# /// script
# requires-python = ">=3.12"
# dependencies = ["marimo==0.25.2"]
# ///
# Generated by scripts/learn-notebooks/build.py; standalone and editable.
import marimo
__generated_with = "0.25.2"
app = marimo.App(width="medium")
@app.cell
def _():
import marimo as mo
import math
import struct
from fractions import Fraction
import json
import inspect
import hashlib
from datetime import datetime, timezone
from copy import deepcopy
return Fraction, datetime, deepcopy, hashlib, inspect, json, math, mo, struct, timezone
@app.cell
def _():
protocol = {'version': 2,
'title': 'Learning rate on synthetic linear regression',
'reference': {'rate': 0.25, 'start': -2, 'steps': 20},
'controls': [{'key': 'rate',
'label': 'Learning rate',
'options': [{'value': 0.01, 'label': '0.01'},
{'value': 0.05, 'label': '0.05'},
{'value': 0.1, 'label': '0.1'},
{'value': 0.25, 'label': '0.25'},
{'value': 0.5, 'label': '0.5'},
{'value': 1, 'label': '1'},
{'value': 1.25, 'label': '1.25'},
{'value': 1.5, 'label': '1.5'},
{'value': 2, 'label': '2'}]},
{'key': 'start',
'label': 'Starting slope',
'options': [{'value': -4, 'label': '-4'},
{'value': -2, 'label': '-2'},
{'value': 0, 'label': '0'},
{'value': 3, 'label': '3'},
{'value': 4, 'label': '4'}]},
{'key': 'steps',
'label': 'Number of updates',
'options': [{'value': 5, 'label': '5'},
{'value': 10, 'label': '10'},
{'value': 20, 'label': '20'},
{'value': 40, 'label': '40'}]}],
'fixed': '300 synthetic observations: y = 2x + 1 + uniform noise in [−0.6, 0.6), seed 42. Fixed '
'split: 240 training / 60 validation. Model ŷ = wx + b; initial b = 0. Full-batch '
'gradient descent uses training rows only. Loss = ½ mean squared error. Every run starts '
'afresh; no preprocessing or resampling.',
'sources': [{'title': 'How neural networks learn',
'url': 'https://indepth.dev/posts/1001/en/how-neural-networks-learn-backpropagation-gradient-descent'},
{'title': 'The effect of hyperparameters on training',
'url': 'https://indepth.dev/posts/1012/en/effect-of-hyperparameters-on-training'}],
'id': 'learning-rate',
'guided': 'Inspect the 300 records and fixed 240/60 train/validation split. Keep starting slope '
'−2, intercept 0, and 20 updates fixed. Predict, then test rates 0.01, 1, 1.5, and 2 '
'against the 0.25 reference. Compare both losses and intermediate slopes/intercepts.',
'extension': 'Choose two starting slopes at a fixed rate and budget. Then compare 10 versus 40 '
'updates at rate 0.05 as a separate investigation. For a code extension, scale x in '
'every training and validation record inside calculate. Keep the transformed data '
'fixed within each rate comparison, record the scale, and predict whether the same '
'rate remains stable.',
'reasoning': 'Training gradients average error*x and error across only the 240 training rows. At '
'rate 0.25 after 20 updates, training loss is about 0.06156 and validation loss '
'about 0.06321. Rate 0.01 is slow, 1 is faster here, 1.5 oscillates while '
'converging, and 2 diverges. Noisy targets leave a nonzero best training loss. '
'Validation does not enter updates; using it to select rates makes it unsuitable as '
'an unbiased final test. Stability depends on the training inputs and loss scale, '
'not just the generating slope 2.',
'referenceDataset': {'id': 'learning-rate',
'version': 2,
'settings': {'rate': 0.25, 'start': -2, 'steps': 20},
'title': 'Synthetic linear regression',
'unit': 'observation',
'description': '300 observations generated from y = 2x + 1 + noise, with x '
'sampled uniformly from [−2, 2) and noise uniformly from '
