Add JAX training tutorial#1302
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yk5
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Jun 2, 2026
| [{"tokens": np.arange(np.random.randint(2, 6))} for _ in range(16)] | ||
| ) | ||
| ragged = ragged.batch(4, batch_fn=pad_collate, drop_remainder=True) | ||
| print(ragged[0]["tokens"].shape) |
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Can you add a couple more to show that they are ragged?
yk5
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Jun 2, 2026
| ds = ds.map(jax.device_put) # transfer still on iter thread | ||
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| first = next(iter(ds)) | ||
| print(first["image"].shape, first["image"].sharding) |
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print another one or two to show the sharding would change?
yk5
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Jun 2, 2026
| ) | ||
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| batch = next(iter(ds)) | ||
| print(batch["image"].sharding) |
yk5
reviewed
Jun 3, 2026
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| for step, batch in zip(range(2), ds): | ||
| batch = jax.device_put(batch) | ||
| print(step, batch["image"].sharding) |
yk5
reviewed
Jun 3, 2026
| ```{code-cell} ipython3 | ||
| :id: jx-template-code | ||
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| BATCH = 256 |
yk5
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Jun 3, 2026
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This PR adds a guide on plugging Grain dataset in JAX training.
📚 Documentation preview 📚: https://google-grain--1302.org.readthedocs.build/