10 ms·

Deploying Transformers on the Apple Neural Engine

121 points by vletal 4y ago

axg11 4y ago
Apple is still behind the rest of FAANG in terms of publishing ML research, but they've taken the lead in terms of real world impact of ML. If you go back a couple of years the general consensus was that Apple is very far behind and lacks any credible machine learning groups. As with most of their products, they waited for the technology to mature (a bit) and then made magical experiences from it. Some examples: - On-device semantic image search - Text highlighting (images and videos) - FaceID - Computational photography, depth mapping (sensor fusion) - Background removal (iOS 16) - Activity detection (Apple Watch) Siri is still far behind vs. Alexa and Google Assistant, but for every other ML/AI application I'd argue that Apple has the smoothest experience. This should be a lesson for others building ML-powered products. You don't need to use the best, state of the art models to compete. You can compete on the overall experience. Sharing details and publications doesn't seem to be a core part of Apple's culture, so I'm glad they're going out of their way to publish more details like this article. Their push to optimize ML inference for on-device rather than cloud is going to have the biggest impact on consumer experience. I'm fairly sure this is part of their AR/VR strategy too. Low-latency local machine learning that powers magical experiences.
- jjoonathan 4y ago
  Add anti-spam to the places where they need to do better, but generally agreed. The iPad handwriting recognition is pretty smooth too, so long as you don't need to write acronyms / model numbers.
- MrBuddyCasino 4y ago
  Discovered a few days ago while hiking that if you take a photo, you can tap on the info icon and it will tell you what kind of plant you just photographed. It works and is so low friction! This is how its done.
- BonoboIO 4y ago
  Picture tagging, Face ID are really great, but Siri is such a letdown. Absolutely useless beside setting a timer. Over the years it got better, but it is still dimensions behind Alexa. Amazon and Google are since the begging “web companies”, Apple comes from the hardware side and has problems keeping up with „information services“, but the Apple owned Search Engine is probably only a few years away.
- jstx1 4y ago
  I'm much more interested in the companies like Apple that are building products with ML as opposed to the ones that publish papers for the sake of publishing papers and getting more publicity like DeepMind. (Which is a personal preference, nothing wrong with what DeepMind is doing, I just don't really care about it that much)
  - warangal 4y ago
    We started a company[0] to make it easy for developers to integrate ML models in their existing applications. [0] https://ramanlabs.in https://ramanlabs.in
- samatman 4y ago
  I don't think Siri is behind from Apple's perspective, because it has different goals. From a user perspective, Siri doesn't do as much, that's clear enough. However Siri is predictable, the others are creating an uncanny valley where it does more but you never quite know what or how. Apple has been consistent in signaling that they want to push as much AI to the device as they can, for example on-device speech recognition was a heavily promoted milestone. That approach is broadly incompatible with using one enormous cloud model to provide rich interactions. I think the goal for Siri is to become a better and better decision tree, rather than a conversation-driven knowledge engine like Google and Amazon have. I support that, I have actual humans to talk to and like that my interactions with Siri are based on simple phrases which do something and don't bottom out in a Google search or (yuck) a pitch to buy some service. Addendum: Siri does bottom out on a search of the web, yes, what I mean is that Siri encourages use patterns (at least for me) where this basically never happens.
  - gmac 4y ago
    Somewhat disagree on this: in my experience, Siri is really bad even at what it supports. For example: * Alexa (in my kitchen) almost always plays the music I ask for on Spotify. Siri (in my car) gets it wrong about half the time — often in comical and bizarre ways. * Siri remains unable to call one of my family contacts: every time, she gets into a loop asking for clarification between two options (when I clarify, she just asks again). * I set up an automation with Siri to start the radio playing on BBC Sounds (to stop me falling back to sleep when my alarm goes in the morning). This was stupidly fiddly to set up, and it just doesn't work: Siri tells me to unlock the phone first, which destroys the whole point. I guess the caveat is that my experience with Siri is a bit limited, because in the light of her uselessness I often don't even try to get her to do things.
    - Pulcinella 4y ago
      Yes Siri can be weirdly non-deterministic. Asking Siri to turn on the lights in the kitchen: “Here is what I found in the web for ‘Turn on the lights in the kitchen’” Asking a second time: “Ok, all the lights in the kitchen are on.”
  - 4y ago
- bla3 4y ago
