Intel 14700K

This is a new computer build.

Some facts on the CPU: it is currently #89 on PassMark [53,737], with a turbo speed of 5.6 GHz and TDP of 125W to 253W. It is 20 cores/28 threads, first seen Q4 2023. 8 of those are “Performance Cores”, 12 are “Efficient Cores”. It is #2 on single-thread rating [4,484] behind the i9-13900K at [4,641].

CPUIntel Core i7 14700K 3.4GHz LGA 1700 125W$399
CoolerCorsair iCUE H150i Elite LCD XT Liquid, AF120 RGB Fans, 360mm Radiator$249
RAMG.SKILL Trident Z5 RGB 64GB (2x32GB) DDR5 5600$204
MotherboardASUS ROG STRIX Z790-A Gaming Wifi LGA 1700, 2.5Gb LAN, Wi-Fi 7 MLO, PCIe 5.0 16x slot, 5x M.2 PCIe 4.0 mode$359
Power SupplySeasonic FOCUS GX-850 80+ Gold, fully modular, fan control, ATX12V, EPS12V$134
VideoGIGABYTE GeForce RTX 4070 Ti Super AERO OC 16GB GDDR6X, PCIe x4.0, DLSS 3, 8,448 CUDA cores$880
CaseCorsair iCUE 5000D RGB Mid-Tower, True White 3x AF120 Fans$219
SS DriveSamsung 990 Pro 4TB PCIe Gen 4×4 NVMe, 7450MB/sec, M2.2280 ($339 on 2024/mar)$249
HD Drivenone
OSWindows 11 Professional 64 bit OEM no media$169
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Better pattern for @Autowired

Just ran across this in a video about Spring Boot by Frank Moley and I thought it deserved a callout.

When creating your Beans (e.g. Controller), you can either:

@RestController() public class ItemController { @Autowired; private ItemService itemService;

Or you can do this:

@RestController() public class ItemController { private final ItemService itemService; @Autowired; public ItemController(ItemService itemService) { this.itemService = itemService }

Both work, but the second one makes JUnit testing way easier. The first one @Autowires the actual field, and is really hard to isolate for test. The second one lets the field stay POJO, and @Autowires the constructor. Both are Spring friendly, but only the second one is JUnit friendly.

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Argo CD notes

Argo CD (github source) is a Continuous Delivery tool done “GitOps” style. Here, that means keeping all of your (Kubernetes) application definitions and configurations under source code control (git).

Assuming you follow GitOps CI/CD best practices of separate repositories (one for application code and a second for your YAML configurations), your development process is likewise split into two discrete problems:

  1. Application Code – build the container images (CI)
  2. Configuration Code – keep your (Kubernetes) configuration files synchronized with the repository (CD)

Argo CD solves the second bullet. And does it pretty well. It supports writing your configuration code in any of: plain YAML, Helm charts. Kustomize, and Jsonnet.

It also supports enforcing that all Kubernetes changes “from the command line” are at least detected (and if desired, automatically rolled back). E.g. if some rogue administrator issues a “kubectl scale –replicas=3 …” command, Argo CD will at least change the status to “Out of Sync”, and is capable of automatically re-synchronizing, and therefore un-doing the change.

Reference: compare Argo CD versus Tekton versus Jenkins X

Question: do you need separate repositories or just separate directories? i.e. Argo CD takes a repository and a path to configure an “application” – does it ignore commits to files outside of the directory? Answer: yes, it ignores them, so no, you don’t technically need separate repositories. Which means all of the Monorepo fans out there are in luck.

Posted in Software Engineering | Comments Off on Argo CD notes

Add HTTPS lock to AWS S3

The goal was to change the “Not secure” banner to the little lock (aka enable https) for

Previously, that site was hosted using only AWS S3 – which does not support “https”.

This is the documentation for the final configurations for AWS S3, AWS CloudFront. AWS Certificate Manager, and AWS Route 53.

The basic idea is to put a CloudFront distribution in front of your AWS S3 website, create a certificate, and then make sure all of the configuration settings between S3/CloudFront/Route53 agree and work with each other.


Bucket NamePublicStatic websiteNotes
www.timtiemens.comYesDisabledNo longer used
cftimtiemensdotcomYesEnabled/Bucket hostingBucket policy set to allow access to S3GetObject from Cloudfront

Since all of the files in AWS S3 are auto-deployed from a github repository using “aws s3 sync”, it was simple for me to change the target bucket from “s3://” to “s3://cftimtiemensdotcom”. Note that for AWS S3 static websites, the bucket name has to match ( but for CloudFront, the bucket name can be anything (cftimtiemensdotcom).

AWS CloudFront

Configuration ItemValueNotes
Price ClassUse all edge locationsIf there is ever a bill for this, switch to “Use only North America and Europ”
Custom SSL CertificateARN of certificateSelected from dropdown list
Alternate domain names2
Origin Domaincftimtiemens.comFrom dropdown choices
HTTP only, port 80
“Name” is greyed-out, no edit
Path pattern*
Origin and origin
ViewRedirect HTTP to HTTPS
Allowed HTTP methodsGET, HEAD

AWS Certificate Manager

Configuration ItemValueNotes
timtiemens.comRequires DNS CNAME confirmation
*.timtiemens.comRequires same DNS CNAME confirmation
In UseYes

After created, pushed the “Create records in Route 53” button. This creates the required CNAME records in the hosted zone.

