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.

This is where off-site backups come into play. For this purpose, I recommend Borg backup. It has sophisticated features for compression and encryption, and allows you to mount any version of your backups as a filesystem to recover the data from. Set this up on a cronjob as well for as frequently as you feel the need to make backups, and send them off-site to another location, which itself should have storage facilities following the rest of the recommendations from this article. Set up another cronjob to run borg check and send you the results on a schedule, so that their conspicuous absence may indicate that something fishy is going on. I also use Prometheus with Pushgateway to make a note every time that a backup is run, and set up an alarm which goes off if the backup age exceeds 48 hours. I also have periodic test alarms, so that the alert manager’s own failures are noticed.

RAID is complicated, and getting it right is difficult. You don’t want to wait until your drives are failing to learn about a gap in your understanding of RAID. For this reason, I recommend ZFS to most. It automatically makes good decisions for you with respect to mirroring and parity, and gracefully handles rebuilds, sudden power loss, and other failures. It also has features which are helpful for other failure modes, like snapshots. Set up Zed to email you reports from ZFS. Zed has a debug mode, which will send you emails even for working disks — I recommend leaving this on, so that their conspicuous absence might alert you to a problem with the monitoring mechanism. Set up a cronjob to do monthly scrubs and review the Zed reports when they arrive. ZFS snapshots are cheap - set up a cronjob to take one every 5 minutes, perhaps with zfs-auto-snapshot.

these days hardware RAID is almost always a mistake. Most operating systems have software RAID implementations which can achieve the same results without a dedicated RAID card.

Failing disks can show signs of it in advance — degraded performance, or via S.M.A.R.T reports. Learn the tools for monitoring your storage medium, such as smartmontools, and set it up to report failures to you (and test the mechanisms by which the failures are reported to you).

RAID gets more creative with three or more hard drives, utilizing parity, which allows it to reconstruct the contents of failed hard drives from still-online drives.

A more reliable solution is to store the data on a hard drive1. However, hard drives are rated for a limited number of read/write cycles, and can be expected to fail eventually.

The worst way I can think of is to store it on a microSD card. These fail a lot. I couldn’t find any hard data, but anecdotally, 4 out of 5 microSD cards I’ve used have experienced failures resulting in permanent data loss.

As it stands, sudo -i is the most practical, clean way to gain a root environment. On the other hand, those using sudo -s will find they can gain a root shell without the ability to touch the root environment, something that has added security benefits.

Much like sudo su, the -i flag allows a user to get a root environment without having to know the root account password. sudo -i is also very similar to using sudo su in that it’ll read all of the environmental files (.profile, etc.) and set the environment inside the shell with it.

This means that unlike a command like sudo -i or sudo su, the system will not read any environmental files. This means that when a user tells the shell to run sudo -s, it gains root but will not change the user or the user environment. Your home will not be the root home, etc. This command is best used when the user doesn’t want to touch root at all and just wants a root shell for easy command execution.

Though there isn’t very much difference from “su,” sudo su is still a very useful command for one important reason: When a user is running “su” to gain root access on a system, they must know the root password. The way root is given with sudo su is by requesting the current user’s password. This makes it possible to gain root without the root password which increases security.

“su” is best used when a user wants direct access to the root account on the system. It doesn’t go through sudo or anything like that. Instead, the root user’s password has to be known and used to log in with.

A good philosophy to live by at work is to “always be quitting”. No, don’t be constantly thinking of leaving your job 😱. But act as if you might leave on short notice 😎.

An incomplete list of skills senior engineers need, beyond coding

if a module's name has no dots, it is not considered to be part of a package. It doesn't matter where the file actually is on disk.

if you imported moduleX (note: imported, not directly executed), its name would be package.subpackage1.moduleX. If you imported moduleA, its name would be package.moduleA. However, if you directly run moduleX from the command line, its name will instead be __main__, and if you directly run moduleA from the command line, its name will be __main__. When a module is run as the top-level script, it loses its normal name and its name is instead __main__.

