call traced functions. This is particularly useful when you need to use control-flow around a simple feed-forward model. For instance the beam search of a sequence to sequence model will typically be written in script but can call an encoder module generated using tracing.
Example (calling a traced function in script):
import torch
def foo(x, y):
return 2 * x + y
traced_foo = torch.jit.trace(foo, (torch.rand(3), torch.rand(3)))
@torch.jit.script
def bar(x):
return traced_foo(x, x)
Traced functions can call script functions. This is useful when a small part of a model requires some control-flow even though most of the model is just a feed-forward network. Control-flow inside of a script function called by a traced function is preserved correctly.
Example (calling a script function in a traced function):
import torch
@torch.jit.script
def foo(x, y):
if x.max() > y.max():
r = x
else:
r = y
return r
def bar(x, y, z):
return foo(x, y) + z
traced_bar = torch.jit.trace(bar, (torch.rand(3), torch.rand(3), torch.rand(3)))
This composition also works for nn.Module
s as well, where it can be used to generate
a submodule using tracing that can be called from the methods of a script module.
Example (using a traced module):
import torch
import torchvision
class MyScriptModule(torch.nn.Module):
def __init__(self):
super(MyScriptModule, self).__init__()
self.means = torch.nn.Parameter(torch.tensor([103.939, 116.779, 123.68])
.resize_(1, 3, 1, 1))
self.resnet = torch.jit.trace(torchvision.models.resnet18(),
torch.rand(1, 3, 224, 224))
def forward(self, input):
return self.resnet(input - self.means)
my_script_module = torch.jit.script(MyScriptModule())
TorchScript Language¶
TorchScript is a statically typed subset of Python, so many Python features apply directly to TorchScript. See the full TorchScript Language Reference for details.
Built-in Functions and Modules¶
TorchScript supports the use of most PyTorch functions and many Python built-ins. See TorchScript Builtins for a full reference of supported functions.
PyTorch Functions and Modules¶
TorchScript supports a subset of the tensor and neural network
functions that PyTorch provides. Most methods on Tensor as well as functions in
the torch
namespace, all functions in torch.nn.functional
and
most modules from torch.nn
are supported in TorchScript.
See TorchScript Unsupported Pytorch Constructs for a list of unsupported PyTorch functions and modules.
Python Functions and Modules¶
Many of Python’s built-in functions are supported in TorchScript.
The math
module is also supported (see math-module for details), but no other Python modules
(built-in or third party) are supported.
Python Language Reference Comparison¶
For a full listing of supported Python features, see Python Language Reference Coverage.
Debugging¶
Disable JIT for Debugging¶
-
PYTORCH_JIT
¶ Setting the environment variable
PYTORCH_JIT=0
will disable all script and tracing annotations. If there is hard-to-debug error in one of your TorchScript model, you can use this flag to force everything to run using native Python. Since TorchScript (scripting and tracing) are disabled with this flag, you can use tools likepdb
to debug the model code.Given an example
@torch.jit.script def scripted_fn(x : torch.Tensor):
- for i in range(12):
x = x + x
return x
- def fn(x):
x = torch.neg(x) import pdb; pdb.set_trace() return scripted_fn(x)
traced_fn = torch.jit.trace(fn, (torch.rand(4, 5),)) traced_fn(torch.rand(3, 4))
Debugging this script with
pdb
works except for when we invoke<EMAIL_ADDRESS>function. We can globally disable JIT, so that we can call<EMAIL_ADDRESS>function as a normal Python function and not compile it. If the above script is calleddisable_jit_example.py
, we can invoke it like so:$ PYTORCH_JIT=0 python disable_jit_example.py
and we will be able to step into the
@torch.jit.script
function as a normal Python function. To disable the TorchScript compiler for a specific function<EMAIL_ADDRESS>.
Inspecting Code¶
TorchScript provides a code pretty-printer for all ScriptModule
instances. This
pretty-printer gives an interpretation of the script method’s code as valid
Python syntax. For example:
@torch.jit.script
def foo(len):
# type: (int) -> torch.Tensor
rv = torch.zeros(3, 4)
for i in range(len):
if i < 10:
rv = rv - 1.0
else:
rv = rv + 1.0
return rv
print(foo.code)
A ScriptModule
with a single forward
method will have an attribute
code
, which you can use to inspect the ScriptModule
’s code.
If the ScriptModule
has more than one method, you will need to access
.code
on the method itself and not the module. We can inspect the
code of a method named foo
on a ScriptModule
by accessing .foo.code
.
The example above produces this output:
def foo(len: int) -> Tensor:
rv = torch.zeros([3, 4], dtype=None, layout=None, device=None, pin_memory=None)
rv0 = rv
for i in range(len):
if torch.lt(i, 10):
rv1 = torch.sub(rv0, 1., 1)
else:
rv1 = torch.add(rv0, 1., 1)
rv0 = rv1
return rv0
This is TorchScript’s compilation of the code for the forward
method.
You can use this to ensure TorchScript (tracing or scripting) has captured
your model code correctly.
Interpreting Graphs¶
TorchScript also has a representation at a lower level than the code pretty- printer, in the form of IR graphs.
TorchScript uses a static single assignment (SSA) intermediate representation (IR) to represent computation. The instructions in this format consist of ATen (the C++ backend of PyTorch) operators and other primitive operators, including control flow operators for loops and conditionals. As an example:
@torch.jit.script
def foo(len):
# type: (int) -> torch.Tensor
rv = torch.zeros(3, 4)
for i in range(len):
if i < 10:
rv = rv - 1.0
else:
rv = rv + 1.0
return rv
print(foo.graph)
graph
follows the same rules described in the Inspecting Code section
with regard to forward
method lookup.
