on_boarding.md

graph LR
    Core_ML_Utilities["Core ML Utilities"]
    Generative_Models_Suite["Generative Models Suite"]
    Reinforcement_Learning_Algorithms["Reinforcement Learning Algorithms"]
    Transformer_Model_Implementations["Transformer Model Implementations"]
    Large_Language_Model_LLM_Specifics["Large Language Model (LLM) Specifics"]
    Graph_Neural_Networks["Graph Neural Networks"]
    Other_Neural_Network_Architectures["Other Neural Network Architectures"]
    Distributed_Training_Scaling["Distributed Training & Scaling"]
    Core_ML_Utilities -- "Provides/Utilizes Training Infrastructure & Data Handling" --> Generative_Models_Suite
    Generative_Models_Suite -- "Utilizes/Provided by Training Infrastructure & Data Handling" --> Core_ML_Utilities
    Core_ML_Utilities -- "Provides/Utilizes Training Infrastructure & Data Handling" --> Reinforcement_Learning_Algorithms
    Reinforcement_Learning_Algorithms -- "Utilizes/Provided by Training Infrastructure & Data Handling" --> Core_ML_Utilities
    Core_ML_Utilities -- "Provides/Utilizes Common Layers & Utilities" --> Transformer_Model_Implementations
    Transformer_Model_Implementations -- "Utilizes/Provided by Common Layers & Utilities" --> Core_ML_Utilities
    Core_ML_Utilities -- "Provides/Utilizes Training Infrastructure & Data Handling" --> Large_Language_Model_LLM_Specifics
    Large_Language_Model_LLM_Specifics -- "Utilizes/Provided by Training Infrastructure & Data Handling" --> Core_ML_Utilities
    Core_ML_Utilities -- "Provides/Utilizes Training Infrastructure & Data Handling" --> Graph_Neural_Networks
    Graph_Neural_Networks -- "Utilizes/Provided by Training Infrastructure & Data Handling" --> Core_ML_Utilities
    Core_ML_Utilities -- "Provides/Utilizes Training Infrastructure & Data Handling" --> Other_Neural_Network_Architectures
    Other_Neural_Network_Architectures -- "Utilizes/Provided by Training Infrastructure & Data Handling" --> Core_ML_Utilities
    Transformer_Model_Implementations -- "Supplies Transformer Building Blocks" --> Generative_Models_Suite
    Transformer_Model_Implementations -- "Provides Foundational Architectures" --> Large_Language_Model_LLM_Specifics
    Large_Language_Model_LLM_Specifics -- "Optimizes/Leverages Distributed Training" --> Distributed_Training_Scaling
    Distributed_Training_Scaling -- "Leveraged by/Optimizes Distributed Training" --> Large_Language_Model_LLM_Specifics
    click Core_ML_Utilities href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/annotated_deep_learning_paper_implementations/Core_ML_Utilities.md" "Details"
    click Generative_Models_Suite href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/annotated_deep_learning_paper_implementations/Generative_Models_Suite.md" "Details"
    click Reinforcement_Learning_Algorithms href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/annotated_deep_learning_paper_implementations/Reinforcement_Learning_Algorithms.md" "Details"
    click Transformer_Model_Implementations href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/annotated_deep_learning_paper_implementations/Transformer_Model_Implementations.md" "Details"
    click Large_Language_Model_LLM_Specifics href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/annotated_deep_learning_paper_implementations/Large_Language_Model_LLM_Specifics.md" "Details"
    click Graph_Neural_Networks href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/annotated_deep_learning_paper_implementations/Graph_Neural_Networks.md" "Details"
    click Other_Neural_Network_Architectures href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/annotated_deep_learning_paper_implementations/Other_Neural_Network_Architectures.md" "Details"
    click Distributed_Training_Scaling href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/annotated_deep_learning_paper_implementations/Distributed_Training_Scaling.md" "Details"

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Details

The annotated_deep_learning_paper_implementations project is architected as a modular ML toolkit, primarily designed to showcase diverse deep learning model implementations. At its core, the Core ML Utilities component provides foundational services such as data handling, training loop management, and common neural network layers, acting as a central backbone for the entire library. Specialized model suites, including Generative Models Suite, Reinforcement Learning Algorithms, Transformer Model Implementations, Large Language Model (LLM) Specifics, Graph Neural Networks, and Other Neural Network Architectures, each represent distinct functional blocks. These model implementations extensively leverage the Core ML Utilities for shared functionalities. Furthermore, there are specific inter-component dependencies, such as Large Language Model (LLM) Specifics building upon Transformer Model Implementations and utilizing Distributed Training & Scaling for performance optimization. This component-based architecture facilitates clear data and control flow, making it highly suitable for a flow graph representation where each model type is a primary architectural unit interacting with shared utilities and specialized modules.

Core ML Utilities [Expand]

Provides foundational utilities, data handling, training infrastructure, and common neural network building blocks.

Related Classes/Methods:

• labml_nn.helpers.trainer.run:409-415
• labml_nn.helpers.datasets
• labml_nn.optimizers.adam
• labml_nn.normalization.batch_norm

Generative Models Suite [Expand]

Implementations of various generative models (e.g., Diffusion Models, GANs, SketchRNN).

Related Classes/Methods:

• labml_nn.diffusion.ddpm.experiment.main:225-247
• labml_nn.diffusion.stable_diffusion.scripts.text_to_image.main:104-153
• labml_nn.gan.cycle_gan.__init__.train:663-683

Reinforcement Learning Algorithms [Expand]

Implementations of reinforcement learning algorithms (e.g., DQN, PPO, CFR).

Related Classes/Methods:

• labml_nn.rl.dqn.experiment.main:254-285
• labml_nn.rl.ppo.experiment.main:353-391
• labml_nn.cfr.kuhn.__init__.main:226-243

Transformer Model Implementations [Expand]

Core transformer architectures, attention mechanisms, and positional encoding schemes.

Related Classes/Methods:

• labml_nn.transformers.mha
• labml_nn.transformers.positional_encoding
• labml_nn.transformers.gpt.__init__.main:220-259

Large Language Model (LLM) Specifics [Expand]

Dedicated modules for large-scale language models, including specific architectures (NeoX, RWKV) and fine-tuning techniques (LoRA).

Related Classes/Methods:

• labml_nn.neox.model
• labml_nn.neox.utils.trainer.train:76-79
• labml_nn.lora.gpt2.__init__:140-167

Graph Neural Networks [Expand]

Implementations of neural networks for graph-structured data (e.g., GAT, GATv2).

Related Classes/Methods:

• labml_nn.graphs.gat.experiment.main:288-304
• labml_nn.graphs.gatv2

Other Neural Network Architectures [Expand]

Diverse neural network models not categorized elsewhere (e.g., ResNet, UNet, Capsule Networks).

Related Classes/Methods:

• labml_nn.capsule_networks.mnist.capsule_network_model:152-155
• labml_nn.resnet.experiment.main:54-77
• labml_nn.unet/experiment

Distributed Training & Scaling [Expand]

Modules for optimizing training of large models, focusing on memory and computational efficiency (e.g., ZeRO-3).

Related Classes/Methods:

• labml_nn.scaling.zero3.__init__

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