A file is loaded as the top-level script if you execute it directly, for instance by typing python myfile.py on the command line. It is loaded as a module if you do python -m myfile, or if it is loaded when an import statement is encountered inside some other file.

After working on the problem for a while, we boiled it down to a 4-turn (2 per player), 9 roll (including doubles) game. Detail on each move given below. If executed quickly enough, this theoretical game can be played in 21 seconds (see video below).

The majority of Python packaging tools also act as virtualenv managers to gain the ability to isolate project environments. But things get tricky when it comes to nested venvs: One installs the virtualenv manager using a venv encapsulated Python, and create more venvs using the tool which is based on an encapsulated Python. One day a minor release of Python is released and one has to check all those venvs and upgrade them if required. PEP 582, on the other hand, introduces a way to decouple the Python interpreter from project environments. It is a relative new proposal and there are not many tools supporting it (one that does is pyflow), but it is written with Rust and thus can't get much help from the big Python community. For the same reason it can't act as a PEP 517 backend.

After suggestions from comments below, I read A Philosophy of Software Design (2018) by John Ousterhout and found it to be a much more positive experience. I would be happy to recommend it over Clean Code.

If you insist on using Git and insist on tracking many large files in version control, you should definitely consider LFS. (Although, if you are a heavy user of large files in version control, I would consider Plastic SCM instead, as they seem to have the most mature solution for large files handling.)

In Mercurial, use of LFS is a dynamic feature that server/repo operators can choose to enable or disable whenever they want. When the Mercurial server sends file content to a client, presence of external/LFS storage is a flag set on that file revision. Essentially, the flag says the data you are receiving is an LFS record, not the file content itself and the client knows how to resolve that record into content.

If you adopt LFS today, you are committing to a) running an LFS server forever b) incurring a history rewrite in the future in order to remove LFS from your repo, or c) ceasing to provide an LFS server and locking out people from using older Git commits.

:/<text>, e.g. :/fix nasty bug A colon, followed by a slash, followed by a text, names a commit whose commit message matches the specified regular expression.

git worktree add <path> automatically creates a new branch whose name is the final component of <path>, which is convenient if you plan to work on a new topic. For instance, git worktree add ../hotfix creates new branch hotfix and checks it out at path ../hotfix.

LocationManager can provide a GPS location (lat,long) every second. Meanwhile, TelephonyManager gives the cellID=(mmc,mcc,lac,cid) the radio is currently camping on. A cellID database[1], allows to know the (lat,long) of each CellID. What is left is to draw the itinerary (in red) and, for each second, a cellID-color-coded connection to the cell.

For instance, when someone is not confident in what they are saying or know that they are lying, they may take a step backwards as if stepping back from the situation.

People with ADHD are highly sensitive to micro expressions.

There are seven expressions that are universal to all humans no matter which country they are born: anger, fear, disgust, sadness, happiness, surprise, and contempt.

A macro expression is the normal facial expression you notice that lasts between ½ a second to 4 seconds. These are fairly easy to notice and tend to match the tone and content of what is being said.A micro expression is an involuntary facial expressions that lasts less than half a second. These expressions are often missed altogether but they reveal someones true feelings about what they are saying.

From an employers perspective, I believe there are many advantages:

The research found that working 55 hours or more a week was associated with a 35% higher risk of stroke and a 17% higher risk of dying from heart disease, compared with a working week of 35 to 40 hours.

What I did have going for me was what might be considered hacker sensibilities - lack of fear of computers in general, and in trying unknown things. A love of exploring, learning by experimenting, putting stuff together on the fly.

The only problem is that Kubeflow Pipelines must be deployed on a Kubernetes Cluster. You will struggle with permissions, VPC and lots of problems to deploy and use it if you are in a small company that uses sensitive data, which makes it a bit difficult to be adoptedVertex AI solves this problem with a managed pipeline runner: you can define a Pipeline and it will executed it, being responsible to provision all resources, store all the artifacts you want and pass them through each of the wanted steps.

