Model Runtime: User Guide
Version: latest
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
Model Runtime: User Guide
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
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v: latest