  Google ships all these too, either in Android or Google Photos. The UI is sometimes less smooth than Apple's, but ML is competitive. (Which I think is your point: Apple does have competitive ML.)
  - axg11 4y ago
    I'm only familiar with Android from a distance - does Google Photos perform all the analysis on device?
    - mupuff1234 4y ago
      So how can you claim that apple has taken the lead if you're not familiar with the biggest direct competitor?
      - isodev 4y ago
        I think the question was rhetorical. At this time, Apple is the only FAANG(M) with affinity for building features with on-device algorithms.
    - criddell 4y ago
      > does Google Photos perform all the analysis on device? Does Apple? If I upload photos from my computer it does analysis in the cloud, doesn't it?
      - willseth 4y ago
        No. Apple has published several papers about how it works. It all happens on device.
        criddell 4y ago
        How does that work when you upload via the browser from a Windows or Linux PC? Do they get stored in iCloud without being processed?
- nerdjon 4y ago
  So I have not used Alexa (and never used Google Assistant) in a long time. So maybe both of them have improved this situation. But one of the things I was most impressed about Siri on the HomePod is it seemed to take context (not sure if that is the right word in this case...) into account more often. For example: On Alexa regardless of if I asked "how cold is it", "what is the temperature", "is it raining", "what is the weather"... etc etc etc. It would always say the same long weather response. On my HomePod if I ask "How cold is it" I get "I don't find that particularly cold, it is cloudy and 72 degrees". If I ask "what is the temperature" I get "its 72 degrees right now". If I ask "is it raining" "no, I don't think its raining right now". Then finally if I ask what the weather is I get the full thing. Does Siri lack a lot of smarts to ask random questions and give the answer? yes, But I found when I switched to HomePod the things I normally do (like weather, timers, and similar) Siri did better. Like I said, maybe Alexa has gotten better about this. But that is my experience. So for me Siri was better for my use cases. Home commands could use a lot of work, but that seems like an issue that no one has figured out how to make it actually better than flicking a light switch (outside of remote stuff).
  - criddell 4y ago
    I'm slowly learning to hate my Echo because of how Amazon so often tries to suggest more things I can do ("By the way..."). All I want is for it to tell me the time, weather, do timers, add stuff to my grocery app, and tell me if the Leafs won or lost yesterday. I bought a HomePod to see if it could do everything I need and oddly enough, the Echo does a better job integrating with my iOS-based grocery list (AnyList). With the Echo, I can say "Alexa, add eggs" and it will either add eggs to AnyList or tell me if eggs are already on my list. With Siri I have to say something like "Hey Siri, using AnyList, add eggs to my grocery list".
    - secabeen 4y ago
      Curse her out. Say "Alexa, STFU" every time she hits you with a "By the way..." It seems to help; I get BTW messages quite rarely compared to other users.
    - nharada 4y ago
- alphabetting 4y ago
  >they've taken the lead in terms of real world impact of ML Maybe from a US lens this is true but FB and Google (android) have much larger worldwide impact on most of these fronts where they did the leg work, published their research (which certainly aided apple who rarely publishes) and had the features out earlier.
- victor106 4y ago
  I love most things Apple but unfortunately Siri is not one of them. Siri on HomePods are a disaster. You have to shout at it even for it to pickup what you are saying. Mostly when the volume is a little high. I haven't tried Alexa and Google Assistant but from what I saw other friends operating it seems more of a smooth experience but I could be wrong.
  - jamil7 4y ago
    I think it's at least partly due to Siri attempting to be more privacy respecting and doing more work on-device rather than server-side like Google and Amazon's offerings.
  - AceJohnny2 4y ago
    Funny, I have the opposite experience with Siri on HomePods, especially compared to our Nest Hub which is shockingly bad.
  - evercast 4y ago
    Counterpoint: I have a HomePod mini in one of my rooms and it reacts easily to any mention of Siri in other rooms. So I am actually impressed with how good it is in reacting.
    - ellisv 4y ago
      I had a similar issue where a HomePod mini in our living room always responded when in the kitchen, so I just disabled the microphone on the living room HomePod minis.
  - jeromegv 4y ago
    >Siri on HomePods are a disaster. You have to shout at it even for it to pickup what you are saying. Mostly when the volume is a little high. I have the opposite experience on the HomePod mini, it's actually one of the thing that they are better at. My google home device is entirely useless to try to talk to if something is already playing on it. Compared to Siri that will understand me. The issue is more with Siri itself being dumb and not that great.
- wiredfool 4y ago
