Hi folks, I had a chance to attend NeurIPS this year and wrote a blog post outlining my impressions, sharing here in the hopes that they are useful for people and spark a conversation!

https://alexkolchinski.com/2019/12/30/neurips-2019/

Comments on what you agree/disagree with, other things you noticed, links to different perspectives etc. would be much appreciated.

I would really appreciate it if you have a look at my Statement of Purpose (SoP) and give your feedback on it. If you’re willing, just leave a comment. I’ll DM you!

As the 2010’s draw to a close, it’s worth taking a look back at the monumental progress that has been made in Deep Learning in this decade.

This post is an overview of some the most influential Deep Learning papers of the last decade. My hope is to provide a jumping-off point into many disparate areas of Deep Learning by providing succinct and dense summaries that go slightly deeper than a surface level exposition, with many references to the relevant resources.

https://leogao.dev/2019/12/31/The-Decade-of-Deep-Learning/

In this piece, we’ll look at some of the top open source machine learning projects in 2019

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https://heartbeat.fritz.ai/2019s-top-open-source-machine-learning-projects-3cd082a02f78

https://www.humblebundle.com/books/python-machine-learning-packt-books

i just saw this bundle on HumbeBundle is it worth buying or it’s just waste of money ?

Hello world!

I’m running a project requiring a significant amount of compute where it’ll be cheaper to buy RTX cards outright (+electricity) instead of running them on the cloud.

I’m looking for a rack-mountable option that can house 8 GPU (can settle for 6 GPUs+ 1 network card).

So far I’ve found:

• ASUS 4RU ESC8000 – excellent fit for a hefty price tag (AUD 10K) that I’d rather put toward the GPU.
• Mining cases/motherboard – very cheap but rubbish layout.
• A few online tutorials for building smaller systems but they are limited to < 4 GPU.

Other requirements:

• Dual PSU support, for obvious reason.
• Will need to allow a minimum 128GB of RAM on the motherboard, ideally 256GB+
• Ideally PCIe x16 Gen 4 but can settle with Gen 3.

I’ve also considered:

• Electricity: yes, I have 3-phase feed, won’t burn the house down 🙂
• Cooling – high powered split AC (for the small room where this live)
• Networking – second hand InfiniBand.

Now the big question:

Are you aware of there that fits this bill?

Budget: $1000-2000 for the case + motherboard. Probably another grand for the CPU (not too important), and however much the RAM are gonna cost, lolz.

Thanks in advance!

TL;DR: custom pip wheels for TF 2.0 / 2.1 for Py 3.7 / 3.8 and CUDA 10.1 / 10.2: https://github.com/inoryy/tensorflow-optimized-wheels

I’m sharing my pip wheels for TF built from source for some non-standard versions, notably Python 3.8 + CUDA 10.2 and Python 3.7 + CUDA 10.1, the latter is “compatible” with PyTorch 1.3 so you can have them share a single env.

The builds also enable various performance flags like XLA JIT support and modern CPU opt flags, including SIMD support (AVX2, SSE4, FMA). If you have a CPU released after ~2013 then you’ll likely benefit from these on e.g. data pre-processing. Though I should note that if you have Intel CPU then you might not see a large difference since now TF comes pre-built with MKL which can dispatch required intrinsics at runtime.

Finally, I’ve enabled additional compute capabilities support (5.0, 6.1, 7.0), which means these wheels should also work on older GPUs (7xx – 9xx families).