Add AOTAutogradCache to caching tutorials #3177
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/tutorials/3177
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit e78f450 with merge base 25f1156 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
oulgen
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Dec 6, 2024
masnesral
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Dec 6, 2024
| @@ -23,7 +23,7 @@ Before starting this recipe, make sure that you have the following: | |||
| Inductor Cache Settings | |||
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| Most of these caches are in-memory, only used within the same process, and are transparent to the user. An exception is the FX graph cache that stores compiled FX graphs. This cache allows Inductor to avoid recompilation across process boundaries when it encounters the same graph with the same Tensor input shapes (and the same configuration). The default implementation stores compiled artifacts in the system temp directory. An optional feature also supports sharing those artifacts within a cluster by storing them in a Redis database. | |||
| Most of these caches are in-memory, only used within the same process, and are transparent to the user. An exception are caches tha stores compiled FX graphs (FXGraphCache, AOTAutogradCache). These caches allow Inductor to avoid recompilation across process boundaries when it encounters the same graph with the same Tensor input shapes (and the same configuration). The default implementation stores compiled artifacts in the system temp directory. An optional feature also supports sharing those artifacts within a cluster by storing them in a Redis database. | |||
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"An exception are" -> "An exception is" or "Exceptions are"
"tha" -> "that"
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Add descriptions of new configuration settings exposed by AOTAutogradCache (pytorch/pytorch#141981).
cc @williamwen42 @msaroufim @anijain2305