'[−0.6, 0.6). A fixed shuffled split assigns 240 rows to '
'training and 60 to validation.',
'columns': [{'key': 'id',
'label': 'Row',
'type': 'category',
'role': 'identifier',
'description': 'Stable identifier within this synthetic '
'fixture.'},
{'key': 'x',
'label': 'Input x',
'type': 'number',
'role': 'feature',
'description': 'Raw input, rounded to six decimals. No fitted '
'normalization.'},
{'key': 'target',
'label': 'Target y',
'type': 'number',
'role': 'target',
'description': '2x + 1 plus sampled noise, rounded to six '
'decimals.'},
{'key': 'split',
'label': 'Split',
'type': 'category',
'role': 'condition',
'description': 'Training rows determine gradients; validation '
'rows are used only to evaluate the fitted '
'line.'}],
'rows': [{'id': '001', 'x': -0.990619, 'target': -1.475488, 'split': 'train'},
{'id': '002', 'x': 0.309125, 'target': 1.285315, 'split': 'train'},
{'id': '003', 'x': -0.497359, 'target': -0.563921, 'split': 'train'},
{'id': '004', 'x': -0.210875, 'target': 0.120402, 'split': 'train'},
{'id': '005', 'x': 1.495255, 'target': 4.584071, 'split': 'train'},
{'id': '006', 'x': 1.412811, 'target': 3.825234, 'split': 'train'},
{'id': '007', 'x': 0.568004, 'target': 2.569755, 'split': 'train'},
{'id': '008', 'x': 0.385879, 'target': 1.280658, 'split': 'train'},
{'id': '009',
'x': -1.439161,
'target': -1.338216,
'split': 'validation'},
{'id': '010', 'x': 1.698222, 'target': 4.863807, 'split': 'train'},
{'id': '011', 'x': 0.202033, 'target': 1.020686, 'split': 'train'},
{'id': '012', 'x': 0.200342, 'target': 1.111444, 'split': 'train'},
{'id': '013', 'x': 1.772487, 'target': 4.930775, 'split': 'train'},
{'id': '014', 'x': -1.378824, 'target': -1.362374, 'split': 'train'},
{'id': '015', 'x': -0.132311, 'target': 0.208224, 'split': 'train'},
{'id': '016', 'x': -1.910179, 'target': -2.775611, 'split': 'train'},
{'id': '017',
'x': 1.319841,
'target': 4.054081,
'split': 'validation'},
{'id': '018', 'x': 0.723672, 'target': 2.304254, 'split': 'train'},
{'id': '019', 'x': 0.342627, 'target': 1.920931, 'split': 'train'},
{'id': '020', 'x': 0.845462, 'target': 2.953266, 'split': 'train'},
{'id': '021', 'x': 1.96115, 'target': 4.862003, 'split': 'train'},
{'id': '022', 'x': -1.608294, 'target': -2.586675, 'split': 'train'},
{'id': '023', 'x': -0.210486, 'target': 0.131429, 'split': 'train'},
{'id': '024', 'x': 1.07506, 'target': 3.262585, 'split': 'train'},
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{'id': '026', 'x': -1.136924, 'target': -1.547377, 'split': 'train'},
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'split': 'validation'},
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{'id': '201', 'x': 1.197283, 'target': 3.367601, 'split': 'train'},
{'id': '202', 'x': 0.586884, 'target': 2.423291, 'split': 'train'},
{'id': '203', 'x': -1.517657, 'target': -2.57674, 'split': 'train'},
{'id': '204', 'x': 0.224554, 'target': 1.72409, 'split': 'train'},
{'id': '205', 'x': 1.42356, 'target': 3.648491, 'split': 'train'},
{'id': '206', 'x': -0.467018, 'target': 0.657682, 'split': 'train'},
{'id': '207', 'x': -0.858674, 'target': -0.368607, 'split': 'train'},
{'id': '208', 'x': -1.150238, 'target': -0.788766, 'split': 'train'},
{'id': '209', 'x': -0.796317, 'target': -0.810531, 'split': 'train'},
{'id': '210',
'x': -0.850777,
'target': -0.251823,
'split': 'validation'},
{'id': '211', 'x': 1.146092, 'target': 3.048792, 'split': 'train'},
{'id': '212', 'x': 0.634467, 'target': 2.113738, 'split': 'train'},
{'id': '213', 'x': -1.723631, 'target': -2.994396, 'split': 'train'},
