Record chip kind alongside process and device count on every run

README.md

@@ -55,13 +55,24 @@ shapes XLA compiles for. Neither is addressable by seeding, and both are the
 same non-associativity that makes any reduction order-dependent.
 
 So `--seed N` plus the configuration does not pin a number; the topology is
-part of it. Every run records `data_processes` and `devices` in its study's
-`records.jsonl`, alongside `system.process_count` in `result.json`.
-
-All studies here ran at 4 processes over 16 chips except `batch-sweep-500M`,
-whose 20-tokens-per-parameter arm ran at 1 process over 8. Those two arms are
-separate experiments — their token budgets differ — so the split does not cross
-a comparison that was being made.
+part of it. Every run records `chip`, `data_processes` and `devices` in its
+study's `records.jsonl`, alongside the full `system` block in `result.json`,
+and the dashboards show them beside each run.
+
+| study | hardware |
+|---|---|
+| `batch-sweep-60M` | TPU v4 — 4 processes, 16 chips |
+| `lr-batch-sweep-125M` | TPU v4 — 4 processes, 16 chips |
+| `batch-sweep-250M` | TPU v4 — 4 processes, 16 chips |
+| `batch-sweep-500M` | **mixed**: 6 runs TPU v4 (4 proc, 16 chips), 6 runs TPU v6 lite (1 proc, 8 chips) |
+| `lr-transfer-250M` | TPU v4 — 4 processes, 16 chips |
+| `lr-sweep-8k-60M` | TPU v4 — 4 processes, 16 chips |
+| `moe-lr-sweep-8k` | TPU v4 — 4 processes, 16 chips |
+
+`batch-sweep-500M` is the only study spanning two chip types, and the split
+follows its 5- against 20-tokens-per-parameter arms. Those are separate
+experiments whose losses were never comparable, so the topology change does not
+cross a comparison that was being made.
 
 ## The format
 

batch-sweep-250M/README.md

@@ -11,7 +11,7 @@ The 250M rung, extended to batch 512 to see whether the batch optimum keeps movi
 - **learning rates:** 2^-7, 2^-8, 2^-9
 - **seeds:** 1337, 1338, 1339
 - **budget:** 5 tokens per parameter (~1.2B tokens), 2,331-18,650 steps
-- **data processes:** 4 (16 chips) — the data stream itself is invariant under this, but gradient reduction order and per-chip batch are not, so the same seed on a different count lands 0.004-0.023 nats away
+- **hardware:** TPU v4 — 4 processes, 16 chips — the data stream is invariant under this, but gradient reduction order and per-chip batch are not, so the same seed on a different topology lands 0.004-0.023 nats away
 - **dataset:** FineWeb-Edu, GPT-2 tokenizer (`fineweb-8b-gpt2`)
 - **parameterization:** Complete(d)P
 - **optimizer:** AdamW, beta1 0.9, beta2 0.95, weight decay 0.1, no gradient clipping

batch-sweep-500M/README.md

@@ -12,7 +12,7 @@ Whether the optima found at 5 tokens per parameter survive a four-times-longer h
 - **seeds:** 1337, 1338, 1339 (5 TPP arm); 1337, 1338 (20 TPP arm)
 - **budgets:** 5 and 20 tokens per parameter -- 9,586 to 153,382 steps
 - **note:** the two budgets are different experiments; their losses are not comparable to each other, which is why run names carry the budget
-- **data processes:** 6 runs at 1 (8 chips), 6 runs at 4 (16 chips) — the data stream itself is invariant under this, but gradient reduction order and per-chip batch are not, so the same seed on a different count lands 0.004-0.023 nats away
+- **hardware:** 6 runs on TPU v6 lite — 1 process, 8 chips; 6 runs on TPU v4 — 4 processes, 16 chips — the data stream is invariant under this, but gradient reduction order and per-chip batch are not, so the same seed on a different topology lands 0.004-0.023 nats away
 - **dataset:** FineWeb-Edu, GPT-2 tokenizer (`fineweb-8b-gpt2`)
 - **parameterization:** Complete(d)P
 - **optimizer:** AdamW, beta1 0.9, beta2 0.95, weight decay 0.1, no gradient clipping

batch-sweep-60M/README.md

@@ -11,7 +11,7 @@ The widest grid in the collection: every batch size against every learning rate,
 - **learning rates:** 2^-6, 2^-7, 2^-8, 2^-9, 2^-10
 - **seeds:** 1337, 1338, 1339
 - **budget:** 5 tokens per parameter (~300M tokens), 571-9,143 steps depending on batch
-- **data processes:** 4 (16 chips) — the data stream itself is invariant under this, but gradient reduction order and per-chip batch are not, so the same seed on a different count lands 0.004-0.023 nats away
+- **hardware:** TPU v4 — 4 processes, 16 chips — the data stream is invariant under this, but gradient reduction order and per-chip batch are not, so the same seed on a different topology lands 0.004-0.023 nats away
 - **dataset:** FineWeb-Edu, GPT-2 tokenizer (`fineweb-8b-gpt2`)
 - **parameterization:** Complete(d)P
 - **optimizer:** AdamW, beta1 0.9, beta2 0.95, weight decay 0.1, no gradient clipping

