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https://github.com/rohitg00/ai-engineering-from-scratch.git
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chore(catalog): auto-regen
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+144
-8
@@ -2,11 +2,11 @@
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"schema_version": 1,
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"totals": {
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"phases": 20,
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"lessons": 435,
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"skills": 378,
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"lessons": 439,
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"skills": 382,
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"prompts": 99,
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"agents": 0,
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"code_files": 515
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},
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"phases": [
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{
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@@ -12304,7 +12304,7 @@
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"num": 19,
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"slug": "19-capstone-projects",
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"title": "Capstone Projects",
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"lesson_count": 17,
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"lesson_count": 21,
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"lessons": [
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{
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"num": 1,
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@@ -12568,8 +12568,14 @@
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"has_quiz": true,
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"has_notebook": true,
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"code_files": [
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"dashboard.ts",
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"main.py"
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"main.py",
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"ts/src/cost.ts",
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"ts/src/index.ts",
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"ts/src/migrations.ts",
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"ts/src/server.ts",
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"ts/src/types.ts",
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"ts/tests/cost.test.ts",
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"ts/tests/migrations.test.ts"
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],
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"outputs": [
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{
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@@ -12601,7 +12607,14 @@
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"has_notebook": true,
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"code_files": [
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"main.py",
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"team.ts"
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"ts/src/agent.ts",
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"ts/src/coordinator.ts",
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"ts/src/index.ts",
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"ts/src/runtime.ts",
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"ts/src/types.ts",
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"ts/src/workspace.ts",
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"ts/tests/coordinator.test.ts",
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"ts/tests/runtime.test.ts"
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],
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"outputs": [
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{
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@@ -12633,7 +12646,14 @@
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"has_notebook": true,
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"code_files": [
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"main.py",
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"server.ts"
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"ts/src/index.ts",
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"ts/src/pricing.ts",
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"ts/src/rollup.ts",
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"ts/src/server.ts",
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"ts/src/spans.ts",
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"ts/src/types.ts",
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"ts/tests/rollup.test.ts",
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"ts/tests/spans.test.ts"
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],
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"outputs": [
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{
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@@ -12847,6 +12867,122 @@
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]
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}
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]
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},
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{
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"num": 46,
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"slug": "46-gradient-accumulation",
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"title": "Gradient Accumulation",
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"path": "phases/19-capstone-projects/46-gradient-accumulation",
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"has_docs": true,
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"has_code": true,
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"has_quiz": true,
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"has_notebook": false,
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"code_files": [
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"main.py",
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"test_main.py"
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],
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"outputs": [
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{
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"type": "skill",
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"name": "gradient-accumulation",
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"path": "phases/19-capstone-projects/46-gradient-accumulation/outputs/skill-gradient-accumulation.md",
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"version": "1.0.0",
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"description": "Train at an effective batch larger than device memory by scaling micro-batch losses and stepping the optimizer once per window.",
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"tags": [
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"training",
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"batch-size",
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"distributed",
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"scaling"
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]
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}
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]
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},
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{
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"num": 47,
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"slug": "47-checkpoint-save-resume",
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"title": "Checkpoint Save and Resume",
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"path": "phases/19-capstone-projects/47-checkpoint-save-resume",
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"has_docs": true,
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"has_code": true,
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"has_quiz": true,
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"has_notebook": false,
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"code_files": [
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"main.py",
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"test_main.py"
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],
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"outputs": [
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{
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"type": "skill",
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"name": "checkpoint-save-resume",
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"path": "phases/19-capstone-projects/47-checkpoint-save-resume/outputs/skill-checkpoint-save-resume.md",
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"version": "1.0.0",
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"description": "Atomic, sharded checkpoints with full RNG capture so a killed run resumes mid-epoch with the same loss trajectory.",
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"tags": [
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"training",
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"durability",
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"resume",
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"sharded-state"
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]
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}
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]
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},
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{
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"num": 48,
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"slug": "48-distributed-fsdp-ddp",
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"title": "Distributed Data Parallel and FSDP from Scratch",
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"path": "phases/19-capstone-projects/48-distributed-fsdp-ddp",
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"has_docs": true,
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"has_code": true,
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"has_quiz": true,
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"has_notebook": false,
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"code_files": [
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"main.py",
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"test_main.py"
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],
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"outputs": [
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{
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"type": "skill",
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"name": "distributed-fsdp-ddp",
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"path": "phases/19-capstone-projects/48-distributed-fsdp-ddp/outputs/skill-distributed-fsdp-ddp.md",
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"version": "1.0.0",
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"description": "Bring up multi-rank training with a from-scratch DDP wrapper and an FSDP parameter sharding sketch on the gloo or nccl backend.",
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"tags": [
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"distributed",
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"ddp",
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"fsdp",
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"collectives"
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]
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}
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]
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},
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{
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"num": 49,
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"slug": "49-lm-eval-harness",
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"title": "Language Model Evaluation Harness",
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"path": "phases/19-capstone-projects/49-lm-eval-harness",
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"has_docs": true,
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"has_code": true,
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"has_quiz": true,
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"has_notebook": false,
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"code_files": [
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"main.py",
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"test_main.py"
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],
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"outputs": [
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{
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"type": "skill",
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"name": "lm-eval-harness",
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"path": "phases/19-capstone-projects/49-lm-eval-harness/outputs/skill-lm-eval-harness.md",
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"version": "1.0.0",
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"description": "Minimal language model evaluation harness with JSONL task spec, five metrics, swappable adapter, and leaderboard JSON output.",
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"tags": [
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"evaluation",
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"metrics",
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"leaderboard",
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"harness"
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]
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}
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]
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}
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]
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}
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