markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
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The helper function `toplevel_defs()` helps saving and restoring the environment before and after redefining the function under repair. | class Repairer(Repairer):
def toplevel_defs(self, tree: ast.AST) -> List[str]:
"""Return a list of names of defined functions and classes in `tree`"""
visitor = DefinitionVisitor()
visitor.visit(tree)
assert hasattr(visitor, 'definitions')
return visitor.definitions
class Def... | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Here's an example for `fitness()`: | repairer = Repairer(middle_debugger, log=1)
good_fitness = repairer.fitness(middle_tree())
good_fitness
# ignore
assert good_fitness >= 0.99, "fitness() failed"
bad_middle_tree = ast.parse("def middle(x, y, z): return x")
bad_fitness = repairer.fitness(bad_middle_tree)
bad_fitness
# ignore
assert bad_fitness < 0.5, "fi... | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
RepairingNow for the actual `repair()` method, which creates a `population` and then evolves it until the fitness is 1.0 or the given number of iterations is spent. | import traceback
class Repairer(Repairer):
def initial_population(self, size: int) -> List[ast.AST]:
"""Return an initial population of size `size`"""
return [self.target_tree] + \
[self.mutator.mutate(copy.deepcopy(self.target_tree))
for i in range(size - 1)]
def re... | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
EvolvingThe evolution of our population takes place in the `evolve()` method. In contrast to the `evolve_middle()` function, above, we use crossover to create the offspring, which we still mutate afterwards. | class Repairer(Repairer):
def evolve(self, population: List[ast.AST]) -> List[ast.AST]:
"""Evolve the candidate population by mutating and crossover."""
n = len(population)
# Create offspring as crossover of parents
offspring: List[ast.AST] = []
while len(offspring) < n:
... | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
A second difference is that we not only sort by fitness, but also by tree size – with equal fitness, a smaller tree thus will be favored. This helps keeping fixes and patches small. | class Repairer(Repairer):
def fitness_key(self, tree: ast.AST) -> Tuple[float, int]:
"""Key to be used for sorting the population"""
tree_size = len([node for node in ast.walk(tree)])
return (self.fitness(tree), -tree_size) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
SimplifyingThe last step in repairing is simplifying the code. As demonstrated in the chapter on [reducing failure-inducing inputs](DeltaDebugger.ipynb), we can use delta debugging on code to get rid of superfluous statements. To this end, we convert the tree to lines, run delta debugging on them, and then convert it ... | class Repairer(Repairer):
def reduce(self, tree: ast.AST) -> ast.AST:
"""Simplify `tree` using delta debugging."""
original_fitness = self.fitness(tree)
source_lines = astor.to_source(tree).split('\n')
with self.reducer:
self.test_reduce(source_lines, original_fitness)
... | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
As dicussed above, we simplify the code by having the test function (`test_reduce()`) declare reaching the maximum fitness obtained so far as a "failure". Delta debugging will then simplify the input as long as the "failure" (and hence the maximum fitness obtained) persists. | class Repairer(Repairer):
def test_reduce(self, source_lines: List[str], original_fitness: float) -> None:
"""Test function for delta debugging."""
