Lambda CDM model Puns

Currently I'm trying to get into the Lambda-CDM Model theory but I'm struggling finding some review articles to read. I was wondering if maybe there is some kind of article I could approach to start learning about this specific topic. At the moment I have only managed to find some articles talking about the problems of the Model but not the Model itself

Read this at http://en.wikipedia.org/wiki/%CE%9BCDM but couldn't understand how this could be true if causality is to be preserved: https://secure.wikimedia.org/wikipedia/en/wiki/Special_theory_of_relativity#Causality_and_prohibition_of_motion_faster_than_light

So some person decided that modern cosmology is actually on really tenuous ground and that they should write a novel about it on /r/philosophy. It was deleted. So they reposted it to /r/philosophyofscience, where it so far hasn't been deleted.

There's a vast amount wrong with it, so I'll have to take it piece by piece, but the overall gist of it is that this person is shocked to learn that our current cosmological simulations don't model every single hydrogen atom in the entire universe, and because of this, they think that such simulations are really bad.

>Images such as this one of the Hercules Cluster present the imagination with an incredible depth and complexity of form in the universe. Ours does not seem (yet) a universe that is winding down in a process of never-ending entropic dispersal.

Yes, it most certainly does seem to be winding down. Star formation rates peaked when the universe was around ~3 billion years old, and they've been declining ever since. The gas reserves in galaxies are much sparser than they used to be, and there is not an efficient mechanism to cool intergalactic gas and get it into galaxies, certainly not on a timescale that could outrun cosmic expansion.

And posting a picture of the Hercules Cluster is meaningless and does nothing whatsoever to support this argument. Might as well post a kitty picture and talk about how kitties make us think deeply about the world.

>How "early" in the universe could life exist?

For complex carbon-based life similar to Earth life, probably not much earlier than a gigayear or so after the Big Bang. There's a variety of considerations, but basically you need a certain level of heavy element (aka anything with more than 2 protons) abundance in order to form a system which can host terrestrial planets, and then it will likely take the planet hundreds of millions of years to cool down sufficiently to host life as happened on Earth. Of course, this is sticking very closely to Earth history, but wandering far from our one example of abiogenesis results in a lot of unbounded speculation.

>Maybe entropy is simply a tendency of nature, not a rule.

Yes and no. It's a tendency which is a rule. Entropy derives from statistical mechanics. It's mathematically provable that a system will tend toward

In this article, we show you how to push an NER spacy transformer model to Huggingface and deploy the model on AWS Lambda to run predictions. Deploying models without the need to manage backend servers will enable developers and small startups who do not have devops resources to start deploying models ready to use in production.

Full Article => https://towardsdatascience.com/deploying-serverless-spacy-transformer-model-with-aws-lambda-364b51c42999

Did you ever look at your Lambda bill thinking:

𝗪𝗵𝗮𝘁 𝘁𝗵𝗲 𝗵𝗲𝗹𝗹 𝗮𝗿𝗲 𝗚𝗕-𝘀𝗲𝗰𝗼𝗻𝗱𝘀? 🤨

It doesn't sound initiative, but it's also not complex.

A breakdown including examples ↓

𝗣𝗿𝗲𝗳𝗮𝗰𝗲

One of Lambda's major differences to services like EC2 or Fargate is the pay-per-use pricing: 𝘆𝗼𝘂'𝗿𝗲 𝗼𝗻𝗹𝘆 𝗽𝗮𝘆𝗶𝗻𝗴 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂𝗿 𝗰𝗼𝗱𝗲 𝗶𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗲𝘅𝗲𝗰𝘂𝘁𝗲𝗱.

In detail, you're paying for GB-seconds.

Let's have a look into that:

There are several measures to take for #Lambda

• number of executions

• execution durations

• assigned memory to your Lambda functions

Calculation of the duration starts when the code inside your handler function is executed & stops when it returns or is terminated.

What's worth noting: global code (outside your handler) is executed at cold starts & 𝗶𝘀𝗻'𝘁 𝗯𝗶𝗹𝗹𝗲𝗱 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝟭𝟬 𝘀𝗲𝗰𝗼𝗻𝗱𝘀.

But back to the cost calculation with a look at AWS free tier.

For Lambda, it is 𝟰𝟬𝟬,𝟬𝟬𝟬 𝗚𝗕-𝘀𝗲𝗰𝗼𝗻𝗱𝘀 per month.

Breakdown: we're paying for 𝗴𝗶𝗴𝗮𝗯𝘆𝘁𝗲𝘀 𝗼𝗳 𝗺𝗲𝗺𝗼𝗿𝘆 assigned to your function 𝗽𝗲𝗿 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝘀𝗲𝗰𝗼𝗻𝗱.

For our free tier, this means: we got 400,000 seconds worth of a 1GB memory function.

This means more than 110 hours or 4 days!

If you change the memory assignment of your function, we'll get other numbers for the free tier:

• 128MB => ~880 hours / 36 days

• 256 MB => ~440 hours / 18 days

• 512MB => ~ 220 hours / 9 days

• 3072MB => ~37 hours / 1.5 days

As seen, calculations are not complex at all.

