Comments (4)
umut-sahin
commented on June 5, 2024
Hello, np.sum will be available in the next release of concrete-numpy (v0.6.0, which will be released in a month). As for np.array(), we cannot support it directly because of technical limitations, but we might be able to provide hnp.array(...) to create encrypted arrays. Though, I cannot think of a situation in which you would want to do that.
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umut-sahin
commented on June 5, 2024
With the release of v0.6.0, np.sum is now supported.
import concrete.numpy as cnp
import numpy as np
@cnp.compiler({"x": "encrypted"})
def function(x):
return np.sum(x)
inputset = [np.random.randint(0, 10, size=(2, 2)) for _ in range(10)]
circuit = function.compile(inputset)
sample = np.array([[1, 2], [3, 4]])
assert circuit.encrypt_run_decrypt(sample) == np.sum(sample)The creation of encrypted arrays will come in v0.8.0 in a few months.
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umut-sahin
commented on June 5, 2024
Closing due to inactivity. Please create another issue if your problem is not resolved.
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zxcCEn
commented on June 5, 2024
Thank you for your help. I will give you feedback after the experiment.
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主题: Re: [zama-ai/concrete-numpy] Is it possible to implement.sum and.array in numpy? Is that possible (Issue #26)
With the release of v0.6.0, np.sum is now supported.
import concrete.numpy as cnp import numpy as np @cnp.compiler({"x": "encrypted"}) def function(x): return np.sum(x) inputset = [np.random.randint(0, 10, size=(2, 2)) for _ in range(10)] circuit = function.compile(inputset) sample = np.array([[1, 2], [3, 4]]) assert circuit.encrypt_run_decrypt(sample) == np.sum(sample)
The creation of encrypted arrays will come in v0.7.0 in a few months.
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Related Issues (14)
- Use multiprocessing to avoid crashes
- Ipykernel dies when .compile() method runs/completes HOT 2
- Fix broken link to the table lookups tutorial in the README file HOT 1
- [Question]: Disabling concrete-csprng warning HOT 2
- Not installing with pip in googlecloab HOT 1
- AttributeError: 'FHECircuit' object has no attribute 'encrypt_run_decrypt' HOT 2
- Unable to install through PIP HOT 5
- Inputset for table lookups HOT 4
- Float usage HOT 9
- Could not find a version that satisfies the requirement concrete-compiler HOT 14
- Docker M1 Illegal instruction HOT 1
- Allow large models to be compiled, avoiding `RuntimeError: Can't emit artifacts` HOT 1
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