The example script above produces the graph:
graph(%len.1 : int):
%24 : int = prim::Constant[value=1]()
%17 : bool = prim::Constant[value=1]() # test.py:10:5
%12 : bool? = prim::Constant()
%10 : Device? = prim::Constant()
%6 : int? = prim::Constant()
%1 : int = prim::Constant[value=3]() # test.py:9:22
%2 : int = prim::Constant[value=4]() # test.py:9:25
%20 : int = prim::Constant[value=10]() # test.py:11:16
%23 : float = prim::Constant[value=1]() # test.py:12:23
%4 : int[] = prim::ListConstruct(%1, %2)
%rv.1 : Tensor = aten::zeros(%4, %6, %6, %10, %12) # test.py:9:10
%rv : Tensor = prim::Loop(%len.1, %17, %rv.1) # test.py:10:5
block0(%i.1 : int, %rv.14 : Tensor):
%21 : bool = aten::lt(%i.1, %20) # test.py:11:12
%rv.13 : Tensor = prim::If(%21) # test.py:11:9
block0():
%rv.3 : Tensor = aten::sub(%rv.14, %23, %24) # test.py:12:18
-> (%rv.3)
block1():
%rv.6 : Tensor = aten::add(%rv.14, %23, %24) # test.py:14:18
-> (%rv.6)
-> (%17, %rv.13)
return (%rv)
Take the instruction %rv.1 : Tensor = aten::zeros(%4, %6, %6, %10, %12) # test.py:9:10
for
example.
%rv.1 : Tensor
means we assign the output to a (unique) value namedrv.1
, that value is ofTensor
type and that we do not know its concrete shape.aten::zeros
is the operator (equivalent totorch.zeros
) and the input list(%4, %6, %6, %10, %12)
specifies which values in scope should be passed as inputs. The schema for built-in functions likeaten::zeros
can be found at `Builtin Functions`_.# test.py:9:10
is the location in the original source file that generated this instruction. In this case, it is a file named test.py, on line 9, and at character 10.
Notice that operators can also have associated blocks
, namely the
prim::Loop
and prim::If
operators. In the graph print-out, these
operators are formatted to reflect their equivalent source code forms
to facilitate easy debugging.
Graphs can be inspected as shown to confirm that the computation described
by a ScriptModule
is correct, in both automated and manual fashion, as
described below.
Tracer¶
Tracing Edge Cases¶
There are some edge cases that exist where the trace of a given Python function/module will not be representative of the underlying code. These cases can include:
Tracing of control flow that is dependent on inputs (e.g. tensor shapes)
Tracing of in-place operations of tensor views (e.g. indexing on the left-hand side of an assignment)
Note that these cases may in fact be traceable in the future.
Automatic Trace Checking¶
One way to automatically catch many errors in traces is by using check_inputs
on the torch.jit.trace()
API. check_inputs
takes a list of tuples
of inputs that will be used to re-trace the computation and verify the
results. For example:
def loop_in_traced_fn(x):
result = x[0]
for i in range(x.size(0)):
result = result * x[i]
return result
inputs = (torch.rand(3, 4, 5),)
check_inputs = [(torch.rand(4, 5, 6),), (torch.rand(2, 3, 4),)]
traced = torch.jit.trace(loop_in_traced_fn, inputs, check_inputs=check_inputs)
Gives us the following diagnostic information:
ERROR: Graphs differed across invocations!
Graph diff:
graph(%x : Tensor) {
%1 : int = prim::Constant[value=0]()
%2 : int = prim::Constant[value=0]()
%result.1 : Tensor = aten::select(%x, %1, %2)
%4 : int = prim::Constant[value=0]()
%5 : int = prim::Constant[value=0]()
%6 : Tensor = aten::select(%x, %4, %5)
%result.2 : Tensor = aten::mul(%result.1, %6)
%8 : int = prim::Constant[value=0]()
%9 : int = prim::Constant[value=1]()
%10 : Tensor = aten::select(%x, %8, %9)
- %result : Tensor = aten::mul(%result.2, %10)
+ %result.3 : Tensor = aten::mul(%result.2, %10)
? ++
%12 : int = prim::Constant[value=0]()
%13 : int = prim::Constant[value=2]()
%14 : Tensor = aten::select(%x, %12, %13)
+ %result : Tensor = aten::mul(%result.3, %14)
+ %16 : int = prim::Constant[value=0]()
+ %17 : int = prim::Constant[value=3]()
+ %18 : Tensor = aten::select(%x, %16, %17)
- %15 : Tensor = aten::mul(%result, %14)
? ^ ^
+ %19 : Tensor = aten::mul(%result, %18)
? ^ ^
- return (%15);
? ^
+ return (%19);
? ^
}
This message indicates to us that the computation differed between when
we first traced it and when we traced it with the check_inputs
. Indeed,
the loop within the body of loop_in_traced_fn
depends on the shape
of the input x
, and thus when we try another x
with a different
shape, the trace differs.
In this case, data-dependent control flow like this can be captured using
torch.jit.script()
instead:
def fn(x):
result = x[0]
for i in range(x.size(0)):
result = result * x[i]
return result
inputs = (torch.rand(3, 4, 5),)
check_inputs = [(torch.rand(4, 5, 6),), (torch.rand(2, 3, 4),)]
scripted_fn = torch.jit.script(fn)
print(scripted_fn.graph)
#print(str(scripted_fn.graph).strip())
for input_tuple in [inputs] + check_inputs:
torch.testing.assert_allclose(fn(*input_tuple), scripted_fn(*input_tuple))
Which produces:
graph(%x : Tensor) {
%5 : bool = prim::Constant[value=1]()
%1 : int = prim::Constant[value=0]()
%result.1 : Tensor = aten::select(%x, %1, %1)
%4 : int = aten::size(%x, %1)
%result : Tensor = prim::Loop(%4, %5, %result.1)
block0(%i : int, %7 : Tensor) {
%10 : Tensor = aten::select(%x, %1, %i)
%result.2 : Tensor = aten::mul(%7, %10)
-> (%5, %result.2)
}
return (%result);
}
Tracer Warnings¶
The tracer produces warnings for several problematic patterns in traced computation. As an example, take a trace of a function that contains an in-place assignment on a slice (a view) of a Tensor:
def fill_row_zero(x):
x[0] = torch.rand(*x.shape[1:2])
return x
traced = torch.jit.trace(fill_row_zero, (torch.rand(3, 4),))
print(traced.graph)
Produces several warnings and a graph which simply returns the input:
fill_row_zero.py:4: TracerWarning: There are 2 live references to the data region being modified when tracing in-place operator copy_ (possibly due to an assignment). This might cause the trace to be incorrect, because all other views that also reference this data will not reflect this change in the trace! On the other hand, if all other views use the same memory chunk, but are disjoint (e.g. are outputs of torch.split), this might still be safe.
x[0] = torch.rand(*x.shape[1:2])
fill_row_zero.py:6: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
Not within tolerance rtol=1e-05 atol=1e-05 at input[0, 1] (0.09115803241729736 vs. 0.6782537698745728) and 3 other locations (33.00%)
traced = torch.jit.trace(fill_row_zero, (torch.rand(3, 4),))
graph(%0 : Float(3, 4)) {
return (%0);
}
We can fix this by modifying the code to not use the in-place update, but
rather build up the result tensor out-of-place with torch.cat
:
def fill_row_zero(x):
x = torch.cat((torch.rand(1, *x.shape[1:2]), x[1:2]), dim=0)
return x
traced = torch.jit.trace(fill_row_zero, (torch.rand(3, 4),))
print(traced.graph)
Built-in Functions and Modules¶
See TorchScript Builtins for a full reference of supported functions.
