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README.md

@@ -16,81 +16,253 @@ tags:
 
 # Parameter Golf Competition Data
 
-Pre-tokenized [FineWeb](https://huggingface.co/datasets/HuggingFaceFW/fineweb) shards for the [OpenAI Parameter Golf](https://github.com/openai/parameter-golf) competition.
+Pre-tokenized [FineWeb](https://huggingface.co/datasets/HuggingFaceFW/fineweb) shards for the [OpenAI Parameter Golf](https://github.com/openai/parameter-golf) competition. Two tokenizations included. Free checkpoint persistence API. Zero setup friction.
 
-**Two tokenizations included:**
-- **SP1024** — SentencePiece BPE, 1024 tokens (competition default)
-- **Scylla** — TokenMonster-derived, 998 tokens (community alternative from [PR #1143](https://github.com/openai/parameter-golf/pull/1143))
+---
+
+## How It Works (The Simple Version)
+
+Think of it like plumbing:
 
-## Why this exists
+1. **The reservoir** is this dataset — 26GB of competition data, pre-processed and ready to flow.
+2. **The pipe** is `huggingface-cli download` — one command, and data flows to your GPU pod. Fast, resumable. If the pipe breaks mid-transfer, reconnect and it picks up where it left off.
+3. **Your pod** is the sink — data arrives at `/workspace/data/`, ready to use. No processing, no conversion, no waiting.
+4. **The safety valve** is checkpoint persistence — every N steps, your training progress flows out to cloud storage. Pod dies? New pod picks up the flow from the last save. No lost work.
 
-Every time you launch a GPU pod, re-downloading 16+ GB of data from the competition repo costs 10-30 minutes of billable GPU time. This dataset provides the same data via `huggingface-cli download` — fast, resumable, and available from any provider (RunPod, Modal, Colab, Vast.ai).
+That's it. Data flows in. Checkpoints flow out. You train in between.
 