try:
source = "\n".join(source_lines)
tree = ast.parse(source)
fitness = self.fitness(tree)
assert fitn... | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
End of Excursion Repairer in ActionLet us go and apply `Repairer` in practice. We initialize it with `middle_debugger`, which has (still) collected the passing and failing runs for `middle_test()`. We also set `log` for some diagnostics along the way. | repairer = Repairer(middle_debugger, log=True) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
We now invoke `repair()` to evolve our population. After a few iterations, we find a best tree with perfect fitness. | best_tree, fitness = repairer.repair()
print_content(astor.to_source(best_tree), '.py')
fitness | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Again, we have a perfect solution. Here, we did not even need to simplify the code in the last iteration, as our `fitness_key()` function favors smaller implementations. Removing HTML MarkupLet us apply `Repairer` on our other ongoing example, namely `remove_html_markup()`. | def remove_html_markup(s): # type: ignore
tag = False
quote = False
out = ""
for c in s:
if c == '<' and not quote:
tag = True
elif c == '>' and not quote:
tag = False
elif c == '"' or c == "'" and tag:
quote = not quote
elif not tag:... | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
To run `Repairer` on `remove_html_markup()`, we need a test and a test suite. `remove_html_markup_test()` raises an exception if applying `remove_html_markup()` on the given `html` string does not yield the `plain` string. | def remove_html_markup_test(html: str, plain: str) -> None:
outcome = remove_html_markup(html)
assert outcome == plain, \
f"Got {repr(outcome)}, expected {repr(plain)}" | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Now for the test suite. We use a simple fuzzing scheme to create dozens of passing and failing test cases in `REMOVE_HTML_PASSING_TESTCASES` and `REMOVE_HTML_FAILING_TESTCASES`, respectively. Excursion: Creating HTML Test Cases | def random_string(length: int = 5, start: int = ord(' '), end: int = ord('~')) -> str:
return "".join(chr(random.randrange(start, end + 1)) for i in range(length))
random_string()
def random_id(length: int = 2) -> str:
return random_string(start=ord('a'), end=ord('z'))
random_id()
def random_plain() -> str:
... | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
End of Excursion Here is a passing test case: | REMOVE_HTML_PASSING_TESTCASES[0]
html, plain = REMOVE_HTML_PASSING_TESTCASES[0]
remove_html_markup_test(html, plain) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Here is a failing test case (containing a double quote in the plain text) | REMOVE_HTML_FAILING_TESTCASES[0]
with ExpectError():
html, plain = REMOVE_HTML_FAILING_TESTCASES[0]
remove_html_markup_test(html, plain) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
We run our tests, collecting the outcomes in `html_debugger`. | html_debugger = OchiaiDebugger()
for html, plain in (REMOVE_HTML_PASSING_TESTCASES +
REMOVE_HTML_FAILING_TESTCASES):
with html_debugger:
remove_html_markup_test(html, plain) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
The suspiciousness distribution will not be of much help here – pretty much all lines in `remove_html_markup()` have the same suspiciousness. | html_debugger | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Let us create our repairer and run it. | html_repairer = Repairer(html_debugger, log=True)
best_tree, fitness = html_repairer.repair(iterations=20) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
We see that the "best" code is still our original code, with no changes. And we can set `iterations` to 50, 100, 200... – our `Repairer` won't be able to repair it. | quiz("Why couldn't `Repairer()` repair `remove_html_markup()`?",
[
"The population is too small!",
"The suspiciousness is too evenly distributed!",
"We need more test cases!",
"We need more iterations!",
"There is no statement in the source with a correct condition!",
... | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
You can explore all of the hypotheses above by changing the appropriate parameters, but you won't be able to change the outcome. The problem is that, unlike `middle()`, there is no statement (or combination thereof) in `remove_html_markup()` that could be used to make the failure go away. For this, we need to mutate an... | def all_conditions(trees: Union[ast.AST, List[ast.AST]],
tp: Optional[Type] = None) -> List[ast.expr]:
"""
Return all conditions from the AST (or AST list) `trees`.
If `tp` is given, return only elements of that type.
"""
if not isinstance(trees, list):
assert isinstance(... | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
`all_conditions()` uses a `ConditionVisitor` class to walk the tree and collect the conditions: | class ConditionVisitor(NodeVisitor):
def __init__(self) -> None:
self.conditions: List[ast.expr] = []
self.conditions_seen: Set[str] = set()
super().__init__()
def add_conditions(self, node: ast.AST, attr: str) -> None:
elems = getattr(node, attr, [])
if not isinstance(e... | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Here are all the conditions in `remove_html_markup()`. This is some material to construct new conditions from. | [astor.to_source(cond).strip()
for cond in all_conditions(remove_html_markup_tree())] | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Mutating ConditionsHere comes our `ConditionMutator` class. We subclass from `StatementMutator` and set an attribute `self.conditions` containing all the conditions in the source. The method `choose_condition()` randomly picks a condition. | class ConditionMutator(StatementMutator):
"""Mutate conditions in an AST"""
def __init__(self, *args: Any, **kwargs: Any) -> None:
"""Constructor. Arguments are as with `StatementMutator` constructor."""
