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AI and Machine Learning: Difference between revisions
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Rephrase, add link to tutorial, fix typo
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(Rephrase, add link to tutorial, fix typo) |
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* Filesystem [[Storage and file management#Filesystem_quotas_and_policies|quotas]] on Compute Canada clusters limit the number of filesystem objects; | * Filesystem [[Storage and file management#Filesystem_quotas_and_policies|quotas]] on Compute Canada clusters limit the number of filesystem objects; | ||
* Your software could | * Your software could be significantly slowed down from streaming lots of small files from <tt>/project</tt> (or <tt>/scratch</tt>) to a compute node. | ||
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If your computations are long, you should use checkpointing. For example, if your training time is 3 days, you | If your computations are long, you should use checkpointing. For example, if your training time is 3 days, you should split it in 3 chunks of 24 hours. This will prevent you from losing all the work in case of an outage, and give you an edge in terms of priority (more nodes are available for short jobs). Most machine learning libraries natively support checkpointing; the typical case is covered in our [[Tutoriel_Apprentissage_machine/en#Checkpointing_a_long-running_job|tutorial]]. If your program does not natively support this, we provide a [[Points de contrôle/en|general checkpointing solution]]. | ||
== Running many similar jobs == <!--T:17--> | == Running many similar jobs == <!--T:17--> | ||