-## Quick Start
+### Step by Step
+
+**I just want to train. What do I do?**
 
 ```bash
-# Full SP1024 dataset (~11 GB)
+# Step 1: Install huggingface-cli (if you don't have it)
+pip install huggingface-hub
+
+# Step 2: Download the competition data
 huggingface-cli download LightSpeedUp/parameter-golf-data --include "fineweb_sp1024/*" --local-dir /workspace/data
 
-# Full Scylla dataset (~11 GB)
+# Step 3: That's it. Train.
+python train_gpt.py --data_dir /workspace/data/fineweb_sp1024
+```
+
+**I want to save checkpoints so I don't lose work:**
+
+```bash
+# After every N steps in your training script, save:
+curl -X PUT \
+  -H "Authorization: Bearer YOUR_GITHUB_TOKEN" \
+  --data-binary @checkpoint.pt \
+  https://pgolf-api.lightspeedup.com/put/YOUR_GITHUB_USERNAME/my-run/checkpoint.pt
+
+# On a new pod, resume:
+curl -o checkpoint.pt \
+  -H "Authorization: Bearer YOUR_GITHUB_TOKEN" \
+  "https://pgolf-api.lightspeedup.com/download?run_id=my-run&filename=checkpoint.pt"
+```
+
+**I want the fully automated experience:**
+
+Use our RunPod template: `matotezitanka/proteus-pytorch:community`
+
+Set these env vars before launch:
+- `PGOLF_DATA=sp1024` (or `scylla`)
+- `PGOLF_SHARDS=full` (or `mini` for testing)
+- `PGOLF_GITHUB_TOKEN=ghp_yourtoken` (optional, for checkpoints)
+- `PGOLF_USER=yourgithubname` (optional)
+- `PGOLF_RUN=my-experiment-1` (optional)
+
+Hit deploy. SSH in when it's ready. Everything is there.
+
+---
+
+## Comprehensive Guide
+
+### Available Data
+
+| Tokenizer | Vocab | Size | Use case |
+|-----------|-------|------|----------|
+| **SP1024** | 1024 tokens | ~15 GB | Competition default. Most PRs use this. |
+| **Scylla** | 998 tokens | ~11 GB | TokenMonster-derived. Used by top entries. |
+
+Each includes 80 training shards + 1 validation shard + tokenizer models.
+
+### Download Options
+
+```bash
+# === DATA SELECTION ===
+
+# SP1024 — competition default (~15 GB)
+huggingface-cli download LightSpeedUp/parameter-golf-data --include "fineweb_sp1024/*" --local-dir /workspace/data
+
+# Scylla — TokenMonster 998-token vocab (~11 GB)
 huggingface-cli download LightSpeedUp/parameter-golf-data --include "fineweb_scylla/*" --local-dir /workspace/data
 
-# Tokenizers only
+# Both datasets + all tokenizers (~26 GB)
+huggingface-cli download LightSpeedUp/parameter-golf-data --local-dir /workspace/data
+
+# Just tokenizer models (tiny, < 1 MB)
 huggingface-cli download LightSpeedUp/parameter-golf-data --include "tokenizers/*" --local-dir /workspace/data
 
-# Mini subset for smoke tests (~2 GB, 10 shards + val)
+
+# === SHARD SUBSETS (save time/bandwidth) ===
+
+# Mini — 10 shards for smoke tests (~2 GB)
 huggingface-cli download LightSpeedUp/parameter-golf-data \
     --include "fineweb_sp1024/fineweb_train_00000?.bin" \
     --include "fineweb_sp1024/fineweb_val*" \
     --local-dir /workspace/data
 
-# Val only (~200 MB)
+# Half — 40 shards (~7 GB)
 huggingface-cli download LightSpeedUp/parameter-golf-data \
+    --include "fineweb_sp1024/fineweb_train_0000[0-3]?.bin" \
     --include "fineweb_sp1024/fineweb_val*" \
     --local-dir /workspace/data
+
+# Val only — just validation data (~200 MB)
+huggingface-cli download LightSpeedUp/parameter-golf-data \
+    --include "fineweb_sp1024/fineweb_val*" \
+    --local-dir /workspace/data
+```
+
+**No HuggingFace account required.** This is a public dataset. No login, no token, no signup.
+
+**Downloads are resumable.** If your connection drops, re-run the same command and it picks up where it left off.
+
+### Checkpoint Persistence API
+
+Save and resume training across pod preemptions. Your GitHub token is your identity — no accounts to create.
+
+```bash
+# === CHECKPOINT API (https://pgolf-api.lightspeedup.com) ===
+
+# Upload a checkpoint
+curl -X POST https://pgolf-api.lightspeedup.com/upload \
+  -H "Authorization: Bearer ghp_yourtoken" \
+  -H "Content-Type: application/json" \
+  -d '{"run_id": "my-run", "filename": "checkpoint_step500.pt"}'
+
+# Then PUT the file
+curl -X PUT https://pgolf-api.lightspeedup.com/put/yourusername/my-run/checkpoint_step500.pt \
+  -H "Authorization: Bearer ghp_yourtoken" \
+  --data-binary @checkpoint_step500.pt
+
+# Download a checkpoint
+curl -o checkpoint.pt \
+  -H "Authorization: Bearer ghp_yourtoken" \
+  "https://pgolf-api.lightspeedup.com/download?run_id=my-run&filename=checkpoint_step500.pt"
+
+# List your checkpoints
+curl -H "Authorization: Bearer ghp_yourtoken" \
+  "https://pgolf-api.lightspeedup.com/list?run_id=my-run"
+
+# Delete a run's checkpoints
+curl -X DELETE -H "Authorization: Bearer ghp_yourtoken" \
+  "https://pgolf-api.lightspeedup.com/clean?run_id=my-run"
+```
+
+**Limits:** 10 checkpoints per user, 2 GB max each. Auto-deleted after 7 days.
+
+### Docker Image
+
+```bash
+docker pull matotezitanka/proteus-pytorch:community
 ```
 
-## Dataset Structure
+Includes: PyTorch 2.11.0 + CUDA 12.8 + Flash Attention 3 + all competition deps (brotli, tokenmonster, sentencepiece) + tools (cpu_test.py, retokenizer, swap_pytorch.sh) + automated boot script.
+
+Works on RunPod, Vast.ai, or any Docker host with NVIDIA GPUs.
+
+### Data Integrity
+
+Every file has a SHA256 checksum. After downloading:
+
+```bash
+cd /workspace/data
+sha256sum -c SHA256SUMS.txt
+```
+
+If any checksum fails, re-download that file. The download is resumable — you won't re-download files that are already correct.
+
+### Dataset Structure
 
 ```
 parameter-golf-data/
 ├── fineweb_sp1024/
-│   ├── fineweb_train_000000.bin ... fineweb_train_000079.bin  (80 shards, ~11 GB)
-│   └── fineweb_val_000000.bin                                 (1 shard, ~200 MB)
+│   ├── fineweb_train_000000.bin ... fineweb_train_000079.bin  (80 shards)
+│   └── fineweb_val_000000.bin
 ├── fineweb_scylla/
-│   ├── fineweb_train_000000.bin ... fineweb_train_000079.bin  (80 shards, ~11 GB)
-│   └── fineweb_val_000000.bin                                 (1 shard)
+│   ├── fineweb_train_000000.bin ... fineweb_train_000079.bin  (80 shards)
+│   └── fineweb_val_000000.bin
 ├── tokenizers/
 │   ├── fineweb_1024_bpe.model         (SP1024 SentencePiece)
 │   ├── scylla/candidate.vocab         (TokenMonster 998)
 │   └── scylla/candidate.meta.npz     (byte LUTs)
-└── SHA256SUMS.txt                     (integrity manifest)
+├── SHA256SUMS.txt
+└── PATENTS.md
 ```
 
