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JORLDY: OpenSource Reinforcement Learning Framework
2 projects | reddit.com/r/reinforcementlearning | 8 Nov 2021
Distributed RL algorithms are provided using ray
Python stands to lose its GIL, and gain a lot of speed
5 projects | reddit.com/r/programming | 20 Oct 2021
I had a similar use case and ended up using ray. https://github.com/ray-project/ray
How to deploy a rllib-trained model?
3 projects | reddit.com/r/reinforcementlearning | 16 Oct 2021
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/190213 projects | reddit.com/r/reinforcementlearning | 16 Oct 2021
[HELP] Converting many individual workstations into a HPC cluster
1 project | reddit.com/r/HPC | 11 Oct 2021
Unless you have infiniband, you might want to build it as a kubernetes cluster and look at something like (ray-project)[https://github.com/ray-project/ray] it has a ton of distributed plugin packages that are Ethernet based.
Show HN: SpotML – Managed ML Training on Cheap AWS/GCP Spot Instances
6 projects | news.ycombinator.com | 3 Oct 2021
Neat. Congratulations on the launch!
Apart from the fact that it could deploy to both GCP and AWS, what does it do differently than AWS Batch ?
When we had a similar problem, we ran jobs on spots with AWS Batch and it worked nicely enough.
Some suggestions (for a later date):
1. Add built-in support for Ray  (you'd essentially be then competing with Anyscale, which is a VC funded startup, just to contrast it with another comment on this thread) and dbt .
2. Support deploying coin miners (might be good to widen the product's reach; and stand it up against the likes of consensys).
3. Get in front of many cost optimisation consultants out there, like the Duckbill Group.
If I may, where are you building this product from? And how many are on the team?
Writing your First Distributed Python Application with Ray (without multiprocessing)
4 projects | reddit.com/r/Python | 23 Aug 2021
Here is an older discussion on dask vs ray from the creators of both projects: https://github.com/ray-project/ray/issues/642
[D] Kubeflow vs. Argo for ML Pipeline Tool
2 projects | reddit.com/r/MachineLearning | 17 Aug 2021
Here is link number 1 - Previous text "Ray"2 projects | reddit.com/r/MachineLearning | 17 Aug 2021
If you are looking for a developer-friendly tool, I'd ditch the task/workflow orchestration paradigm altogether and use something like Ray. It's made by and for ML practitioners, it's much more versatile, has no unwarranted DSLs (pure python), and you can test locally before deploying with pretty much the same code.
1 project | news.ycombinator.com | 8 Jun 2021
[P] optimization of Hugging Face Transformer models to get Inference < 1 Millisecond Latency + deployment on production ready inference server
3 projects | reddit.com/r/MachineLearning | 5 Nov 2021
There are plenty of different options to do that in OSS, the most well known being optuna (https://github.com/optuna/optuna).
Best practices for hyperparameters tuning (code/framework)
1 project | reddit.com/r/deeplearning | 18 Sep 2021
I use Optuna, which is a hyperparameter optimizer, it was both the easiest to use/integrate and the one with most features when I searched for one a year ago or so. https://optuna.org/
How to improve the performance of a machine learning (ML) model
1 project | dev.to | 16 Sep 2021
We recommend using publicly available libraries to help with hyperparameter tuning, e.g. optuna. We’ll have a separate article for hyperparameter tuning in the future.
[P] easyopt: zero-code hyperparameters optimization framework
2 projects | reddit.com/r/MachineLearning | 16 Sep 2021
It is basically an optuna wrapper that does all the boring stuff for you.
[D] What kind of Hyperparameter Optimisation do you use?
3 projects | reddit.com/r/MachineLearning | 30 Aug 2021
[D] Best Bayesian Optimization Library in R?
1 project | reddit.com/r/MachineLearning | 25 Jun 2021
I'm looking for an R-library to optimize any multivariate objective function with Bayesian Optimization (BO). In python, I usually use Optuna (https://optuna.org/) for BO. Do you have any recommendations for equivalent libraries in R?
Do you often find hyperparam tuning does very little?
1 project | reddit.com/r/datascience | 23 Apr 2021
As for doing a full gridsearch, I recommend using a better strategy, e.g. bayesian optimization. Optuna is great for this.
[D] When do you start optimizing hyperparameters when trying out a new idea?
1 project | reddit.com/r/MachineLearning | 21 Apr 2021
[D] Efficient ways of choosing number of layers/neurons in a neural network
3 projects | reddit.com/r/statistics | 20 Apr 2021
optuna, hyperopt, nni, plenty of less-known tools too.
Easy to implement algorithm for choosing correct values
2 projects | reddit.com/r/algorithms | 19 Apr 2021
Black box optimization: https://optuna.org/, https://hyperopt.github.io/hyperopt/, https://iminuit.readthedocs.io/en/stable/
What are some alternatives?
Faust - Python Stream Processing
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python
gevent - Coroutine-based concurrency library for Python
SCOOP (Scalable COncurrent Operations in Python) - SCOOP (Scalable COncurrent Operations in Python)
Thespian Actor Library - Python Actor concurrency library
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
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.
pipelines - An experimental programming language for data flow
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
Wallaroo - Distributed Stream Processing