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The training cache enables you to persist data between training jobs. This can significantly improve iteration speed by skipping expensive downloads and data transformations.

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How to Use the Training Cache

Set the cache configuration in your Runtime: 
from truss_train import definitions

training_runtime = definitions.Runtime(
    # ... other configuration options
    cache_config=definitions.CacheConfig(enabled=True)
)

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Cache Directory

The cache will be mounted at /root/.cache/user_artifacts, which can be accessed via the $BT_RW_CACHE_DIR environment variable.

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Legacy HF Cache

We recommend using the new cache directory at /root/.cache/user_artifacts instead. However, if you need to access data mounted to /root/.cache/huggingface for compatibility reasons, you can set enable_legacy_hf_mount=True in your CacheConfig. Note that this legacy option is not recommended for new projects.

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Seeding Your Data

For multi-gpu training, you should ensure that your data is seeded before running multi-process training jobs. You can do this by separating your training script into training script and data loading script.

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Performance Benefits

For a 400 GB HF Dataset, you can expect to save nearly an hour of compute time for each job - data download and preparation have been done already!

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Cache Management

You can inspect the contents of the cache through CLI with truss train cache summarize <project_name or project_id>. This visibility into what’s in the cache can help you verify your code is working as expected, and additionally manage files and artifacts you no longer need.
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