DeepSpeed is an advanced deep learning optimization library designed to improve the performance and scalability of training large models. It provides features like mixed precision training, gradient accumulation, and efficient memory management, making it a popular choice for researchers and developers working with large-scale models.
When using DeepSpeed, you might encounter an issue where the optimizer state is not loaded as expected. This can manifest as an error message or unexpected behavior during model training or resumption from a checkpoint.
"Optimizer state not loaded" is a typical error message indicating that the optimizer's state was not correctly restored, which can affect the training process.
The root cause of this issue often lies in the configuration settings of DeepSpeed. If the optimizer state loading is not enabled or is incorrectly configured in the DeepSpeed configuration file, the optimizer will not load its state from a checkpoint, leading to this error.
Ensure that your DeepSpeed configuration file includes the necessary settings to enable optimizer state loading. This typically involves setting the appropriate flags and paths for state restoration.
Open your DeepSpeed configuration file (usually a JSON file) and check for the following settings:
{
"optimizer": {
"type": "Adam",
"params": {
"lr": 0.001
}
},
"zero_optimization": {
"stage": 2,
"offload_optimizer": true
},
"checkpoint": {
"load_optimizer_states": true
}
}
Ensure that "load_optimizer_states": true
is set under the "checkpoint"
section.
Verify that the checkpoint path specified in your training script or configuration file is correct and accessible. The path should point to the directory where the optimizer state was saved.
Ensure you are using the latest version of DeepSpeed, as updates may include bug fixes and improvements. You can update DeepSpeed using the following command:
pip install deepspeed --upgrade
For more information on configuring DeepSpeed, refer to the DeepSpeed Configuration Documentation. If you continue to experience issues, consider reaching out to the DeepSpeed GitHub Issues page for community support.
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