  Apple watch activity detection is kinda crap. - Sleep detection sort of works for going to bed, but doesn't recognize a lie in after sleepily hitting the button and dozing off for another hour. It also doesn't recognize being awake in the night, unless I actively get up. - It detects maybe half of the walks I go on, and usually well after I've already triggered cyclemeter. (So, there's a simple heuristic that I'm on a walk -- I've told it so) - It interprets the cat headbutting my wrist as a cue to change the watch face.
- wodenokoto 4y ago
  They did release a paper where they said there are more important measures of AI than minimizing error, and that was ease of use and real world impact.
- fartcannon 4y ago
  So is this just it? Do we get another year of this great sounding but soon to be disproven/softened and then downplayed hype every year for the rest of time? That's almost worse to me than the e-waste.
- vimota 4y ago
  Dictation (speech-to-text) is also pretty crappy compared to Google's.
- yunohn 4y ago
  Honestly, all of these examples have actually been solved by Google/Android before Apple/iOS, except Face ID. Google even pioneered Federated Learning with gBoard ages ago. The only difference is that a lot of it was via cloud, not on device. The point is about ML chops, not processing locality.
vletal 4y ago
I wonder how approachable it would be to optimise a custom model for ANE. According to the code examples at the bottom, the current implementation seems to be a custom model, so no generic solution. Anyway, it seems that we are at dawn of deploying mode cool models which formerly required cloud computation to the hands of the users. Really cool! Are we going to see more federated learning being pushed to user devices or is it a dead branch only useful for a few use cases?
- lupex 4y ago
  To be honest, I would expect Apple to provide an automl optimization service for this. Architectural model search is not something new. Apple has the unique access to their chip and a large enough customer base to make it feasible.
amelius 4y ago
Suprised to find out that there is not a simple abstraction layer that makes it the same across all platforms.
- criddell 4y ago
  I would have been more surprised to find out there was. Simple is rarely goes along with ML / AI libraries. Any abstraction layer will add overhead and probably present a lowest common denominator interface. The audience willing to accept those costs is small.
  - amelius 4y ago
    > Any abstraction layer will add overhead and probably present a lowest common denominator interface. The audience willing to accept those costs is small. Eh ... lots of people use Tensorflow or Torch.
    - hprotagonist 4y ago
      which together are about 5 separate abstraction layers that don’t agree with each other. (tf 1,2, keras, torch, torch functional, …) so we add onnx and trt on top! that’ll help, right?
- nmfisher 4y ago
  That was the idea behind ONNX, which has been somewhat successful. But if you’ve ever had to deploy these models, you’ll know it’s never as simple as promised. Different libraries use different ops, have weird export requirements or even noticeable performance issues.
navanchauhan 4y ago
First party support for models hosted on HuggingFace for optimisation/conversion! Pretty stoked about playing with it
pavlov 4y ago
Recently I bought a Mac Studio and wanted to experiment with Apple's GPU-accelerated ML API under the Metal Performance Shaders framework. I downloaded a sample code project from WWDC 2019: https://developer.apple.com/documentation/metalperformanceshaders/training_a_neural_network_with_metal_performance_shaders https://developer.apple.com/documentation/metalperformancesh... It didn't build on latest Xcode on the Mac Studio. I'm experienced with Cocoa and Apple's APIs, but couldn't fix the problem in 30 minutes of poking around. Then I found another sample code project from WWDC 2020 which is apparently using a similarly named but different API for the same purpose: https://developer.apple.com/documentation/metalperformanceshadersgraph/training_a_neural_network_using_mps_graph https://developer.apple.com/documentation/metalperformancesh... This one looked promising, but failed with a runtime assertion and I was unable to figure it out. At this point I wish Apple spent more effort on making their existing frameworks usable. If the only available sample code doesn't even work on a brand new Mac, the API isn't going to be used by third parties.
- Invictus0 4y ago
  I agree. It's a miracle anything gets written for iOS at all. Poking around in Apple's documentation is just miserable.
- hedgehog 4y ago
  Depending what you want to do you do you might find it faster to prototype with the Python bindings: https://coremltools.readme.io/docs/model-prediction https://coremltools.readme.io/docs/model-prediction
  - pavlov 4y ago
    I don't want to use CoreML or Python, I wanted to learn about the lower-level implementation using Metal Performance Shaders. And the only available sample code doesn't even run. This is sadly not a unique situation for Apple's more specialized public frameworks — they're more like semi-private because nobody outside Apple can make them work.
    - 4y ago