Let's have a look at a detailed example:

Running a function for 𝗼𝗻𝗲 𝘀𝗲𝗰𝗼𝗻𝗱 (𝟭𝟬𝟬𝟬𝗺𝘀) with 𝟭𝟮𝟴𝗠𝗕 for 𝗼𝗻𝗲 𝗺𝗶𝗹𝗹𝗶𝗼𝗻 𝘁𝗶𝗺𝗲𝘀.

We're paying for:

• 1ms: $𝟬.𝟬𝟬𝟬𝟬𝟬𝟬𝟬𝟬𝟮𝟭

• 1s: $𝟬.𝟬𝟬𝟬𝟬𝟬𝟮𝟭

=> 1m executions: $𝟮.𝟭𝟬

Is this included?

Yes, the free tier covers this completely.

We receive 400,000 GB-seconds.

That means:

=> 400,000 GB-seconds = 𝟯,𝟮𝟬𝟬,𝟬𝟬𝟬 128MB-seconds

In our example, we're only using 𝟭,𝟬𝟬𝟬,𝟬𝟬𝟬 128MB-seconds! 🤩

Let's switch from a 128MB to a 10GB function.

Now we end up with $𝟬.𝟬𝟬𝟬𝟬𝟬𝟬𝟭𝟲𝟲𝟳 per 10GB-ms.

Which means:

• 1s: $𝟬.𝟬𝟬𝟬𝟭𝟲𝟲𝟳

=> 1m executions: $𝟭𝟲𝟲,𝟳 🤯 🔥

Which are equal to 10,000,000 GB-secs!

So free tier doesn't do a lot here with its 400,000 GB-secs.

This calculation is an example.

Your function will execute computations way faster with those memory (& theref

With this new theory that unifies dark matter and dark energy into stuff with negative mass, there's a few questions.

1. Does this account for the Casimir effect? Could it be the negative mass pushing things together when in a vacuum? If so, does that mean this accounts for zero-point energy?
2. Does this eliminate the need for virtual particles, or does this simply describe the effect of virtual particles? It might be too early to tell.
3. Could negative-mass particles be explained by the recent Infinite-dimensional R Symmetries theory?
4. What is the likelihood that these particles are what's needed by the Alcubierre drive to function?

Hello. I need help with a problem where a notification about google stopping working won't hide and it reappears every 2 seconds. It says: cdm.google.android.apps.gsa.tasks.m:EXCLUSIVE background:task UPDATE_HOTWORD_MODELS crashed at com.google.android.apps.gsa.shared.util.c.a.bw.run(SourceFile:1) at ...

Currently want to move on from just doing analysis and modeling in notebooks and making apis through flask, so I want to give a shot on a serverless framework but the UI is quite daunting, thank you

Lambda calculus is the foundation of functional languages, in the sense that it can almost be seen as a subset of a functional language, up to minor syntactic change.

Lambda calculus is also a model of computation, according to https://en.wikipedia.org/wiki/Model_of_computation.

Is there a model of computation which has similar influence to imperative languages, as Lambda calculus to functional languages?

• Turing machine is used for computability study. Is writing a program on a universal Turing machine like writing a program in a procedural language?

• RAM is used for complexity study. Does it have the same influence to procedural imperative languages as Lambda calculus to functional languages?

Thanks.

About once a month I'll try out a different pattern for MLops, whether the way models are versioned or how they're hosted in a web service.

The other weekend I deployed a basic ~100mb sklearn model on lambda with Docker the other weekend and thought it might be of general interest. Cold starts were about 30 seconds but latency was great after that.

Here's the repo if anyone wants to play around with it: https://github.com/ktrnka/mlops_example_lambda

I trained a model for image classifcation on SageMaker, and now I want to deploy it on my website.

Having an endpoint is too expensive for me, the cheapest instance costs $0.065/h, which adds up to $46.8, and I probably will only need at maximum 5,000 calls spread throughout the day, so I don't need it running during the intervals.

I want to reduce the cost, and from what I read AWS Lambda is a good option. But I can't find a solution to loading TensorFlow on the code. I'm using a model that needs TensorFlow 2.3, and I saw no AWS Lambda Layer that supports it.

What is the best option to deploy then? Is using the SageMaker endpoint my only and cheapest option?

EDIT: the best solution for me was using the tflite.runtime library, and downloading the model from an S3 bucket/ from a layer.

I've recently been working a lot with Lambda vs. SageMaker for realtime ML inference with my startup, and realized that Lambda was way more cost-efficient for a lot of use-cases without too much of a performance hit. I created this quick visualization to compare at various resource / usage levels.

https://modelzoo.dev/lambda-vs-sagemaker-cost/

Also wrote a blog on my take here, biased towards Lambda: https://modelzoo.dev/blog/lambda.html.

I'm curious to get a discussion going -- what do you use for realtime inference workloads?

In this article, we show you how to push an NER spacy transformer model to Huggingface and deploy the model on AWS Lambda to run predictions. Deploying models without the need to manage backend servers will enable developers and small startups who do not have devops resources to start deploying models ready to use in production.

Full Article => https://towardsdatascience.com/deploying-serverless-spacy-transformer-model-with-aws-lambda-364b51c42999