AWS Route 53

timtiemens.comAAlias, to
www.timtiemens.comAAlias, to
various “_xxyyzz”CNAMEas set by AWS Certificate Manager, for various certificates to be validated by DNS
blog.timtiemens.comA34.236.123.127 (separate webserver, not under this CloudFront distribution)

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Design Problem from Tic Tac Toe

It is fascinating how quickly a simple problem can escalate to a very difficult design problem.

Here are the major pieces needed to frame one of these difficult problems in Tic Tac Toe:

CellValue :   X, O, EMPTY

Board     :   state -> CellValue[9]

Game      :   board -> Board

Everything is going well until we get to Game.getWinner(). In Tic Tac Toe, the winner is one of four things: X, O, Draw or “unknown/in progress”. The above definitions already includes “X” and “O” – so, can getWinner() return type CellValue? That is a bit awkward, since “EMPTY” makes sense for a Board, but “EMPTY” is a poor name to use for “Draw”. And what string should “EMPTY” return? The “-” to use when printing a board, or “Draw” for printing the winner?

One possible solution is this design:

Player    :   X, O

CellValue :   X(Player.X), O(Player.O), EMPTY

Winner    :   X(Player.X), O(Player.O), DRAW, INPROGRESS

Board     :   state -> CellValue[9]

Game      :   board -> Board

Now, Game.getWinner() returns type Winner, and there are no “null” values floating around to spawn runtime errors. The two similar “Player” concepts are captured explicitly (i.e. “X” can be both a value on a Board, and a winner of a Game), with only a small glitch around things like “Does board.state[3] equal game.getWinner()”? i.e. the sub-problem of comparing a CellValue with a Winner, when they currently share no useful common parent type.

If your goal is to get a good grade in a software engineering class, then use the second design. If your goal is a software project that can generate Kaggle CSV dataset files, the first design will suffice (See tictactoe).

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You’re here because you can’t get the unit tests for the Coursera Improving Deep Neural Networks course, Week 3, exercise number 1, to pass. You are trying to correctly implement

def compute_total_loss(logits, labels):

And the unit test keeps failing with an off-by-a-large number (i.e. not the usual “shapes are incompatible” error).

Sorry, this post isn’t going to just give you the answer.

But, since Coursera (instructions and code) and Tensorflow (documentation) have come together to make an incredibly frustrating riddle, this will provide hints on where they went wrong, and thus where you went wrong.

Another guess on why you’re here: you entered “tf.keras.losses.categorical_crossentropy example” into a search engine, and followed the link tf.keras.losses.CategoricalCrossEntropy, and that page shows the first argument as “from_logits=False,”. And you just figured that was close enough.

Yet another guess on why you’re here: you read the instructions – “It’s important to note that the “y_pred” and “y_true” inputs of tf.keras.losses.categorical_crossentropy are expected to be of shape (number of examples, num_classes)”. And you noticed the code gives you “logits” and “labels”, in that order. So Coursera has said “y_pred/y_true”, then said “logits/labels”, and the first documentation link doesn’t even have the first two arguments. But even with all of these naming mis-matches, you still entered the arguments in the order given (implied?) by the instructions.

Final clue: use the link provided by the instructions – tf.keras.losses.categorical_crossentropy and pay attention to the order of the arguments.

Then, channel Jeff Atwood on the “hard things in computer science” and write your own post about how using poor names can cause a ton of problems…

Posted in Machine Learning | Comments Off on compute_total_loss

Teraflops Comparison

Documenting some various GPU hardware

single precision
tensor perf (FP16)
RTX 3080Ti34.113627332010,24012 GB
141126405,12016 GB
20.311845,8888 GB
GTX 10808.82,5608 GB
Xbox X12.1
Xbox One1.4

Note that the V100 is used in the AWS p3.2xlarge instance type. The V100 numbers are in general smaller than the 3080Ti, and with the WSL2 tensorflow 2.12 libraries, the 3080Ti out-performs the V100 on the 50,000 epoch test 736 seconds to 928 seconds – here the 3080Ti is 26% faster.) (Caveat – extremely small test set – only my ml-style-transfer code.)

(Using the “Windows Native tensorflow 2.11” libraries, the V100 out-performed the 3080Ti on the 50,000 epoch test 928 seconds to 1063 seconds – here the V100 is 12% faster).

It looks like the p3.2xlarge has been around since late 2017. It started at $3.06/hour, and is still the same price today (2023/Apr). The V100 prices seems to have dropped from $6,000 in 2019 to $3,500 today.

Node Replacement Factor (NRF) – nvidia documentation

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AWS EC2 GPU instance comparison

These are the results from running the ml-style-transfer project on three different AWS EC2 instance types.

