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machine-learning/jax-haiku-dask-dataframe-distributed-example.ipynb
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…ipynb Co-authored-by: Matthew Rocklin <mrocklin@gmail.com>
| " df_one_partition = ddf_one_partition.compute()\n", | ||
| " scaled_x = jnp.array(df_one_partition[[\"scaled_x\"]].values)\n", | ||
| " y = jnp.array(df_one_partition[[\"y\"]].values)\n", | ||
| " params, opt_state = update(params, opt_state, scaled_x, y)" |
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It might be worth taking a look at some of the functionality in dask-ml, which might do some of these things for you already if you're interested.
cc'ing @stsievert and @TomAugspurger
| " futures = []\n", | ||
| " for ddf_one_partition in ddf_train.partitions:\n", | ||
| " # Compute the gradients in parallel\n", | ||
| " futures.append(client.submit(dask_compute_grads_one_partition_wrapper, ddf_one_partition, params))\n", |
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I recommend instead ...
from dask.distributed import futures_of
futures = futures_of(df.map_partitions(func, **params).persist())There was a problem hiding this comment.
Thanks, I've tried this but .map_partitions() requires you to return either a Dask.DataFrame or Dask.Series (I think?). My function returns a set of gradients, grads, which is a Python dictionary (with more python dicts inside, i.e. a tree-like structure), so I don't think this will work in this case (please correct me if I am mistaken).
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You can probably work around that with to_delayed() instead of map_partitions. I can take a closer look later.
machine-learning/jax-haiku-dask-dataframe-distributed-example.ipynb
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| " # Bring the gradients back to the client, and update the model with the optimizer on the client\n", | ||
| " grads = future.result()\n", | ||
| " updates, opt_state = optimizer.update(grads, opt_state)\n", | ||
| " params = optix.apply_updates(params, updates)" |
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This is also the kind of thing for which Actors is probably a decent fit.
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Yes, I've been trying to think how to perform training with shared parameters (and optimizer state) among workers via Actors. Haven't quite got my head around how this might work yet.
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This might be a start: https://docs.dask.org/en/latest/futures.html#example-parameter-server
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That example doesn't run, maybe a bad merge. I've put in a PR to correct that: dask/dask#6449
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This notebook example is a learning exercise during the Scipy2020 Dask sprint to establish how dask might be used to parallelize jax/dm-haiku deep learning model training and prediction.
I've committed my notebook that is working end-to-end, and demonstrates a neural network for learning the sine function.