Train your JAX models using model.fit(...) in Keras 3

Keras, JAX, and Model Training: A Deep Dive

Key Concepts: Keras ecosystem, JAX backend, model.fit, progressive disclosure of complexity, callbacks, multi-device distribution, device mesh, layout map, Keras Hub, pre-trained models, presets, task models (backbone, preprocessor, task head), fine-tuning, LoRA, Q-LoRA, Keras Recommenders, two-phase approach (retrieval, ranking), multitask models, distributed embedding layer, TPUs, sparse-core processors.

1. Introduction to Keras and JAX Integration

• Main Topic: Overview of the Keras ecosystem and its integration with JAX for training neural networks.
• Key Points:
  • Keras provides a comprehensive toolbox for deep learning tasks with JAX.
  • It supports various levels of abstraction, from simple model.fit loops to advanced model parallel distribution.
  • Keras works with TensorFlow and PyTorch, allowing backend selection via the KERAS_BACKEND environment variable.
  • Data pipelines (e.g., tf.data) can be used independently of the chosen backend (e.g., JAX).

2. Training with model.fit: Progressive Complexity

• Main Topic: Using the model.fit API for training JAX models, showcasing different levels of customization.
• Key Points:
  • Simple Workflow: Compile the model with an optimizer, loss function, and metrics, then call model.fit with training data, batch size, epochs, and validation data.
  • Customization with Callbacks: Use callbacks like EarlyStopping to modify the training loop based on validation loss.
  • Multi-Device Distribution: Utilize keras.distribution APIs to enable multi-device training.
    • Device Mesh: Create a grid of available devices, defining its shape and assigning names to each axis (e.g., "data," "model" in a 2x4 grid).
    • Layout Map: Specify how to shard the model's weights across the device mesh.
    • Distribution Strategy: Configure the training process according to the sharding configuration.
  • Overriding Core Training Steps: Customize the training loop by overriding methods like compute_loss and compute_updates.

3. Keras Hub: Pre-trained Models and Presets

• Main Topic: Introduction to Keras Hub, a model garden with pre-trained models and seamless integrations.
• Key Points:
  • Keras Hub offers over 211 model variants based on 44+ state-of-the-art architectures (e.g., Gemma, Llama, Qwen, Stable Diffusion).
  • Models are ready to use with JAX, TensorFlow, or PyTorch.
  • Integrations with Kaggle and Hugging Face provide access to models, code snippets, and community guides.
  • Presets: Neatly packaged directories containing everything needed to load a pre-trained model (config, checkpoints, extra files).
    • Presets can be loaded using built-in identifiers (e.g., gemma_2b_en), Kaggle URLs, Hugging Face URLs, or local paths.
    • Custom models can be built with custom configurations.
  • Keras Hub models work with over 200,000 checkpoints via Kaggle and Hugging Face integrations.

4. Keras Hub Model Architecture: Task Models, Backbones, and Preprocessors

• Main Topic: Deep dive into the underlying structure of Keras Hub models.
• Key Points:
  • Task Models: High-level entry points that combine pre-processing and modeling into one class (e.g., causal LLM, image classifier, text classifier).
  • Backbone: Responsible for mapping preprocessed inputs to the model's latent space. Contains the architecture and pre-trained parameters.
  • Preprocessor: Converts raw data (images, text, audio) into a format the model can understand (tensors). Includes tools like tokenizers, audio converters, and image converters.
  • All components (backbone, preprocessor, tokenizer) can be instantiated using the from_preset method.

5. Fine-tuning Keras Hub Models: LoRA and Q-LoRA

• Main Topic: Techniques for fine-tuning Keras Hub models efficiently.
• Key Points:
  • Models can be fine-tuned using standard model.fit training loops.
  • LoRA (Low-Rank Adaptation): Reduces the number of trainable parameters by freezing the original model and training only two smaller matrices.
    • Enable LoRA by calling enable_lora and specifying the rank on the backbone.
  • Q-LoRA (Quantized LoRA): Adds quantization to LoRA, converting model data into smaller, more efficient formats.
    • Quantize the weights, then enable LoRA as usual.
  • Trained model weights can be uploaded to Kaggle and Hugging Face using keras_hub.upload_preset.

6. Keras Recommenders: Building Recommendation Systems

• Main Topic: Introduction to Keras Recommenders, a library for building state-of-the-art recommendation systems.
• Key Points:
  • Two-Phase Approach:
    • Retrieval Phase: Efficiently filters the item pool to identify a manageable set of candidates.
    • Ranking Phase: Uses a more intensive scoring method to select and order the most relevant items.
  • Multitask Models: Combine retrieval and ranking tasks, often outperforming single-task models due to transfer learning.
  • Keras Recommenders provides specialized layers, losses, and metrics for training recommender models.
  • Components are backend-agnostic, supporting JAX, TensorFlow, and Torch.
  • Examples and code for standard architectures are provided.

7. Example: Building a Sequential Recommender Model (GRU4Rec)

• Main Topic: Practical example of building a sequential recommender model using Keras Recommenders.
• Key Points:
  • Sequential recommenders leverage a user's past interactions to make predictions.
  • Two-Tower Architecture:
    • Query Tower: Represents the user, using a Gated Recurrent Unit (GRU) to process the sequence of past items.
    • Candidate Tower: Represents the items, using a simple embedding layer.
  • Retrieval Layer: Performs the actual recommendations.
  • Compute Loss Method: Overridden to calculate the affinity scores between the query tower output and the candidate tower output.
  • Categorical Cross-Entropy Loss: Used to quantify how far the affinity scores are from a perfect match.
  • The model is trained using model.fit with user-item interaction data.

8. Distributed Embedding Layer for Large-Scale Recommendations

• Main Topic: Using the distributed embedding layer to handle large embedding tables in recommender systems.
• Key Points:
  • Embedding tables can become too large for a single processing unit.
  • Google's TPUs with sparse-core processors are optimized for handling large embedding models.
  • The distributed embedding layer automatically shards the embedding tables across TPUs and sparse-core processors.
  • All embedding lookups are performed in a single pass for maximum performance.
  • Configuration:
    • Specify the dimensionality of each embedding table.
    • Define the combiner function (how to reduce embeddings obtained from looking up a sequence of items).
    • Configure features and connect them with the embedding tables.
  • The distributed embedding layer is available in Keras RS for both JAX and TensorFlow backends.

9. Conclusion

• Main Takeaways: The Keras ecosystem provides a comprehensive set of tools for building and customizing machine learning models, particularly with JAX. From simple training loops to advanced distributed training and pre-trained models, Keras offers a progressive and flexible approach to deep learning. Keras Hub and Keras Recommenders extend the ecosystem with specialized tools for specific domains, simplifying the development of state-of-the-art models.