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Fastai:README.rSt.md

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+# Welcome to fastai
+
+<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
+
+[![CI](https://github.com/fastai/fastai/actions/workflows/main.yml/badge.svg)](https://github.com/fastai/fastai/actions/workflows/main.yml)
+[![PyPI](https://img.shields.io/pypi/v/fastai?color=blue&label=pypi%20version.png)](https://pypi.org/project/fastai/#description)
+[![Conda (channel
+only)](https://img.shields.io/conda/vn/fastai/fastai?color=seagreen&label=conda%20version.png)](https://anaconda.org/fastai/fastai)
+![docs](https://github.com/fastai/fastai/workflows/docs/badge.svg)
+
+## Installing
+
+You can use fastai without any installation by using [Google
+Colab](https://colab.research.google.com/). In fact, every page of this
+documentation is also available as an interactive notebook - click “Open
+in colab” at the top of any page to open it (be sure to change the Colab
+runtime to “GPU” to have it run fast!) See the fast.ai documentation on
+[Using Colab](https://course.fast.ai/start_colab) for more information.
+
+You can install fastai on your own machines with conda (highly
+recommended), as long as you’re running Linux or Windows (NB: Mac is not
+supported). For Windows, please see the “Running on Windows” for
+important notes.
+
+We recommend using
+[miniconda](https://docs.conda.io/en/latest/miniconda.html) (or
+miniforge). First install PyTorch using the conda line shown
+[here](https://pytorch.org/get-started/locally/), and then run:
+
+``` bash
+conda install -c fastai fastai
+```
+
+To install with pip, use: `pip install fastai`.
+
+If you plan to develop fastai yourself, or want to be on the cutting
+edge, you can use an editable install (if you do this, you should also
+use an editable install of
+[fastcore](https://github.com/fastai/fastcore) to go with it.) First
+install PyTorch, and then:
+
+    git clone https://github.com/fastai/fastai
+    pip install -e "fastai[dev]"
+
+## Learning fastai
+
+The best way to get started with fastai (and deep learning) is to read
+[the
+book](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527),
+and complete [the free course](https://course.fast.ai).
+
+To see what’s possible with fastai, take a look at the [Quick
+Start](https://docs.fast.ai/quick_start.html), which shows how to use
+around 5 lines of code to build an image classifier, an image
+segmentation model, a text sentiment model, a recommendation system, and
+a tabular model. For each of the applications, the code is much the
+same.
+
+Read through the [Tutorials](https://docs.fast.ai/tutorial.html) to
+learn how to train your own models on your own datasets. Use the
+navigation sidebar to look through the fastai documentation. Every
+class, function, and method is documented here.
+
+To learn about the design and motivation of the library, read the [peer
+reviewed paper](https://www.mdpi.com/2078-2489/11/2/108/htm).
+
+## About fastai
+
+fastai is a deep learning library which provides practitioners with
+high-level components that can quickly and easily provide
+state-of-the-art results in standard deep learning domains, and provides
+researchers with low-level components that can be mixed and matched to
+build new approaches. It aims to do both things without substantial
+compromises in ease of use, flexibility, or performance. This is
+possible thanks to a carefully layered architecture, which expresses
+common underlying patterns of many deep learning and data processing
+techniques in terms of decoupled abstractions. These abstractions can be
+expressed concisely and clearly by leveraging the dynamism of the
+underlying Python language and the flexibility of the PyTorch library.
+fastai includes:
+
+- A new type dispatch system for Python along with a semantic type
+  hierarchy for tensors
+- A GPU-optimized computer vision library which can be extended in pure
+  Python
+- An optimizer which refactors out the common functionality of modern
+  optimizers into two basic pieces, allowing optimization algorithms to
+  be implemented in 4–5 lines of code
+- A novel 2-way callback system that can access any part of the data,
+  model, or optimizer and change it at any point during training
+- A new data block API
+- And much more…
+
+fastai is organized around two main design goals: to be approachable and
+rapidly productive, while also being deeply hackable and configurable.