Frequently Asked Questions¶
Q: I would like to train a model on GPU and do inference on CPU. What are the best practices?
First convert your model from GPU to CPU and then save it, like so:
cpu_model = gpu_model.cpu() sample_input_cpu = sample_input_gpu.cpu() traced_cpu = torch.jit.trace(traced_cpu, sample_input_cpu) torch.jit.save(traced_cpu, "cpu.pth") traced_gpu = torch.jit.trace(traced_gpu, sample_input_gpu) torch.jit.save(traced_gpu, "gpu.pth") # ... later, when using the model: if use_gpu: model = torch.jit.load("gpu.pth") else: model = torch.jit.load("cpu.pth") model(input)This is recommended because the tracer may witness tensor creation on a specific device, so casting an already-loaded model may have unexpected effects. Casting the model before saving it ensures that the tracer has the correct device information.
Q: How do I store attributes on a ScriptModule
?
Say we have a model like:
class Model(nn.Module): def __init__(self): super(Model, self).__init__() self.x = 2 def forward(self): return self.x m = torch.jit.script(Model())If
Model
is instantiated it will result in a compilation error since the compiler doesn’t know aboutx
. There are 4 ways to inform the compiler of attributes onScriptModule
:1.
nn.Parameter
- Values wrapped innn.Parameter
will work as they do onnn.Module
s2.
register_buffer
- Values wrapped inregister_buffer
will work as they do onnn.Module
s. This is equivalent to an attribute (see 4) of typeTensor
.3. Constants - Annotating a class member as
Final
(or adding it to a list called__constants__
at the class definition level) will mark the contained names as constants. Constants are saved directly in the code of the model. See `Python-defined Constants`_ for details.4. Attributes - Values that are a `supported type`_ can be added as mutable attributes. Most types can be inferred but some may need to be specified, see `Module Attributes`_ for details.
Q: I would like to trace module’s method but I keep getting this error:
RuntimeError: Cannot insert a Tensor that requires grad as a constant. Consider making it a parameter or input, or detaching the gradient
This error usually means that the method you are tracing uses a module’s parameters and you are passing the module’s method instead of the module instance (e.g.
my_module_instance.forward
vsmy_module_instance
).
Invoking
trace
with a module’s method captures module parameters (which may require gradients) as constants.On the other hand, invoking
trace
with module’s instance (e.g.my_module
) creates a new module and correctly copies parameters into the new module, so they can accumulate gradients if required.To trace a specific method on a module, see
torch.jit.trace_module
Appendix¶
Migrating to PyTorch 1.2 Recursive Scripting API¶
This section details the changes to TorchScript in PyTorch 1.2. If you are new to TorchScript you can skip this section. There are two main changes to the TorchScript API with PyTorch 1.2.
1. torch.jit.script
will now attempt to recursively compile functions,
methods, and classes that it encounters. Once you call torch.jit.script
,
compilation is “opt-out”, rather than “opt-in”.
2. torch.jit.script(nn_module_instance)
is now the preferred way to create
ScriptModule
s, instead of inheriting from torch.jit.ScriptModule
.
These changes combine to provide a simpler, easier-to-use API for converting
your nn.Module
s into ScriptModule
s, ready to be optimized and executed in a
non-Python environment.
The new usage looks like this:
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.conv1 = nn.Conv2d(1, 20, 5)
self.conv2 = nn.Conv2d(20, 20, 5)
def forward(self, x):
x = F.relu(self.conv1(x))
return F.relu(self.conv2(x))
my_model = Model()
my_scripted_model = torch.jit.script(my_model)
The module’s
forward
is compiled by default. Methods called fromforward
are lazily compiled in the order they are used inforward
.To compile a method other than
forward
that is not called fromforward
<EMAIL_ADDRESS>.To stop the compiler from compiling a method, add
@torch.jit.ignore
<EMAIL_ADDRESS>.@ignore
leaves themethod as a call to python, and
@unused
replaces it with an exception.@ignored
cannot be exported;@unused
can.Most attribute types can be inferred, so
torch.jit.Attribute
is not necessary. For empty container types, annotate their types using PEP 526-style class annotations.Constants can be marked with a
Final
class annotation instead of adding the name of the member to__constants__
.Python 3 type hints can be used in place of
torch.jit.annotate
- As a result of these changes, the following items are considered deprecated and should not appear in new code:
The
@torch.jit.script_method
decoratorClasses that inherit from
torch.jit.ScriptModule
The
torch.jit.Attribute
wrapper classThe
__constants__
arrayThe
torch.jit.annotate
function
Modules¶
Warning
The @torch.jit.ignore
annotation’s behavior changes in
PyTorch 1.2. Before PyTorch 1.2 the @ignore decorator was used to make a function
or method callable from code that is exported. To get this functionality back,
use @torch.jit.unused()
. @torch.jit.ignore
is now equivalent
to @torch.jit.ignore(drop=False)
. See @torch.jit.ignore
and @torch.jit.unused
for details.
When passed to the torch.jit.script
function, a torch.nn.Module
’s data is
copied to a ScriptModule
and the TorchScript compiler compiles the module.
The module’s forward
is compiled by default. Methods called from forward
are
lazily compiled in the order they are used in forward
, as well as any
@torch.jit.export
methods.