lr-batch-sweep-125M/README.md

@@ -11,7 +11,7 @@ The 125M rung of the batch/learning-rate grid. The same product read either way:
 - **learning rates:** 2^-7, 2^-8, 2^-9
 - **seeds:** 1337, 1338, 1339
 - **budget:** 5 tokens per parameter (~617M tokens), 2,355-9,419 steps
-- **data processes:** 4 (16 chips) — the data stream itself is invariant under this, but gradient reduction order and per-chip batch are not, so the same seed on a different count lands 0.004-0.023 nats away
+- **hardware:** TPU v4 — 4 processes, 16 chips — the data stream is invariant under this, but gradient reduction order and per-chip batch are not, so the same seed on a different topology lands 0.004-0.023 nats away
 - **dataset:** FineWeb-Edu, GPT-2 tokenizer (`fineweb-8b-gpt2`)
 - **parameterization:** Complete(d)P
 - **optimizer:** AdamW, beta1 0.9, beta2 0.95, weight decay 0.1, no gradient clipping

lr-sweep-8k-60M/README.md

@@ -11,7 +11,7 @@ Whether the learning-rate optimum moves when the context length grows eight-fold
 - **learning rates:** 2^-6, 2^-7, 2^-8, 2^-9, 2^-10
 - **seeds:** 1337, 1338, 1339
 - **budget:** 5 tokens per parameter, 2,286 steps -- identical to the 1,024-context ladder
-- **data processes:** 4 (16 chips) — the data stream itself is invariant under this, but gradient reduction order and per-chip batch are not, so the same seed on a different count lands 0.004-0.023 nats away
+- **hardware:** TPU v4 — 4 processes, 16 chips — the data stream is invariant under this, but gradient reduction order and per-chip batch are not, so the same seed on a different topology lands 0.004-0.023 nats away
 - **dataset:** FineWeb-Edu, GPT-2 tokenizer (`fineweb-8b-gpt2`)
 - **parameterization:** Complete(d)P
 - **optimizer:** AdamW, beta1 0.9, beta2 0.95, weight decay 0.1, no gradient clipping

lr-transfer-250M/README.md

@@ -11,7 +11,7 @@ Run to settle a 250M result that a single seed had gotten wrong, and the evidenc
 - **learning rates:** 2^-6, 2^-7, 2^-8, 2^-9
 - **seeds:** 1337, 1338, 1339
 - **budget:** 5 tokens per parameter, 9,325 steps
-- **data processes:** 4 (16 chips) — the data stream itself is invariant under this, but gradient reduction order and per-chip batch are not, so the same seed on a different count lands 0.004-0.023 nats away
+- **hardware:** TPU v4 — 4 processes, 16 chips — the data stream is invariant under this, but gradient reduction order and per-chip batch are not, so the same seed on a different topology lands 0.004-0.023 nats away
 - **dataset:** FineWeb-Edu, GPT-2 tokenizer (`fineweb-8b-gpt2`)
 - **parameterization:** Complete(d)P
 - **optimizer:** AdamW, beta1 0.9, beta2 0.95, weight decay 0.1, no gradient clipping

moe-lr-sweep-8k/README.md

@@ -17,7 +17,7 @@ dropped — actually does.
 - **learning rates:** 2^-6, 2^-7, 2^-8, 2^-9, 2^-10 at 60M; 2^-7, 2^-8, 2^-9 at 125M
 - **batch size:** 16 sequences — 131,072 tokens per step, matching the dense ladder
 - **budget:** 5 tokens per parameter, sized by *active* parameters
-- **data processes:** 4 (16 chips) — the data stream itself is invariant under this, but gradient reduction order and per-chip batch are not, so the same seed on a different count lands 0.004-0.023 nats away
+- **hardware:** TPU v4 — 4 processes, 16 chips — the data stream is invariant under this, but gradient reduction order and per-chip batch are not, so the same seed on a different topology lands 0.004-0.023 nats away
 - **dataset:** FineWeb-Edu, GPT-2 tokenizer (`fineweb-8b-gpt2`)
 - **parameterization:** Complete(d)P
 - **optimizer:** AdamW, beta1 0.9, beta2 0.95, weight decay 0.1, no gradient clipping