Which GPU(s) to Get for Deep Learning – by Tim Dettmers

Tim Dettmer’s article on choice of GPUs for deep learning is a must-read for us AIDLers. This time he update his popular article “Which GPU(s) to Get for Deep Learning” to include the latest RTX 2080. We like his conclusion about RTX 2080 than 2080 Ti: he believes that 2080 is more cost-effective.

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Layman Description of Technical Terms in Deep Learning.

Here is a nice layman description of deep learning terminologies.

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Understanding an Algorithm

Many of you asked AIDL whether you can understand deep learning after taking several beginner courses. As ML practitioners, our answer is to make sure you can implement classic algorithms in training or inference yourself, compare your work with SOTA, and understand the strengths and weaknesses of various open source package down to the code level.

We think François Chollet explains better:

“A popular quote goes “if you can’t explain it in simple terms, you don’t understand it well enough” (often incorrectly attributed to Einstein or Feynman).

I think a more accurate take is: “if you can’t explain it in arbitrarily precise terms, you don’t understand it well enough””

“In particular, if you understand something clearly, you should be able to describe it in precise algorithmic terms to a computer: you should be able to implement it from scratch (as a simulation, as a framework, etc).””

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OpenAI Five Lost to Human Experts in Dota 2

Few weeks ago, we learn that OpenAI created OpenAI Five to compete in one of the most competitive eSport game: Dota 2. We have covered previous Open AI’s Bot for DotA at length in Issue 25 Fact-checking section, we noted that it would be much tougher to move from a 1v1 mode to 5v5 mode because that require learning, complex strategy. But then OpenAI surprised us by showing that they can beat semi-pros in 5v5! So we did feel optimistic for the prospect for this week game at The International.

The difference between “The International” Match and the previous report, (also shared in this issue) is that the bot was dealing with semi-pro rather than genuine professional players. Unfortunately OpenAI failed. But then when you look at data and the phenomena they saw, we might be just hitting a scalability problem. Perhaps training for few more months would make the bot strong enough. See our analysis in the next item.

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OpenAI Five

We included an an older link from OpenAI so that readers can have a sense of previous OpenAI bot publicized in Jun this year. One major difference, as we mentioned previously is that this time OpenAI bot is dealing with pro players who collect prize money for living. More importantly is that three of the players were playing together competitively.

So what can we say about the strength of current OpenAI Five? First of all, when the players are at solo MMR around 5500, Five doesn’t seem to have problem to beat the team formed by the players in that level. Whereas the pros Five deal with have MMR rating 7000+ (e.g. xiao8’s rating is 7333). So just like machine’s long history of defeating humans in chess, we might be just looking at a scalability problem.

You may ask: is it a scalability problem which we can solve in reasonable amount of time? For example, it seems that machines MMR rating doesn’t just improve linearly over time. For example, we know that from March to June, a three month period, we see the bot trained from scratch can now beat a semi-pro at 5500, but then another three month period of time from June to now, machines don’t quite make up the gap of 1800 points.

Our discussion so far is based on solo MMR, if you look at the players records, team MMR could be a better metric to decide relative strength of a team. Would it be possible to estimate that number? So far, we don’t see such number published yet.

The true picture is also obscured by the fact that there are change of rules of courier. As OpenAI point out, it forces the machine to use a more aggressive strategy, which humans can exploit.

If we were OpenAI researchers, we would probably spend time to analyze the two lost games, and ask how can we learn even faster than now? Given what we see so far, we still place our bet on the machine, and it sounds like we are almost there.

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