super().__init__(*args, **kwargs)
self.conditions = all_conditions(self.source)
... | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
The actual mutation takes place in the `swap()` method. If the node to be replaced has a `test` attribute (i.e. a controlling predicate), then we pick a random condition `cond` from the source and randomly chose from:* **set**: We change `test` to `cond`.* **not**: We invert `test`.* **and**: We replace `test` by `cond... | class ConditionMutator(ConditionMutator):
def choose_bool_op(self) -> str:
return random.choice(['set', 'not', 'and', 'or'])
def swap(self, node: ast.AST) -> ast.AST:
"""Replace `node` condition by a condition from `source`"""
if not hasattr(node, 'test'):
return super().swa... | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
We can use the mutator just like `StatementMutator`, except that some of the mutations will also include new conditions: | mutator = ConditionMutator(source=all_statements(remove_html_markup_tree()),
log=True)
for i in range(10):
new_tree = mutator.mutate(remove_html_markup_tree()) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Let us put our new mutator to action, again in a `Repairer()`. To activate it, all we need to do is to pass it as `mutator_class` keyword argument. | condition_repairer = Repairer(html_debugger,
mutator_class=ConditionMutator,
log=2) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
We might need more iterations for this one. Let us see... | best_tree, fitness = condition_repairer.repair(iterations=200)
repaired_source = astor.to_source(best_tree)
print_content(repaired_source, '.py') | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Success again! We have automatically repaired `remove_html_markup()` – the resulting code passes all tests, including those that were previously failing. Again, we can present the fix as a patch: | original_source = astor.to_source(remove_html_markup_tree())
for patch in diff(original_source, repaired_source):
print_patch(patch) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
However, looking at the patch, one may come up with doubts. | quiz("Is this actually the best solution?",
[
"Yes, sure, of course. Why?",
"Err - what happened to single quotes?"
], 1 << 1) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Indeed – our solution does not seem to handle single quotes anymore. Why is that so? | quiz("Why aren't single quotes handled in the solution?",
[
"Because they're not important. I mean, who uses 'em anyway?",
"Because they are not part of our tests? "
"Let me look up how they are constructed..."
], 1 << 1) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Correct! Our test cases do not include single quotes – at least not in the interior of HTML tags – and thus, automatic repair did not care to preserve their handling. How can we fix this? An easy way is to include an appropriate test case in our set – a test case that passes with the original `remove_html_markup()`, ye... | with html_debugger:
remove_html_markup_test("<foo quote='>abc'>me</foo>", "me") | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Let us repeat the repair with the extended test set: | best_tree, fitness = condition_repairer.repair(iterations=200) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Here is the final tree: | print_content(astor.to_source(best_tree), '.py') | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
And here is its fitness: | fitness | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
The revised candidate now passes _all_ tests (including the tricky quote test we added last). Its condition now properly checks for `tag` _and_ both quotes. (The `tag` inside the parentheses is still redundant, but so be it.) From this example, we can learn a few lessons about the possibilities and risks of automated r... | # ignore
print_content(middle_source, '.py') | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
We set up a function `middle_test()` that tests it. The `middle_debugger` collects testcases and outcomes: | middle_debugger = OchiaiDebugger()
for x, y, z in MIDDLE_PASSING_TESTCASES + MIDDLE_FAILING_TESTCASES:
with middle_debugger:
middle_test(x, y, z) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
The repairer attempts to repair the invoked function (`middle()`). The returned AST `tree` can be output via `astor.to_source()`: | middle_repairer = Repairer(middle_debugger)
tree, fitness = middle_repairer.repair()
print(astor.to_source(tree), fitness) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Here are the classes defined in this chapter. A `Repairer` repairs a program, using a `StatementMutator` and a `CrossoverOperator` to evolve a population of candidates. | # ignore
from ClassDiagram import display_class_hierarchy
# ignore
display_class_hierarchy([Repairer, ConditionMutator, CrossoverOperator],
abstract_classes=[
NodeVisitor,
NodeTransformer
],
p... | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Lessons Learned* Automated repair based on genetic optimization uses five ingredients: 1. A _test suite_ to determine passing and failing tests 2. _Defect localization_ (typically obtained from [statistical debugging](StatisticalDebugger.ipynb) with the test suite) to determine potential locations to be fixed ... | from Assertions import square_root # minor dependency
with ExpectError():
square_root_of_zero = square_root(0) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Can your `ValueMutator` automatically fix this failure? **Solution.** Your solution will be effective if it also includes named constants such as `None`. | import math
def square_root_fixed(x): # type: ignore
assert x >= 0 # precondition
approx = 0 # <-- FIX: Change `None` to 0
guess = x / 2
while approx != guess:
approx = guess
guess = (approx + x / approx) / 2
assert math.isclose(approx * approx, x)
return approx
square_root_... | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
BLERSSI Seldon serving Clone Cisco Kubeflow Starter pack repository | BRANCH_NAME="master" #Provide git branch name "master" or "dev"
! git clone -b $BRANCH_NAME https://github.com/CiscoAI/cisco-kubeflow-starter-pack.git | Cloning into 'cisco-kubeflow-starter-pack'...