121 points by vletal 4y ago

Apple is still behind the rest of FAANG in terms of publishing ML research, but they've taken the lead in terms of real world impact of ML. If you go back a couple of years the general consensus was that Apple is very far behind and lacks any credible machine learning groups. As with most of their products, they waited for the technology to mature (a bit) and then made magical experiences from it. Some examples: - On-device semantic image search - Text highlighting (images and videos) - FaceID - Computational photography, depth mapping (sensor fusion) - Background removal (iOS 16) - Activity detection (Apple Watch) Siri is still far behind vs. Alexa and Google Assistant, but for every other ML/AI application I'd argue that Apple has the smoothest experience. This should be a lesson for others building ML-powered products. You don't need to use the best, state of the art models to compete. You can compete on the overall experience. Sharing details and publications doesn't seem to be a core part of Apple's culture, so I'm glad they're going out of their way to publish more details like this article. Their push to optimize ML inference for on-device rather than cloud is going to have the biggest impact on consumer experience. I'm fairly sure this is part of their AR/VR strategy too. Low-latency local machine learning that powers magical experiences.

jjoonathan 4y ago
Add anti-spam to the places where they need to do better, but generally agreed. The iPad handwriting recognition is pretty smooth too, so long as you don't need to write acronyms / model numbers.
MrBuddyCasino 4y ago
Discovered a few days ago while hiking that if you take a photo, you can tap on the info icon and it will tell you what kind of plant you just photographed. It works and is so low friction! This is how its done.
BonoboIO 4y ago
Picture tagging, Face ID are really great, but Siri is such a letdown. Absolutely useless beside setting a timer. Over the years it got better, but it is still dimensions behind Alexa. Amazon and Google are since the begging “web companies”, Apple comes from the hardware side and has problems keeping up with „information services“, but the Apple owned Search Engine is probably only a few years away.
jstx1 4y ago
I'm much more interested in the companies like Apple that are building products with ML as opposed to the ones that publish papers for the sake of publishing papers and getting more publicity like DeepMind. (Which is a personal preference, nothing wrong with what DeepMind is doing, I just don't really care about it that much)
- warangal 4y ago
  We started a company[0] to make it easy for developers to integrate ML models in their existing applications. [0] https://ramanlabs.in https://ramanlabs.in
samatman 4y ago
I don't think Siri is behind from Apple's perspective, because it has different goals. From a user perspective, Siri doesn't do as much, that's clear enough. However Siri is predictable, the others are creating an uncanny valley where it does more but you never quite know what or how. Apple has been consistent in signaling that they want to push as much AI to the device as they can, for example on-device speech recognition was a heavily promoted milestone. That approach is broadly incompatible with using one enormous cloud model to provide rich interactions. I think the goal for Siri is to become a better and better decision tree, rather than a conversation-driven knowledge engine like Google and Amazon have. I support that, I have actual humans to talk to and like that my interactions with Siri are based on simple phrases which do something and don't bottom out in a Google search or (yuck) a pitch to buy some service. Addendum: Siri does bottom out on a search of the web, yes, what I mean is that Siri encourages use patterns (at least for me) where this basically never happens.
- gmac 4y ago
  Somewhat disagree on this: in my experience, Siri is really bad even at what it supports. For example: * Alexa (in my kitchen) almost always plays the music I ask for on Spotify. Siri (in my car) gets it wrong about half the time — often in comical and bizarre ways. * Siri remains unable to call one of my family contacts: every time, she gets into a loop asking for clarification between two options (when I clarify, she just asks again). * I set up an automation with Siri to start the radio playing on BBC Sounds (to stop me falling back to sleep when my alarm goes in the morning). This was stupidly fiddly to set up, and it just doesn't work: Siri tells me to unlock the phone first, which destroys the whole point. I guess the caveat is that my experience with Siri is a bit limited, because in the light of her uselessness I often don't even try to get her to do things.
  - Pulcinella 4y ago
    Yes Siri can be weirdly non-deterministic. Asking Siri to turn on the lights in the kitchen: “Here is what I found in the web for ‘Turn on the lights in the kitchen’” Asking a second time: “Ok, all the lights in the kitchen are on.”
- 4y ago
bla3 4y ago
Google ships all these too, either in Android or Google Photos. The UI is sometimes less smooth than Apple's, but ML is competitive. (Which I think is your point: Apple does have competitive ML.)
- axg11 4y ago
  I'm only familiar with Android from a distance - does Google Photos perform all the analysis on device?
  - mupuff1234 4y ago
    So how can you claim that apple has taken the lead if you're not familiar with the biggest direct competitor?
    - isodev 4y ago
      I think the question was rhetorical. At this time, Apple is the only FAANG(M) with affinity for building features with on-device algorithms.
  - criddell 4y ago
    > does Google Photos perform all the analysis on device? Does Apple? If I upload photos from my computer it does analysis in the cloud, doesn't it?
