Progressbar
This document introduces the progressbar feature that provides visual feedback on the execution progress of large circuits, which can take considerable time to execute.
The following Python code demonstrates how to enable and use the progressbar:
import time
import matplotlib.pyplot as plt
import numpy as np
import randimage
from concrete import fhe
configuration = fhe.Configuration(
enable_unsafe_features=True,
use_insecure_key_cache=True,
insecure_key_cache_location=".keys",
# To enable displaying progressbar
show_progress=True,
# To enable showing tags in the progressbar (does not work in notebooks)
progress_tag=True,
# To give a title to the progressbar
progress_title="Evaluation:",
)
@fhe.compiler({"image": "encrypted"})
def to_grayscale(image):
with fhe.tag("scaling.r"):
r = image[:, :, 0]
r = (r * 0.30).astype(np.int64)
with fhe.tag("scaling.g"):
g = image[:, :, 1]
g = (g * 0.59).astype(np.int64)
with fhe.tag("scaling.b"):
b = image[:, :, 2]
b = (b * 0.11).astype(np.int64)
with fhe.tag("combining.rgb"):
gray = r + g + b
with fhe.tag("creating.result"):
gray = np.expand_dims(gray, axis=2)
result = np.concatenate((gray, gray, gray), axis=2)
return result
image_size = (16, 16)
image_data = (randimage.get_random_image(image_size) * 255).round().astype(np.int64)
print()
print(f"Compilation started @ {time.strftime('%H:%M:%S', time.localtime())}")
start = time.time()
inputset = [np.random.randint(0, 256, size=image_data.shape) for _ in range(100)]
circuit = to_grayscale.compile(inputset, configuration)
end = time.time()
print(f"(took {end - start:.3f} seconds)")
print()
print(f"Key generation started @ {time.strftime('%H:%M:%S', time.localtime())}")
start = time.time()
circuit.keygen()
end = time.time()
print(f"(took {end - start:.3f} seconds)")
print()
print(f"Evaluation started @ {time.strftime('%H:%M:%S', time.localtime())}")
start = time.time()
grayscale_image_data = circuit.encrypt_run_decrypt(image_data)
end = time.time()
print(f"(took {end - start:.3f} seconds)")
fig, axs = plt.subplots(1, 2)
axs = axs.flatten()
axs[0].set_title("Original")
axs[0].imshow(image_data)
axs[0].axis("off")
axs[1].set_title("Grayscale")
axs[1].imshow(grayscale_image_data)
axs[1].axis("off")
plt.show()
When you run this code, you will see a progressbar like this one:
Evaluation: 10% |█████.............................................| 10% (scaling.r)
^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^
Title Progressbar Tag
As the execution proceeds, the progress bar updates:
Evaluation: 30% |███████████████...................................| 30% (scaling.g)
Evaluation: 50% |█████████████████████████.........................| 50% (scaling.b)
Once the progressbar fills and execution completes, you will see the following figure:

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