Comments (1)
As you mentioned, if the same task is executed, it'd be nice to just reuse the previous computation. For example, da.zeros([10 * da.BLOCK_SIZE])
will call ra.zeros([da.BLOCK_SIZE])
10 times (with exactly the same arguments), and since objects are immutable, we could get away with executing it just once.
However, there are situations where this may not make sense. In particular, if the function involves randomness. If we call da.random.normal([10 * da.BLOCK_SIZE])
, we probably want to execute ra.random.normal([da.BLOCK_SIZE])
10 times.
This thread is also relevant for thinking about how we want to do fault tolerance #19.
from ray-legacy.
Related Issues (20)
- Exception at end of L-BFGS example application HOT 3
- Better error messages when composing remote functions HOT 1
- Equality testing for objects in the object store HOT 1
- Unicode Strings are not serializable by Ray HOT 1
- Cluster install using Anaconda HOT 1
- Let scheduler GRPC methods return before calling schedule
- Installation on CentOS
- Placing large array in object store causes segfault
- Fail to put dictionary of tuples HOT 2
- Attempting to return a non-serializable object in a remote function kills the worker HOT 1
- Don't sleep(0.1) in ray.select
- Fix the order in which remote functions, reusable variables, and functions to run get exported to workers.
- Handle recursive objects or throw an exception. HOT 1
- Error visualizing computation graph HOT 1
- Error when starting too many workers. HOT 9
- Putting and getting a float does not preserve the exact value HOT 3
- Set distribution of Processes over the remote nodes
- Memory on remote node increases HOT 5
- How to catch an error in a remote call? HOT 1
- ValueError: all the input arrays must have same number of dimensions, but the array at index 0 has 2 dimension(s) and the array at index 1 has 1 dimension(s) HOT 1
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from ray-legacy.