跟讀練習: How I deploy serverless containers for free - 通過影片學習英語口語

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One of the most annoying things in the world is when you're trying to steal an image from the internet, and it looks like it's going to have a transparent background, but when you download it, it looks like this.

Luckily, nowadays there are all kinds of good tools for removing backgrounds from images, like RemoveBG or the new AI tools in Photoshop.

Now if you watch my videos, you'll notice I use a lot of images with the background removed, and it's extremely inefficient to have to go into Photoshop, upload an image, remove its background,

and then re-export it and bring it back into Adobe Premiere.

As a developer, this inefficiency is totally unacceptable, and my only option was to build my own app from scratch.

In today's video, I want to show you how I built this background remover from scratch, but more importantly, I want to talk about why I dockerized it, and explain how I deployed it to the cloud for free.

Earlier this week on the main channel, I made a Docker video, which you should definitely check out first if you have no idea what Docker is, but that video was sponsored by Docker.

This video is not sponsored.

I don't work with sponsors very often, but when I do, it's usually with tools that I have first-hand experience with, and Docker is something I use all the time.

By containerizing this background remover, which is just a Python web app, I'm able to run it locally with the click of a button, and also deploy it to the cloud with a single command.

Without Docker, I would have to go into my terminal, make sure I have the right Python dependencies installed, and then run the app in the background every time I want to use it.

And on top of that, deployment to the cloud would be a lot more complex, and also more expensive.

First I want to talk about the app itself.

The reason I built this app in Python, and not my typical choice of JavaScript, is that there's a python package called removeBG,

which is based on the U2net model to magically remove the background with AI.

Oh man, I just realized I said the A word again.

I guess we have to reset the counter, but really this video has nothing to do with AI.

The model itself is heavily abstracted, to the point where all we do is open an image with Pillow, call the remove function from this library, which returns a new image with the background removed.

It's an extremely practical use case for image models, but I don't want to use it from the terminal.

I want to be able to drag and drop images directly from my browser into it, so I can then drag the result directly back into my Adobe Premiere timeline.

To do that, I built a little app with Flask, which creates a single HTTP route that handles both git and post methods.

Git displays the initial webpage, then when we drag an image into that webpage, it makes a post request, which calls that remove background function.

Now the website itself is rendered in this index.html file, which uses nothing but plain JavaScript and CSS.

There's an HTML form with a file input, then when that form is submitted, it makes a post request to the root URL.

I also wrote a little bit of JavaScript here that will automatically submit the form when a file is dropped onto it, just to make the process even more efficient.

That's the entire app, and now I can run it from the terminal with the Python command.

But now here's where Docker comes in.

I want to be able to use this code on multiple computers, and I also want to deploy it to the web

so I could even use it from my phone or some other device.

And I want everyone in the world to have the opportunity to use my awesome invention.

First, you'll need to have Docker installed.

I'm doing that through Docker Desktop, but in the past I've used tools like Podman, which is developed by Red Hat and is also a good option.

But now we need to go into our code and create a Docker file.

The Docker file itself is very simple.

It starts with the official Python base image, creates a working directory for the app, installs the dependencies, copies the code, exposes a port, and then runs the app.

The only unusual thing I'm doing here is taking the actual AI model weights and copying them into the Docker image.

The weights are about 175 megabytes, and doing this prevents the actual Python package from downloading asynchronously, which will slow things down in general.

Now let's build the image and make sure to give it a tag.

That'll take a minute, then if we go into Docker Desktop, we should see it here in the images panel.

To actually run the image as a container, we simply hit the play button, and make sure to map the port to something we can use on localhost.

And now the app is always ready to go in the background with Docker.

Pretty cool, and that's how I use this tool 90% of the time, but I also want to show you how to deploy it to the web.

There's a bunch of different options for deploying containers to the cloud, and there's also some free options if you're cheap like me.

The most well-known option is Elastic Container Engine on AWS with a related service called Fargate

that can deploy your container in a serverless way, which means it will scale down to zero when it's not in use

and then scale back up once the requests start coming in.

You've also got services like the app platform on DigitalOcean, which starts at $0 a month, but my go-to for deploying random utilities like this is Google Cloud Run.

To deploy something that's dockerized, though, you first need to get your image on a registry.

Every cloud has one built in, and on Google, it's called Artifact Registry.

What you do is create a repository for your images that'll store them in a specific region, and then you can copy this link up here, which can be used as a tag on your images,

so it knows where to upload them.

