README.md

metadata

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.