computer architecture – A question about the pipeline cycle

This is my question, I am so confused with my answer that I am looking for help!
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This is my answer:
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However, the instructor said that if an instruction is waiting before the ID, the next IF phase instruction must start in 6th place in the 3rd (what I did)

Therefore, I am looking for help and I want someone to discuss and verify the idea with me. Thank you very much for the help!

For example, which of the two IFs is appropriate?

Monogame: Load SpriteFont using the .spritefont file without using the Pipeline tool

There is a way to load a sprite font without using the Monogame Pipeline tool?

I am creating a Game-Engine using Monogame, I cannot use the Monogame Pipeline tool because the way Game-Engine manages the content files is different, and you need to read the source file, not the .xnb file that the Monogame Pipeline tool creates .

Monogame: load SpriteFont using the .spritefont file without the Pipeline tool

There is a way to load a sprite font without using the Monogame Pipeline tool?

I am creating a Game-Engine using Monogame, I cannot use the Monogame Pipeline tool because the way Game-Engine manages the content files is different, and you need to read the source file, not the .xnb file that the Monogame Pipeline tool creates .

Is Azure DevOps Pipeline CI a good practice when creating your own pipe?

I am using Azure DevOps Pipeline for CI and CD (Continuous Integration and Continuous Implementation). I realize that there are many ways to define the construction of Pipeline. Let me explain with examples:

For one of my projects, I started creating 2 Pipelines. Both aimed at the same repository, the same solution but one for desktop compilation and the other for web API compilation (publishing method).

I realize that this takes twice as long because each pipe definition works independently. Also for all my tests, I decided to merge these 2 pipes as 2 agents into a single pipe.

Then I decided to merge my agent 2 in one and move only my compilation post into this unique agent. So now I have a pipeline with an agent but 2 compilation tasks.

It is right. For now it satisfies my needs, but what about the tests? Good practices for a reason I still ignore.

For example, my version 2 is copying the files into a single drop artifact. I am thinking of creating 2 artifacts. What are the rules?

I don't know what are the best practices or the reasons to design your compilation definition.

asynchronous: Python AsyncIO pipeline

I wrote a small fragment to implement Asyncio Pipeline, an object that connects the layers and allows them to create and pass through the objects. Each layer could perform some IOs to create / store an object or calculate something to create on an object on the fly or delete an object. Layers are connected to Queues, and have states with Events activated when that state changes.

The most questionable part was how to let the entire process (I mean the pipeline run, not the system process) from anywhere in the layer or outside the pipeline. With that I want to get help (or recommendation).

Code snipper in GitHub Gist

import typing
import asyncio


class Layer:
    class STATES:
        IDLE = 1
        RUNNING = 2
        GOING_TO_STOP = 3
        STOPPED = 4

    class DEFAULT:
        QUEUE_MAX_SIZE = ...

    needs_next_layer: bool = False
    next_layer_type: typing.Type('Layer') or None = None

    def __init__(self, queue_max_size: int = DEFAULT.QUEUE_MAX_SIZE):
        self.next_layer = None
        self.queue_max_size = queue_max_size

        self.state = self.STATES.IDLE
        self.queue: asyncio.Queue = asyncio.Queue(maxsize=queue_max_size)
        self.running_task: asyncio.Task = None

        self.started_event = asyncio.Event()
        self.stopping_event = asyncio.Event()
        self.stopped_event = asyncio.Event()

    def connect_next_layer(self, next_layer: 'Layer'):
        if not isinstance(next_layer, self.next_layer_type or Layer):
            raise TypeError
        self.next_layer = next_layer

    async def start(self):
        self.state = self.STATES.RUNNING
        self.started_event.set()

        self.running_task = asyncio.create_task(self._start())
        await self.running_task
        await self.stop()

    async def stop(self):
        self.state = self.STATES.GOING_TO_STOP
        self.stopping_event.set()

        await self.queue.join()
        await self._stop()

        self.running_task.cancel()

        self.state = self.STATES.STOPPED
        self.stopped_event.set()

    async def _start(self):
        pass

    async def _stop(self):
        pass

    async def stop_at_event(self, event: asyncio.Event):
        await event.wait()
        await self.stop()

    async def forward_item(self, obj):
        await self.next_layer.queue.put(obj)

    async def read_item(self):
        return await self.queue.get()

    def done_item(self):
        self.queue.task_done()

    def cancel(self):
        self.running_task.cancel()


class Pipeline:

    def __init__(self, layers: typing.Sequence(Layer)):
        self.layers = tuple(layers)
        self._connect_layers()