Kubeflow Pipelines comes to solve this problem. KFP, for short, is a toolkit dedicated to run ML Workflows (as experiments for model training) on Kubernetes, and it does it in a very clever way:Along with other ways, Kubeflow lets us define a workflow as a series of Python functions that pass results, and Artifacts for one another.For each Python function, we can define dependencies (for libs used) and Kubeflow will create a container to run each function in an isolated way, and passing any wanted object to a next step on the workflow. We can set needed resources, (as memory or GPUs) and it will provision them for our workflow step. It feels like magic.Once you’ve ran your pipeline, you will be able to see it in a nice UI, like this:

Vertex AI came from the skies to solve our MLOps problem with a managed — and reasonably priced—alternative. Vertex AI comes with all the AI Platform classic resources plus a ML metadata store, a fully managed feature store, and a fully managed Kubeflow Pipelines runner.

Method 1: We can grab the PDF Versions of Google’s TotT episodes or create our own posters that are more relevant to the company and put them in places where both developers and testers can’t be ignored.Method 2 : We can initiate something called ‘Tip of the day’ Mailing System from Quality Engineering Department.

They started to write flyers about everything from dependency injection to code coverage and then regularly plaster the bathrooms in all over Google with each episode, almost 500 stalls worldwide.

Dogfooding → Internal adoption of software that is not yet released. The phrase “eating your own dogfood” is meant to convey the idea that if you make a product to sell to someone else, you should be willing to use it yourself to find out if it is any good.

It's Faster

It's even more discoverable if your shell has smart tab completion: for example, on my current project, if you enter the aws/ directory and type make<TAB>, you'll see a list that includes things like docker-login, deploy-dev and destroy-sandbox.

Variables. Multiple lines. No more escaped quotation marks. Comments.

People might think I’m not experiencing new things, but I think the secret to a good life is to enjoy your work. I could never stay indoors and watch TV. I hear London is a place best avoided. I think living in a city would be terrible – people living on top of one another in great tower blocks. I could never do it. Walking around the farm fills me with wonder. What makes my life is working outside, only going in if the weather is very bad.

pronunciation cues in an individual’s speech communicate their social status more accurately than the content of their speech.

reciting seven random words is sufficient to allow people to discern the speaker’s social class with above-chance accuracy.

Cookiecutter takes a source directory tree and copies it into your new project. It replaces all the names that it finds surrounded by templating tags {{ and }} with names that it finds in the file cookiecutter.json. That’s basically it. [1]

In short, MLflow makes it far easier to promote models to API endpoints on various cloud vendors compared to Kubeflow, which can do this but only with more development effort.

Bon Appétit?

MLflow is a single python package that covers some key steps in model management. Kubeflow is a combination of open-source libraries that depends on a Kubernetes cluster to provide a computing environment for ML model development and production tools.

I decided to embrace imperfection and use it as the unifying theme of the album.

I start by sampling loops from songs I like, and then use the concatenative synthesis and source separations tools to extract interesting musical ideas (e.g., melodies, percussion, ambiance). These results can be used directly, with a little FX (Replica XT, Serum FX), or translated into MIDI and resynthesized (Serum, VPS Avenger). This gives me the building blocks of the song (the tracks), each a few bars long, which I carefully combine using Ableton Live's session view into something that sounds "good". Later, each track is expanded by sampling more loops from the original songs until I have plenty of content to create something with enough structure to be called a "song".

Another extremely interesting and underrated task in the audio domain is translating audio into MIDI notes. Melodyne is state of the art in the area, but Ableton's Convert Audio to Midi works really well too.

imperfect separations are sometimes the most interesting, especially the results from models trained to separate something that isn't in the source sound (e.g., extract vocals from an instrumental track)

Sound Source Separation

concatenative sound synthesis. The main idea is simple: given a database of sound snippets, concatenate or mix these sounds in order to create new and original ones.

Calibri has been the default font for all things Microsoft since 2007, when it stepped in to replace Times New Roman across Microsoft Office. It has served us all well, but we believe it’s time to evolve. To help us set a new direction, we’ve commissioned five original, custom fonts to eventually replace Calibri as the default.

