etrain

Joined 15 years ago

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1,737
Submissions
201
Member since
Apr 12
Present: Founder at Stealth. Past: VP AI Solutions and AI Cloud at HPE, Founder/CEO at Determined AI (acquired by HPE), Ph.D in AI/Distributed Systems in the UC Berkeley AMPLab, engineer at Recorded Future, and senior quant analyst at MDT Advisers.
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Recent submissions

Comment · 4 years ago
Comment · 5 years ago
Users expose their model to our Trial API (https://docs.determined.ai/latest/topic-guides/model-definit...), the base class then implements a training loop (which can be enhanced with user-supplied callbacks, metrics, etc.) that has a whole bunch of bells and whistles. Easy distributed (multi-GPU and multi-node) training, automatic checkpointing, fault tolerance, etc.

Concretely, the system is regularly taking checkpoints (which include model weights and optimizer state) and so if the spots disappear (as they do), the system has enough information to resume from where things were last checkpointed when resources become available again.

Comment · 5 years ago
Check out Determined https://github.com/determined-ai/determined to help manage this kind of work at scale: Determined leverages Horovod under the hood, automatically manages cloud resources and can get you up on spot instances, T4's, etc. and will work on your local cluster as well. Gives you additional features like experiment management, scheduling, profiling, model registry, advanced hyperparameter tuning, etc.

Full disclosure: I'm a founder of the project.

Comment · 5 years ago
Comment · 6 years ago
The exposition is tight.
Comment · 7 years ago
Relational database queries are supposed to be _declarative_. As a user, you're not supposed to think about the mechanics of execution because the database system is supposed to be able to decide how to execute your query using whatever magic it wants, as long as it satisfies the contract that it gives you the right answer.

It's absolutely useful as a debugging tool to build up some semantic understanding of what the query means, and I encourage every database user to learn to use EXPLAIN, but relying on a mental execution model is borderline dangerous.

Comment · 7 years ago
Assume 100 pages on each onion address (it’s probably power-law but let’s just assume that’s the mean). Latency with Tor is super high. Assume average of 5s to load a single page. This is generous because tail latency will probably dominate mean latency in this setting.

These things can happen in parallel but let’s also assume no more than 32 simultaneous TCP connections per host through a Tor proxy.

So we’re looking at ~75k1005/32 seconds = 14 days to run through all of them. You may not need to distribute this but there are situations (e.g. I want a fresh index daily) where it is warranted.

Comment · 7 years ago
Comment · 7 years ago
It’s also hard to separate the design of the neural architecture from the definition of the feature extractor.
Comment · 9 years ago
Comment · 9 years ago
Crunchbase has a good deal of data here - it is incomplete particularly for smaller companies, but is pretty good for larger investment rounds.
Comment · 9 years ago
A word of caution - these talks are highly technical and theoretical and I don't recommend them for anyone looking to understand practical applications of ML. If, however, you're interested in intro to cutting edge work at the frontier of theoretical underpinnings of ML, this is a fabulous group talks from some of the sharpest academics in the field.

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