Now since the “compiling to bytecode” step above takes a noticeable amount of time when you import a module, Python stores (marshalls) the bytecode into a .pyc file, and stores it in a folder called __pycache__. The __cached__ parameter of the imported module then points to this .pyc file.When the same module is imported again at a later time, Python checks if a .pyc version of the module exists, and then directly imports the already-compiled version instead, saving a bunch of time and computation.

Bytecode is a set of micro-instructions for Python’s virtual machine. This “virtual machine” is where Python’s interpreter logic resides. It essentially emulates a very simple stack-based computer on your machine, in order to execute the Python code written by you.

Python is compiled. In fact, all Python code is compiled, but not to machine code — to bytecode

Python always runs in debug mode by default.The other mode that Python can run in, is “optimized mode”. To run python in “optimized mode”, you can invoke it by passing the -O flag. And all it does, is prevents assert statements from doing anything (at least so far), which in all honesty, isn’t really useful at all.

np = __import__('numpy') # Same as doing 'import numpy as np'

This refers to the module spec. It contains metadata such as the module name, what kind of module it is, as well as how it was created and loaded.

let’s say you only want to support integer addition with this class, and not floats. This is where you’d use NotImplemented

class MyNumber:
    def __add__(self, other):
        if isinstance(other, float):
            return NotImplemented

        return other + 42

__radd__ operator, which adds support for right-addition

class MyNumber:
    def __add__(self, other):
        return other + 42

    def __radd__(self, other):
        return other + 42

Now I should mention that all objects in Python can add support for all Python operators, such as +, -, +=, etc., by defining special methods inside their class, such as __add__ for +, __iadd__ for +=, and so on.

class MyNumber:
    def __add__(self, other):
        return other + 42

>>> num = MyNumber()
>>> num + 3
45

NotImplemented is used inside a class’ operator definitions, when you want to tell Python that a certain operator isn’t defined for this class.

Doing that would even catch KeyboardInterrupt, which would make you unable to close your program by pressing Ctrl+C.

every exception is a subclass of BaseException, and nearly all of them are subclasses of Exception, other than a few that aren’t supposed to be normally caught.

print(dir(__builtins__))

builtin scope in Python:It’s the scope where essentially all of Python’s top level functions are defined, such as len, range and print.When a variable is not found in the local, enclosing or global scope, Python looks for it in the builtins.

Global scope (or module scope) simply refers to the scope where all the module’s top-level variables, functions and classes are defined.

you can use the nonlocal keyword in Python to tell the interpreter that you don’t mean to define a new variable in the local scope, but you want to modify the one in the enclosing scope.

The enclosing scope (or nonlocal scope) refers to the scope of the classes or functions inside which the current function/class lives.

The local scope refers to the scope that comes with the current function or class you are in.

A builtin in Python is everything that lives in the builtins module.

in Python 3.0 (alongside 2.6), A new method was added to the str data type: str.format. Not only was it more obvious in what it was doing, it added a bunch of new features, like dynamic data types, center alignment, index-based formatting, and specifying padding characters.

So, while DELETE operations are free, LIST operations (to get a list of objects) are not free (~$.005 per 1000 requests, varying a bit by region).

TypedDict is a dictionary whose keys are always string, and values are of the specified type. At runtime, it behaves exactly like a normal dictionary.

you should only use reveal_type to debug your code, and remove it when you’re done debugging.

What this says is “function double takes an argument n which is an int, and the function returns an int.

This tells mypy that nums should be a list of integers (List[int]), and that average returns a float.

from typing import List

def average(nums: List[int]) -> float:

for starters, use mypy --strict filename.py

few battle-hardened options, for instance: Airflow, a popular open-source workflow orchestrator; Argo, a newer orchestrator that runs natively on Kubernetes, and managed solutions such as Google Cloud Composer and AWS Step Functions.

To make ML applications production-ready from the beginning, developers must adhere to the same set of standards as all other production-grade software. This introduces further requirements:

In contrast, a defining feature of ML-powered applications is that they are directly exposed to a large amount of messy, real-world data which is too complex to be understood and modeled by hand.