Let's go into the terminal and use the docker tag command to tag our existing image with this namespace.

Once that's done, we can use the docker push command to upload it to Google Cloud.

Now, one caveat is that of course you need a Google Cloud account, and you'll also need the gcloud CLI tool installed on your system.

But once that's done, you should then be able to see the image in Google Cloud.

One nice thing about this is that if you want to use this image on a different machine, you can simply pull it from this repo.

But now let's head over to Cloud Run and deploy it to the internet.

Create a new service, then the first thing you'll do is select that container image.

Now from here we have a bunch of configuration options, but if you want to make this a public web service, the most important one is to allow unauthenticated invocations.

That means anybody can access it from a public API or URL.

The next option is CPU allocation.

One problem with serverless deployments is that when the app is not being used, it scales down to zero, which is great because it means you're not paying for anything, but the tradeoff is a cold start,

which means it takes like four or five seconds for the thing to boot up when the next request comes in.

In my case, that's not a problem, but if you want to eliminate cold starts, you can make sure that the CPU is always allocated.

It's just going to cost more because you'll always be burning through these free CPU seconds every month.

Now from there, let's go down to the container options, and one thing we'll also want to change here is the allocated memory for the container.

It takes a lot of memory to run the AI model, so let's bump this up to two gigabytes.

One other thing I want to do is also decrease the amount of auto-scaling this thing can do.

Instead of 100 maximum instances, I'm only going to allow three.

We just don't need to be prepared to scale for this type of app, but it's nice to have that option if you're building something viral.

Let's go ahead and deploy it, and a few minutes later, we should have a URL where we can actually access our Python app on the web.

Pretty awesome, and one huge benefit of having this all dockerized is that our code is portable, so if we want to get off Cloud Run, we could take that to any other cloud service and deploy it there just as easily.

And that's basically all there is to it.

There's a ton of other stuff we could talk about when it comes to Docker and Cloud run, but let me know what you want to see next in the comments.

I do have one update for Fireship Pro members.

I'm currently finishing up a new Stripe course that's designed specifically for people building software as a service products.

More details to come on that soon.

Thanks for watching, and I will see you in the next one.

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本片段的口語練習目標

這段影片非常適合想要提升英語影子跟讀能力和流暢度的學習者。內容圍繞技術工具的製作與部署展開，包含大量實用的說明性表達，能幫助你掌握清晰描述步驟、解釋原因的口語技巧，尤其適合雅思口語練習中“描述過程”類話題。通過跟讀，你能快速提升在專業場景下的語言組織能力，實現從“能說”到“說清楚”的 milestones。

實用表達積累

“One of the most annoying things in the world is when...”（世界上最煩人的事之一是……）——用於引出問題，增強表達感染力。
“Luckily, nowadays there are all kinds of good tools for...”（幸運的是，現在有各種好用的工具來……）——轉折並提出解決方案，邏輯清晰。
“By containerizing this..., I'm able to...”（通過將……容器化，我能夠……）——技術場景中描述方法與效果的經典句式。
“The reason I... is that...”（我……的原因是……）——解釋動機，讓表達更有深度。

攻克發音與節奏難點

影片中存在大量長句和專業術語（如“Docker”“containerize”），其連讀和重音處理是練習重點。比如“containerizing this background remover”中，“containerizing”的重音在第二個音節，“background remover”需連讀流暢。通過英語影子跟讀，你能模仿 native speaker 的節奏，解決“卡殼”問題。此外，影片中的自然停頓（如在解釋步驟時的短暫停頓）也值得學習，能讓你的口語更具層次感，有效提高英語發音和表達流暢度。每天堅持10分鐘，一週即可感受到明顯進步！

無論是英語口語練習還是技術英語提升，這段影片都是絕佳素材。趕緊開啟影片，跟著“shadowspeaks”的節奏，一起練起來吧！

影片中的語法

說話人最常用的結構，並附上影片中的原話：

結構	影片中的用法
被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的	was sponsored · is not sponsored · is based
定語從句 who / which + 從句 — 補充說明人或事物	remover, which is · Podman, which is · way, which means

什麼是跟讀法？

跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。

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跟讀練習: How I deploy serverless containers for free - 通過影片學習英語口語

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本片段的口語練習目標

實用表達積累

攻克發音與節奏難點

影片中的語法

什麼是跟讀法？