{'id': '214', 'x': 1.52082, 'target': 4.069796, 'split': 'train'},
{'id': '215', 'x': -0.172726, 'target': 1.139092, 'split': 'train'},
{'id': '216', 'x': -0.426817, 'target': 0.577751, 'split': 'train'},
{'id': '217',
'x': -1.48075,
'target': -2.055935,
'split': 'validation'},
{'id': '218', 'x': -0.040207, 'target': 0.54822, 'split': 'train'},
{'id': '219',
'x': 1.59583,
'target': 3.952332,
'split': 'validation'},
{'id': '220', 'x': 1.984887, 'target': 4.776255, 'split': 'train'},
{'id': '221',
'x': 0.394187,
'target': 1.311471,
'split': 'validation'},
{'id': '222', 'x': -1.737221, 'target': -2.119119, 'split': 'train'},
{'id': '223', 'x': -1.486648, 'target': -1.510203, 'split': 'train'},
{'id': '224', 'x': 0.895833, 'target': 2.967658, 'split': 'train'},
{'id': '225',
'x': -0.35742,
'target': -0.02377,
'split': 'validation'},
{'id': '226',
'x': -1.781151,
'target': -2.766943,
'split': 'validation'},
{'id': '227', 'x': 1.112379, 'target': 3.143858, 'split': 'train'},
{'id': '228', 'x': -1.32764, 'target': -2.170616, 'split': 'train'},
{'id': '229', 'x': 0.230686, 'target': 1.545892, 'split': 'train'},
{'id': '230', 'x': -0.632682, 'target': -0.791028, 'split': 'train'},
{'id': '231', 'x': 1.466711, 'target': 3.412823, 'split': 'train'},
{'id': '232', 'x': 1.903709, 'target': 5.066469, 'split': 'train'},
{'id': '233', 'x': 1.544926, 'target': 4.419481, 'split': 'train'},
{'id': '234', 'x': -0.994444, 'target': -0.902124, 'split': 'train'},
{'id': '235',
'x': -1.488281,
'target': -1.909227,
'split': 'validation'},
{'id': '236', 'x': 0.534836, 'target': 2.295161, 'split': 'train'},
{'id': '237', 'x': -0.162801, 'target': 0.314845, 'split': 'train'},
{'id': '238', 'x': 0.446803, 'target': 1.995153, 'split': 'train'},
{'id': '239', 'x': 1.614659, 'target': 4.73241, 'split': 'train'},
{'id': '240',
'x': -0.750782,
'target': -0.9985,
'split': 'validation'},
{'id': '241', 'x': -0.079804, 'target': 1.271001, 'split': 'train'},
{'id': '242', 'x': 1.527982, 'target': 4.198407, 'split': 'train'},
{'id': '243', 'x': 0.394985, 'target': 1.5929, 'split': 'train'},
{'id': '244', 'x': 1.101674, 'target': 3.574926, 'split': 'train'},
{'id': '245',
'x': 1.112711,
'target': 3.525476,
'split': 'validation'},
{'id': '246', 'x': -0.632493, 'target': -0.321445, 'split': 'train'},
{'id': '247', 'x': 0.794659, 'target': 2.442714, 'split': 'train'},
{'id': '248', 'x': -0.598829, 'target': -0.583138, 'split': 'train'},
{'id': '249', 'x': 1.434955, 'target': 3.86665, 'split': 'train'},
{'id': '250', 'x': -0.626899, 'target': -0.374074, 'split': 'train'},
{'id': '251', 'x': -0.601532, 'target': -0.142518, 'split': 'train'},
{'id': '252', 'x': 1.843639, 'target': 4.524313, 'split': 'train'},
{'id': '253',
'x': 0.053867,
'target': 0.861106,
'split': 'validation'},
{'id': '254', 'x': 0.499606, 'target': 1.871351, 'split': 'train'},
{'id': '255',
'x': 0.004331,
'target': 0.582819,
'split': 'validation'},
{'id': '256', 'x': -1.105216, 'target': -1.05154, 'split': 'train'},
{'id': '257', 'x': 1.978425, 'target': 4.555959, 'split': 'train'},
{'id': '258',
'x': 0.756795,
'target': 2.982258,
'split': 'validation'},
{'id': '259', 'x': 0.443616, 'target': 1.774186, 'split': 'train'},
{'id': '260',
'x': 0.422899,
'target': 1.728249,
'split': 'validation'},
{'id': '261', 'x': 1.004964, 'target': 3.056222, 'split': 'train'},
{'id': '262', 'x': -1.032814, 'target': -0.966233, 'split': 'train'},
{'id': '263', 'x': 1.651451, 'target': 3.711726, 'split': 'train'},
{'id': '264', 'x': 0.58168, 'target': 1.698995, 'split': 'train'},
