Distributed training with Horovod on Databricks. Does it automatically parallelize any ML model?

No, Horovod on Databricks does not automatically parallelize any ML model. It is specifically designed to parallelize the training of deep learning models using data parallelism. Here's a detailed explanation:

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What Horovod does parallelize:

Horovod focuses on distributing the training of deep learning models written with frameworks like TensorFlow, Keras, and PyTorch. It achieves this by employing data parallelism, where:

• The training dataset is divided across multiple worker nodes.
• Each node trains a copy of the model on its subset of the data.
• Gradients from each node are exchanged and averaged across the cluster to update the model’s weights.

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What Horovod does not automatically parallelize:

• Traditional Machine Learning Algorithms: Models like linear regression, decision trees, or random forests (e.g., from scikit-learn) are not distributed by Horovod. These often have their own parallelization mechanisms, but Horovod is not designed to handle them across a cluster.
• Arbitrary Python Code: Horovod is built for deep learning training loops and cannot distribute general-purpose Python code automatically.

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How Horovod Achieves Parallelism:

Horovod uses communication primitives like all-reduce to efficiently exchange gradients between worker nodes. This requires explicit integration with your training loop through Horovod's API.

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Horovod with Databricks (HorovodRunner):

Databricks simplifies distributed training using HorovodRunner, which wraps Horovod to manage distributed environments on Spark clusters. However, your training code still needs to integrate with Horovod's API.

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Example (Distributed Training with TensorFlow and Horovod):

import tensorflow as tf
import horovod.tensorflow.keras as hvd

# Initialize Horovod
hvd.init()

# Pin GPU to local rank
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
    tf.config.experimental.set_memory_growth(gpu, True)
if gpus:
    tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], 'GPU')

# Build your Keras model
model = tf.keras.models.Sequential(...)

# Wrap optimizer with Horovod's DistributedOptimizer
optimizer = tf.keras.optimizers.Adam(0.001 * hvd.size())
optimizer = hvd.DistributedOptimizer(optimizer)

# Compile the model
model.compile(loss='...', optimizer=optimizer, metrics=['...'])

# Add Horovod callbacks
callbacks = [
    hvd.callbacks.BroadcastGlobalVariablesCallback(0),
    hvd.callbacks.MetricAverageCallback(),
    tf.keras.callbacks.ReduceLROnPlateau(patience=10, verbose=1),
]

# Train the model
model.fit(..., callbacks=callbacks)

This example demonstrates that you must:

1. Initialize Horovod (hvd.init()).
2. Wrap your optimizer with hvd.DistributedOptimizer.
3. Add Horovod-specific callbacks to manage distributed training.

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Key Takeaways:

• Horovod is a powerful tool for distributed deep learning training but requires code modifications to integrate with its API.
• It does not automatically parallelize arbitrary ML models or Python code.
• For traditional ML models or general-purpose code, consider other parallelization tools.

By leveraging Horovod on Databricks, you can achieve efficient distributed training for deep learning models, provided you adapt your training loop to integrate with Horovod's API.