Instance Name$Cost/hour250 epochs2,500 epochs50,000 epochs
Comparing p3.2xlarge GPU with t2.large non-GPU

The p3.2xlarge is 32x more expensive per hour. Yet, for the 2,500 epoch tests, it is 266x faster, which combines to be 8x more cost-effective (e.g. $0.05 versus $0.40 for 2,500 epochs).

The p3.2xlarge also gets results faster (wall clock) – the 50,000 epoch run on p3.2xlarge only took 15 minutes wall clock, yet the projected run on t2.large is 4,892 minutes (over 3 days).

The c5.4xlarge is a “compute optimized” – large vCPU, large RAM. It is 7x the hourly price of the t2.large, and on the 2,500 epoch test, delivers about that much wall clock improvement – so, basically the same cost, but 7x faster delivery of results.

Note: the total test time was approximately 40 minutes on the p3.2xlarge ($2.04) and 280 minutes on the t2.large ($0.44). But, for a mere 5x total cost difference, the p3.2xlarge performed a 50,000 epoch run that would have taken forever on the t2.large.

In contrast, purchasing a RTX 3080Ti for $900 and using it for the 2,500 epoch run took 16 seconds (which is barely slower than the current record-holder p3.2xlarge at 14 seconds).

Next up is to upgrade my AWS account to allow “spot” pricing for p3.2xlarge – if Amazon will allow it for my non-commercial account. The $3.06 on-demand price seems to drop to about $1.01 for spot instances. Update (1 day later): “We have approved and processed your service quota increase request”. So my “EC2->Limits->All P Spot Instance Requests now says “8 vCPUs”, which is enough for one p3.2xlarge instance.

Just FYI: when that AMI is run under a non-GPU machine type, the first run takes an extra 5+ minutes, as the system says “Matplotlib is building the font cache; this may take a moment.” It only does this the first run. (This is an example of a thing that cause differences in “python elapsed time” and “wall clock elapsed time”.)

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Read, Do, Aha

This is a variation of “Be, Do, Have” (“Be, Do Have” == Be a photographer, Do take a bunch of photos, and then Have/Buy expensive equipment). It records my recent epiphany in Machine Learning. This variation is “Read, Do, Aha”.

I have read a lot of material on machine learning (linear regression, logistic regression, neural nets, supervised learning, CNNs, etc.) I have done some certifications (mainly from Coursera, from Andrew Ng, in the Machine Learning Specialization).

I have already completed the Convolutional Neural Networks course (fourth of the five of the Deep Learning Specialization) and recently decided to “go back” and start with the first course in this specialization – Neural Networks and Deep Learning.

In that course, one of the exercises is to build a simple neural net (simple = two layers total, no hidden layers). So simple it is equivalent to a Logistic Regression model. A very basic plot of a Logistic Regression model is:

In the example above, two inputs (plotted above as x and y) are given a binary classification (blue dot or orange dot), and then a single-line regression is computed, and the graph shaded into blue and orange areas. Thereafter, you can use that line to test the accuracy of known (training) data, and to make predictions on never-seen-before (test) data. In the above example, the training accuracy is quite good, and it seems safe to say the test accuracy will/would be quite good as well.

The course uses “Cat or not-Cat” as the binary class definitions, and flattens a 64x64x3 image into a an input of 12,288 values. After everything is implemented and run against the training data, you have a 12,000+ dimension hyperplane that divides the input space into Cat/not-Cat.

The “Aha!” moment looks like this:

  1. Fact: It can be trained to 99% accuracy to correctly separate the Cats from the not-Cats, in a very small amount of time/iterations.
  2. Fact: It has a terrible accuracy (70%) on the test set (aka never-seen-before data). Just guessing would have a 50% accuracy if the test set was split 50-50. i.e. the hyperplane is terribly overfit

  3. Aha! This makes sense because you tried to use 12,288 pixels individually to decide between Cat/not-Cat. (i.e. you had no hidden layers, no “build up of knowledge”, no “higher level constructs”). It only used individual pixels. When looked at from that perspective, it is amazing that it can get 99% accuracy on the training data, and completely un-amazing that it doesn’t generalize to the test data.
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Another Dell SFF

The goal of this machine was, well, it was basically too inexpensive to pass up. It was refurbished, from SJ Computers LLC. It arrived in a box that used the standard “conform to interior” expanding foam, and was very well packed. The CPU was “First Seen” in 2013.

Note that Windows 10 Pro OEM itself sells for approximately $145.

Some facts on the CPU: the Core i5-4570@3.2GHz is currently #1390 on PassMark [5,220] and is a LGA1150 at 84W. It is a 4 core 4 thread model with turbo speed of 3.6GHz. It supports a maximum of 32 GB of DDR3 RAM.

All product links are from the actual vendor.

SystemDell OptiPlex 7020 SFF Computer$138
CPUIntel Core i5-4570 3.2Ghzincl.
RAM32GB (2x 16GB) DDR3 DRAMincl.
Power Supplyincl.
VideoIntel graphicsincl.
DVD/CDThin form factor DVDincl.
SSD Drive512GB SSD (TIMETEC brand)incl.
HHD Drive
Wi-FiUSB wi-fi adapterincl.
Keyboard/MouseDell wired keyboard; Dell optical mouseincl.
OSWindows 10 Proincl.
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