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Developer preview of TensorFlow Lite

developers.googleblog.com

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Re: Developer preview of TensorFlow Lite

#23

Post by rajatmonga »

MBCook wrote: post #7

Is it possible that a future version may be able to leverage CoreML on iOS?

With TensorFlow and TF Lite we are looking to provide a great experience across all platforms, and are exploring ways to provide a simpler experience with good acceleration on iOS as well.

Re: Developer preview of TensorFlow Lite

#24

Post by 1_over_n »

how does this relate to other hardware beyond iOS / Javascript i.e. raspberry pi, nvidia jetson etc andddddd.........whats the likelihood of libraries that sit on top of TF supporting this like keras and pytorch. Just some questions that spring to my mind

Re: Developer preview of TensorFlow Lite

#26

Post by infnorm »

lern_too_spel wrote: post #13
rajatmonga wrote: post #2

TensorFlow Lite is TensorFlow’s lightweight solution for mobile and embedded devices! TensorFlow has always run on many platforms, from racks of servers to tiny devices, but as the adoption of machine learning models has grown over the last few years, so has the need to deploy them on mobile and embedded devices. TensorFlow Lite enables low-latency inference of on-device machine learning models. Looking forward to yo…

What about using XLA to compile libraries for mobile deployment fusing only the operations needed by the model?

TensorFlow Lite is an interpreter in contrast with XLA which is a compiler. The advantage of TensorFlow lite is that a single interpreter can handle several models rather than needing specialized code for each model and each target platform. TensorFlow Lite’s core kernels have also been hand-optimized for common machine learning patterns. The advantage of compiler approaches is fusing many operations to reduce memory bandwidth (and thus speed). TensorFlow lite fuses many common patterns in the TensorFlow converter. We are of course excited about the possibility of using JIT techniques and using XLA technology within the TensorFlow Lite interpreter or as part of the TensorFlow Lite converter as a possible future direction.

Re: Developer preview of TensorFlow Lite

#27

Post by sarasuec »

bitmapbrother wrote: post #14
rajatmonga wrote: post #2

TensorFlow Lite is TensorFlow’s lightweight solution for mobile and embedded devices! TensorFlow has always run on many platforms, from racks of servers to tiny devices, but as the adoption of machine learning models has grown over the last few years, so has the need to deploy them on mobile and embedded devices. TensorFlow Lite enables low-latency inference of on-device machine learning models. Looking forward to yo…

Will this leverage the Pixel Visual Core SoC on a Pixel 2 device?

This release of Tensorflow Lite doesn't leverage the Pixel Visual Core. We will explore different hardware options available to us in the future.

Re: Developer preview of TensorFlow Lite

#28

Post by infnorm »

barbolo wrote: post #16

Would that be a viable option to deploy TensorFlow models on serverless environments (Lambda, Functions)?

The main TensorFlow interpreter provides a lot of functionality for larger machines like servers (e.g. Desktop GPU support and distributed support). Of course, TensorFlow lite does run on standard PCs and servers, so using it on non-mobile/small devices is possible. If you wanted to create a very small microservice, TensorFlow lite would likely work, and we’d love to hear about your experiences, if you try this.

Re: Developer preview of TensorFlow Lite

#29

Post by infnorm »

mtgx wrote: post #17

How would this differ from uTensor? Did they make uTensor redundant? https://github.com/neil-tan/uTensor

We developed TensorFlow lite to be small enough to target really small devices that lack MMU’s like the ARM Cortex M MCU series, but we haven’t done the actual work to target those devices. That being said, we are excited when the ecosystem and community around machine learning expands.
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