remote: Enumerating objects: 63, done.[K
remote: Counting objects: 100% (63/63), done.[K
remote: Compressing objects: 100% (44/44), done.[K
remote: Total 4630 (delta 16), reused 44 (delta 11), pack-reused 4567[K
Receiving objects: 100% (4630/4630), 17.61 MiB | 48.72 MiB... | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Install the required packages | ! pip install pandas sklearn seldon_core dill alibi==0.3.2 --user | Collecting pandas
Downloading pandas-1.0.5-cp36-cp36m-manylinux1_x86_64.whl (10.1 MB)
[K |████████████████████████████████| 10.1 MB 21.3 MB/s eta 0:00:01
[?25hCollecting sklearn
Downloading sklearn-0.0.tar.gz (1.1 kB)
Collecting seldon_core
Downloading seldon_core-1.2.1-py3-none-any.whl (104 kB)
[K |██... | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Restart Notebook kernel | from IPython.display import display_html
display_html("<script>Jupyter.notebook.kernel.restart()</script>",raw=True) | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Import Libraries | from __future__ import division
from __future__ import print_function
import tensorflow as tf
import pandas as pd
import numpy as np
import shutil
import yaml
import random
import re
import os
import dill
import logging
import requests
import json
from time import sleep
from sklearn.preprocessing import OneHotEncoder
... | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Get NamespaceGet current k8s namespace | def is_running_in_k8s():
return os.path.isdir('/var/run/secrets/kubernetes.io/')
def get_current_k8s_namespace():
with open('/var/run/secrets/kubernetes.io/serviceaccount/namespace', 'r') as f:
return f.readline()
def get_default_target_namespace():
if not is_running_in_k8s():
return 'defa... | anonymous
| Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Check GPUs availability | gpus = len(tf.config.experimental.list_physical_devices('GPU'))
if gpus == 0:
print("Model will be trained using CPU")
elif gpus >= 0:
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
tf.config.experimental.list_physical_devices('GPU')
print("Model will be trained ... | Model will be trained using CPU
| Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Declare Variables | path="cisco-kubeflow-starter-pack/apps/networking/ble-localization/onprem"
BLE_RSSI = pd.read_csv(os.path.join(path, "data/iBeacon_RSSI_Labeled.csv")) #Labeled dataset
# Configure model options
TF_DATA_DIR = os.getenv("TF_DATA_DIR", "/tmp/data/")
TF_MODEL_DIR = os.getenv("TF_MODEL_DIR", "blerssi/")
TF_EXPORT_DIR = os.... | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
BLERSSI Input Dataset Attribute Informationlocation: The location of receiving RSSIs from ibeacons b3001 to b3013; symbolic values showing the column and row of the location on the map (e.g., A01 stands for column A, row 1).date: Datetime in the format of ‘d-m-yyyy hh:mm:ss’b3001 - b3013: RSSI readings corre... | BLE_RSSI.head(10) | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Definition of Serving Input Receiver Function | feature_columns = make_feature_cols()
inputs = {}
for feat in feature_columns:
inputs[feat.name] = tf.placeholder(shape=[None], dtype=feat.dtype)
serving_input_receiver_fn = tf.estimator.export.build_raw_serving_input_receiver_fn(inputs) | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Train and Save BLE RSSI Model | # Feature columns
COLUMNS = list(BLE_RSSI.columns)
FEATURES = COLUMNS[2:]
LABEL = [COLUMNS[0]]
b3001 = tf.feature_column.numeric_column(key='b3001',dtype=tf.float64)
b3002 = tf.feature_column.numeric_column(key='b3002',dtype=tf.float64)
b3003 = tf.feature_column.numeric_column(key='b3003',dtype=tf.float64)
b3004 = tf.... | INFO:tensorflow:ParameterServerStrategy with compute_devices = ('/device:CPU:0',), variable_device = '/device:CPU:0'
Number of devices: 1
INFO:tensorflow:Initializing RunConfig with distribution strategies.
INFO:tensorflow:Not using Distribute Coordinator.