-
torch.jit.
export
(fn)[source]¶ This decorator indicates that a method on an
nn.Module
is used as an entry point into aScriptModule
and should be compiled.forward
implicitly is assumed to be an entry point, so it does not need this decorator. Functions and methods called fromforward
are compiled as they are seen by the compiler, so they do not need this decorator either.Example (using
@torch.jit.export
on a method):import torch import torch.nn as nn class MyModule(nn.Module): def implicitly_compiled_method(self, x): return x + 99 # `forward` is implicitly decorated with<EMAIL_ADDRESS># so adding it here would have no effect def forward(self, x): return x + 10 @torch.jit.export def another_forward(self, x): # When the compiler sees this call, it will compile # `implicitly_compiled_method` return self.implicitly_compiled_method(x) def unused_method(self, x): return x - 20 # `m` will contain compiled methods: # `forward` # `another_forward` # `implicitly_compiled_method` # `unused_method` will not be compiled since it was not called from # any compiled methods and wasn't decorated with<EMAIL_ADDRESS>m = torch.jit.script(MyModule())
Functions¶
Functions don’t change much, they can be decorated with @torch.jit.ignore
or torch.jit.unused
if needed.
# Same behavior as pre-PyTorch 1.2
@torch.jit.script
def some_fn():
return 2
# Marks a function as ignored, if nothing
# ever calls it then this has no effect
@torch.jit.ignore
def some_fn2():
return 2
# As with ignore, if nothing calls it then it has no effect.
# If it is called in script it is replaced with an exception.
@torch.jit.unused
def some_fn3():
import pdb; pdb.set_trace()
return 4
# Doesn't do anything, this function is already
# the main entry point
@torch.jit.export
def some_fn4():
return 2
TorchScript Classes¶
Warning
TorchScript class support is experimental. Currently it is best suited
for simple record-like types (think a NamedTuple
with methods
attached).
Everything in a user defined `TorchScript Class`_ is exported by default, functions
can be decorated with @torch.jit.ignore
if needed.
Attributes¶
The TorchScript compiler needs to know the types of `module attributes`_. Most types
can be inferred from the value of the member. Empty lists and dicts cannot have their
types inferred and must have their types annotated with PEP 526-style class annotations.
If a type cannot be inferred and is not explicitly annotated, it will not be added as an attribute
to the resulting ScriptModule
Old API:
from typing import Dict
import torch
class MyModule(torch.jit.ScriptModule):
def __init__(self):
super(MyModule, self).__init__()
self.my_dict = torch.jit.Attribute({}, Dict[str, int])
self.my_int = torch.jit.Attribute(20, int)
m = MyModule()
New API:
from typing import Dict
class MyModule(torch.nn.Module):
my_dict: Dict[str, int]
def __init__(self):
super(MyModule, self).__init__()
# This type cannot be inferred and must be specified
self.my_dict = {}
# The attribute type here is inferred to be `int`
self.my_int = 20
def forward(self):
pass
m = torch.jit.script(MyModule())
Constants¶
The Final
type constructor can be used to mark members as `constant`_. If members are not marked constant, they will be copied to the resulting ScriptModule
as an attribute. Using Final
opens opportunities for optimization if the value is known to be fixed and gives additional type safety.
Old API:
class MyModule(torch.jit.ScriptModule):
__constants__ = ['my_constant']
def __init__(self):
super(MyModule, self).__init__()
self.my_constant = 2
def forward(self):
pass
m = MyModule()
New API:
try:
from typing_extensions import Final
except:
# If you don't have `typing_extensions` installed, you can use a
# polyfill from `torch.jit`.
from torch.jit import Final
class MyModule(torch.nn.Module):
my_constant: Final[int]
def __init__(self):
super(MyModule, self).__init__()
self.my_constant = 2
def forward(self):
pass
m = torch.jit.script(MyModule())
Variables¶
Containers are assumed to have type Tensor
and be non-optional (see
`Default Types`_ for more information). Previously, torch.jit.annotate
was used to
tell the TorchScript compiler what the type should be. Python 3 style type hints are
now supported.
import torch
from typing import Dict, Optional
@torch.jit.script
def make_dict(flag: bool):
x: Dict[str, int] = {}
x['hi'] = 2
b: Optional[int] = None
if flag:
b = 2
return x, b


{{ site.title }}
{{ site.description }}
{% include social-links.html %}
About
Miri is a web developer who lives in Japan. She does coding, designing and sometimes animation.


XLS-NoC Glossary
Adaptive Routing
An adaptive routing algorithm derives a route using any information about the network’s state.
Channel
A channel interconnects a pair of nodes and is the medium that transfers data amongst the pair. A node is either a router input, a router output or an endpoint port. A channel may be unidirectional, transferring the data in a single direction, or bidirectional, transferring the data in both directions.
Endpoint
A node connected to the network that communicates to other endpoints using the network.
Endpoint Port
An endpoint port is a port that connects to an endpoint. There are two types of endpoint ports: a send port and a receive port. A send port enables an endpoint to send data to the network and a receive port enables an endpoint to receive data from the network. The naming convention for a send port and receive port is from the perspective of the endpoints.
Deadlock
A deadlock occurs when a set of agents holding resources are waiting on another set of resources such that a cycle of waiting agents is formed, implying that agents are unable to make progress.
Figure Deadlock_Example. A deadlock example with two agents and two resources.
Figure Deadlock_Example shows a deadlock example with two agents and two resources. In the example, agent 0 is waiting for resource B that is assigned to agent 1, and agent 1 is waiting for resource A that is assigned to agent 0. The dependency cycle created with agent 0 and 1 demonstrate that the agents are unable to make progress.
Flit
A flow control digit, or flit, is the smallest unit of resource allocation in a router. Variable length packets are divided into one or more fixed length flits to simplify the management and allocation of resources. Flits may be divided further into phits when traversing a router.
Flow Control
Flow control is the scheduling and allocation of a network’s resources. For example, a virtual channel can have a wormhole flow control.
Hotspot (Hot-spot)
A hotspot resource is one whose demand is significantly greater than other, similar resources. For example, a particular destination terminal becomes a hotspot in a shared memory multicomputer when many processors are simultaneously reading from the same memory location (for example, a shared lock or data structure). Another example is traffic congestion within an area of the network.
