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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

By default, the cache will be mounted in two locations
  • /root/.cache/user_artifacts, which can be accessed via the $BT_PROJECT_CACHE_DIR environment variable. This cache is shared by all jobs in a project.
  • /root/.cache/team_artifacts, which can be accessed via the $BT_TEAM_CACHE_DIR environment variable. This cache is shared by all jobs for a team.

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Hugging Face Cache Mount

You can mount your cache to the Hugging Face cache directory by setting HF_HOME to one of the provided mount points plus /huggingface. For example, you can set HF_HOME=$BT_PROJECT_CACHE_DIR/huggingface to use the project cache directory. However, there are considerable technical pitfalls when trying to read from the cache with multiple processes, as Huggingface doesn’t work well with distributed filesystems. To help enable this use case, ensure your dataset processors or process count is set to 1 to minimize the number of concurrent readers.

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Seeding Your Data and Models

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