    - willseth 4y ago
      No. Apple has published several papers about how it works. It all happens on device.
      - criddell 4y ago
        How does that work when you upload via the browser from a Windows or Linux PC? Do they get stored in iCloud without being processed?
nerdjon 4y ago
So I have not used Alexa (and never used Google Assistant) in a long time. So maybe both of them have improved this situation. But one of the things I was most impressed about Siri on the HomePod is it seemed to take context (not sure if that is the right word in this case...) into account more often. For example: On Alexa regardless of if I asked "how cold is it", "what is the temperature", "is it raining", "what is the weather"... etc etc etc. It would always say the same long weather response. On my HomePod if I ask "How cold is it" I get "I don't find that particularly cold, it is cloudy and 72 degrees". If I ask "what is the temperature" I get "its 72 degrees right now". If I ask "is it raining" "no, I don't think its raining right now". Then finally if I ask what the weather is I get the full thing. Does Siri lack a lot of smarts to ask random questions and give the answer? yes, But I found when I switched to HomePod the things I normally do (like weather, timers, and similar) Siri did better. Like I said, maybe Alexa has gotten better about this. But that is my experience. So for me Siri was better for my use cases. Home commands could use a lot of work, but that seems like an issue that no one has figured out how to make it actually better than flicking a light switch (outside of remote stuff).
- criddell 4y ago
  I'm slowly learning to hate my Echo because of how Amazon so often tries to suggest more things I can do ("By the way..."). All I want is for it to tell me the time, weather, do timers, add stuff to my grocery app, and tell me if the Leafs won or lost yesterday. I bought a HomePod to see if it could do everything I need and oddly enough, the Echo does a better job integrating with my iOS-based grocery list (AnyList). With the Echo, I can say "Alexa, add eggs" and it will either add eggs to AnyList or tell me if eggs are already on my list. With Siri I have to say something like "Hey Siri, using AnyList, add eggs to my grocery list".
  - secabeen 4y ago
    Curse her out. Say "Alexa, STFU" every time she hits you with a "By the way..." It seems to help; I get BTW messages quite rarely compared to other users.
  - nharada 4y ago
alphabetting 4y ago
>they've taken the lead in terms of real world impact of ML Maybe from a US lens this is true but FB and Google (android) have much larger worldwide impact on most of these fronts where they did the leg work, published their research (which certainly aided apple who rarely publishes) and had the features out earlier.
victor106 4y ago
I love most things Apple but unfortunately Siri is not one of them. Siri on HomePods are a disaster. You have to shout at it even for it to pickup what you are saying. Mostly when the volume is a little high. I haven't tried Alexa and Google Assistant but from what I saw other friends operating it seems more of a smooth experience but I could be wrong.
- jamil7 4y ago
  I think it's at least partly due to Siri attempting to be more privacy respecting and doing more work on-device rather than server-side like Google and Amazon's offerings.
- AceJohnny2 4y ago
  Funny, I have the opposite experience with Siri on HomePods, especially compared to our Nest Hub which is shockingly bad.
- evercast 4y ago
  Counterpoint: I have a HomePod mini in one of my rooms and it reacts easily to any mention of Siri in other rooms. So I am actually impressed with how good it is in reacting.
  - ellisv 4y ago
    I had a similar issue where a HomePod mini in our living room always responded when in the kitchen, so I just disabled the microphone on the living room HomePod minis.
- jeromegv 4y ago
  >Siri on HomePods are a disaster. You have to shout at it even for it to pickup what you are saying. Mostly when the volume is a little high. I have the opposite experience on the HomePod mini, it's actually one of the thing that they are better at. My google home device is entirely useless to try to talk to if something is already playing on it. Compared to Siri that will understand me. The issue is more with Siri itself being dumb and not that great.
wiredfool 4y ago
Apple watch activity detection is kinda crap. - Sleep detection sort of works for going to bed, but doesn't recognize a lie in after sleepily hitting the button and dozing off for another hour. It also doesn't recognize being awake in the night, unless I actively get up. - It detects maybe half of the walks I go on, and usually well after I've already triggered cyclemeter. (So, there's a simple heuristic that I'm on a walk -- I've told it so) - It interprets the cat headbutting my wrist as a cue to change the watch face.
wodenokoto 4y ago
They did release a paper where they said there are more important measures of AI than minimizing error, and that was ease of use and real world impact.
fartcannon 4y ago
So is this just it? Do we get another year of this great sounding but soon to be disproven/softened and then downplayed hype every year for the rest of time? That's almost worse to me than the e-waste.
vimota 4y ago
Dictation (speech-to-text) is also pretty crappy compared to Google's.
yunohn 4y ago
Honestly, all of these examples have actually been solved by Google/Android before Apple/iOS, except Face ID. Google even pioneered Federated Learning with gBoard ages ago. The only difference is that a lot of it was via cloud, not on device. The point is about ML chops, not processing locality.