+It is built on top of a hierarchy of lower-level APIs which provide
+composable building blocks. This way, a user wanting to rewrite part of
+the high-level API or add particular behavior to suit their needs does
+not have to learn how to use the lowest level.
+
+<img alt="Layered API" src="images/layered.png" width="345">
+
+## Migrating from other libraries
+
+It’s very easy to migrate from plain PyTorch, Ignite, or any other
+PyTorch-based library, or even to use fastai in conjunction with other
+libraries. Generally, you’ll be able to use all your existing data
+processing code, but will be able to reduce the amount of code you
+require for training, and more easily take advantage of modern best
+practices. Here are migration guides from some popular libraries to help
+you on your way:
+
+- [Plain PyTorch](https://docs.fast.ai/examples/migrating_pytorch.html)
+- [Ignite](https://docs.fast.ai/examples/migrating_ignite.html)
+- [Lightning](https://docs.fast.ai/examples/migrating_lightning.html)
+- [Catalyst](https://docs.fast.ai/examples/migrating_catalyst.html)
+
+## Windows Support
+
+Due to python multiprocessing issues on Jupyter and Windows,
+`num_workers` of `Dataloader` is reset to 0 automatically to avoid
+Jupyter hanging. This makes tasks such as computer vision in Jupyter on
+Windows many times slower than on Linux. This limitation doesn’t exist
+if you use fastai from a script.
+
+See [this
+example](https://github.com/fastai/fastai/blob/master/nbs/examples/dataloader_spawn.py)
+to fully leverage the fastai API on Windows.
+
+We recommend using Windows Subsystem for Linux (WSL) instead – if you do
+that, you can use the regular Linux installation approach, and you won’t
+have any issues with `num_workers`.
+
+## Tests
+
+To run the tests in parallel, launch:
+
+`nbdev_test`
+
+For all the tests to pass, you’ll need to install the dependencies
+specified as part of dev_requirements in settings.ini
+
+`pip install -e .[dev]`
+
+Tests are written using `nbdev`, for example see the documentation for
+`test_eq`.
+
+## Contributing
+
+After you clone this repository, make sure you have run
+`nbdev_install_hooks` in your terminal. This install Jupyter and git
+hooks to automatically clean, trust, and fix merge conflicts in
+notebooks.
+
+After making changes in the repo, you should run `nbdev_prepare` and
+make additional and necessary changes in order to pass all the tests.
+
+## Docker Containers
+
+For those interested in official docker containers for this project,
+they can be found
+[here](https://github.com/fastai/docker-containers#fastai).

LMLM_Presentation_Notebook.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# LMLM \u2014 Large Multimodal Learning Model\n",
+        "## Intelligence, Orchestrated.\n",
+        "\n",
+        "**Technical Presentation Notebook \u00b7 v1.0**\n",
+        "\n",
+        "This notebook presents LMLM as a model-agnostic intelligence orchestration architecture connecting multimodal inputs, specialized models, agents, memory, retrieval, tools, execution, policy, and verification."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 3D Visual Overview\n\n",
+        "![LMLM 3D presentation overview](assets/lmlm_3d_overview.png)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# LMLM reference architecture\n",
+        "lmlm = {\n",
+        "    \"input\": [\"text\", \"image\", \"audio\", \"video\", \"code\", \"documents\", \"data\", \"sensors\"],\n",
+        "    \"intelligence\": [\"task_understanding\", \"reasoning\", \"planning\"],\n",
+        "    \"coordination\": [\"model_registry\", \"capability_routing\", \"agent_orchestration\"],\n",
+        "    \"state\": [\"context\", \"working_memory\", \"long_term_memory\", \"project_state\"],\n",
+        "    \"action\": [\"tools\", \"apis\", \"code_execution\", \"cloud\", \"local\", \"edge\"],\n",
+        "    \"control\": [\"policy\", \"permissions\", \"verification\", \"recovery\"],\n",
+        "    \"output\": [\"result\", \"evidence\", \"status\", \"artifacts\"]\n",
+        "}\n",
+        "\n",
+        "print(\"LMLM layers:\", len(lmlm))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 01 \u2014 LMLM\n",
+        "## Large Multimodal Learning Model\n\n",
