PopXL User Guide and API (experimental)

• 1. Introduction
• 2. Concepts
  • 2.1. IRs
  • 2.2. Graphs
  • 2.3. Tensors
  • 2.4. Operations
• 3. Simple example
• 4. Data types
  • 4.1. 8-bit floating point datatypes
  • 4.2. 8-bit floating point inference model example
• 5. Tensors
  • 5.1. Constant tensors
  • 5.2. Variable tensors
  • 5.3. Intermediate tensors
• 6. Supported operations
  • 6.1. Data input and output
  • 6.2. List of available operations
• 7. Custom operations
  • 7.1. Environment
  • 7.2. Parameter struct
  • 7.3. Operation class
  • 7.4. Opx class
  • 7.5. Gradient operation class
  • 7.6. Gradient opx class
  • 7.7. Python bindings
  • 7.8. Python wrapper
  • 7.9. Auto-compiling custom operations with cppimport
  • 7.10. Using your custom operation
• 8. Graphs
  • 8.1. Main graph
  • 8.2. Graphs
  • 8.3. Creating a graph
  • 8.4. Calling a graph
  • 8.5. Calling a graph in a loop
  • 8.6. Graph replication
  • 8.7. Code loading from Streaming Memory
    • 8.7.1. Minimal example
    • 8.7.2. Controlling liveness between multiple calls
    • 8.7.3. Optimisation: merging the code load operations
    • 8.7.4. Advanced example: nested code loading
    • 8.7.5. Advanced concept: code loading in dynamic branches
• 9. Transforms
  • 9.1. Autodiff
• 10. Context manager
• 11. Session
  • 11.1. Running a session
  • 11.2. Getting and setting tensor data
    • 11.2.1. When transfers will occur between host and device
  • 11.3. Nested Session Contexts
  • 11.4. Number of host transfers
  • 11.5. Data input shape
  • 11.6. Device Types
• 12. Variables in Streaming Memory
  • 12.1. Remote buffers
  • 12.2. Remote variable tensors
  • 12.3. Variable tensors for replicated tensor sharding
• 13. Replication
  • 13.1. Graph replication
  • 13.2. Replica grouping
  • 13.3. Code examples
  • 13.4. Retrieval modes
• 14. Distributed PopXL Instances
  • 14.1. Introduction
  • 14.2. Primer on using PopDist and PopRun to run multi-instance applications
  • 14.3. Basic Example
  • 14.4. Variable ReplicaGroupings and multiple instances
    • 14.4.1. Detail of inter-instance communication
  • 14.5. Sharding and multiple instances
    • 14.5.1. Basic Example
    • 14.5.2. Using shard_over
    • 14.5.3. Complex Example
• 15. Application example: MNIST
  • 15.1. Import the necessary libraries
  • 15.2. Prepare dataset
  • 15.3. Create IR for training
    • 15.3.1. Create network
    • 15.3.2. Calculate gradients and update weights
  • 15.4. Run the IR to train the model
  • 15.5. Create an IR for testing and run the IR to test the model
• 16. Application example: MNIST with replication and RTS
  • 16.1. Add support for replications
  • 16.2. Change variable tensors to RTS variable tensors
• 17. Environment variables
  • 17.1. Logging
    • 17.1.1. POPART_LOG_LEVEL
    • 17.1.2. POPART_LOG_DEST
    • 17.1.3. POPART_LOG_CONFIG
  • 17.2. Generating DOT files
  • 17.3. Caching of compiled executables
• 18. Python API Reference
  • 18.1. Ir
  • 18.2. Contexts
  • 18.3. Data types
  • 18.4. Graphs
  • 18.5. Modules
  • 18.6. Random seeds
  • 18.7. Remote buffers
  • 18.8. Replica grouping
  • 18.9. Session
  • 18.10. Streams
  • 18.11. Tensors
  • 18.12. Tensor locations
  • 18.13. Transforms
  • 18.14. Utils
  • 18.15. Ops available in PopXL
• 19. Index
• 20. Legal notices