Livelock
Livelock occurs when a packet is not able to make progress in the network and is never delivered to its destination. Unlike deadlock, a livelocked packet continues to move through the network.
Figure Livelock_Example. A example with livelocked packet P1.
Figure Livelock_Example shows a livelock example. In the example, there are two packets, P0 and P1. The destination router for the packets is router R2. R1 uses an adaptive routing algorithm. At timestep 1, P0 and P1 are at router R0. Given the routing algorithm at R0, R0 routes packet P0 to R2 and P1 to R1 (timestep 2). At timestep 3, another instance of P0 is routed to R0 and P1 is routed to R2. At timestep 4, P1 is routed to R0. After the arrival of P1 at R0. The state of R0 is identical to timestep1 where these sequence of events will repeat. In the end, P1 does not arrive to its destination although it makes progress, thus livelocked.
Network-On-Chip (NoC)
At a high level, a Network-On-Chip (NoC) is a network designed for one chip. It is composed of routers, channels and endpoint ports. It transports data between endpoints connected to it. Although the design is intended for a single chip, the logical description can be partitioned across multiple chips.
Oblivious Routing
An oblivious routing algorithm derives a route without using any information about the network’s state, where, fundamentally, the route is computed using solely the source and the destination.
Packet
Packets are the unit of routing within an interconnection network. Messages are broken into one or more variable, but bounded, length packets for processing by the network. All data contained within a packet follow the same route through the network and packets are reassembled into messages at the destination node. A packet is divided further into flits.
Phit
A physical digit, or phit, is the smallest unit of data processed (e.g. traversing or accessed) by a router. One or more phits are combined to form a flit.
Port
A port is a physical gateway to a component (input port) or from a component (output port).
Router
A router receives packets on its inputs, determines the packets' destination based on the routing algorithm, and forwards the packets to the appropriate output.
Routing Algorithm
The series of steps for choosing a path for a packet through the network. For a packet, the routing algorithm determines the router's output from its input.
Topology
The static arrangement of router nodes, channels, and endpoint ports in a network. The topology affects the routing in the network.
Virtual Channel
A virtual channel (VC) is a logical representation of a channel at a router's input or output. It is composed of flit buffers within the router. In a router that handles virtual channels, a packet or flit is assigned to a virtual channel. Hence, the presence of virtual channels at a router's input or output enables the transfer of multiple packets through a single channel.
Wormhole Flow Control
Wormhole flow control defines the allocation of a resource at the flit granularity. Upon a successful allocation, the transfer of the flit is permitted to commence.
References
William James Dally and Brian Patrick Towles. 2004. Principles and Practices of Interconnection Networks. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.


Report: Netanyahu will be required to leave ministerial positions
Attorney General will make clear to Binyamin Netanyahu that he will not be able to hold office because of indictments against him.
by Mordechai SonesAttorney General Avichai Mandelblit will announce in coming hours that Prime Minister Binyamin Netanyahu must resign ministerial positions due to the indictments against him, journalist Baruch Kara reported on Channel 13 News.
Netanyahu is currently serving as Prime Minister, as well as Health Minister, Welfare Minister, Diaspora Affairs Minister, and Agriculture Minister.
According to law, he is not allowed to serve as minister after he has been indicted.
The political system estimates the Prime Minister will rush to hand over at least three of the positions to Likud MKs, possibly to Gideon Sa'ar supporters to weaken criticism against him from those quarters.
At the same time, United Torah Judaism Chairman Deputy Health Minister Yaakov Litzman may soon be appointed Health Minister if Prime Minister Netanyahu resigns.
The dilemma of what Litzman would do if Netanyahu resigns was voted on by the Agudat Yisrael Torah Council, which approved the move.
In the past Litzman had already held the title of Health Minister but resigned and was "demoted" to rank of Deputy Minister due to Sabbath desecration on Israel Railways.


Cost optimisation standards
This content explains:
- the Public Cloud Team’s (PCT) help with cost optimisation
- pillars of cost optimisation
- explanation and how to assess your maturity scale
- how you can achieve various levels of the maturity scale
PCT's help with cost optimisation
Cost optimisation is not a one-time piece of work. It should be a continual process of inspection and implementation. The PCT want to help the Home Office use cloud services efficiently. We want to help your team realise the best savings possible. So, we will be running a regular working group to share best practices, feed in ideas and new explore tooling. If you would like to join these meetings, please contact<EMAIL_ADDRESS>More information on PCT Working Groups.
Pillars of optimisation
Right size your instances
Make sure that the instance you provision matches what your needs e.g. CPU, network, storage.
Increase elasticity/Make environments scalable
Optimise environments to meet dynamic needs. Schedule non-prod environments.
Leverage the right pricing model
Utilise on-demand, spot and reserved instances for the right workloads.
Optimise storage
Do not just rely on using EBS and S3 storage types. Identify the most appropriate destination for each type of data.
Measure, monitor and improve
Apply cost optimisation tags. Define metrics, set targets and review regularly.
Cost optimisation maturity scale
The Home Office has produced a cost efficiency model which your project can be scored against.
- Level 0 - On Demand = 0% saving
- Level 1 - Rightsizing = 10% to 50% base instance or storage saving
- Level 2 - Reserved Instances (RI’s) = 36% compute saving
- Level 3 - On Demand (0%) + Scheduling (64%) = 64% compute saving
- Level 3A - With autoscaling in response to workload
- Level 4 - Spot = 80% compute saving
- Level 4A - With autoscaling in response to workload
- Level 5 - Spot (80%) + Scheduling (64%) = 92% compute saving
- Level 5A - With autoscaling in response to workload
How to assess your maturity scale
You can complete a maturity decision tree questionnaire to assess what level your project or service can achieve.
How to achieve different levels
The Home Office has produced a cost efficiency model which your project can be scored against.
Achieving Level 1
Right sizing
AWS Cost Explorer can provide quick and easy recommendations for resizing existing instance types. Steps to get recommendations:
- Log in to the AWS Console and go to the Cost Explorer
- Select the dartboard style icon
- Select resource optimisation recommendations and view all
You can also get cost optimisation recommendations via Trusted Advisor. If your service consistently runs at under 40%, this is a clear indicator that the service is wrongly sized. So, the number of resources assigned to the services needs to be reduced. You can change your instance types via normal deployment routes.
