Supports distributed machine learning environments.

Supports Distributed Machine Learning Environments

Pathfinder is designed to facilitate distributed machine learning operations, essential for handling large-scale data and complex models.

Distributed Training Support

• Data Parallelism:

  • Synchronized Training:

    • Splits data across multiple nodes, each training a copy of the model.

    • Aggregates gradients to update the global model synchronously.

  • Asynchronous Training:

    • Nodes train independently, updating the global model asynchronously to improve efficiency.

• Model Parallelism:

  • Model Segmentation:

    • Divides a large model across multiple nodes, each handling different layers or components.

    • Enables the training of models that exceed the memory capacity of a single node.

Framework Compatibility

• Integration with Machine Learning Libraries:

  • Support for TensorFlow, PyTorch, etc.:

    • Compatible with popular machine learning frameworks that facilitate distributed training.

  • Custom Frameworks:

    • Capable of integrating with proprietary or specialized machine learning tools as needed.

• Data Management:

  • Distributed File Systems:

    • Utilizes systems like HDFS or distributed databases to manage large datasets.

  • Data Preprocessing Pipelines:

    • Efficiently processes data in a distributed manner to prepare it for training.