As a character, I believe that I am quite a responsible and hardworking person. The combination of the two becomes dangerous when you lose the measure of how much you should work to get a job done right.

UPDATE--SHA-1, the 25-year-old hash function designed by the NSA and considered unsafe for most uses for the last 15 years, has now been “fully and practically broken” by a team that has developed a chosen-prefix collision for it.

So, what’s a better way to illustrate JOIN operations? JOIN diagrams!

State of Data Science and Machine Learning 2021

iOS 15.0 introduces a new feature: an iPhone can be located with Find My even while the iPhone is turned "off"

Argo Workflow is part of the Argo project, which offers a range of, as they like to call it, Kubernetes-native get-stuff-done tools (Workflow, CD, Events, Rollouts).

Argo is designed to run on top of k8s. Not a VM, not AWS ECS, not Container Instances on Azure, not Google Cloud Run or App Engine. This means you get all the good of k8s, but also the bad.

If you are already heavily invested in Kubernetes, then yes look into Argo Workflow (and its brothers and sisters from the parent project).The broader and harder question you should ask yourself is: to go full k8s-native or not? Look at your team’s cloud and k8s experience, size, growth targets. Most probably you will land somewhere in the middle first, as there is no free lunch.

In order to reduce the number of lines of text in Workflow YAML files, use WorkflowTemplate . This allow for re-use of common components.

Wet behind the ears: the ones that have just started working as a professional programmers and need lots of guidance. Sort of knows what they are doing: professional programmers, who have sort of figured what is going on and can mostly finish their tasks on their own. Experienced: the ones who have been around the block for a while and are most likely the brains behind all the major design decisions of a large software project. Coworkers usually turn to them when they need advice for a hard technical problem. Some programmers will never reach this level, despite their official title stating otherwise (but more on that later).

The problem with the first approach is that it considers software development to be a deterministic process when, in fact, it’s stochastic. In other words, you can’t accurately determine how long it will take to write a particular piece of code unless you have already written it.

Reading books, being aware of the curiosity gap, and asking a lot of questions:

The difference between smart and curious people and only smart people is that curiosity helps you move forward in life. If you shut the door to curiosity. You shut the door to learning. And when you don’t learn. You don’t move forward. You must be curious to learn. Otherwise, you won’t even consider learning.

Smart people become even smarter because they are smart enough to understand that they don’t have all the answers.

You probably shouldn't use Alpine for Python projects, instead use the slim Docker image versions.

Cost — it’s by far the most affordable headset in its class, and while I have a tendency to be lavish with my gadgetry I’m still a cheapskate: I love a good deal and a favorably skewed cost/benefit ratio even more.

Adapting to the new environment is immediate, like moving between rooms, and since the focal length in the headset matches regular human vision there’s no acclimating or adjustment.

I do highly contextual work, with multiple work orders and their histories open, supporting reference documentation, API specifications, several areas of code (and calls in the stack), tests, logs, databases, and GUIs — plus Slack, Spotify, clock, calendar, and camera feeds. I tend to only look at 25% of that at once, but everything is within a comfortable glance without tabbing between windows. Protecting that context and augmenting my working memory maintains my flow.

Realism will increase (perhaps to hyperrealism) and our ability to perceive and interact with simulated objects and settings will be indistinguishable to our senses. Acting in simulated contexts will have physical consequences as systems interpret and project actions into the world — telepresence will take a quantum leap, removing limitations of time and distance. Transcending today’s drone piloting, remote surgery, etc., we will see through remote eyes and work through remote hands anywhere.

It has a ton of promise, but… I don’t really care for the promise it’s making. 100% of what you can do in Workrooms is feasible in a physical setting, although it would be really expensive (lots of smart hardware all over the place). But that’s the thing: it’s imitating life within a tool that doesn’t share the same limitations, so as a VR veteran I find it bland and claustrophobic. That’s going to be really good for newcomers or casual users because the skeuomorphism is familiar, making it easy to immediately orient oneself and begin working together — and that illustrates a challenge in design vocabulary. While the familiar can provide a safe and comfortable starting point, the real power of VR requires training users for potentially unfamiliar use cases. Also, if you can be anywhere, why would you want to be in a meeting room, virtual-Lake Tahoe notwithstanding?