{'id': '265', 'x': 0.912482, 'target': 2.377127, 'split': 'train'},
{'id': '266', 'x': 0.773852, 'target': 3.054777, 'split': 'train'},
{'id': '267', 'x': -1.012966, 'target': -0.609896, 'split': 'train'},
{'id': '268', 'x': -1.056967, 'target': -0.624035, 'split': 'train'},
{'id': '269',
'x': -1.092042,
'target': -1.628512,
'split': 'validation'},
{'id': '270', 'x': 0.898238, 'target': 2.749073, 'split': 'train'},
{'id': '271', 'x': -0.19605, 'target': 0.183359, 'split': 'train'},
{'id': '272', 'x': 1.879891, 'target': 4.312975, 'split': 'train'},
{'id': '273', 'x': -0.678339, 'target': -0.067969, 'split': 'train'},
{'id': '274',
'x': 1.647966,
'target': 4.891402,
'split': 'validation'},
{'id': '275', 'x': -0.72952, 'target': -0.015259, 'split': 'train'},
{'id': '276', 'x': 0.199023, 'target': 1.535983, 'split': 'train'},
{'id': '277', 'x': 1.552463, 'target': 4.668743, 'split': 'train'},
{'id': '278', 'x': -0.325343, 'target': 0.398584, 'split': 'train'},
{'id': '279', 'x': -1.560083, 'target': -2.178436, 'split': 'train'},
{'id': '280', 'x': -0.976149, 'target': -0.548309, 'split': 'train'},
{'id': '281', 'x': -1.736849, 'target': -2.138787, 'split': 'train'},
{'id': '282',
'x': 0.981737,
'target': 2.475597,
'split': 'validation'},
{'id': '283',
'x': 0.570873,
'target': 2.545035,
'split': 'validation'},
{'id': '284', 'x': -0.072741, 'target': 1.231127, 'split': 'train'},
{'id': '285', 'x': -0.174256, 'target': 0.685987, 'split': 'train'},
{'id': '286', 'x': 0.182249, 'target': 1.036668, 'split': 'train'},
{'id': '287', 'x': 1.697597, 'target': 4.795111, 'split': 'train'},
{'id': '288', 'x': -0.685596, 'target': 0.068241, 'split': 'train'},
{'id': '289', 'x': 1.016799, 'target': 3.318894, 'split': 'train'},
{'id': '290', 'x': 0.00667, 'target': 1.001373, 'split': 'train'},
{'id': '291', 'x': -0.549716, 'target': 0.240336, 'split': 'train'},
{'id': '292', 'x': -0.944465, 'target': -1.286489, 'split': 'train'},
{'id': '293', 'x': 1.962728, 'target': 4.512404, 'split': 'train'},
{'id': '294',
'x': -0.939278,
'target': -0.734267,
'split': 'validation'},
{'id': '295', 'x': 0.12539, 'target': 1.292688, 'split': 'train'},
{'id': '296', 'x': 1.368797, 'target': 3.282515, 'split': 'train'},
{'id': '297', 'x': -0.664893, 'target': -0.752038, 'split': 'train'},
{'id': '298', 'x': -0.16682, 'target': 0.748294, 'split': 'train'},
{'id': '299', 'x': 0.005943, 'target': 1.297422, 'split': 'train'},
{'id': '300',
'x': -0.046947,
'target': 0.939551,
'split': 'validation'}],
'notes': ['Generation seed: 42. A 32-bit LCG (1664525 × state + 1013904223, '
'modulo 2³²) produces inputs and noise, then a Fisher–Yates '
'shuffle assigns the split.',
'The same 300 rows and split are used for every learning-rate '
'comparison. Neither initialization nor learning rate regenerates '
'the dataset.',
'Only x is a model feature. The target, split, and row identifier '
'are never used as input features. There is no fitted '
'preprocessing.',
'Validation loss is useful for these comparisons, but repeated '
'tuning on it does not produce an unbiased test score. There is no '
'separate test set.',
'Noise prevents a single straight line from fitting every target '
'exactly. This is synthetic regression, not MNIST or a GPU '
'benchmark.']},
'lessonUrl': 'https://indepth.dev/learn/experiments/machine-learning/learning-rate'}
return protocol,
@app.cell(hide_code=True)
def _(mo, protocol):
mo.md(f"""
# {protocol['title']}
[Read the lesson]({protocol['lessonUrl']}). This notebook is an editable Python companion.