To summarize, implementing ML in a production environment doesn't only mean deploying your model as an API for prediction. Rather, it means deploying an ML pipeline that can automate the retraining and deployment of new models. Setting up a CI/CD system enables you to automatically test and deploy new pipeline implementations. This system lets you cope with rapid changes in your data and business environment. You don't have to immediately move all of your processes from one level to another. You can gradually implement these practices to help improve the automation of your ML system development and production.

🐛 (home, components): Resolve issue with modal collapses 🚚 (home): Move icons folder ✨ (newsletter): Add Newsletter component

feat(home, components): Add login modal fix(home, components): Resolve issue with modal collapses chore(home): Move icons folder

When audio quality is high (vs low), people judge the content as better and more important. They also judge the speaker as more intelligent, competent, and likable.In an experiment, people rated a physicist’s talk at a scientific conference as 19.3% better when they listened to it in high quality audio vs slightly distorted, echo-prone audio.

Just write your markdown as text/plain, email clients are broken.

Bring up the board that shows the status of every item the team is working on.Starting in the right-most column, get an update for each ticket in the column.Be sure to ask: “What’s needed to move this ticket to the next stage of progress?”Highlight blockers that people bring up and define what’s needed to unblock work.Move to the next column to the left and get updates for each ticket in that column.Continue until you get to the left-most column.

More people brings more status updates.More updates mean more information that others won’t care about.The team may be working on multiple projects at once.If the team has customers, ad-hoc work will regularly come up.

I start thinking about my update to prove I should keep my jobPeople zone out when a teammate starts talking about how they worked on something that doesn’t affect me.It’s finally my turn to give an update. No one is listening except for the facilitator.We go over our time and end when the next team starts lurking outside the room.

What did I complete yesterday that contributed to the team?What do I plan to complete today to contribute to the team?What impediments do I see that prevent me or the team from achieving its goals?

On the median case, Colab is going to assign users a K80, and the GTX 1080 is around double the speed, which does not stack up particularly well for Colab. However, on occasion, when a P100 is assigned, the P100 is an absolute killer GPU (again, for FREE).

With Spark 3.1, the Spark-on-Kubernetes project is now considered Generally Available and Production-Ready.

The key libraries of TFX are as follows

The console is a killer SQLite feature for data analysis: more powerful than Excel and more simple than pandas. One can import CSV data with a single command, the table is created automatically

This is not a problem if your DBMS supports SQL recursion: lots of data can be generated with a single query. The WITH RECURSIVE clause comes to the rescue.

Interrupt if: T3F > RTW (IO + T3E)

When you seek advice, first write down everything you’ve tried.

If you’re stuck for over an hour, seek help.

Simple … a single Linode VPS.

When peeking at your brain may help with mental illness

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We use Prometheus to collect time-series metrics and Grafana for graphs, dashboards, and alerts.

large machine learning job spans many nodes and runs most efficiently when it has access to all of the hardware resources on each node. This allows GPUs to cross-communicate directly using NVLink, or GPUs to directly communicate with the NIC using GPUDirect. So for many of our workloads, a single pod occupies the entire node.

We use Kubernetes mainly as a batch scheduling system and rely on our autoscaler to dynamically scale up and down our cluster — this lets us significantly reduce costs for idle nodes, while still providing low latency while iterating rapidly.

For high availability, we always have at least 2 masters, and set the --apiserver-count flag to the number of apiservers we’re running (otherwise Prometheus monitoring can get confused between instances).

We’ve increased the max etcd size with the --quota-backend-bytes flag, and the autoscaler now has a sanity check not to take action if it would terminate more than 50% of the cluster.

Another helpful tweak was storing Kubernetes Events in a separate etcd cluster, so that spikes in Event creation wouldn’t affect performance of the main etcd instances.

The root cause: the default setting for Fluentd’s and Datadog’s monitoring processes was to query the apiservers from every node in the cluster (for example, this issue which is now fixed). We simply changed these processes to be less aggressive with their polling, and load on the apiservers became stable again:

We then moved the etcd directory for each node to the local temp disk, which is an SSD connected directly to the instance rather than a network-attached one. Switching to the local disk brought write latency to 200us, and etcd became healthy!