vletal 4y ago

I wonder how approachable it would be to optimise a custom model for ANE. According to the code examples at the bottom, the current implementation seems to be a custom model, so no generic solution. Anyway, it seems that we are at dawn of deploying mode cool models which formerly required cloud computation to the hands of the users. Really cool! Are we going to see more federated learning being pushed to user devices or is it a dead branch only useful for a few use cases?

lupex 4y ago
To be honest, I would expect Apple to provide an automl optimization service for this. Architectural model search is not something new. Apple has the unique access to their chip and a large enough customer base to make it feasible.

amelius 4y ago

Suprised to find out that there is not a simple abstraction layer that makes it the same across all platforms.

criddell 4y ago
I would have been more surprised to find out there was. Simple is rarely goes along with ML / AI libraries. Any abstraction layer will add overhead and probably present a lowest common denominator interface. The audience willing to accept those costs is small.
- amelius 4y ago
  > Any abstraction layer will add overhead and probably present a lowest common denominator interface. The audience willing to accept those costs is small. Eh ... lots of people use Tensorflow or Torch.
  - hprotagonist 4y ago
    which together are about 5 separate abstraction layers that don’t agree with each other. (tf 1,2, keras, torch, torch functional, …) so we add onnx and trt on top! that’ll help, right?
nmfisher 4y ago
That was the idea behind ONNX, which has been somewhat successful. But if you’ve ever had to deploy these models, you’ll know it’s never as simple as promised. Different libraries use different ops, have weird export requirements or even noticeable performance issues.

navanchauhan 4y ago

First party support for models hosted on HuggingFace for optimisation/conversion! Pretty stoked about playing with it

pavlov 4y ago

Recently I bought a Mac Studio and wanted to experiment with Apple's GPU-accelerated ML API under the Metal Performance Shaders framework. I downloaded a sample code project from WWDC 2019: https://developer.apple.com/documentation/metalperformanceshaders/training_a_neural_network_with_metal_performance_shaders https://developer.apple.com/documentation/metalperformancesh... It didn't build on latest Xcode on the Mac Studio. I'm experienced with Cocoa and Apple's APIs, but couldn't fix the problem in 30 minutes of poking around. Then I found another sample code project from WWDC 2020 which is apparently using a similarly named but different API for the same purpose: https://developer.apple.com/documentation/metalperformanceshadersgraph/training_a_neural_network_using_mps_graph https://developer.apple.com/documentation/metalperformancesh... This one looked promising, but failed with a runtime assertion and I was unable to figure it out. At this point I wish Apple spent more effort on making their existing frameworks usable. If the only available sample code doesn't even work on a brand new Mac, the API isn't going to be used by third parties.

Invictus0 4y ago
I agree. It's a miracle anything gets written for iOS at all. Poking around in Apple's documentation is just miserable.
hedgehog 4y ago
Depending what you want to do you do you might find it faster to prototype with the Python bindings: https://coremltools.readme.io/docs/model-prediction https://coremltools.readme.io/docs/model-prediction
- pavlov 4y ago
  I don't want to use CoreML or Python, I wanted to learn about the lower-level implementation using Metal Performance Shaders. And the only available sample code doesn't even run. This is sadly not a unique situation for Apple's more specialized public frameworks — they're more like semi-private because nobody outside Apple can make them work.
  - 4y ago