Only Pretraining available, downstream tasks are not yet.

https://github.com/graykode/ALBERT-Pytorch

Hi everyone,

I have created a yet another reinforcement learning library. https://github.com/vwxyzjn/cleanrl

This repository focuses on a clean and minimal implementation of reinforcement learning algorithms. The highlights features of this repo are:

• Most algorithms are self-contained in single files with a common dependency file common.py that handles different gym spaces.
• Easy logging of training processes using Tensorboard and Integration with wandb.com to log experiments on the cloud. Check out https://app.wandb.ai/costa-huang/cleanrltest.
• Easily customizable and being able to debug directly in Python’s interactive shell.
• Convenient use of commandline arguments for hyper-parameters tuning.

Currently I support A2C, PPO, and DQN. If you are interested, please consider giving it a try 🙂

Motivation:

There are two types of RL library on the two ends of the spectrum. The first one is the demo kind that really just demos what the algorithm is doing, only deals with one gym environment and hard to record experiments and tune parameters.

On the other end of the spectrum, we have OpenAI/baselines, ray-project/ray, and couple google repos. My personal experience with them is that I could only run benchmark with them. They try to write modular code and employ good software engineering practices, but the problem is python is a dynamic language without IDE support. As a result, I had no idea what variable types in different files are and it was very difficult to do any kind of customization. I had to see through dozens of files before even able to try some experiments.

That’s why I created this repo that leans towards the first kind, but has more actual experimental support. I support multiple gym spaces (still working on it), command line arguments to tune parameters, and very seamless experiment logging, all of which are essential characteristics for building a pipeline for research I believe.

I want to start building a foundation in machine learning to ultimately do research in the intersection of machine learning and economics/finance. Would The Elements of Statistical Learning (Hastie et. al. 2009) be the best place to start? I fear that the text may be outdated in 2019, but I’m coming to ML with only econometric/statistical knowledge.

Also, would An Introduction to Statistical Learning with Applications in R (Gareth et al 2017) be too basic for my goals? Are there better texts I should start with than Hastie et al (2009)?

Blog post on gradient.pub with quantitative extensive analysis of PyTorch vs TensorFlow adoption in research.

TensorFlow will always have a captive audience within Google/DeepMind, but I wonder whether Google will eventually relax this. Even now, many of the researchers that Google wants to recruit will already prefer PyTorch at varying levels, and I’ve heard grumblings that many researchers inside Google would like to use a framework other than TensorFlow.

In addition, PyTorch’s dominance might start to cut off Google researchers from the rest of the research community. Not only will they have a harder time building on top of outside research, outside researchers will also be less likely to build on top of code published by Google.

https://thegradient.pub/state-of-ml-frameworks-2019-pytorch-dominates-research-tensorflow-dominates-industry/

I am not able to implement the object detection algorithms on code, and need an example where the three main algos are applied on the same dataset, so I can compare their speed myself.

Any help with all this?

Detectron2: A PyTorch-based modular object detection library

• Blog post
• Github page

Improvements in Detectron2

PyTorch: The original Detectron was implemented in Caffe2. PyTorch provides a more intuitive imperative programming model that allows researchers and practitioners to iterate more rapidly on model design and experiments. Because we’ve rewritten Detectron2 from scratch in PyTorch, users can now benefit from PyTorch’s approach to deep learning as well as the large and active community that continually improves PyTorch

Modular, extensible design: In Detectron2, we’ve introduced a modular design that allows users to plug custom module implementations into almost any part of an object detection system. This means that many new research projects can be written in hundreds of lines of code with a clean separation between the core Detectron2 library and the novel research implementation. We continue to refine the modular, extensible design by implementing new models and discovering new ways in which we can make Detectron2 more flexible.