Scaling
You can also reduce the number of instances you provision by architecting your environments for elasticity/scaling. You can find guidance on how to do this on Amazon's EC2 Autoscaling page.
Generally, you should aim to scale ‘horizontally’ with more instances instead of ‘vertically’ with more CPU/RAM per instance. You can also look at re-platforming your instances to use AWS lambda or serverless technology.
Housekeeping
You should use the latest instance types in EC2 & RDS families. You can see the latest instances available on Amazon's EC2 Instance types.
Pick the right storage type for your use case. List of storage is available on Amazon's Cloud Storage. When shutting down instances you need to look out for orphaned snapshots/EBS instances or idle Elastic Load Balances (ELB). Your trusted advisor report will give you a list.
Achieving Level 2
Reserved Instances
AWS Cost Explorer can provide quick and easy recommendations for Reserved Instance (RI) recommendations. Reserved Instances should only be purchased for instances that will be running 24/7.
There are several distinct types of reserved instances. We recommend that you look at purchasing one year all upfront instances. If you wish to purchase RI’s please email<EMAIL_ADDRESS>as we need treasury approval to process upfront payments.
Achieving Level 3
Scheduling
You can reduce instance costs by up to 64% by only running them during business hours. Additionally, you can scale your containers or instances based upon expected usage and set different limits outside of business hours. All development instances should be able to be scheduled and turned off outside of 9-5/7-7.
Achieving Level 4
Spot Instances
You can use EC2 Spot instances to save up to 80% of instance costs by bidding against unused EC2 capacity. However, when you use a spot instance, Amazon can claim the capacity back with 2 minutes notice. Your architecture needs to be immutable and highly available to deal with this. You can also configure Kubernetes nodes to use spot instances.
Achieving Level 5
Spot and Scheduling
By combining scheduling & spot instances you can saving up to 92% of instance costs vs on demand EC2 costs.
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Enter B- program source code
Compiler error messages
Compiling and running the Javascript output using the Oracle JDK :
- Copy and paste Javascript code to file: source.js
- Append "oracle_jjs_adapter" to the end of the file: cat source.js adapters/oracle_jjs_adapter.js > program.js
- Run: jjs program.js


| # | Item Code | Available | Stock Date |
|---|---|---|---|
| 1 | AB678 | 45 | 06/01/2018 |
| 2 | CD345 | 200 | 26/08/2017 |
| 3 | FG456 | 130 | 23/09/2017 |
| 4 | GH799 | 405 | 12/12/2016 |
| 5 | LK789 | 150 | 09/12/2017 |
@1 rate kitna hai?
@2 kab tak pahuchega?
@1 How much are available?
@3 I am ready. What is the final price?
@4 Pictures are not clear can you please send them again?
| Post No. | Item Code | Offer Description | No. of Requests | Status |
|---|---|---|---|---|
| 1 | AB567 | 10% Discount | 13 | Unsold |
| 1 | CD789 | Buy 200 get 250 | 45 | Sold Out |
| 3 | AB567 | 10% Discount | 13 | Unsold |
| 4 | CD789 | Buy 200 get 250 | 45 | Sold Out |
| 5 | AB567 | 10% Discount | 13 | Unsold |
| 6 | CD789 | Buy 200 get 250 | 45 | Sold Out |
| 7 | AB567 | 10% Discount | 13 | Unsold |
| 8 | CD789 | Buy 200 get 250 | 45 | Sold Out |


| 6 May 2006 |
Version 1.7.1New features:
Enhancements:
|
|
| 1 April 2006 |
Version 1.7New features:
Bug fixes:
|
|
| 7 March 2006 |
Version 1.6.1 |
|
| 12 February 2006 |
Version 1.6 This version uses Cocoa instead of Carbon for the Mac implementation. The Carbon implementation is no longer supported.
There have been many additions and changes. See the CHANGES file for details. |
|
| 4 July 2004 |
Version 1.5.1Bug fixes:
|
|
| 27 June 2004 |
Version 1.5 There is now a GLView class for 3D graphics using OpenGL. |
|
| 21 June 2004 |
Version 1.4 Image and Pixmap classes and a shrink_wrap method have been added, and some bugs have been fixed. See the CHANGES file for details. |
|
| 14 June 2004 |
Version 1.3 For the first time, PyGUI has two simultaneously working implementations covering different platforms: MacOS X using Carbon, and Linux using Gtk. The Gtk version should also be usable on other Unix/X11 platforms. and, at a pinch, on Windows, although a native Windows version would be preferable. |


--- layout: post title: Tracking website errors in Google Analytics date: 2014-10-28 17:01:58.000000000 -04:00 type: post published: true status: publish categories: - Analytics - Tuesday Tips tags: - errors - google analytics - tips & tricks meta: _wpcom_is_markdown: '1' _publicize_twitter_user: "@chris_vannoy" _edit_last: '1' image: '' _thumbnail_id: '69' _wpas_done_all: '1' _wpas_mess: 'Tuesday Tip: Tracking website errors in Google Analytics http://wp.me/p5eaMq-19' _wpas_skip_9013372: '1' _wpas_skip_9017901: '1' _yoast_wpseo_metadesc: Why tracking your errors - 404s and 500s - in Google Analytics are a good idea and best done through custom events. thumb: http://jekyll-blog-cv.s3.amazonaws.com/product-icon.png ---
Note: This post originally appeared on the blog of my since-shuttered analytics firm: Axiomatic. That said, if you need some analytics work, get in touch
One of the things your website analytics should do the absolute best for you is letting you know when things went wrong.
When your search engine referrals fall of a cliff or when your conversion rate falls, you should be able to rely on your site analytics to tell you that it happened.
Today, though, let's talk about all your other errors - 404s and 500s primarily - and how best to track (and report on) with Google Analytics.
If you're a developer (or friendly with your dev staff), chances are you already have error tracking for your site or apps1. So, why bother to track it in GA as well?
First, chances are you're not going to have the marketing department traipsing through your dev error tracking system, right?
Secondly, most developer error tracking services are geared more to individual issues: What went wrong and how to fix it. Tracking your errors in GA unlocks all of the other GA features - segmenting, technology, trends and the like - to tell you less about what went wrong and more about who's been affected.