For meeting with those not in VR, or if I have a video call that needs input rather than passive attendance, I’ll frequently use a virtual webcam to attend by avatar. It’s sufficiently demonstrative for most team meetings, and the crew has gotten used to me showing up as a digital facsimile. I’ll surface from VR and use a physical webcam for anything sensitive or personal, however.

Meetings are best in person, in VR, in MURAL, and in Zoom — in that order. As a remote worker of several years, “in person” is a rarity for me — so I use VR to preserve the feeling of shared presence, of inhabiting a place with other people, especially when good spatial audio is used. Hand tracking enables meaningful gestures and animated expression, despite the avatars cartoonish appearance — somehow it all “just works”, your brain accepts that these people you know are embodied through these virtual puppets, and you get on with communicating instead of quibbling about missing realism (which will be a welcome improvement as it becomes available but doesn’t stop this from working right now).

What’s it like to actually use? In a word: comfortable. Given a few more words, I’d choose productive and effective. I can resize, reposition, add, or remove as much screen space as I need. I never have to squint or lean forward, crane my neck, hunt for an application window I just had open, or struggle to find a place for something. Many trade-offs and compromises from the past no longer apply — I put my apps in convenient locations I can see at a glance, and without getting in my way. I move myself and my gaze enough throughout the day that I’m not stiff at the end of it and experience less eye strain than I ever did with a bunch of desk-bound LCDs.

Since all I need is a keyboard, mouse, and a place to park myself, I’ve completely ditched the traditional desk. I can use a floor setup for part of the day and mix it up with a standing arrangement for the rest.

Supporting 4×4 MIMO takes a lot more power, and for battery powered devices, runtime is FAR more important.

How did your router even get a 'rating' of 5300 Mbps in the first place? Router manufacturers combine/add the maximum physical network speeds for ALL wifi bands (usually 2 or 3 bands) in the router to produce a single aggregate (grossly inflated) Mbps number. But your client device only connects to ONE band (not all bands) on the router at once. So, '5300 Mbps' is all marketing hype.

The only thing that really matters to you is the maximum speed of a single 5 GHz band (using all MIMO antennas).

You have 1 Gbps Internet, and just bought a very expensive AX11000 class router with advertised speeds of up to 11 Gbps, but when you run a speed test from your iPhone XS Max (at a distance of around 32 feet), you only get around 450 Mbps (±45 Mbps). Same for iPad Pro. Same for Samsung Galaxy S8. Same for a laptop computer. Same for most wireless clients. Why? Because that is the speed expected from these (2×2 MIMO) devices!

Before we dive into the details, here's a brief summary of the most important changes:

It’s been a hot, hot year in the world of data, machine learning and AI. 

we will be releasing KServe 0.7 outside of the Kubeflow Project and will provide more details on how to migrate from KFServing to KServe with minimal disruptions

You can attach Visual Studio Code to this container by right-clicking on it and choosing the Attach Visual Studio Code option. It will open a new window and ask you which folder to open.

You don’t have to download them manually, as a docker-compose.yml will do that for you. Here’s the code, so you can copy it to your machine:

Kafka version 2.8.0 introduced early access to a Kafka version without Zookeeper, but it’s not ready yet for production environments.

Kafka consumer — A program you write to get data out of Kafka. Sometimes a consumer is also a producer, as it puts data elsewhere in Kafka.

Kafka producer — An application (a piece of code) you write to get data to Kafka.

Kafka topic — A category to which records are published. Imagine you had a large news site — each news category could be a single Kafka topic.

Kafka broker — A single Kafka Cluster is made of Brokers. They handle producers and consumers and keeps data replicated in the cluster.

Kafka — Basically an event streaming platform. It enables users to collect, store, and process data to build real-time event-driven applications. It’s written in Java and Scala, but you don’t have to know these to work with Kafka. There’s also a Python API.

Then I noticed in the extension details that the "Source" was listed as the location of the "bypass-paywalls-chrome-master" file. I reasoned that the extension would load from there, and I think it does at every browser start-up. So before I re-installed it, I moved the source folder to a permanent location and then dragged it over the extensions page to install it. It has never disappeared since. You need to leave the source folder in place after installation.