These small deterministic calculations run on CPU; a GPU is unnecessary.
They do not reproduce full training runs from the source articles.
**Protocol:** predict → run a reference → change one condition → compare → explain → design a variation.
{protocol['fixed']}
**First run:** use the reference settings shown below and write your prediction.
**Then investigate:** {protocol['guided']}
Runs remain in this running notebook session. Download your report before restarting or leaving.
Editing the function does not recalculate old evidence; each new run saves the function source.
""")
return
@app.cell(hide_code=True)
def _(json, mo, protocol):
_dataset = protocol['referenceDataset']
mo.vstack([mo.md('## Inspect the reference inputs\n\n' + _dataset['description']),
mo.ui.table(_dataset['rows'], selection=None, show_column_summaries=False),
mo.md('\n'.join('- ' + note for note in _dataset['notes'])),
mo.md('These are reference inputs. The input-dependent settings and full calculation source are saved with each run.'),
mo.download(json.dumps(_dataset, indent=2).encode(), filename=protocol['id'] + '-reference-inputs.json', mimetype='application/json', label='Download reference inputs')])
return
@app.cell
def _(math, protocol):
def calculate(s):
data = protocol['referenceDataset']['rows']
training = [row for row in data if row['split'] == 'train']
validation = [row for row in data if row['split'] == 'validation']
weight, bias = s['start'], 0
rows = []
def loss(records):
try:
return sum((weight * row['x'] + bias - row['target']) ** 2 for row in records) / (2 * len(records))
except OverflowError:
return math.inf
for step in range(s['steps'] + 1):
train_loss, validation_loss = loss(training), loss(validation)
if not math.isfinite(train_loss) or not math.isfinite(validation_loss):
return {'metrics': {}, 'rows': rows, 'status': f'Non-finite loss at update {step}; no finite final score.'}
rows.append({'Update': step, 'Slope': weight, 'Intercept': bias, 'Training loss': train_loss, 'Validation loss': validation_loss})
if step == s['steps']:
break
dw, db = 0, 0
for row in training:
error = weight * row['x'] + bias - row['target']
dw += error * row['x']
db += error
weight -= s['rate'] * dw / len(training)
bias -= s['rate'] * db / len(training)
return {'metrics': {'Final training loss': rows[-1]['Training loss'], 'Final validation loss': rows[-1]['Validation loss'], 'Final slope': weight, 'Final intercept': bias, 'Updates': s['steps']}, 'rows': rows, 'status': 'Completed'}
return calculate,
@app.cell
def _(mo):
get_runs, set_runs = mo.state([])
return get_runs, set_runs
@app.cell
def _(calculate, datetime, deepcopy, hashlib, inspect, mo, protocol, set_runs, timezone):
def record_run(value):
if value is None or not value['prediction'].strip():
return
settings = {key: value[key] for key in protocol['reference']}
source = inspect.getsource(calculate)
snapshot = {
'recordedAt': datetime.now(timezone.utc).isoformat(),
'settings': settings,
'prediction': value['prediction'].strip(),
'implementationNote': value['implementation'],
'calculationSource': source,
'calculationSha256': hashlib.sha256(source.encode()).hexdigest(),
'result': calculate(settings),
}
set_runs(lambda previous: [*previous, deepcopy(snapshot)])
_controls = {}
_parts = []
for _control in protocol['controls']:
_options = {choice['label']: choice['value'] for choice in _control['options']}
_default = next(label for label, value in _options.items() if value == protocol['reference'][_control['key']])
_controls[_control['key']] = mo.ui.dropdown(_options, value=_default, label=_control['label'])
_parts.append('{' + _control['key'] + '}')
_controls['prediction'] = mo.ui.text_area(label='Prediction and reason for this run', full_width=True)
_controls['implementation'] = mo.ui.text_area(value='Original lesson calculation', label='Describe any code changes', full_width=True)
_parts.extend(['{prediction}', '{implementation}'])
experiment_form = mo.md('\n\n'.join(_parts)).batch(**_controls).form(
submit_button_label='Run experiment',
validate=lambda value: None if value and value['prediction'].strip() else 'Write a prediction and reason before running.',
on_change=record_run,
)
experiment_form
return experiment_form,
@app.cell(hide_code=True)
def _(get_runs, mo):
runs = get_runs()
baseline = mo.ui.dropdown({f"Run {i + 1}": i for i in range(len(runs))}, value='Run 1' if runs else None, label='Comparison baseline')
mo.vstack([mo.md(f'## Recorded evidence — {len(runs)} runs'), baseline])
return baseline, runs
@app.cell(hide_code=True)
def _(baseline, math, mo, runs):
mo.stop(not runs, mo.md('Write a prediction, then select **Run experiment** to record the reference.'))
