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Came to say the same. I’ve always assumed the target audience of these machines is just video editing? What are the requirements on running, say, TensorFlow? I

by azag0 9y ago

Came to say the same. I’ve always assumed the target audience of these machines is just video editing? What are the requirements on running, say, TensorFlow? Isn’t it just the GPU there?

jrs95 9y ago
You can't run tensorflow on it because it has an AMD GPU.
- arcanus 9y ago
  Tensorflow has been supported on AMD GPUs since at least November. https://www.anandtech.com/show/12032/amd-announces-wider-epyc-availability-and-rocm-17-with-tensorflow-support https://www.anandtech.com/show/12032/amd-announces-wider-epy...
  - PeterisP 9y ago
    No, AMD has claimed that tensorflow works for them since 2016 IIRC, but it's not exactly correct. The main tensorflow branch does not support AMD GPUs at all. There's a bunch of experimental code by AMD that possibly works in certain conditions, but is neither properly supported nor widely used. For example, there's hiptensorflow which seems to be a TF port stuck on 1.0.1 (TF is now up to 1.5, with many major releases and key changes), not being actively developed (the last commit seems to be at the end of 2017) and core parts of it (ROCm) don't work in OS X (tensorflow as such runs just fine on OSX, just CPU-only, which is a major performance limitation compared to any machine with a good nvidia gpu), and thus are not that relevant for discussing Apple systems. There are also some OpenCL port efforts, none of which are stable, up to date, and actually usable to get the expected performance improvements. All these projects are interesting, promising, difficult, and their developers deserve all the best, but calling them "Tensorflow has been supported" is an extreme exaggeration, PR vaporware. Proper support is not there yet. I believe that there are likely ways to jury-rig some code together and run it on AMD hardware, but that's not comparable to the maturity level of these tools on nvidia (essentially, run the install, and it works). AMD still has to do their homework and fulfil their promises. They've spent far too long saying that the software support is coming soon, and now their claims have to be taken with a grain of salt. They can hope that the community will take over effective development a year or two after they do that and people in the ML scene start widely using their hardware, but not earlier. Currently, even if you have a nice AMD GPU, it's cheaper to just buy another high-end GPU from nvidia rather than spend a week of engineer-time to possibly maybe get their shoddy software to do what you need.
  - danieldk 9y ago
    Sorry, this is not just a box that you tick off. The CUDA code paths in Tensorflow have been optimized and stabilized for years. Why would anyone drop 5000-10000 Euro on a Tesla-class GPU and get a subpar experience when you can just buy NVIDIA? AMD has a lot more work to do in this area before people will seriously consider them in GPU computing.
matt4077 9y ago
I worked in bioinformatics at a Max Planck Institute during my studies, and made good use of a Mac Pro. Some of the tools we used were interactive, such as 3D-modelling. Many other tasks, such as DNA sequence searches (BLAST), just happened to involve datasets that were too large for most notebooks, but manageable with the 32GB or RAM the Mac Pro had. We did use a compute cluster, but using it drastically reduced the turnaround time during development in my experience.

by azag0 9y ago

Came to say the same. I’ve always assumed the target audience of these machines is just video editing? What are the requirements on running, say, TensorFlow? Isn’t it just the GPU there?

jrs95 9y ago

You can't run tensorflow on it because it has an AMD GPU.

arcanus 9y ago
Tensorflow has been supported on AMD GPUs since at least November. https://www.anandtech.com/show/12032/amd-announces-wider-epyc-availability-and-rocm-17-with-tensorflow-support https://www.anandtech.com/show/12032/amd-announces-wider-epy...
- PeterisP 9y ago
  No, AMD has claimed that tensorflow works for them since 2016 IIRC, but it's not exactly correct. The main tensorflow branch does not support AMD GPUs at all. There's a bunch of experimental code by AMD that possibly works in certain conditions, but is neither properly supported nor widely used. For example, there's hiptensorflow which seems to be a TF port stuck on 1.0.1 (TF is now up to 1.5, with many major releases and key changes), not being actively developed (the last commit seems to be at the end of 2017) and core parts of it (ROCm) don't work in OS X (tensorflow as such runs just fine on OSX, just CPU-only, which is a major performance limitation compared to any machine with a good nvidia gpu), and thus are not that relevant for discussing Apple systems. There are also some OpenCL port efforts, none of which are stable, up to date, and actually usable to get the expected performance improvements. All these projects are interesting, promising, difficult, and their developers deserve all the best, but calling them "Tensorflow has been supported" is an extreme exaggeration, PR vaporware. Proper support is not there yet. I believe that there are likely ways to jury-rig some code together and run it on AMD hardware, but that's not comparable to the maturity level of these tools on nvidia (essentially, run the install, and it works). AMD still has to do their homework and fulfil their promises. They've spent far too long saying that the software support is coming soon, and now their claims have to be taken with a grain of salt. They can hope that the community will take over effective development a year or two after they do that and people in the ML scene start widely using their hardware, but not earlier. Currently, even if you have a nice AMD GPU, it's cheaper to just buy another high-end GPU from nvidia rather than spend a week of engineer-time to possibly maybe get their shoddy software to do what you need.
- danieldk 9y ago
  Sorry, this is not just a box that you tick off. The CUDA code paths in Tensorflow have been optimized and stabilized for years. Why would anyone drop 5000-10000 Euro on a Tesla-class GPU and get a subpar experience when you can just buy NVIDIA? AMD has a lot more work to do in this area before people will seriously consider them in GPU computing.

matt4077 9y ago

I worked in bioinformatics at a Max Planck Institute during my studies, and made good use of a Mac Pro. Some of the tools we used were interactive, such as 3D-modelling. Many other tasks, such as DNA sequence searches (BLAST), just happened to involve datasets that were too large for most notebooks, but manageable with the 32GB or RAM the Mac Pro had. We did use a compute cluster, but using it drastically reduced the turnaround time during development in my experience.