Some problems with the feature

One thing to note is that match and case are not real keywords but “soft keywords”, meaning they only operate as keywords in a match ... case block.

Use variable names that are set if a case matches Match sequences using list or tuple syntax (like Python’s existing iterable unpacking feature) Match mappings using dict syntax Use * to match the rest of a list Use ** to match other keys in a dict Match objects and their attributes using class syntax Include “or” patterns with | Capture sub-patterns with as Include an if “guard” clause

It’s tempting to think of pattern matching as a switch statement on steroids. However, as the rationale PEP points out, it’s better thought of as a “generalized concept of iterable unpacking”.

We have decided to spend some extra time refactoring the Goose Protocol. The impact of this is that we will have to push the release out a week, but it will save us time when we implement the Duck and Chicken protocols next quarter.

Often, when you ask someone to perform a task, you have an expectation of what the output of that task will look like. Making sure that you communicate that expectation clearly means that they are set up for success and that you get the outcome you anticipated.

I am finding that it is often more important to bring wider context (“Ah, that would align with the work that Team Scooby are working on.") and ask the right questions (“How are we going to roll back if this fails?"), allowing the engineers (more experienced than you or not) to do their best work.

Machine Unlearning is a new area of research that aims to make models selectively “forget” specific data points it was trained on, along with the “learning” derived from it i.e. the influence of these training instances on model parameters. The goal is to remove all traces of a particular data point from a machine learning system, without affecting the aggregate model performance.Why is it important? Work is motivated in this area, in part by growing concerns around privacy and regulations like GDPR and the “Right to be Forgotten”. While multiple companies today allow users to request their private data be deleted, there is no way to request that all context learned by algorithms from this data be deleted as well. Furthermore, as we have covered previously, ML models suffer from information leakage and machine unlearning can be an important lever to combat this. In our view, there is another important reason why machine unlearning is important: it can help make recurring model training more efficient by making models forget those training examples that are outdated, or no longer matter.

The difference appears to be what the primary causes of that burnout are. The top complaint was “being asked to take on more work” after layoffs, consolidations or changing priorities. Other top beefs included toxic workplaces, being asked to work faster and being micromanaged.

What’s left are two options, but only one for the WWW. To capture the most internet users, the best option is to use a .is TLD; however, for true anonimity and control, a .onion is superior.

When you notice someone feeling anxious at work, try grabbing a notebook and helping them to get their concerns and questions onto paper. Often this takes the form of a list of to-dos or scenarios.

Anxiety comes from not seeing the full picture.

Overreaction is often a sign that something else might be going on that you aren’t aware of. Perhaps they didn’t get enough sleep or recently had a fight with a friend. Maybe something about the situation is triggering an unresolved trauma from their childhood — a phenomenon called transference.

In 2020, 35% of respondents said they used Docker. In 2021, 48.85% said they used Docker. If you look at estimates for the total number of developers, they range from 10 to 25 million. That's 1.4 to 3 million new users this year.

kind, microk8s, or k3s are replacements for Docker Desktop. False. Minikube is the only drop-in replacement. The other tools require a Linux distribution, which makes them a non-starter on macOS or Windows. Running any of these in a VM misses the point – you don't want to be managing the Kubernetes lifecycle and a virtual machine lifecycle. Minikube abstracts all of this.

The best practice is this: #!/usr/bin/env bash #!/usr/bin/env sh #!/usr/bin/env python

As python supports virtual environments, using /usr/bin/env python will make sure that your scripts runs inside the virtual environment, if you are inside one. Whereas, /usr/bin/python will run outside the virtual environment.

Here's my bash boilerplate with some sane options explained in the comments

This post is about all the major disadvantages of Julia. Some of it will just be rants about things I particularly don't like - hopefully they will be informative, too.

