Sacred VS Keras

🔗 https://github.com/IDSIA/sacred

I'm using Omniboard (https://github.com/vivekratnavel/omniboard) with Sacred (https://github.com/IDSIA/sacred) for tracking experiments. You can specify custom Observers in Sacred so the model metrics and logs will be saved to a local directory or to a remote DB (e.g., MongoDB). I use a MongoDB database hosted on Atlas. Unlike other suggested options, Sacred and Omniboard are free. Atlas free tier comes with 512MB of free storage which is a huge amount if you're uploading only log files to it.

I'm using Omniboard (https://github.com/vivekratnavel/omniboard) with Sacred (https://github.com/IDSIA/sacred) for tracking experiments. You can specify custom Observers in Sacred so the model metrics and logs will be saved to a local directory or to a remote DB (e.g., MongoDB). I use a MongoDB database hosted on Atlas. Unlike other suggested options, Sacred and Omniboard are free. Atlas free tier comes with 512MB of free storage which is a huge amount if you're uploading only log files to it. ex = Experiment() ex.observers.append(FileStorageObserver(EXPERIMENTS_ROOT)) ex.observers.append(MongoObserver(url=MONGODB_URL, db_name='sacred'))

sacred helped me log my experiments. I did setup my environment only once 4 years ago, and since then I have a list of all my training runs with the hyperparameters and results.

For 1, setup an experiment tracking framework. I found Sacred to be helpful https://github.com/IDSIA/sacred.

After setting the variables for the environment, the next step is to install dependencies. To use Gemma, KerasNLP is the dependency used. KerasNLP is a collection of natural language processing (NLP) models implemented in Keras and runnable on JAX, PyTorch, and TensorFlow.

All breaking changes are listed here: https://github.com/keras-team/keras/issues/18467

You can use this migration guide to identify and fix each of these issues (and further, making your code run on JAX or PyTorch): https://keras.io/guides/migrating_to_keras_3/

I'm guessing the "real" keras code is coming from the keras repository. Is that a correct assumption? How does that version of Keras get there? If I wanted to write my own activation layer next to ELU, where exactly would I do that?

Other popular machine learning tools include PyTorch, Keras, and Scikit-learn. PyTorch is an open-source machine learning library developed by Facebook that is known for its ease of use and flexibility. Keras is a high-level neural networks API that is written in Python and is known for its simplicity. Scikit-learn is a machine learning library for Python that is used for data analysis and data mining tasks.

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I'm not aware of anything off-the-shelf, but if you have sufficient programming experience, one way to do this would be to build a large dataset of reference images and pictures and use something like keras to train a convolutional neural network on them.

Keras documentation: https://keras.io/

3. Keras Keras is a high-level neural networks API written in Python that’s built on top of TensorFlow. It’s designed to enable fast experimentation with deep learning, allowing you to build and train models with just a few lines of code. If you’re new to deep learning or just want a more user-friendly interface, Keras is the way to go.