_current = runs[-1]
_base = runs[baseline.value if baseline.value is not None else 0]
_changes = [key for key, value in _current['settings'].items() if value != _base['settings'][key]]
_code_changed = _current['calculationSha256'] != _base['calculationSha256']
_metrics = [{'Measurement': key, 'Baseline': _base['result']['metrics'].get(key), 'Latest run': value,
'Difference': value - _base['result']['metrics'][key] if key in _base['result']['metrics'] else None}
for key, value in _current['result']['metrics'].items()]
_outputs = [mo.md(f"**Latest run:** {len(runs)} · {_current['result']['status']}"),
mo.md('**Prediction:** ' + _current['prediction']),
mo.md('**Changed settings:** ' + (', '.join(_changes) or 'none') + '. **Calculation changed:** ' + str(_code_changed) + '.'),
mo.md('Compare runs that differ in one intended condition. Check the saved code when comparing implementations.')]
if _metrics:
_outputs.append(mo.ui.table(_metrics, selection=None, show_column_summaries=False))
if _current['result']['rows'] and 'Training loss' in _current['result']['rows'][0]:
_training = [math.log10(1 + row['Training loss']) for row in _current['result']['rows']]
_validation = [math.log10(1 + row['Validation loss']) for row in _current['result']['rows']]
_top = max(1, max(_training), max(_validation))
_lines = []
for _values, _color, _dash in [(_training, '#2337ff', ''), (_validation, '#ae5a0b', '7 4')]:
_points = ' '.join(f'{40 + 500 * i / max(1, len(_values) - 1)},{200 - 160 * value / _top}' for i, value in enumerate(_values))
_lines.append(f'')
_outputs.append(mo.Html(f''))
_outputs.append(mo.md('Blue solid = training; orange dashed = validation. Use the table for exact losses.'))
_outputs.append(mo.accordion({'Latest run: all intermediate evidence': mo.ui.table(_current['result']['rows'], selection=None, show_column_summaries=False)}))
_outputs.append(mo.accordion({f"Run {i + 1}: settings, prediction, code, and evidence": mo.json(run) for i, run in enumerate(runs)}))
mo.vstack(_outputs)
return
@app.cell(hide_code=True)
def _(mo, protocol):
observations = mo.ui.text_area(label='Observations: cite runs and measured values', full_width=True, debounce=False)
explanation = mo.ui.text_area(label='Explanation: why did this happen?', full_width=True, debounce=False)
limitations = mo.ui.text_area(label='Limitations: what remains unresolved?', full_width=True, debounce=False)
next_test = mo.ui.text_area(label='Your next test and a result that would challenge your explanation', full_width=True, debounce=False)
mo.vstack([mo.md('## Explain, then extend'), observations, explanation, limitations,
mo.md('### Design your own investigation\n\n' + protocol['extension']), next_test,
mo.accordion({'Check the reasoning after writing your explanation': mo.md(protocol['reasoning'])})])
return explanation, limitations, next_test, observations
@app.cell(hide_code=True)
def _(baseline, datetime, explanation, json, limitations, mo, next_test, observations, protocol, runs, timezone):
report = {'schemaVersion': 1, 'protocol': protocol, 'exportedAt': datetime.now(timezone.utc).isoformat(),
'comparisonRunNumber': baseline.value + 1 if baseline.value is not None else None, 'runs': runs, 'notes': {'observations': observations.value, 'explanation': explanation.value,
'limitations': limitations.value, 'nextTest': next_test.value}}
mo.download(json.dumps(report, indent=2, allow_nan=False).encode(), filename=protocol['id'] + '-notebook-report.json',
mimetype='application/json', label='Download report', disabled=not runs)
return report,
if __name__ == '__main__':
app.run()