And back in the day, everything was GitHub issues. The company internal blog was an issue-only repository. Blog posts were issues, you’d comment on them with comments on issues. Sales deals were tracked with issue threads. Recruiting was in issues - an issue per candidate. All internal project planning was tracked in issues.

set -euo pipefail

The real world is the polar opposite. You’ll have some ultra-vague end goal, like “help people in sub-Saharan Africa solve their money problems,” based on which you’ll need to prioritize many different sub-problems. A solution’s performance has many different dimensions (speed, reliability, usability, repeatability, cost, …)—you probably don’t even know what all the dimensions are, let alone which are the most important.

k3d is basically running k3s inside of Docker. It provides an instant benefit over using k3s on a local machine, that is, multi-node clusters. Running inside Docker, we can easily spawn multiple instances of our k3s Nodes.

Kubernetes in Docker (KinD) is similar to minikube but it does not spawn VM's to run clusters and works only with Docker. KinD for the most part has the least bells and whistles and offers an intuitive developer experience in getting started with Kubernetes in no time.

Contrary to the name, it comes in a larger binary of 150 MB+. It can be run as a binary or in DinD mode. k0s takes security seriously and out of the box, it meets the FIPS compliance.

k3s is a lightweight Kubernetes distribution from Rancher Labs. It is specifically targeted for running on IoT and Edge devices, meaning it is a perfect candidate for your Raspberry Pi or a virtual machine.

All of the tools listed here more or less offer the same feature, including but not limited to

There are multiple tools for running Kubernetes on your local machine, but it basically boils down to two approaches on how it is done

Before we move on to talk about all the tools, it will be beneficial if you installed arkade on your machine.

The hard truth that many companies struggle to wrap their heads around is that they should be paying their long-tenured engineers above market rate. This is because an engineer that’s been working at a company for a long time will be more impactful specifically at that company than at any other company.

This one implements the behavior of git checkout when running it only against a branch name. So you can use it to switch between branches or commits.

When you provide just a branch or commit as an argument for git checkout, then it will change all your files to their state in the corresponding revision, but if you also specify a filename, it will only change the state of that file to match the specified revision.

if you are in the develop branch and want to change the test.txt file to be the version from the main branch, you can do it like this

CBL-Mariner is an internal Linux distribution for Microsoft’s cloud infrastructure and edge products and services.

Open source is not a good business model.If you want to make money do literally anything else: try to sell software, do consulting, build a SAAS and charge monthly for it, rob a bank.

The only way for one person to even attempt cross-platform app is to use a UI abstraction layer like Qt, WxWidgets or Gtk.The problem is that Gtk is ugly, Qt is extremely bloated and WxWidgets barely works.

However, human babies are also able to immediately detect objects and identify motion, such as a finger moving across their field of vision, suggesting that their visual system was also primed before birth. 

A new Yale study suggests that, in a sense, mammals dream about the world they are about to experience before they are even born. 

The visual and spacing differences between proportional and monospaced fonts

you don’t want to miss out on a great engineer just because they spent all of their energy making great products for prior employers rather than blogging, speaking and coding in public.

Why do 87% of data science projects never make it into production?

Cats don't understand smiles, but they have an equivalent: Slow blinking. Slow blinking means: We are cool, we are friends. Beyond that you can just physically pet them, use gentle voice.

PyPA still advertises pipenv all over the place and only mentions poetry a couple of times, although poetry seems to be the more mature product.

The main selling point for Anaconda back then was that it provided pre-compiled binaries. This was especially useful for data-science related packages which depend on libatlas, -lapack, -openblas, etc. and need to be compiled for the target system.

Many of Python’s standard tools allow already for configuration in pyproject.toml so it seems this file will slowly replace the setup.cfg and probably setup.py and requirements.txt as well. But we’re not there yet.

When I'm at the very beginning of a learning journey, I tend to focus primarily on guided learning. It's difficult to build anything in an unguided way when I'm still grappling with the syntax and the fundamentals!As I become more comfortable, though, the balance shifts.

I try and act like a scientist. If I have a hypothesis about how this code is supposed to work, I test that hypothesis by changing the code, and seeing if it breaks in the way I expect. When I discover that my hypothesis is flawed, I might detour from the tutorial and do some research on Google. Or I might add it to a list of "things to explore later", if the rabbit hole seems to go too deep.