        self.start_layers_future = self._create_start_future()
        self.stop_layers_future = self._create_stop_future()
        self.stop_self_task: asyncio.Task = None
        self.running_future: asyncio.Future = None

    async def start(self):
        self.stop_self_task = asyncio.create_task(
            self.stop_at_event(self.layers(-1).stopped_event)
        )
        try:
            self.running_future = asyncio.gather(
                self.start_layers_future,
                self.stop_layers_future,
                self.stop_self_task,
                return_exceptions=True,
            )
        except Exception as exc:
            print(exc)
        await self.running_future
        await self.stop()

    async def stop(self):
        for layer in self.layers:
            await layer.stop()
            layer.cancel()
        self.start_layers_future.cancel()

    async def stop_at_event(self, event: asyncio.Event):
        await event.wait()
        await self.stop()

    def _create_start_future(self) -> asyncio.Future:
        coros = (layer.start() for layer in self.layers)
        return asyncio.gather(*coros, return_exceptions=True,)

    def _create_stop_future(self) -> asyncio.Future:
        coros = ()
        for idx in range(1, len(self.layers)):
            layer = self.layers(idx-1)
            layer_to_stop = self.layers(idx)
            coro = layer_to_stop.stop_at_event(event=layer.stopped_event)
            coros.append(coro)
        return asyncio.gather(*coros, return_exceptions=True,)

    def _connect_layers(self):
        for idx in range(1, len(self.layers)):
            prev_layer = self.layers(idx-1)
            next_layer = self.layers(idx)
            prev_layer.connect_next_layer(next_layer)

PD: You have plans to extend Pipeline to run multiple instances of the exact layer at the same time. Therefore, it could have, say, 4 first concurrent layers and 16 concurrent second layers with shared queue. But I really don't know if I should create another question

execute – Use the pipeline operator ("|") when executing system commands

I would like to emulate

ls -tlra | grep 

in Wolfram I have tried

Module[{},
  SetDirectory[dir]
  RunProcess[{"ls", "-tlra", "|", "grep", }, "StandardOutput"]
]

that does not seem to work Is there any way to use the "pipe" operator when commands are discarded in Wolfram?

spo: SharePoint Online migration pipeline configuration for Metalogix

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g suite: publish Bitbucket pipeline artifacts to Google Drive

I am managing some documents in the version control of Git, hosted in Bitbucket.

After a boost to Bitbucket, I would like the latest version of these documents to be visible to a select group of people.

It would be easy for us if these documents were written in a Google Drive folder (which grants fine-grained "share" permissions allowing fine-grained access control to documents.

Is there a standard solution for publishing Bitbucket Pipeline artifacts in a G Suite folder (through the GSuite API)? (As there is for other data stores such as AWS).

python – Tokenizer in sklearn pipeline

Please help me add Tokenizer to the pipe
I'm using

max_words = 10000
max_len = 40
tok = Tokenizer(num_words=max_words, filters='!"#$%&()*+,-./;<=>?@(\)^_`{|}~tn', lower=True, split=' ')
tok.fit_on_texts(X_train)
sequences = tok.texts_to_sequences(X_train)
sequences_matrix = sequence.pad_sequences(sequences,maxlen=max_len)

It works well. But I want to use this with the pipe
I'm trying

pipe = make_pipeline(Tokenizer(num_words=max_words, filters='!"#$%&()*+,-./;<=>?@(\)^_`{|}~tn', lower=True, split=' '))
pipe.fit(X_train)
sequences = pipe.texts_to_sequences(X_train)
sequences_matrix = sequence.pad_sequences(sequences,maxlen=max_len)

It does not work giving error

TypeError: Last step of Pipeline should implement fit or be the string 'passthrough'. '(('tokenizer', )

sharepoint server: the item was canceled because the pipeline did not respond in a timely manner. This item will be tried again in the next crawl

We face two problems related to SharePoint search

Problem 1:

The article was aborted because the pipe did not respond within the appropriate time. This item will be tried again in the next trace.

Previous error in SharePoint 2013 search crawl.

We have tried to follow,

  1. Index reset in the search service application.
  2. Stop the timer service -> Clear configuration cache. -> Start the timer service.
  3. Restart Search Host Control Service.
  4. A new search service application was created.
  5. Set the level of search performance to a reduced and increased timeout value in agricultural search management.

The error is majority on the DispForm.aspx page.
The solution suggested below also does not work

The pipe did not respond within the appropriate time

Something we are on mission for?

Number 2

We have created the managed property of type DateTime "ReceivedDate" in the search scheme that can be searched, queried, retrieved.

But when we try to search only using Receipt date> 01/01/2015 We are not getting any results. But if we concatenate with another managed property of text type, we can see the filtered results.