easyopt
wandb
| easyopt | wandb | |
|---|---|---|
| 2 | 16 | |
| 10 | 8,211 | |
| - | 1.6% | |
| 5.0 | 9.9 | |
| 3 months ago | 7 days ago | |
| Python | Python | |
| GNU General Public License v3.0 only | MIT License |
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.
easyopt
- Easyopt: Zero-Code Hyperparameter Optimization
-
[P] easyopt: zero-code hyperparameters optimization framework
I got tired of writing over and over again the same boilerplate code to do hyperparameters optimization so i built easyopt
wandb
-
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
-
The last sentence of Lowes conveniently missing from OpenAI...
HuggingFace and wandb.ai (both competitors of OpenAI) both also have "do own research"
-
Efficient way to tune a network by changing hyperparameters?
Wandb is the best! https://wandb.ai/
-
[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.
-
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.
-
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.
-
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/?
-
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?
tuneta - Intelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning models
tensorboard - TensorFlow's Visualization Toolkit
evalml - EvalML is an AutoML library written in python.
aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
AgileRL - Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
guildai - Experiment tracking, ML developer tools
optuna - A hyperparameter optimization framework
pytorch-summary - Model summary in PyTorch similar to `model.summary()` in Keras
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
Tetris-deep-Q-learning-pytorch - Deep Q-learning for playing tetris game