Things never go smoothly when it comes to software development. Inevitably, we'll hit a rough patch where the code doesn't do what we expect.This can either lead to a downward spiral—one full of frustration and self-doubt and impostor syndrome—or it can be seen as a fantastic learning opportunity. Nothing helps you learn faster than an inscrutable error message, if you have the right mindset.Honestly, we learn so much more from struggling and failing than we do from effortless success. With a growth mindset, the struggle might not be fun exactly, but it feels productive, like a good workout.

I had a concrete goal, something I really wanted, I was able to push through the frustration and continue making progress. If I had been learning this stuff just for fun, or because I thought it would look good on my résumé, I would have probably given up pretty quickly.

Any import statement compiles to a series of bytecode instructions, one of which, called IMPORT_NAME, imports the module by calling the built-in __import__() function.

Furthermore, in order to build a comprehensive pipeline, the code quality, unit test, automated test, infrastructure provisioning, artifact building, dependency management and deployment tools involved have to connect using APIs and extend the required capabilities using IaC.

59% of organizations are following agile principles, but only 4% of these organizations are getting the full benefit.

databases is an async SQL query builder that works on top of the SQLAlchemy Core expression language.

The fact that FastAPI does not come with a development server is both a positive and a negative in my opinion. On the one hand, it does take a bit more to serve up the app in development mode. On the other, this helps to conceptually separate the web framework from the web server, which is often a source of confusion for beginners when one moves from development to production with a web framework that does have a built-in development server (like Django or Flask).

FastAPI makes it easy to deliver routes asynchronously. As long as you don't have any blocking I/O calls in the handler, you can simply declare the handler as asynchronous by adding the async keyword like so:

Fixtures are created when first requested by a test, and are destroyed based on their scope: function: the default scope, the fixture is destroyed at the end of the test.

When pytest goes to run a test, it looks at the parameters in that test function’s signature, and then searches for fixtures that have the same names as those parameters. Once pytest finds them, it runs those fixtures, captures what they returned (if anything), and passes those objects into the test function as arguments.

“Fixtures”, in the literal sense, are each of the arrange steps and data. They’re everything that test needs to do its thing.

Get the `curl-format.txt` from github and then run this curl command in order to get the output $ curl -L -w "@curl-format.txt" -o tmp -s $YOUR_URL

there is a drawback, docker-compose runs on a single node which makes scaling hard, manual and very limited. To be able to scale services across multiple hosts/nodes, orchestrators like docker-swarm or kubernetes comes into play.

We had to build the image for our python API every-time we changed the code, we had to run each container separately and manually insuring that out database container is running first. Moreover, We had to create a network before hand so that we connect the containers and we had to add these containers to that network and we called it mynet back then. With docker-compose we can forget about all of that.

A deep interest in a topic makes people work harder than any amount of discipline can.

Whereas what drives me now, writing essays, is the flaws in them. Between essays I fuss for a few days, like a dog circling while it decides exactly where to lie down. But once I get started on one, I don't have to push myself to work, because there's always some error or omission already pushing me.

And if you think there's something admirable about working too hard, get that idea out of your head. You're not merely getting worse results, but getting them because you're showing off — if not to other people, then to yourself.

That limit varies depending on the type of work and the person. I've done several different kinds of work, and the limits were different for each. My limit for the harder types of writing or programming is about five hours a day. Whereas when I was running a startup, I could work all the time. At least for the three years I did it; if I'd kept going much longer, I'd probably have needed to take occasional vacations.

There are three ingredients in great work: natural ability, practice, and effort. You can do pretty well with just two, but to do the best work you need all three: you need great natural ability and to have practiced a lot and to be trying very hard.

One thing I know is that if you want to do great things, you'll have to work very hard.

rebase is really nothing more than a merge (or a series of merges) that deliberately forgets one of the parents of each merge step.

Here is how you can create a fully configured new project in a just a couple of minutes (assuming you have pyenv and poetry installed already).

After reading through PEP8, you may wonder if there is a way to automatically check and enforce these guidelines? Flake8 does exactly this, and a bit more. It works out of the box, and can be configured in case you want to change some specific settings.

Pylint is a very strict and nit-picky linter. Google uses it for their Python projects internally according to their guidelines. Because of it’s nature, you’ll probably spend a lot of time fighting or configuring it. Which is maybe not bad, by the way. Outcome of such strictness can be a safer code, however, as a consequence - longer development time.

