Model Runtime: User Guide

• 1. Introduction
• 2. Model Runtime under the hood
  • 2.1. Finding hardware
  • 2.2. Data transfers
  • 2.3. Executable upload
  • 2.4. Tensor data
  • 2.5. Managing the data sources/targets
  • 2.6. Queues of data
  • 2.7. Buffers
  • 2.8. Conditional execution
• 3. Model Runner deep dive through examples
  • 3.1. Execution modes
  • 3.2. Replication
  • 3.3. Multithreading
  • 3.4. Frozen inputs
• 4. Session
  • 4.1. Creating a session
  • 4.2. Uploading user model onto IPU
  • 4.3. Handlers for model tensors
  • 4.4. Running programs
  • 4.5. Retrieving information from Session
  • 4.6. Managing queues of tensor data
  • 4.7. Verification
• 5. Managing devices
  • 5.1. Device
  • 5.2. Device Manager
• 6. Queue Manager
• 7. Tools
  • 7.1. Callbacks benchmark
  • 7.2. Queues benchmark
  • 7.3. Real data
• 8. Logging
• 9. Appendix
  • 9.1. Files contain helper functions used by examples
  • 9.2. Generating example PopEF file
• 10. Model Runtime C++ API reference
  • 10.1. High level API
    • 10.1.1. Device management
    • 10.1.2. Tensor memory representation
    • 10.1.3. Model Runner
  • 10.2. Low level API
    • 10.2.1. Anchor callback management
    • 10.2.2. Queue memory management
    • 10.2.3. Queue management
    • 10.2.4. Runtime management
• 11. Model Runtime Python API
  • 11.1. High level API
    • 11.1.1. Device management
    • 11.1.2. Tensor memory representation
    • 11.1.3. Model Runner
  • 11.2. Low level API
    • 11.2.1. Anchor callback management
    • 11.2.2. Queue management
    • 11.2.3. Runtime management
• 12. Legal notices