Measure times #439
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Measure times #439
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Summary: Pull Request resolved: pytorch#439 Add two counters, containing the iteration and time blocked on data for the last iteration. These are stored on the state, not sure if the unit would be a better place. They can be accessed at the start of the next iteration. This is done because we want to capture the time of the whole iteration, including callbacks, so it cannot be accessed by callbacks. Another possibility would be to keep a list of all the times and data times, not sure if that would be preferrable. Reviewed By: daniellepintz Differential Revision: D46853794 fbshipit-source-id: 439a9ae17bf867f5722cd86f21cd42599966bd9c
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Summary: Pull Request resolved: pytorch#439 Add two counters, containing the iteration and time blocked on data for the last iteration. These are stored on the state, not sure if the unit would be a better place. Had to rework the approach slightly since the state does not seem the right place. With the changes, it is more natural to make it similar to progress, and store the info in a Stateful. This has the additional advantage that all values should be storable and be restored with the checkpoints. Also rather than the last value, all of them are stored. Reviewed By: daniellepintz Differential Revision: D46853794 fbshipit-source-id: 888c9559c48961eccb1f9a6ecbd976cecdb90100
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This pull request was exported from Phabricator. Differential Revision: D46853794 |
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Summary: Pull Request resolved: pytorch#439 Add two counters, containing the iteration and time blocked on data for the last iteration. These are stored on the state, not sure if the unit would be a better place. Had to rework the approach slightly since the state does not seem the right place. With the changes, it is more natural to make it similar to progress, and store the info in a Stateful. This has the additional advantage that all values should be storable and be restored with the checkpoints. Also rather than the last value, all of them are stored. Differential Revision: D46853794 fbshipit-source-id: 87d0cd58963c6cfd15c2e497801ed993668b6d29
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Summary: Pull Request resolved: pytorch#439 Add two counters, containing the iteration and time blocked on data for the last iteration. These are stored and recorded using a timer that does not synchronise with CUDA, and that only records these two values. - iteration time is recorded in the training loop for all jobs. - data time is recorded if the training loop does the data fetching, otherwise the user needs to instrument the logic which reads the data from the iterable. Had to rework the approach slightly since the state does not seem the right place. With the changes, it is more natural to make it similar to progress, and store the info in a Stateful. This has the additional advantage that all values should be storable and be restored with the checkpoints. Also rather than the last value, all of them are stored. Differential Revision: D46853794 fbshipit-source-id: 64a4f55f8fb0985831c0cd94e93eb7be67a30faa
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This pull request was exported from Phabricator. Differential Revision: D46853794 |
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Summary: Pull Request resolved: pytorch#439 Add two counters, containing the iteration and time blocked on data for the last iteration. These are stored and recorded using a timer that does not synchronise with CUDA, and that only records these two values. - iteration time is recorded in the training loop for all jobs. - data time is recorded if the training loop does the data fetching, otherwise the user needs to instrument the logic which reads the data from the iterable. Had to rework the approach slightly since the state does not seem the right place. With the changes, it is more natural to make it similar to progress, and store the info in a Stateful. This has the additional advantage that all values should be storable and be restored with the checkpoints. Also rather than the last value, the last LOWER_BOUND values are stored. I hardcoded this in the torchtnt state, to not bloat parameters, since intuitively the last 1e4 values for each timer action here should be enough for monitoring purposes. Reviewed By: daniellepintz, ananthsub Differential Revision: D46853794 fbshipit-source-id: 6d5c5d4b2ab3372c606f60a4c12f2a5e0092e867
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Summary: Pull Request resolved: pytorch#439 Add two counters, containing the iteration and time blocked on data for the last iteration. These are stored and recorded using a timer that does not synchronise with CUDA, and that only records these two values. - iteration time is recorded in the training loop for all jobs. - data time is recorded if the training loop does the data fetching, otherwise the user needs to instrument the logic which reads the data from the iterable. Had to rework the approach slightly since the state does not seem the right place. With the changes, it is more natural to make it similar to progress, and store the info in a Stateful. This has the additional advantage that all values should be storable and be restored with the checkpoints. Also rather than the last value, the last LOWER_BOUND values are stored. I hardcoded this in the torchtnt state, to not bloat parameters, since intuitively the last 1e4 values for each timer action here should be enough for monitoring purposes. Reviewed By: daniellepintz, ananthsub Differential Revision: D46853794 fbshipit-source-id: 71178021e60a78c993474ae3bdfeb208aa1e4a88
