| --- |
| language: |
| - en |
| - zh |
| tags: |
| - video |
| - world-model |
| - csgo |
| - training-cache |
| - streaming |
| --- |
| # CSGO training cache |
|
|
| Precomputed feature-only training samples, continuously converted from four source datasets: |
|
|
| - `peih/csgo-3090-v4-overflow-20260923` |
| - `peih/csgo-4090-v4-20260921` |
| - `peih/csgo-4090-v4-overflow-20260923` |
| - `peih/csgo-dual3090-v4-backup` |
|
|
| This dataset is still growing. `manifest.json` is the current verified snapshot; `manifests/vNNNNNN.json` contains immutable historical snapshots. Read only listed shards at their pinned `revision`. New uploads become available after their verification receipt and a manifest update. Do not train from unlisted TAR files. |
|
|
| ## Format |
|
|
| Schema: `csgo_training_cache_features_v1`. Each TAR contains ordered pairs `<sample_id>.pt` and `<sample_id>.json`. A shard usually holds 60 samples and is approximately 1.49 GB; use the exact size in the manifest. |
|
|
| | Tensor | Shape per sample | dtype | |
| |---|---|---| |
| | latent | [1,16,21,60,104] | float32 | |
| | i2v_y | [1,20,21,60,104] | float32 | |
| | dense_features | [1,21,32,30,52] | float32 | |
| | state | [1,21,3,60,104] | float32 | |
| | interaction | [1,21,134] | float32 | |
| | camera_poses | [1,21,4,4] | float32 | |
| | camera_intrinsics | [1,21,4] | float32 | |
| | text_emb | [1,L,4096] | bfloat16 | |
| |
| There is **no raw seven-channel `dense` tensor**. The Dense VAE features are already encoded. No VAE/T5 encoding is required on the training consumer. Model revisions, weight hashes and preprocessing contract are recorded in the manifest. |
| |
| Use `torch.load(..., weights_only=True)`. Verify each PT's bytes and SHA256 before deserialization, and verify the complete TAR SHA256 at EOF. A partially consumed shard has not yet passed the consumer's full-TAR check. |
|
|
| ## Streaming consumer contract |
|
|
| 1. Read `manifest.json` at a fixed dataset commit. |
| 2. Respect train/val splits and `exclude_sample_ids`. The split is stable by match, with an expected validation fraction of 2% (not an exact per-snapshot ratio). |
| 3. Open listed TARs over streaming HTTP at each pinned shard revision. Avoid downloading the entire dataset. |
| 4. Interleave shards; assign disjoint shards to different ranks. Sample IDs must not repeat across ranks in the same epoch. |
| 5. Keep the manifest fixed throughout an epoch. Refresh only at the next epoch boundary to incorporate newly published samples. |
| 6. Checkpoint the manifest, deterministic order/cursor and training state. A restart may reread a TAR prefix from the network but must not train already consumed samples again. |
|
|
| This layout uses a manifest-aware custom TAR reader. Ordinary `datasets.load_dataset(streaming=True)` is not claimed to implement the exclusion, version refresh and exact-resume protocol automatically. |
|
|
| ## Publication |
|
|
| Two conversion machines write separate `shards/<worker>/...` and `receipts/<worker>/...` paths in this repo. One manifest publisher serializes publication with a parent-commit check. Existing verified shards from the four older cache repos are being copied byte-for-byte; this is not re-encoding. |
|
|
| The full dataset is not yet complete. Manifest totals are the authority for currently published samples. Source discovery and conversion continue while consumers train on a fixed snapshot. |
|
|