Also, a class of errors that's just noise to most developers - 404s - can be extremely useful to dig into for marketers.
So, my first recommendation is to track errors, yes, in two places - your dev error tracking of choice for issue resolution and in Google Analytics for trends, impact and marketing knowhow.
As I mentioned up top, there are two primary classes of errors that are especially handy to track in GA: 404s and 500s. Javascript errors are also handy, but we'll save that for next week's Tuesday Tip.
Let's walk through them one by one.
As I alluded to earlier, most development teams treat 404s as simply noise. In most cases, nothing's actually broken from a code standpoint and, to be honest a 404 means your system is likely doing exactly what it should.
If you're a marketer, however, 404s can tell you a great deal:
The key thing here is to not track them as a page view; you don't want errors inflating your other metrics. Each of these error classes is better tracked using Custom Events.
The other important tip is to not redirect the URL to a single 404 page (ala www.yoursite.com/404.html) but rather render the 404 template and keep the URL the same. Essentially, to maximize the tracking's usefulness, we want to keep the referral, visitor and path information intact.
There are of course ways to still track all that formation while redirecting to a fresh page, but it's going to be more work for you in the long run.
To make this work, we're going to need to modify our tracking code a bit.
The default tracking code provided by Google2 looks like the below:
<script>// <![CDATA[
(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
})(window,document,'script','//www.google-analytics.com/analytics.js','ga');
ga('create', <YOUR_UA_ID>, 'auto');
ga('send', 'pageview');
// ]]></script>
So, first off, we want to remove the page view, which is the ga('send', 'pageview');
and replace it instead with some event tracking such as the below:
ga('send', 'event', 'error', '404', window.location.href)
Final full tracking code:
<script>// <![CDATA[
(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
})(window,document,'script','//www.google-analytics.com/analytics.js','ga');
ga('create', <YOUR_UA_ID>, 'auto');
ga('send', 'event', 'error', '404', window.location.href)
// ]]></script>
The nice thing about tracking this way is that you still get all the niceties about your users, acquisition and the like while avoiding pollution of your page view data (including bounce rate).
Once it's in the GA interface, you can still segment by anything you'd like in the Events reporting area (Behavior > Events).
One final note on the above: This change should be made only on the 404 page template. Leave your GA tracking code alone on all other pages.
As a quick recap: 404 errors are when a URL isn't there. The 500 class of errors3 are when something is messed up.
The procedure and technique listed for 404s largely applies here as well - as do the benefits. Again: If you can, don't redirect the page for 500s. Keep them where they are so you can pass the referral information without any extra effort.
Really the only change is in the event action, changing our replacement line of code with:
ga('send', 'event', 'error', '500', window.location.href)
Now, GA isn't going to be able to tell you what the error was, but from a marketing perspective that's immaterial. And besides, your dev team should really be tracking 500s in a more issue-based interface. In this case, GA is handy because you can segment the errors by things like operating system - which can prove hugely helpful for things like errors that might only apply to one browser or operating system.


AS GOOD AS IT GETS A film review by David N. Butterworth Copyright 1997 David N. Butterworth
Rating: **1/2 (Maltin scale)
Meet Melvin Udall, a misanthropic, racist, homophobic bigot with an intense dislike of dogs.
Well, one dog in particular, perhaps--Verdell, a button-cute Brussels Griffon from across the hall. Melvin's had about all he's gonna take from this pint-sized urinating machine and, in a scene we've all seen in the three month's worth of previews, shoves him down the trash chute. Given that old stalwart "no animals were mistreated during the making of this motion picture," Verdell shows up again a little rumpled--doggie dignity included--but none the worse for wear.
This initial man vs. beast confrontation in "As Good As It Gets" does little to endear Melvin to us. After disposing of the furry little critter this reclusive writer of romance novels holes himself up in his Manhattan apartment (turning the lock five times very deliberately) and proceeds to wash his hands in scalding water, wasting two entire bars of Neutrogena in the process.
Melvin, you see, suffers from a chronic obsessive-compulsive disorder, and it's not long before we witness further outrageous behaviors on his part: Melvin insulting Jews, blacks, and Panamanians alike with vicious, political incorrectness; Melvin twisting himself pretzel-like along a busy street in order to avoid stepping on the cracks in the sidewalk. And so on.
Later still we realize that Melvin isn't someone to be hated, however. Misanthropy isn't a choice for him; it's a disease.
When Verdell's owner Simon is badly beaten during a robbery, Melvin is "entrusted" with caring for the cow-eyed canine. (Simon, a neighboring, almost-successful artist is affectionately played by Greg Kinnear.) During Simon's convalescence, Verdell has an extraordinarily humanizing effect on the antisocial author.
It's hard to imagine anyone but Jack Nicholson playing Melvin. He's terrific, in that Jack Nicholson kind of way, and is likely to garner an Oscar nomination for his ebullient, eyebrow-centric performance. Helen Hunt plays Carol, a struggling single mother with an asthmatic son at home. She's the only waitress in the coffee shop that Melvin frequents who can stand him for more than a few minutes. Hunt's puffy-cheeked nervous fidgeting isn't much of an extension of her Jamie Buchman character on TV's "Mad About You."
Things clunk, rather than click, between the two of them.
James L. Brooks, the successful producer of both big and small screen projects--last year's "Jerry Maguire" and TV's "The Tracey Ullman Show" and "The Simpsons" to name a few--has only a small list of directing credits. Clearly he chooses his directing assignments carefully and clearly he likes to employ Nicholson, who won an Academy Award for his supporting work in "Terms of Endearment," and provided "Broadcast News" with a wonderful, uncredited cameo.
As is typical with any Brooks production, the writing in "As Good As It Gets" (by the director and Mark Andrus) has a depth that's rare by Hollywood standards but, at 138 minutes, the film stumbles along and at times seems contrived. That Carol should settle for someone as beyond-her-years as Melvin seems more a product of casting than it does story construct.
That said, "As Good As It Gets" is decent, if not vintage, James L. Brooks, made all the more palatable by that man Jack Nicholson.
