If you notice a throughput spike after saving checkpoints, this is a known artifact of reporting throughput on the user node, caused by the asynchronous nature of execution on Wafer-Scale Cluster. For more details and understanding of this behavior, please refer to Measure throughput of your model.Documentation Index
Fetch the complete documentation index at: https://training-docs.cerebras.ai/llms.txt
Use this file to discover all available pages before exploring further.
Getting Started
CS Torch
Cluster Monitoring
Fundamentals
Support
- Previous Releases
- Troubleshooting
- Cannot Load Cerebras Checkpoints in GPUs
- Custom Pt Training Script Spawns Multiple Compile Jobs
- Loss Compilation Issues With Autogen
- Error Parsing Metadata
- Error Receiving Activation
- Failed Mount Directory During Execution
- Failing To Automatically Load Checkpoints
- Failure To Trace Due To Functionalization Error
- Input Starvation
- Out Of Memory Errors And System Resources
- Model Is Too Large To Fit In Memory
- Modulenotfounderror
- Numerical Issues
- Throughput Spike After Saving Checkpoints
- Training Fails When Logged In As Root
- Vocabulary Size Troubleshooting
- Glossary
Throughput Spike After Saving Checkpoints
⌘I
Assistant
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