+        "![3D illustration for 01 \u2014 LMLM](assets/illustration_01.png)\n\n",
+        "**Concept**  \nLMLM is an intelligent orchestration architecture for coordinating multimodal models, agents, memory, tools, execution, and verification.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 02 \u2014 The Problem\n",
+        "## Fragmented AI Landscape\n\n",
+        "![3D illustration for 02 \u2014 The Problem](assets/illustration_02.png)\n\n",
+        "**Concept**  \nModern AI capability is distributed across specialized models, tools, data stores, agents, and applications. The integration problem becomes a systems problem.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 03 \u2014 The Vision\n",
+        "## Intelligence Orchestration\n\n",
+        "![3D illustration for 03 \u2014 The Vision](assets/illustration_03.png)\n\n",
+        "**Concept**  \nLMLM provides a coordination layer that understands objectives, routes work, maintains state, invokes capabilities, and evaluates outcomes.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 04 \u2014 LMLM Core\n",
+        "## The Orchestration Runtime\n\n",
+        "![3D illustration for 04 \u2014 LMLM Core](assets/illustration_04.png)\n\n",
+        "**Concept**  \nA model-agnostic core coordinates input processing, reasoning, routing, memory, tools, execution, verification, policy, state, and output.\n\n",
+        "### Technical notes\n\n",
+        "Treat the core as a runtime boundary rather than a single neural network. Adapters can expose heterogeneous model providers behind normalized capability interfaces.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 05 \u2014 Multimodal Input\n",
+        "## All Modalities, One Pipeline\n\n",
+        "![3D illustration for 05 \u2014 Multimodal Input](assets/illustration_05.png)\n\n",
+        "**Concept**  \nText, images, audio, video, code, documents, structured data, and sensor information can enter a common task-processing pipeline.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 06 \u2014 Task Understanding\n",
+        "## From Intent to Execution Graph\n\n",
+        "![3D illustration for 06 \u2014 Task Understanding](assets/illustration_06.png)\n\n",
+        "**Concept**  \nLMLM interprets the objective, identifies constraints and dependencies, decomposes the work, and constructs an execution graph.\n\n",
+        "### Technical notes\n\n",
+        "Represent the plan as a DAG or stateful execution graph. Dependencies, parallelism, retries, timeouts, and completion criteria should be explicit.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 07 \u2014 Model Registry\n",
+        "## Capability Discovery\n\n",
+        "![3D illustration for 07 \u2014 Model Registry](assets/illustration_07.png)\n\n",
+        "**Concept**  \nModels register capabilities, modalities, context limits, latency, cost, locality, tool access, and other routing metadata.\n\n",
+        "### Technical notes\n\n",
+        "Capability metadata should support routing decisions: modality, context window, latency, cost, locality, reliability, tool access, and policy constraints.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 08 \u2014 Dynamic Routing\n",
+        "## Right Model, Right Task\n\n",
+        "![3D illustration for 08 \u2014 Dynamic Routing](assets/illustration_08.png)\n\n",
+        "**Concept**  \nThe router selects or composes model capabilities according to task requirements, policy, context, performance, and availability.\n\n",
+        "### Technical notes\n\n",
+        "Routing can be deterministic, score-based, learned, policy-constrained, or hybrid. Preserve the reason for a routing decision for observability.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 09 \u2014 Script.God\n",
+        "## Structured AI Coordination\n\n",
+        "![3D illustration for 09 \u2014 Script.God](assets/illustration_08.png)\n\n",
+        "**Concept**  \nCONNECT, CAPABILITIES, TASK, ACK, CONTEXT, PROGRESS, RESULT, ERROR, BLOCKED, CANCEL, VERIFY, and SYNC form a structured coordination vocabulary.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 10 \u2014 Memory & Context\n",
+        "## Relevant Continuity\n\n",
+        "![3D illustration for 10 \u2014 Memory & Context](assets/illustration_09.png)\n\n",
