Tetris-deep-Q-learning-pytorch
wandb
| Tetris-deep-Q-learning-pytorch | wandb | |
|---|---|---|
| 2 | 16 | |
| 445 | 8,243 | |
| - | 1.6% | |
| 1.2 | 9.9 | |
| about 1 year ago | about 10 hours ago | |
| Python | Python | |
| MIT License | MIT License |
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Tetris-deep-Q-learning-pytorch
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Reinforcement Learning deploy in Modern Tetris project
if the former, this guy trained a DQN agent to play his simple implementation of the game. you can find the code for the game here and make any modifications you need
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AI agent plays Contra
https://github.com/uvipen/Tetris-deep-Q-learning-pytorch For tetris you coult take a look at this one :)
wandb
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A list of SaaS, PaaS and IaaS offerings that have free tiers of interest to devops and infradev
Weights & Biases — The developer-first MLOps platform. Build better models faster with experiment tracking, dataset versioning, and model management. Free tier for personal projects only, with 100 GB of storage included.
- Northlight makes Alan Wake 2 shine
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The last sentence of Lowes conveniently missing from OpenAI...
HuggingFace and wandb.ai (both competitors of OpenAI) both also have "do own research"
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Efficient way to tune a network by changing hyperparameters?
Wandb is the best! https://wandb.ai/
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[D] Monitoring production image models
To track stuff I've used wandb.ai in a company in the past, as someone else pointed out. Regarding metrics... This is really specific to your domain, and it is such a broad question. You could count color pixels, the distribution of intensity histograms, etc etc.
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How to use the colab notebook version of Dall-E mini and bypass the traffic limit - A guide
Step 1: The colab notebook uses wandb.ai, so you need to register for a wandb.ai account beforehand if you want to use the colab notebook. After registering you need to go to your homepage and copy the API key and paste/keep it somewhere.
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Roadmap for learning MLOps (for DevOps engineers)
I want to take a look at tools like https://wandb.ai/ and they would integrate into some of the pipelines I'm playing with.
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What's a sequel that got you thinking "the people who made this COMPLETELY missed the point of the first one"?
does current cgi and ai tech can bring back leslie nielsen? might use unreal engine and https://www.resemble.ai/ or https://wandb.ai/?
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What MLOps tools and processes do you use?
I'm currently working for a MLOps company so I'm heavily using their tools (Weights & Biases) but I've used custom C++ for deployment, Pytorch + fastai for quick experimentation, Weights & Biases for experiment tracking, hyper-parameter tuning + model versioning (hence why I went to work for them), custom database + data pipeline, HoloViz for data visualisation (really nice dashboarding tool), Jenkins for CI/CD, I also love Github Actions.
- [D] Best resources or tools to draw nicer table for comparing different models/frameworks performance
What are some alternatives?
deep-q-learning - Minimal Deep Q Learning (DQN & DDQN) implementations in Keras
tensorboard - TensorFlow's Visualization Toolkit
pytorch-learn-reinforcement-learning - A collection of various RL algorithms like policy gradients, DQN and PPO. The goal of this repo will be to make it a go-to resource for learning about RL. How to visualize, debug and solve RL problems. I've additionally included playground.py for learning more about OpenAI gym, etc.
aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
tritris - A redirect to tritris
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
JS-Beautifier - Beautifier for javascript
guildai - Experiment tracking, ML developer tools
tetris - :memo: Disassembly of Tetris for Game Boy.
pytorch-summary - Model summary in PyTorch similar to `model.summary()` in Keras
pytorch-a2c-ppo-acktr-gail - PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)