-## Data Integrity
+---
 
-Verify your download:
-```bash
-cd /workspace/data
-sha256sum -c SHA256SUMS.txt
-```
+## Security & Privacy
+
+We believe in transparency. Here's exactly what we can and can't see.
+
+### What we CAN access (technically)
+- **Your checkpoint files** — they're stored in our Cloudflare R2 bucket. We have admin access to the bucket. We don't look at them, but we could.
+- **Your checkpoint metadata** — filenames, sizes, upload timestamps. This is visible in the R2 dashboard.
+- **Request logs** — Cloudflare logs request metadata (IP addresses, timestamps, URLs) by default. We do not add any additional logging.
+- **Your GitHub username** — extracted from your token to scope your storage namespace.
+
+### What we CANNOT access
+- **Your training code** — it runs on your pod, never touches our infrastructure.
+- **Your model weights** (unless you upload them as a checkpoint).
+- **Your GitHub token contents** — the token transits our Worker to call GitHub's API, but it is NOT stored, NOT logged, and NOT persisted anywhere. It's used once per request for authentication and discarded.
+- **Other users' data** — the Worker enforces namespace isolation. Your GitHub username is your storage prefix. You cannot read, list, or delete another user's checkpoints.
+
+### What we DO NOT do
+- We do NOT sell, share, or analyze your data.
+- We do NOT train models on your checkpoints.
+- We do NOT log your GitHub token value.
+- We do NOT track your usage beyond standard Cloudflare request metrics.
+
+### What we disclose
+- The checkpoint API Worker code is in our private repo. We plan to open-source it.
+- Cloudflare's privacy policy applies to request metadata: https://www.cloudflare.com/privacypolicy/
+- Checkpoints are automatically deleted after 7 days. We do not keep backups.
+
+### If you don't trust us
+That's fair. You can:
+1. **Use just the HF dataset** — no account, no tokens, no interaction with our API. Just `huggingface-cli download`.
+2. **Save checkpoints locally** — skip the API entirely. Save to `/workspace/` and accept the risk of losing work on preemption.
+3. **Inspect the Worker** — we'll open-source it. Until then, the API surface is 5 endpoints, ~150 lines of JavaScript, zero dependencies.
+
+---
 
 ## Provenance
 
 - **Source:** [FineWeb](https://huggingface.co/datasets/HuggingFaceFW/fineweb) (CommonCrawl-derived, by Hugging Face)
-- **SP1024 tokenization:** SentencePiece BPE trained on FineWeb, 1024 tokens — from [openai/parameter-golf](https://github.com/openai/parameter-golf) competition repo
-- **Scylla tokenization:** TokenMonster vocabulary (998 tokens) by [@simon-marcus](https://github.com/simon-marcus) ([PR #1143](https://github.com/openai/parameter-golf/pull/1143)). Retokenized using our [retokenize_scylla.py](https://github.com/MatoTeziTanka/parameter-golf-private) pipeline.
-- **No modification** to token sequences — these are byte-identical to what you'd get by cloning the competition repo and running the tokenizer yourself.
+- **SP1024 tokenization:** SentencePiece BPE, 1024 tokens — from the [openai/parameter-golf](https://github.com/openai/parameter-golf) competition repo
+- **Scylla tokenization:** TokenMonster vocabulary (998 tokens) by [@simon-marcus](https://github.com/simon-marcus) ([PR #1143](https://github.com/openai/parameter-golf/pull/1143)). Retokenized using our pipeline.
+- **No modification** to token sequences — byte-identical to what you'd produce by tokenizing the raw data yourself.
 
-## Attribution Chain
+### Attribution Chain
 
-FineWeb → CommonCrawl (CC-BY) → Hugging Face (ODC-By 1.0) → This dataset (ODC-By 1.0)
+CommonCrawl (CC-BY) → Hugging Face FineWeb (ODC-By 1.0) → This dataset (ODC-By 1.0)
 
 ## License
 
-**Data:** [Open Data Commons Attribution License (ODC-By 1.0)](https://opendatacommons.org/licenses/by/1-0/) — required by FineWeb upstream license. You may use, share, and adapt this data with attribution.
+**Data:** [Open Data Commons Attribution License (ODC-By 1.0)](https://opendatacommons.org/licenses/by/1-0/)
+
+**Code & Tools:** Apache 2.0 — see [PATENTS.md](PATENTS.md)
+
+---
+
+## Roadmap
 
-**Retokenization code:** Apache 2.0 — see [PATENTS.md](PATENTS.md) for patent boundary notice.
+- SP4096 and SP8192 tokenizations (need tokenizer models — contributions welcome)
+- Automated checkpoint save/resume in the boot script
+- Open-source the CF Worker code
 
 ## Community