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Codecov Report
@@ Coverage Diff @@
## master #439 +/- ##
==========================================
+ Coverage 86.95% 87.02% +0.07%
==========================================
Files 106 106
Lines 8407 8455 +48
==========================================
+ Hits 7310 7358 +48
Misses 1097 1097
... and 26 files with indirect coverage changes 📣 We’re building smart automated test selection to slash your CI/CD build times. Learn more |
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This pull request was exported from Phabricator. Differential Revision: D46853794 |
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Aug 23, 2023
Summary: Pull Request resolved: pytorch#439 Add two counters, containing the iteration and time blocked on data for the last iteration. These are stored and recorded using a timer that does not synchronise with CUDA, and that only records these two values. - iteration time is recorded in the training loop for all jobs. - data time is recorded if the training loop does the data fetching, otherwise the user needs to instrument the logic which reads the data from the iterable. Had to rework the approach slightly since the state does not seem the right place. With the changes, it is more natural to make it similar to progress, and store the info in a Stateful. This has the additional advantage that all values should be storable and be restored with the checkpoints. Also rather than the last value, the last LOWER_BOUND values are stored. I hardcoded this in the torchtnt state, to not bloat parameters, since intuitively the last 1e4 values for each timer action here should be enough for monitoring purposes. Reviewed By: daniellepintz, ananthsub Differential Revision: D46853794 fbshipit-source-id: e0c74d7147a8da57f51ef8c9bd7bbb6ec38858c9
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Summary: Pull Request resolved: pytorch#439 Add two counters, containing the iteration and time blocked on data for the last iteration. These are stored and recorded using a timer that does not synchronise with CUDA, and that only records these two values. - iteration time is recorded in the training loop for all jobs. - data time is recorded if the training loop does the data fetching, otherwise the user needs to instrument the logic which reads the data from the iterable. Had to rework the approach slightly since the state does not seem the right place. With the changes, it is more natural to make it similar to progress, and store the info in a Stateful. This has the additional advantage that all values should be storable and be restored with the checkpoints. Also rather than the last value, the last LOWER_BOUND values are stored. I hardcoded this in the torchtnt state, to not bloat parameters, since intuitively the last 1e4 values for each timer action here should be enough for monitoring purposes. Reviewed By: daniellepintz, ananthsub Differential Revision: D46853794 fbshipit-source-id: dafc454fb790d91debfd3ff452c49c0ff6f84135
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Summary: Pull Request resolved: pytorch#439 Add two counters, containing the iteration and time blocked on data for the last iteration. These are stored and recorded using a timer that does not synchronise with CUDA, and that only records these two values. - iteration time is recorded in the training loop for all jobs. - data time is recorded if the training loop does the data fetching, otherwise the user needs to instrument the logic which reads the data from the iterable. Had to rework the approach slightly since the state does not seem the right place. With the changes, it is more natural to make it similar to progress, and store the info in a Stateful. This has the additional advantage that all values should be storable and be restored with the checkpoints. Also rather than the last value, the last LOWER_BOUND values are stored. I hardcoded this in the torchtnt state, to not bloat parameters, since intuitively the last 1e4 values for each timer action here should be enough for monitoring purposes. Reviewed By: daniellepintz, ananthsub Differential Revision: D46853794 fbshipit-source-id: ac2e9f992f21ac66625f89d1c75d228397740e1f
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Summary:
Add two counters, containing the iteration and time blocked on data for the last iteration. These are stored on the state, not sure if the unit would be a better place.
They can be accessed at the start of the next iteration. This is done because we want to capture the time of the whole iteration, including callbacks, so it cannot be accessed by callbacks.
Another possibility would be to keep a list of all the times and data times, not sure if that would be preferrable.
Differential Revision: D46853794