5 ms·
Meaning even deeper level of vendor lock -- now you cannot even find the chips to run your application elsewhere!
by eva1984 10y ago
Meaning even deeper level of vendor lock -- now you cannot even find the chips to run your application elsewhere!
- dgacmu 10y agoNo - tensorflow is open source and you can run it on many platforms. TPUs are about efficiency. You might not be able to do image recognition as efficiently without one, but you can still perform exactly the same tasks. (I work on TF this year.)
- eva1984 10y agoWell, that is good to know. Thanks for clarification.
- Retric 10y agoI would be shocked if tensorflow optimizations where useful 1:1 for stock Intel chips or GPU's. So, there is still plenty of lock-in even if your process runs. GPU vendors love to play this game by helping optimize games.
- dgacmu 10y agoIt's possible, but I think that the majority of ML optimization as seen by a programmer using tensorflow is more about optimizing the balance of accuracy, training & inference speed, and memory use, and a lot of the solutions in this space are pretty hardware independent. There's an entire other type of optimization about, e.g., making conv2d insanely fast, but that's not something that a typical data scientist-type user deals with. (To elaborate -- it's questions like "how deep should I make this convolution? Should I use tf.relu or tf.sigmoid? How many fully-connected layers should I put here, and how big should I make them?". These are really knotty deep learning design questions, but they're often h/w independent. Not always - we certainly have some ops on TF that we only support in CPUs and not on GPUs, for example - but often.)
- Retric 10y agoI am more thinking in terms of: 1. Best price/performance is tensorflow right now. So, the best software choice is platform X. 2. Then in 2 years.. Well we are using Platform X so tensorflow is clearly the best option. In other words once you pick conv2d, you tend to also stick with whatever conv2d is optimized for. Which also means HW vendors love to help optimize popular platforms.
- visarga 10y agoOthers can make TF hardware too. Now that Google has opened the way, they will compete.
- micro_cam 10y agoAll of the comparable tools are practically locked into nVidia gpus/CUDA. TensorFlow is rapidly reaching performance parity on that hardware [1] and can now be run on this so it's actually sort of the least locked in framework. [1] It has been climbing the charts at https://github.com/soumith/convnet-benchmarks https://github.com/soumith/convnet-benchmarks for example.