+        "**Concept**  \nWorking context, long-term memory, project state, retrieved knowledge, and user context can be managed as distinct information layers.\n\n",
+        "### Technical notes\n\n",
+        "Separate transient working context from durable memory. Retrieval should be relevance- and authorization-aware rather than indiscriminately injecting history.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 11 \u2014 Retrieval\n",
+        "## Evidence Before Action\n\n",
+        "![3D illustration for 11 \u2014 Retrieval](assets/illustration_09.png)\n\n",
+        "**Concept**  \nRetrieval can supply relevant documents, code, records, or knowledge to the reasoning loop while preserving provenance and task context.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 12 \u2014 Tools & External Systems\n",
+        "## From Reasoning to Action\n\n",
+        "![3D illustration for 12 \u2014 Tools & External Systems](assets/illustration_10.png)\n\n",
+        "**Concept**  \nLMLM can connect to repositories, APIs, databases, browsers, containers, CI/CD systems, cloud infrastructure, and other execution environments.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 13 \u2014 Agent Collaboration\n",
+        "## Many Experts, One Goal\n\n",
+        "![3D illustration for 13 \u2014 Agent Collaboration](assets/illustration_11.png)\n\n",
+        "**Concept**  \nSpecialized agents can research, design, implement, test, audit, and verify while the orchestration layer coordinates dependencies and shared state.\n\n",
+        "### Technical notes\n\n",
+        "Agents should communicate through structured task contracts and shared state rather than uncontrolled conversational coupling.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 14 \u2014 Execution Loop\n",
+        "## Observe, Adapt, Succeed\n\n",
+        "![3D illustration for 14 \u2014 Execution Loop](assets/illustration_12.png)\n\n",
+        "**Concept**  \nThe runtime can receive, understand, decompose, execute, observe, evaluate, adapt, and verify rather than assuming a single-pass workflow.\n\n",
+        "### Technical notes\n\n",
+        "Execution should expose state transitions and events so the system can be monitored, replayed, cancelled, and recovered.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 15 \u2014 Error Recovery\n",
+        "## Failure Is a State\n\n",
+        "![3D illustration for 15 \u2014 Error Recovery](assets/illustration_12.png)\n\n",
+        "**Concept**  \nErrors and blocked states become explicit execution states that can trigger diagnosis, recovery, retry, escalation, or cancellation.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 16 \u2014 Verification\n",
+        "## Quality, Safety, Trust\n\n",
+        "![3D illustration for 16 \u2014 Verification](assets/illustration_13.png)\n\n",
+        "**Concept**  \nOutputs can pass through fact checks, code tests, schema validation, security checks, consistency checks, source validation, and policy checks.\n\n",
+        "### Technical notes\n\n",
+        "Verification is multi-dimensional. A result can be syntactically valid but semantically wrong, so verification should test the actual acceptance criteria.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 17 \u2014 Policy & Permissions\n",
+        "## Controlled Capability\n\n",
+        "![3D illustration for 17 \u2014 Policy & Permissions](assets/illustration_13.png)\n\n",
+        "**Concept**  \nTool access, model selection, data access, execution privileges, and external actions should be constrained by explicit policy and authorization.\n\n",
+        "### Technical notes\n\n",
+        "Policy is a first-class control plane. Sensitive actions should require explicit authorization and least-privilege tool scopes.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 18 \u2014 Human + LMLM\n",
+        "## Amplifying Human Potential\n\n",
+        "![3D illustration for 18 \u2014 Human + LMLM](assets/illustration_16.png)\n\n",
+        "**Concept**  \nHumans define objectives, provide judgment, review decisions, approve sensitive actions, and intervene when required.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 19 \u2014 End-to-End Project\n",
