Add tensorboard to display training and evaluation metrics #3163
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This pull request was exported from Phabricator. Differential Revision: D77841795 |
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… implementation to support DLRMv2 (pytorch#3163) Summary: Pull Request resolved: pytorch#3163 ### Major changes - Add tensorboard to the benchmark testbed, specifically in `benchmark_zch.py`. - Count the number of unique values received by each rank in each epoch by revising `benchmark_zch_utils.py`. - Revise `data/non_zch_remapper.py` to not depend on `batch.to_dict()` method, instead it fetch dataclass `batch`'s attribute with the built-in `vars()` method. - Revise DLRMv2 model EBC config initialization to make the table name identical with the feature name. Rollback Plan: Differential Revision: D77841795
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… implementation to support DLRMv2 (pytorch#3163) Summary: Pull Request resolved: pytorch#3163 ### Major changes - Add tensorboard to the benchmark testbed, specifically in `benchmark_zch.py`. - Count the number of unique values received by each rank in each epoch by revising `benchmark_zch_utils.py`. - Revise `data/non_zch_remapper.py` to not depend on `batch.to_dict()` method, instead it fetch dataclass `batch`'s attribute with the built-in `vars()` method. - Revise DLRMv2 model EBC config initialization to make the table name identical with the feature name. - Revise DLRMv2 configuration yaml file to set table size for each feature. - Revise the default value for "num_embeddings" parameter in `arguments.py` to None. Rollback Plan: Differential Revision: D77841795
Summary: Pull Request resolved: pytorch#3127 Differential Revision: D77033290
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… implementation to support DLRMv2 (pytorch#3163) Summary: Pull Request resolved: pytorch#3163 ### Major changes - Add tensorboard to the benchmark testbed, specifically in `benchmark_zch.py`. - Count the number of unique values received by each rank in each epoch by revising `benchmark_zch_utils.py`. - Revise `data/non_zch_remapper.py` to not depend on `batch.to_dict()` method, instead it fetch dataclass `batch`'s attribute with the built-in `vars()` method. - Revise DLRMv2 model EBC config initialization to make the table name identical with the feature name. - Revise DLRMv2 configuration yaml file to set table size for each feature. - Revise the default value for "num_embeddings" parameter in `arguments.py` to None. Rollback Plan: Differential Revision: D77841795
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… implementation to support DLRMv2 (pytorch#3163) Summary: Pull Request resolved: pytorch#3163 ### Major changes - Add tensorboard to the benchmark testbed, specifically in `benchmark_zch.py`. - Count the number of unique values received by each rank in each epoch by revising `benchmark_zch_utils.py`. - Revise `data/non_zch_remapper.py` to not depend on `batch.to_dict()` method, instead it fetch dataclass `batch`'s attribute with the built-in `vars()` method. - Revise DLRMv2 model EBC config initialization to make the table name identical with the feature name. - Revise DLRMv2 configuration yaml file to set table size for each feature. - Revise the default value for "num_embeddings" parameter in `arguments.py` to None. Differential Revision: D77841795
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… implementation to support DLRMv2 (pytorch#3163) Summary: Pull Request resolved: pytorch#3163 ### Major changes - Add tensorboard to the benchmark testbed, specifically in `benchmark_zch.py`. - Count the number of unique values received by each rank in each epoch by revising `benchmark_zch_utils.py`. - Revise `data/non_zch_remapper.py` to not depend on `batch.to_dict()` method, instead it fetch dataclass `batch`'s attribute with the built-in `vars()` method. - Revise DLRMv2 model EBC config initialization to make the table name identical with the feature name. - Revise DLRMv2 configuration yaml file to set table size for each feature. - Revise the default value for "num_embeddings" parameter in `arguments.py` to None. Differential Revision: D77841795
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Differential Revision: D77841795