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