Mixing
Dask how to avoid recomputing things
Using dask I have defined a long pipeline of computations; at some point given constraints in apis and version I need to compute some small result (not lazy) and feed it in the lazy operations. My problem is that at this point the whole computation graph will be executed so that I can produce an intermediate results. Is there a way to not loose the work done at this point and have to recompute everything from scratch when in a following step I am storing the final results to disk?
Is using persist supposed to help with that?
Any help will be very appreciated.
python dask dask-distributed
asked Nov 20 '18 at 11:30
LetsPlayYahtzeeLetsPlayYahtzee
2,01132037
add a comment |
Using dask I have defined a long pipeline of computations; at some point given constraints in apis and version I need to compute some small result (not lazy) and feed it in the lazy operations. My problem is that at this point the whole computation graph will be executed so that I can produce an intermediate results. Is there a way to not loose the work done at this point and have to recompute everything from scratch when in a following step I am storing the final results to disk?
Is using persist supposed to help with that?
Any help will be very appreciated.
python dask dask-distributed
asked Nov 20 '18 at 11:30
LetsPlayYahtzeeLetsPlayYahtzee
2,01132037
add a comment |
Using dask I have defined a long pipeline of computations; at some point given constraints in apis and version I need to compute some small result (not lazy) and feed it in the lazy operations. My problem is that at this point the whole computation graph will be executed so that I can produce an intermediate results. Is there a way to not loose the work done at this point and have to recompute everything from scratch when in a following step I am storing the final results to disk?
Is using persist supposed to help with that?
Any help will be very appreciated.
python dask dask-distributed
asked Nov 20 '18 at 11:30
LetsPlayYahtzeeLetsPlayYahtzee
2,01132037
Using dask I have defined a long pipeline of computations; at some point given constraints in apis and version I need to compute some small result (not lazy) and feed it in the lazy operations. My problem is that at this point the whole computation graph will be executed so that I can produce an intermediate results. Is there a way to not loose the work done at this point and have to recompute everything from scratch when in a following step I am storing the final results to disk?
Is using persist supposed to help with that?
Any help will be very appreciated.
python dask dask-distributed
python dask dask-distributed
asked Nov 20 '18 at 11:30
LetsPlayYahtzeeLetsPlayYahtzee
2,01132037
asked Nov 20 '18 at 11:30
LetsPlayYahtzeeLetsPlayYahtzee
2,01132037
asked Nov 20 '18 at 11:30
LetsPlayYahtzeeLetsPlayYahtzee
2,01132037
asked Nov 20 '18 at 11:30
LetsPlayYahtzeeLetsPlayYahtzee
2,01132037
asked Nov 20 '18 at 11:30
LetsPlayYahtzeeLetsPlayYahtzee
2,01132037
2,01132037
add a comment |
add a comment |
1 Answer
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oldest
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Yes, this is the usecase that persist is for. The trick is figuring out where to apply it - this decision is usually influenced by:
- The size of your intermediate results. These will be kept in memory until all references to them are deleted (e.g.
fooinfoo = intermediate.persist()). - The shape of your graph. It's better to persist only components that would need to be recomputed, to minimize the memory impact of the persisted values. You can use
.visualize()to look at the graph. - The time it takes to compute the tasks. If the tasks are quick to compute, then it may be more beneficial just to recompute them rather than keep them around in memory.
answered Dec 10 '18 at 18:12
jiminy_cristjiminy_crist
1,2681020
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Yes, this is the usecase that persist is for. The trick is figuring out where to apply it - this decision is usually influenced by:
- The size of your intermediate results. These will be kept in memory until all references to them are deleted (e.g.
fooinfoo = intermediate.persist()). - The shape of your graph. It's better to persist only components that would need to be recomputed, to minimize the memory impact of the persisted values. You can use
.visualize()to look at the graph. - The time it takes to compute the tasks. If the tasks are quick to compute, then it may be more beneficial just to recompute them rather than keep them around in memory.
answered Dec 10 '18 at 18:12
jiminy_cristjiminy_crist
1,2681020
add a comment |
Yes, this is the usecase that persist is for. The trick is figuring out where to apply it - this decision is usually influenced by:
- The size of your intermediate results. These will be kept in memory until all references to them are deleted (e.g.
fooinfoo = intermediate.persist()). - The shape of your graph. It's better to persist only components that would need to be recomputed, to minimize the memory impact of the persisted values. You can use
.visualize()to look at the graph. - The time it takes to compute the tasks. If the tasks are quick to compute, then it may be more beneficial just to recompute them rather than keep them around in memory.
answered Dec 10 '18 at 18:12
jiminy_cristjiminy_crist
1,2681020
add a comment |
Yes, this is the usecase that persist is for. The trick is figuring out where to apply it - this decision is usually influenced by:
- The size of your intermediate results. These will be kept in memory until all references to them are deleted (e.g.
fooinfoo = intermediate.persist()). - The shape of your graph. It's better to persist only components that would need to be recomputed, to minimize the memory impact of the persisted values. You can use
.visualize()to look at the graph. - The time it takes to compute the tasks. If the tasks are quick to compute, then it may be more beneficial just to recompute them rather than keep them around in memory.
answered Dec 10 '18 at 18:12
jiminy_cristjiminy_crist
1,2681020
Yes, this is the usecase that persist is for. The trick is figuring out where to apply it - this decision is usually influenced by:
- The size of your intermediate results. These will be kept in memory until all references to them are deleted (e.g.
fooinfoo = intermediate.persist()). - The shape of your graph. It's better to persist only components that would need to be recomputed, to minimize the memory impact of the persisted values. You can use
.visualize()to look at the graph. - The time it takes to compute the tasks. If the tasks are quick to compute, then it may be more beneficial just to recompute them rather than keep them around in memory.
answered Dec 10 '18 at 18:12
jiminy_cristjiminy_crist
1,2681020
answered Dec 10 '18 at 18:12
jiminy_cristjiminy_crist
1,2681020
answered Dec 10 '18 at 18:12
jiminy_cristjiminy_crist
1,2681020
answered Dec 10 '18 at 18:12
jiminy_cristjiminy_crist
1,2681020
1,2681020
add a comment |
add a comment |
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