Translations:AI and Machine Learning/16/en: Difference between revisions

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If your computations are long, you should use checkpointing. For example, if your training time is 3 days, you could split it in 3 chunks of 24 hours. This would prevent you from losing all the work in case of an outage, and would give you an edge in terms of priority (more nodes are available for short jobs). Most machine learning libraries natively support checkpointing. Please see our suggestions about [[Running jobs#Resubmitting_jobs_for_long_running_computations|resubmitting jobs for long running computations]]. If your program does not natively support this, we provide a [[Points de contrôle|general checkpointing solution]].
If your computations are long, you should use checkpointing. For example, if your training time is 3 days, you could split it in 3 chunks of 24 hours. This would prevent you from losing all the work in case of an outage, and would give you an edge in terms of priority (more nodes are available for short jobs). Most machine learning libraries natively support checkpointing. Please see our suggestions about [[Running jobs#Resubmitting_jobs_for_long_running_computations|resubmitting jobs for long running computations]]. If your program does not natively support this, we provide a [[Points de contrôle/en|general checkpointing solution]].

Revision as of 15:04, 26 July 2019

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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]].

If your computations are long, you should use checkpointing. For example, if your training time is 3 days, you could split it in 3 chunks of 24 hours. This would prevent you from losing all the work in case of an outage, and would give you an edge in terms of priority (more nodes are available for short jobs). Most machine learning libraries natively support checkpointing. Please see our suggestions about resubmitting jobs for long running computations. If your program does not natively support this, we provide a general checkpointing solution.

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