Ray
optuna
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Ray | optuna | |
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42 | 32 | |
30,474 | 9,471 | |
2.9% | 2.4% | |
10.0 | 9.9 | |
6 days ago | about 21 hours ago | |
Python | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Ray
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Open Source Advent Fun Wraps Up!
22. Ray | Github | tutorial
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TransformerXL + PPO Baseline + MemoryGym
RLlib
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Elixir Livebook now as a desktop app
I've wondered whether it's easier to add data analyst stuff to Elixir that Python seems to have, or add features to Python that Erlang (and by extension Elixir) provides out of the box.
By what I can see, if you want multiprocessing on Python in an easier way (let's say running async), you have to use something like ray core[0], then if you want multiple machines you need redis(?). Elixir/Erlang supports this out of the box.
Explorer[1] is an interesting approach, where it uses Rust via Rustler (Elixir library to call Rust code) and uses Polars as its dataframe library. I think Rustler needs to be reworked for this usecase, as it can be slow to return data. I made initial improvements which drastically improves encoding (https://github.com/elixir-nx/explorer/pull/282 and https://github.com/elixir-nx/explorer/pull/286, tldr 20+ seconds down to 3).
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preprocessing millions of records - how to speed up the processing
Dask, Ray(ray.io), or pyspark(if you have a cluster)
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3% of 666 Python codebases we checked had a silently failing unit test
https://github.com/ansible-community/ara/pull/358 https://github.com/b12io/orchestra/pull/830 https://github.com/batiste/django-page-cms/pull/210 https://github.com/carpentries/amy/pull/2130 https://github.com/celery/django-celery/pull/612 https://github.com/django-cms/django-cms/pull/7241 https://github.com/django-oscar/django-oscar/pull/3867 https://github.com/esrg-knights/Squire/pull/253https://github.com/Frojd/django-react-templatetags/pull/64 https://github.com/groveco/django-sql-explorer/pull/474 https://github.com/jazzband/django-silk/pull/550 https://github.com/keras-team/keras/pull/16073 https://github.com/ministryofjustice/cla_backend/pull/773 https://github.com/nitely/Spirit/pull/306 https://github.com/python/pythondotorg/pull/1987 https://github.com/rapidpro/rapidpro/pull/1610 https://github.com/ray-project/ray/pull/22396 https://github.com/saltstack/salt/pull/61647 https://github.com/Swiss-Polar-Institute/project-application/pull/483 https://github.com/UEWBot/dipvis/pull/216
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Rust OpenCV - Simple Guide
I'd really want use Rust+OpenCV instead of Python+OpenCV to process a lot of images (xxxxxx pieces on a central NAS). I would want to do it by also splitting the work over multiple worker nodes for speed. Unfortunately, I've so far not had the time to figure this out... Meanwhile, a Rust API for Ray is being worked on! https://github.com/ray-project/ray/issues/20609
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Blazer - HPC python library for MPI workflows
ray.io doesn't support MPI natively. And thus is not "supercomputer" friendly. Blazer runs on MPI which runs across the NUMA (non-unified memory architecture) setup of a supercomputer. The compute interconnect is 100's of times faster than network remoting, which ray.io uses.
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JORLDY: OpenSource Reinforcement Learning Framework
Distributed RL algorithms are provided using ray
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Python stands to lose its GIL, and gain a lot of speed
I had a similar use case and ended up using ray. https://github.com/ray-project/ray
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How to deploy a rllib-trained model?
Currently, rllib's "--export-formats" does nothing; I have folders of checkpoints, but no models. Looks like currently the internal export_model function isn't implemented: https://github.com/ray-project/ray/issues/19021
optuna
- How to test optimal parameters
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How did you make that?!
The network configuration process is usually not particularly scientific and mostly relies on empirical observation. For some cases, tools like Optuna can be used to automatically find the optimal parameters. In others, on others, you can look for modern studies which explore the effect of this parameter on performance, such as this study (2022), but these are typically very specific to one particular architecture.
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
Keras Tuner, Optuna : https://github.com/optuna/optuna ?
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Suggestion to optimize algo
I have used OpenTuner, but I don't think it is maintained anymore. I hear tell that Optuna is what to use now, but have not used it myself. https://optuna.org Optuna - A hyperparameter optimization framework
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[D]How to optimize an ANN?
You can use Optuna, SMAC or hyperopt
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Optuna: An open source hyperparameter optimization framework to automate hyperparameter search
Optuna is a great library and I do use it in tuneta for optimizing technical indicator parameters. However, certain Optuna algos suggest the same parameters in separate trials resulting in many duplicate parameters (issue) which needs to be managed external of the lib.
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The loss function of my model (Not a NN model) is not differentiable, what should I do?
if your parameter set is not too large, you could try black-box optimization via something like Optuna
- SPO600 project part 1
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Trading Algos - 5 Key Metrics and How to Implement Them in Python
Nothing can beat iteration and rapid optimization. Try running things like grid experiments, batch optimizations, and parameter searches. Take a look at various packages like hyperopt or optuna as packages that might be able to help you here!
What are some alternatives?
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
Faust - Python Stream Processing
hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python
rl-baselines3-zoo - A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
gevent - Coroutine-based concurrency library for Python
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
SCOOP (Scalable COncurrent Operations in Python) - SCOOP (Scalable COncurrent Operations in Python)
Thespian Actor Library - Python Actor concurrency library
Dask - Parallel computing with task scheduling
django-celery - Old Celery integration project for Django
pymarl - Python Multi-Agent Reinforcement Learning framework