The goal of this tutorial is to describe Python development ecosystem.

For Windows, there is pyenv for Windows - https://github.com/pyenv-win/pyenv-win. But you’d probably better off with Windows Subsystem for Linux (WSL), then installing it the Linux way.

There are often multiple versions of python interpreters and pip versions present. Using python -m pip install <library-name> instead of pip install <library-name> will ensure that the library gets installed into the default python interpreter.

In addition to SQLAlchemy core queries, you can also perform raw SQL queries

query = notes.insert()
values = [
    {"text": "example2", "completed": False},
    {"text": "example3", "completed": True},
]
await database.execute_many(query=query, values=values)

query = "INSERT INTO notes(text, completed) VALUES (:text, :completed)"
values = [
    {"text": "example2", "completed": False},
    {"text": "example3", "completed": True},
]
await database.execute_many(query=query, values=values)

query = notes.select()
rows = await database.fetch_all(query=query)

query = "SELECT * FROM notes WHERE completed = :completed"
rows = await database.fetch_all(query=query, values={"completed": True})

This means that an event-driven system focuses on addressable event sources while a message-driven system concentrates on addressable recipients. A message can contain an encoded event as its payload.

we want systems that are Responsive, Resilient, Elastic and Message Driven. We call these Reactive Systems.

Resilience is achieved by replication, containment, isolation and delegation.

Today applications are deployed on everything from mobile devices to cloud-based clusters running thousands of multi-core processors. Users expect millisecond response times and 100% uptime. Data is measured in Petabytes.

Even though Kubernetes is moving away from Docker, it will always support the OCI and Docker image formats. Kubernetes doesn’t pull and run images itself, instead the Kubelet relies on container engines like CRI-O and containerd to pull and run the images. These are the two main container engines used with CRI-O and they both support the Docker and OCI image formats, so no worries on this one.

We comment out the failed line, and the Dockerfile now looks like this:

Some options (you will have to use your own judgment, based on your use case)

When you have one layer that downloads a large temporary file and you delete it in another layer, that has the result of leaving the file in the first layer, where it gets sent over the network and stored on disk, even when it's not visible inside your container. Changing permissions on a file also results in the file being copied to the current layer with the new permissions, doubling the disk space and network bandwidth for that file.

making sure the longest RUN command come first and in their own layer (again to be cached), instead of being chained with other RUN commands: if one of those fail, the long command will have to be re-executed. If that long command is isolated in its own (Dockerfile line)/layer, it will be cached.

Docker has a default entrypoint which is /bin/sh -c but does not have a default command.

To prevent this skew, companies like DoorDash and Etsy log a variety of data at online prediction time, like model input features, model outputs, and data points from relevant production systems.

idempotent jobs — you should be able to run the same job multiple times and get the same result.

Uber and Booking.com’s ecosystem was originally JVM-based but they expanded to support Python models/scripts. Spotify made heavy use of Scala in the first iteration of their platform until they received feedback like:some ML engineers would never consider adding Scala to their Python-based workflow.

Spotify has a CLI that helps users build Docker images for Kubeflow Pipelines components. Users rarely need to write Docker files.

Most serving systems are built in-house, I assume for similar reasons as a feature store — there weren’t many serving tools until recently and these companies have stringent production requirements.

Models require a dedicated system because their behavior is determined not only by code, but also by the training data, and hyper-parameters. These three aspects should be linked to the artifact, along with metrics about performance on hold-out data.

five ML platform components stand out which are indicated by the green boxes in the diagram below

we employed a three-stage strategy for validating and deploying the latest binary of the Real-time Prediction Service: staging integration test, canary integration test, and production rollout. The staging integration test and canary integration tests are run against non-production environments. Staging integration tests are used to verify the basic functionalities. Once the staging integration tests have been passed, we run canary integration tests to ensure the serving performance across all production models. After ensuring that the behavior for production models will be unchanged, the release is deployed onto all Real-time Prediction Service production instances, in a rolling deployment fashion.