+        "## Specification to Deployment\n\n",
+        "![3D illustration for 19 \u2014 End-to-End Project](assets/illustration_14.png)\n\n",
+        "**Concept**  \nA complete project can be decomposed into research, architecture, implementation, testing, security, build, deployment, monitoring, and reporting.\n\n",
+        "### Technical notes\n\n",
+        "The end-to-end workflow demonstrates why orchestration matters: no single specialist needs to own the entire project lifecycle.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 20 \u2014 Developer Integration\n",
+        "## LMLM + Codex / GitHub\n\n",
+        "![3D illustration for 20 \u2014 Developer Integration](assets/illustration_10.png)\n\n",
+        "**Concept**  \nLMLM can orchestrate development workflows around repositories, issues, pull requests, code generation, testing, CI/CD, and verification.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 21 \u2014 Local + Cloud + Edge\n",
+        "## Distributed Intelligence\n\n",
+        "![3D illustration for 21 \u2014 Local + Cloud + Edge](assets/illustration_15.png)\n\n",
+        "**Concept**  \nModel capabilities can be distributed across local hardware, private infrastructure, cloud services, and edge devices.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 22 \u2014 Ecosystem\n",
+        "## Everything Connected\n\n",
+        "![3D illustration for 22 \u2014 Ecosystem](assets/illustration_15.png)\n\n",
+        "**Concept**  \nLMLM can act as a connective intelligence layer across AI models, agents, applications, data, infrastructure, automation, and human workflows.\n\n",
+        "### Technical notes\n\n",
+        "The ecosystem model allows LMLM to sit above heterogeneous infrastructure without requiring every capability to be implemented by the same vendor or model family.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 23 \u2014 Application Example\n",
+        "## Build a Complete Application\n\n",
+        "![3D illustration for 23 \u2014 Application Example](assets/illustration_14.png)\n\n",
+        "**Concept**  \nA single objective can become a coordinated lifecycle: understand \u2192 design \u2192 code \u2192 test \u2192 secure \u2192 build \u2192 deploy \u2192 monitor \u2192 report.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 24 \u2014 Future\n",
+        "## The Intelligence Network\n\n",
+        "![3D illustration for 24 \u2014 Future](assets/illustration_15.png)\n\n",
+        "**Concept**  \nThe long-term direction is interoperable intelligence: composable models, coordinated agents, persistent context, tool use, verification, and adaptive execution.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 25 \u2014 Final\n",
+        "## Intelligence, Orchestrated.\n\n",
+        "![3D illustration for 25 \u2014 Final](assets/illustration_01.png)\n\n",
+        "**Concept**  \nLMLM connects intelligence, coordinates capability, executes with purpose, and verifies outcomes.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Implementation Roadmap\n",
+        "\n",
+        "**Phase 1 \u2014 Core Runtime:** task envelope, model adapters, capability registry, routing, state, events.\n",
+        "\n",
+        "**Phase 2 \u2014 Tooling:** GitHub, filesystem, databases, APIs, code execution, containers, CI/CD.\n",
+        "\n",
+        "**Phase 3 \u2014 Agent Coordination:** structured task contracts, Script.God protocol, shared context, progress reporting, cancellation and recovery.\n",
+        "\n",
+        "**Phase 4 \u2014 Memory & Retrieval:** working memory, durable project state, retrieval, provenance, permissions.\n",
+        "\n",
+        "**Phase 5 \u2014 Verification:** automated tests, evidence validation, security checks, policy enforcement, result scoring.\n",
+        "\n",
+        "**Phase 6 \u2014 Distributed LMLM:** local, cloud, and edge model execution with observability and resilient routing.\n",
+        "\n",
+        "## Closing principle\n",
+        "\n",
+        "> **LMLM is not defined by one model. It is defined by how intelligence is connected, coordinated, executed, and verified.**"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.x"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}