-- David N. Butterworth<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


To make the ashes of flowers and herbs leave molds
Some people put quicksilver inside. But, if it is a little work, or fine & delicate foliage, that only has a slender issue, they make two errors: the first, that quicksilver by its heaviness can destroy some delicate tract inside in coming out, the other, that some bit will always linger inside that will make other metals sour & inhibit the perfection of the casting. It is true that if it is to empty the mold of some large animal that has big conduits & passages through which the quicksilver can easily exit, like of a bird or a serpent, one can indeed put some amount of quicksilver to break the calcinated bones of the animal in escaping, because of which the said ☿ will empty out & not remain at all.
at left top margin
The asparagus stalk is so hard that most often it perseveres as charcoal. Because of this, do it rather dried out or wet it with sulfur oil & turpentine, or indeed cast the little branches separately & solder them onto a fat stalk drawn through with a wire.
at left middle margin
If the burnt thing has left behind some filth or ash, let it cool down a little, & with an iron wire wrapped in cotton that can bend with the cavities that you have, investigate. Clean & blow to a fault, either with a soft brush or a clipped brush.
Daisies
They can be cast well in gold. But if you want to enamel them, you have to make them by hand & enamel them & then attach them. Otherwise, the leaves would be so pressured that it would encrust the enamel.
Sand that has already been used
Do not cast it. But because it is mixed with stone allum, you can use it in the mixture of other things & it can serve in place of brick.
Core molding wax figures or lead medals
Rub them with oil with a brush. But be sure that it be so softly that your medal be barely wiped with it & that it hardly appear to have been greased. After rub it with spirits and heat the water with which you will wet your sand, that of plaster, brick & alum, so that being sort of lukewarm when you cast it, the oil will not refuse it, as it does with cold water, & do not forget to moisten your sand with it, mixing it always with a little sal ammoniac.
at left bottom margin
Medals molded in this way are very fine.
Snakes’ blood
If you have to cut some snake inside a mold in order to burn it, cut it far from the intake of the mold so that no blood at all remains, because it makes a crust that afterwards would not be taken away by the same quicksilver & removes impressions from your mold.


Your template engine seems to work perfectly!
This rendering is made running:
$output = $template_engine->render( 'hello.htm', // this is the view file name array( // these are the parameters passed to the view file // as PHP variables 'name' => isset($_GET['name']) ? $_GET['name'] : 'Anonymous' ) ) // which means that, in your view file, you can write: echo $name
To test it, add a "name" variable to your current URI, for instance name=Myname ...
In a view file, $_template is the whole template engine and is array-accessible:
$_template['web_root_path'] // will be rewritten as $_template->getWebRootPath() // and will be searched in the "view" or "template" objects $_template->template->getWebRootPath() $_template->view->getWebRootPath() // in this case: $_template['web_root_path'] = $_template->template->getWebRootPath() => ""
See the documentation for a full PHP objects review.


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Born in Arizona but raised in the foothills of the Rockies in Idaho, I grew up loving the outdoors and the adventure that comes with it. I graduated from Centennial High School in 2009 and quickly went off to college at Boise State University. I only spent a semester there, as I was young and not quite sure what I wanted to do with my life yet. I had many hobbies that I enjoyed, but one of my favorites was Scuba Diving, so after some research I packed my bags and moved to Honduras to get my PADI Scuba Instructor licence. All of the sudden I found myself, at just 19 years old, jetting around the world to teach scuba for different schools and resorts! This ended up being one of the best decisions I ever made as I gained knowledge and expereienc working with people of all ages, from all different countries and backgrounds, where I constantly helped others work through problems so they could reach their goals.
After 4 years and over 10 countries, I found myself missing home and ready for the next adventure. I moved back to Boise in 2014 where I worked a number of different jobs over a few years before being offered a position to help start an indoor cannabis farm in Portland, Oregon. I assisted with the creation of the company for two years, which found me in a dark place. Being the only woman working for the company at the time, I struggled to find my place and felt ostracized for not being "one of the boys". I longed to have more freedom with my career and wished to be in a position where I could let my creativity juices flow, while also being recognized for the hard work that I did.
Jump to present day and you'll find me learning all things coding at Epicodus! I was introduced to this field by my partner and after doing some heavy research I decided that the desk job I always feared might actually be the job that I always wanted. I look forward to learning new things daily, challenging myself, but especially those moments of overcoming problems and reaping the benifits of my hard work. I am so excited to become apart of this field and it's people. I can't wait to see what the future holds for me!
This was my first ever webpage! I was able to create a page talking a little bit about myself and some of my favorite animals...like the Sealion! I used html and css, all done using Visual Studio Code.
On this project, I worked on a team with a fellow programmer. We created a page that showcased three travel packages to New Zealand, all with different offers and at different costs. We used html, css, and bootstrap. All code was built using Visual Studio Code.
This was a team project done with 2 other programmers at Epicodus. We created a page about the iconic band Queen! In this project we used htlm, css, added images and built all of the code using Visual Studio Code.


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[gecode-users] Float overflow exceptions with 'linear' in 6.0.1
Konvicka Filip
Filip.Konvicka at logis.cz
Fri Aug 24 09:48:29 CEST 2018
Hi,
I'm in process of upgrading our code to Gecode 6.0.1 (which looks great by the way!)
I found some cases where code that used to run fine with Gecode 5 now throws exceptions. The following is a simple example that demonstrates this. I was able to work around to defaulting to (Gecode::Float::Limits::max/4) as the default max bound of float variables, but I think it's still probably a bug.
Thanks,
Filip
#include <gecode/float.hh>
#include <iostream>
using namespace Gecode;
int main() {
struct MySpace : public Space {
Space* copy() { return nullptr; }
};
MySpace s;
try {
FloatVarArgs args(2);
args[0] = FloatVar(s, 0., Gecode::Float::Limits::max);
args[1] = FloatVar(s, 0., Gecode::Float::Limits::max);
FloatVar result(s, 0., Gecode::Float::Limits::max);
linear(s, args, FRT_EQ, result);
}
catch (std::exception& e) {
std::cerr << e.what() << "\n";
}
try {
FloatVarArgs args(2);
args[0] = FloatVar(s, 1., 1.);
args[1] = FloatVar(s, 0., Gecode::Float::Limits::max);
FloatVar result(s, 0., Gecode::Float::Limits::max);
linear(s, args, FRT_EQ, result);
}
catch (std::exception& e) {
std::cerr << e.what() << "\n";
}
}
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