We add auto-shadow configuration as part of the model deployment configurations. Real-time Prediction Service can check on the auto-shadow configurations, and distribute traffic accordingly. Users only need to configure shadow relations and shadow criteria (what to shadow and how long to shadow) through API endpoints, and make sure to add features that are needed for the shadow model but not for the primary model.

In a gradual rollout, clients fork traffic and gradually shift the traffic distribution among a group of models.  In shadowing, clients duplicate traffic on an initial (primary) model to apply on another (shadow) model).

we built a model auto-retirement process, wherein owners can set an expiration period for the models. If a model has not been used beyond the expiration period, the Auto-Retirement workflow, in Figure 1 above, will trigger a warning notification to the relevant users and retire the model.

For helping machine learning engineers manage their production models, we provide tracking for deployed models, as shown above in Figure 2. It involves two parts:

Model deployment does not simply push the trained model into Model Artifact & Config store; it goes through the steps to create a self-contained and validated model package

we implemented dynamic model loading. The Model Artifact & Config store holds the target state of which models should be served in production. Realtime Prediction Service periodically checks that store, compares it with the local state, and triggers loading of new models and removal of retired models accordingly. Dynamic model loading decouples the model and server development cycles, enabling faster production model iteration.

The first challenge was to support a large volume of model deployments on a daily basis, while keeping the Real-time Prediction Service highly available.

pip install 'poetry==$POETRY_VERSION'

To address this problem, I offer git undo, part of the git-branchless suite of tools. To my knowledge, this is the most capable undo tool currently available for Git. For example, it can undo bad merges and rebases with ease, and there are even some rare operations that git undo can undo which can’t be undone with git reflog.

the logical and physical page addresses are decoupled. A mapping table, which is stored on the SSD, translates logical (software) addresses to physical (flash) locations. This component is also called Flash Translation Layer (FTL).

NAND flash pages cannot be overwritten. Page writes can only be performed sequentially within blocks that have been erased beforehand.

For example, if one looks at write latency, one may measure results as low as 10us – 10 times faster than a read. However, latency only appears so low because SSDs are caching writes on volatile RAM. The actual write latency of NAND flash is about 1ms – 10 times slower than a read.

Another important difference between disks and SSDs is that disks have one disk head and perform well only for sequential accesses. SSDs, in contrast, consist of dozens or even hundreds of flash chips ("parallel units"), which can be accessed concurrently.

SSDs are often referred to as disks, but this is misleading as they store data on semiconductors instead of a mechanical disk.

It basically takes any command line arguments passed to entrypoint.sh and execs them as a command. The intention is basically "Do everything in this .sh script, then in the same shell run the command the user passes in on the command line".

#!/bin/bash
set -e

... code ...

exec "$@"

The alternative for curl is a credential file: A .netrc file can be used to store credentials for servers you need to connect to.And for mysql, you can create option files: a .my.cnf or an obfuscated .mylogin.cnf will be read on startup and can contain your passwords.

Linux keyring offers several scopes for storing keys safely in memory that will never be swapped to disk. A process or even a single thread can have its own keyring, or you can have a keyring that is inherited across all processes in a user’s session. To manage the keyrings and keys, use the keyctl command or keyctl system calls.

Docker container can call out to a secrets manager for its secrets. But, a secrets manager is an extra dependency. Often you need to run a secrets manager server and hit an API. And even with a secrets manager, you may still need Bash to shuttle the secret into your target application.

Using environment variables for secrets is very convenient. And we don’t recommend it because it’s so easy to leak things

As the sanitized example shows, a pipeline is generally an excellent way to pass secrets around, if the program you’re using will accept a secret via STDIN.

A few notes about storing and retrieving file secrets

vibrating bike (and rider) is absorbing energy that reduces the bike’s speed.

And since we now have very similar tires in widths from 26 to 54 mm, we could do controlled testing of all these sizes. We found that they all perform the same. Even on very smooth asphalt, you don’t lose anything by going to wider tires (at least up to 54 mm). And on rough roads, wider tires are definitely faster.

Tire width influences the feel of the bike, but not its speed. If you like the buzzy, connected-to-the-road feel of a racing bike, choose narrower tires. If you want superior cornering grip and the ability to go fast even when the roads get rough, choose wider tires.