When should you end a conversation? Probably sooner than you think

Why we’re so bad at daydreaming, and how to fix it

The newest generation of image codecs—in particular AVIF and JPEG XL—are a major improvement of the old JPEG and PNG codecs. To be sure, JPEG 2000 and WebP also compress more effectively and offer more features, yet the overall gain is not significant and consistent enough to warrant fast and widespread adoption. AVIF and JPEG XL will do much better—at least that’s what I hope.

Scientists break through the wall of sleep to the untapped world of dreams

'Night owls' may be twice as likely as morning 'larks' to underperform at work

Consider the amount of data and the speed of the data, if low latency is your priority use Akka Streams, if you have huge amounts of data use Spark, Flink or GCP DataFlow.

As we mentioned before, the majority of machine learning implementations are based on running model serving as a REST service, which might not be appropriate for the high volume data processing or usage of the streaming system, which requires re coding/starting systems for model update, for example, TensorFlow or Flink. Model as Data is a great fit for big data pipelines. For online inference, it is quite easy to implement, you can store the model anywhere (S3, HDFS…), read it into memory and call it.

The most common way to deploy a trained model is to save into the binary format of the tool of your choice, wrap it in a microservice (for example a Python Flask application) and use it for inference.

When we are providing our API endpoint to frontend team we need to ensure that we don’t overwhelm them with preprocessing technicalities.We might not always have a Python backend server (eg. Node.js server) so using numpy and keras libraries, for preprocessing, might be a pain.If we are planning to serve multiple models then we will have to create multiple TensorFlow Serving servers and will have to add new URLs to our frontend code. But our Flask server would keep the domain URL same and we only need to add a new route (a function).Providing subscription-based access, exception handling and other tasks can be carried out in the Flask app.

Next, imagine you have more models to deploy. You have three optionsLoad the models into the existing cluster — having one cluster serve all models.Spin up a new cluster to serve each model — having multiple clusters, one cluster serves one model.Combination of 1 and 2 — having multiple clusters, one cluster serves a few models.The first option would not scale, because it’s just not possible to load all models into one cluster as the cluster has limited resources.The second option will definitely work but it doesn’t sound like an effective process, as you need to create a set of resources every time you have a new model to deploy. Additionally, how do you optimize the usage of resources, e.g., there might be unutilized resources in your clusters that could potentially be shared by the rest.The third option looks promising, you can manually choose the cluster to deploy each of your new models into so that all the clusters’ resource utilization is optimal. The problem is you have to manuallymanage it. Managing 100 models using 25 clusters can be a challenging task. Furthermore, running multiple models in a cluster can also cause a problem as different models usually have different resource utilization patterns and can interfere with each other. For example, one model might use up all the CPU and the other model won’t be able to serve anymore.Wouldn’t it be better if we had a system that automatically orchestrates model deployments based on resource utilization patterns and prevents them from interfering with each other? Fortunately, that is exactly what Kubernetes is meant to do!

If you’re running lots of deployments of models then it becomes important to record which versions were deployed and when. This is needed to be able to go back to specific versions. Model registries help with this problem by providing ways to store and version models.

Here is a quick recap table of every technology we discussed in this blog post.

Can the Brain Resist the Group Opinion?

The benefits of applying GitOps best practices are far reaching and provide:

GitOps is a way to do Kubernetes cluster management and application delivery.  It works by using Git as a single source of truth for declarative infrastructure and applications. With GitOps, the use of software agents can alert on any divergence between Git with what's running in a cluster, and if there's a difference, Kubernetes reconcilers automatically update or rollback the cluster depending on the case. With Git at the center of your delivery pipelines, developers use familiar tools to make pull requests to accelerate and simplify both application deployments and operations tasks to Kubernetes.

It was actually very common to have one Dockerfile to use for development (which contained everything needed to build your application), and a slimmed-down one to use for production, which only contained your application and exactly what was needed to run it. This has been referred to as the “builder pattern”.

volumes are often a better choice than persisting data in a container’s writable layer, because a volume does not increase the size of the containers using it, and the volume’s contents exist outside the lifecycle of a given container.