OOM issue faced during Searchspace: Required 338 TB Memory #365
Comments
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Could you share some details on what exactly the |
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Also, I tried to execute your example and found several errors (duplicate names in lists and so on). Please double-check that your code can directly be executed by just copy-pasting it. |
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@khoonun Independent of the issues already raise by the others: We have a more memory-efficient implementation of the search space creation including the constraint application. It is automatically enabled if you install baybe with But I doubt that this will work for the huge space you are trying to build. There exists no solution to this problem, if your space is too large, it is too large. So the only options you have imo are
I should also mention that the memory number estimated by the utility is the |
Hey, so sorry! I was trying to edit the parameter names and there was some mistakes as correctly pointed out by all. I have now corrected the mistakes. :) |
Hey, so sorry! I was trying to edit the parameter names and there was some mistakes as correctly pointed out by all. I have now corrected the mistakes. :) |
**Hi @AVHopp , Enclosed is one example. Supposedly, the Equiv represents the concentration of the respective ingredients. Hope this explains? 
3 levels of constraints above:
Hope this explains as well. I have been thinking of turning to Equiv as a continuous parameter, but with the use of Sustance parameter so that we can leverage of the molecular descriptors to select the compatible chemicals. But, I am not sure whether we can implement here in Baybe yet.** |
@Scienfitz, all along, I have implemented the baybe [chem, polars]. So, it seems like it does not help too. It is really helpful to know that the real memory requirements could be much less. In this case, I would need to find ways to further investigate this. For sure, the use of chemical encoding is very useful and we wish to retain that. Given this case, is there a non-traditional representation approach we can adopt here? |
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@khoonun in your example, you do not actually construct the search space object. The potential effect of the constraints can only happen if you attempt to construct the search space (notice how the estimation does only take the parameters into account!), did you already attempt that? |
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A non-traditional approach will only work if you're willing to reduce the space. Example for Traditional Mixture With Sub-Groups and Constraints # List of substance labels, divided into subgroups
g1 = ["A", "B"]
g2 = ["mol1", "mol2"]
g3 = ["substance1", "substance2"]
# Make continuous concentration parameters for each group
p_g1_concentrations = [
NumericalContinuousParameter(name=f"{name}", bounds=(0, 20)) for name in g1
]
p_g2_concentrations = [
NumericalContinuousParameter(name=f"{name}", bounds=(0, 40)) for name in g2
]
p_g3_concentrations = [
NumericalContinuousParameter(name=f"{name}", bounds=(0, 60)) for name in g3
]
# Ensure total sum is 100
c_total_sum = ContinuousLinearEqualityConstraint(
parameters=g1 + g2 + g3,
coefficients=[1.0] * len(g1 + g2 + g3),
rhs=100.0,
)
# Ensure sum of group 1 is smaller than 40
c_g1_max = ContinuousLinearInequalityConstraint(
parameters=g1,
coefficients=[-1.0] * len(g1),
rhs=-40,
)
# Ensure sum of group 2 is larger than 60
c_g2_min = ContinuousLinearInequalityConstraint(
parameters=g2,
coefficients=[1.0] * len(g2),
rhs=60,
)
searchspace = SearchSpace(
continuous=SubspaceContinuous(
parameters=p_g1_concentrations + p_g2_concentrations + p_g3_concentrations,
constraints_lin_eq=[c_total_sum],
constraints_lin_ineq=[c_g1_max, c_g2_min],
),
)
campaign = Campaign(
searchspace=searchspace,
objective=NumericalTarget(name="MyTarget", mode="MAX").to_objective(),
)
rec = campaign.recommend(10)
print(rec)
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Hi @AVHopp , I have constructed the search space object with the below: I was using the space size estimation on hindsight to roughly understand the constraint I was facing. |
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@khoonun, without having followed the entire conversation so far, I'd like to make one important point that might help you: In particular, the essence of the problem is that these constructors cannot easily account for specific constraints you might have in your use case in a computationally efficient way (that's basically also what @Scienfitz mentioned). For example, So two things you can try:
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This is very helpful, @AdrianSosic ! |
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Since there has been no updates here, I assume that the issue was solved. If not, feel free to reopen. |
**Hi,
Ideally, i should use at least a resolution of 4.
However, even with resolution of 2, I am running into the OOM issue with the below code.
Based on the res1.exp_rep_human_readable, I am supposed to have roughly 338 TB of memory space for the search space generation.
Basically, I have 5 types of chemicals to select, and within each type of chemical, i have a variety of chemical ingredients to choose from. For instance, X10, I will use 1 of the 3 possible candidates, while X20, I will use 2 of the 3 possible candidates and etc.
I understand that Bayesian optimization suffers from the combinatorial explosion of the search space.
And I have already reduced the search space significantly.
Will appreciate if there is any advice on ways to tackle this issue.
So far, I can have at most 128 GiB of memory to handle this operation.**
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