Enable logging for the plan() function, ShardEstimators and TrainingPipeline class constructors - #3521
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…ipeline class constructors (meta-pytorch#3521) Summary: This diff enables the static logging functionality to collect data for: 1) plan() - This will allow us to look at the inputs and outputs to the planner to help with use issue debugging 2) ShardEstimators - This will allow us to look at the inputs and outputs to the ShardEstimators, which gives us the bandwidth inputs to verify if the planner is generating expected values as well as help with debugging OOMs 3) TrainingPipeline - The class type here will be an indicator of which pipeline was used by the training job. The training pipeline has implications on the memory usage and is an important data point to collect to investigate OOMs. Reviewed By: kausv Differential Revision: D86317910
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…ipeline class constructors (meta-pytorch#3521) Summary: This diff enables the static logging functionality to collect data for: 1) plan() - This will allow us to look at the inputs and outputs to the planner to help with use issue debugging 2) ShardEstimators - This will allow us to look at the inputs and outputs to the ShardEstimators, which gives us the bandwidth inputs to verify if the planner is generating expected values as well as help with debugging OOMs 3) TrainingPipeline - The class type here will be an indicator of which pipeline was used by the training job. The training pipeline has implications on the memory usage and is an important data point to collect to investigate OOMs. Reviewed By: kausv Differential Revision: D86317910
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…ipeline class constructors (meta-pytorch#3521) Summary: This diff enables the static logging functionality to collect data for: 1) plan() - This will allow us to look at the inputs and outputs to the planner to help with use issue debugging 2) ShardEstimators - This will allow us to look at the inputs and outputs to the ShardEstimators, which gives us the bandwidth inputs to verify if the planner is generating expected values as well as help with debugging OOMs 3) TrainingPipeline - The class type here will be an indicator of which pipeline was used by the training job. The training pipeline has implications on the memory usage and is an important data point to collect to investigate OOMs. Reviewed By: kausv Differential Revision: D86317910
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…ipeline class constructors (meta-pytorch#3521) Summary: This diff enables the static logging functionality to collect data for: 1) plan() - This will allow us to log the inputs and outputs to the planner to help with use issue debugging 2) ShardEstimators - This will allow us to log the inputs and outputs to the ShardEstimators, which gives us the bandwidth inputs to verify if the planner is generating expected values as well as help with debugging OOMs 3) TrainingPipeline - The class type here will be an indicator of which pipeline was used by the training job. The training pipeline has implications on the memory usage and is an important data point to collect to investigate OOMs. Reviewed By: kausv Differential Revision: D86317910
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…ipeline class constructors (meta-pytorch#3521) Summary: This diff enables the static logging functionality to collect data for: 1) plan() - This will allow us to log the inputs and outputs to the planner to help with use issue debugging 2) ShardEstimators - This will allow us to log the inputs and outputs to the ShardEstimators, which gives us the bandwidth inputs to verify if the planner is generating expected values as well as help with debugging OOMs 3) TrainingPipeline - The class type here will be an indicator of which pipeline was used by the training job. The training pipeline has implications on the memory usage and is an important data point to collect to investigate OOMs. Reviewed By: kausv Differential Revision: D86317910
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…ipeline class constructors (meta-pytorch#3521) Summary: This diff enables the static logging functionality to collect data for: 1) plan() - This will allow us to log the inputs and outputs to the planner to help with use issue debugging 2) ShardEstimators - This will allow us to log the inputs and outputs to the ShardEstimators, which gives us the bandwidth inputs to verify if the planner is generating expected values as well as help with debugging OOMs 3) TrainingPipeline - The class type here will be an indicator of which pipeline was used by the training job. The training pipeline has implications on the memory usage and is an important data point to collect to investigate OOMs. Reviewed By: kausv Differential Revision: D86317910
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…ipeline class constructors (meta-pytorch#3521) Summary: This diff enables the static logging functionality to collect data for: 1) plan() - This will allow us to log the inputs and outputs to the planner to help with use issue debugging 2) ShardEstimators - This will allow us to log the inputs and outputs to the ShardEstimators, which gives us the bandwidth inputs to verify if the planner is generating expected values as well as help with debugging OOMs 3) TrainingPipeline - The class type here will be an indicator of which pipeline was used by the training job. The training pipeline has implications on the memory usage and is an important data point to collect to investigate OOMs. Reviewed By: kausv Differential Revision: D86317910
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…ipeline class constructors (meta-pytorch#3521) Summary: This diff enables the static logging functionality to collect data for: 1) plan() - This will allow us to log the inputs and outputs to the planner to help with use issue debugging 2) ShardEstimators - This will allow us to log the inputs and outputs to the ShardEstimators, which gives us the bandwidth inputs to verify if the planner is generating expected values as well as help with debugging OOMs 3) TrainingPipeline - The class type here will be an indicator of which pipeline was used by the training job. The training pipeline has implications on the memory usage and is an important data point to collect to investigate OOMs. Reviewed By: kausv Differential Revision: D86317910
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…ipeline class constructors (meta-pytorch#3521) Summary: This diff enables the static logging functionality to collect data for: 1) plan() - This will allow us to log the inputs and outputs to the planner to help with use issue debugging 2) ShardEstimators - This will allow us to log the inputs and outputs to the ShardEstimators, which gives us the bandwidth inputs to verify if the planner is generating expected values as well as help with debugging OOMs 3) TrainingPipeline - The class type here will be an indicator of which pipeline was used by the training job. The training pipeline has implications on the memory usage and is an important data point to collect to investigate OOMs. Reviewed By: kausv Differential Revision: D86317910
…ipeline class constructors (meta-pytorch#3521) Summary: This diff enables the static logging functionality to collect data for: 1) plan() - This will allow us to log the inputs and outputs to the planner to help with use issue debugging 2) ShardEstimators - This will allow us to log the inputs and outputs to the ShardEstimators, which gives us the bandwidth inputs to verify if the planner is generating expected values as well as help with debugging OOMs 3) TrainingPipeline - The class type here will be an indicator of which pipeline was used by the training job. The training pipeline has implications on the memory usage and is an important data point to collect to investigate OOMs. Reviewed By: kausv Differential Revision: D86317910
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Summary:
This diff enables the static logging functionality to collect data for:
Reviewed By: kausv
Differential Revision: D86317910