pcmoritz

3 ms·

1.
by pcmoritz 8mo ago
You are right, it is fixed now!
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by pcmoritz 8mo ago
Thanks, fixed!
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by pcmoritz 8mo ago
We are excited about the Tinker API, it exposes the primitives forward_backward, optim_step, sample and checkpoints of an LLM as a REST API, which can be used to implement pretty much arbitrary training recipes while hiding all the infra ch
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Open or Closed Future? Ben Horowitz on AI Through the Lens of the Dotcom Boom [video](youtube.com)
1 points by pcmoritz 3y ago|0 comments
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by pcmoritz 8y ago
If you are interested in learning more about this, there is also a recent PyData talk: https://rise.cs.berkeley.edu/blog/modin-talk-at-pydata-nyc-2...
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by pcmoritz 9y ago
One of the authors here! We are glad you like it! The project is indeed driven by the authors listed on the paper and also the knowledge and experience that was accumulated in the AMPLab (the predecessor of the RISELab, see https:/&#x
7.
by pcmoritz 9y ago
There are two considerations here. (1) Python single threaded performance: Here, most of the libraries we are using are implemented in C++ (like numpy, TensorFlow, Cython to speed up the code, etc.). Ray is orthogonal to that. (2) Python pa
8.
by pcmoritz 9y ago
For Ray, the main use case at the moment is parallel/distributing machine learning algorithms, people have been using it for parallelizing MC(MC) style applications, doing hyperparameter search, (pre-)process data, we are using it for
9.
by pcmoritz 9y ago
Author here. For CapnProto see the answer to the other comment above. Concerning dill, we have been using it for serializing function and classes (and then switched to cloudpickle because it supports some Python functionality better and the
10.
by pcmoritz 9y ago
Author here. From our perspective, CapnProto has similar characteristics as Flatbuffers and the reasons to prefer Arrow over it are the same: We would need to develop a mapping from Python types to CapnProto from scratch and Arrow has many