INFO:tensorflow:Using config: {'_model_dir': 'blerssi/', '_tf_r... | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Define predict function | MODEL_EXPORT_PATH= os.path.join(TF_MODEL_DIR, "export", TF_EXPORT_DIR)
def predict(request):
"""
Define custom predict function to be used by local prediction
and explainer. Set anchor_tabular predict function so it always returns predicted class
"""
# Get model exporter path
for dir in os.lis... | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Initialize and fitTo initialize the explainer, we provide a predict function, a list with the feature names to make the anchors easy to understand. | feature_cols=["b3001", "b3002", "b3003", "b3004", "b3005", "b3006", "b3007", "b3008", "b3009", "b3010", "b3011", "b3012", "b3013"]
explainer = AnchorTabular(predict, feature_cols) | WARNING:tensorflow:From <ipython-input-8-69054218b064>:31: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.loader.load or tf.compa... | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Discretize the ordinal features into quartiles. disc_perc is a list with percentiles used for binning | explainer.fit(x1, disc_perc=(25, 50, 75)) | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Save Explainer fileSave explainer file with .dill extension. It will be used when creating the InferenceService | EXPLAINER_PATH="explainer"
if not os.path.exists(EXPLAINER_PATH):
os.mkdir(EXPLAINER_PATH)
with open("%s/explainer.dill"%EXPLAINER_PATH, 'wb') as f:
dill.dump(explainer,f) | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Create a gatewayCreate a gateway called kubeflow-gateway in namespace anonymous. | gateway=f"""apiVersion: networking.istio.io/v1alpha3
kind: Gateway
metadata:
name: kubeflow-gateway
namespace: {namespace}
spec:
selector:
istio: ingressgateway
servers:
- hosts:
- '*'
port:
name: http
number: 80
protocol: HTTP
"""
gateway_spec=yaml.safe_load(gateway)
custom_api.... | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Adding a new inference server The list of available inference servers in Seldon Core is maintained in the **seldon-config** configmap, which lives in the same namespace as your Seldon Core operator. In particular, the **predictor_servers** key holds the JSON config for each inference server.[Refer to for more informat... | api_client.patch_namespaced_config_map(name="seldon-config", namespace="kubeflow",pretty=True, body={"data":{"predictor_servers":'{"MLFLOW_SERVER":{"grpc":{"defaultImageVersion":"1.2.1","image":"seldonio/mlflowserver_grpc"},"rest":{"defaultImageVersion":"1.2.1","image":"seldonio/mlflowserver_rest"}},"SKLEARN_SERVER":{"... | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Seldon Serving DeploymentCreate an **SeldonDeployment** with a blerssi model | pvcname = !(echo $HOSTNAME | sed 's/.\{2\}$//')
pvc = "workspace-"+pvcname[0]
seldon_deploy=f"""apiVersion: machinelearning.seldon.io/v1alpha2
kind: SeldonDeployment
metadata:
name: blerssi
namespace: {namespace}
spec:
name: blerssi
predictors:
- graph:
children: []
implementation: CUSTOM_INFEREN... | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Wait for state to become available | status=False
while True:
seldon_status=custom_api.get_namespaced_custom_object_status(group="machinelearning.seldon.io", version="v1alpha2", namespace=namespace, plural="seldondeployments", name=seldon_deploy_spec["metadata"]["name"])
if seldon_status["status"]["state"] == "Available":
status=True
... | Status: Creating
Status: Creating
Status: Available
| Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Run a Prediction | CLUSTER='ucs' #where your cluster running 'gcp' or 'ucs'
%%bash -s "$CLUSTER" --out NODE_IP
if [ $1 = "ucs" ]
then
echo "$(kubectl get node -o=jsonpath='{.items[0].status.addresses[0].address}')"
else
echo "$(kubectl get node -o=jsonpath='{.items[0].status.addresses[1].address}')"
fi
%%bash --out INGRESS_PORT
I... | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Data for prediction | df_full = pd.read_csv(os.path.join(path,'data/iBeacon_RSSI_Unlabeled_truncated.csv')) #Labeled dataset
# Input Data Preprocessing
df_full = df_full.drop(['date'],axis = 1)
df_full = df_full.drop(['location'],axis = 1)
df_full[FEATURES] = (df_full[FEATURES])/(-200)
input_data=df_full.to_numpy()[:1]
input_data
headers... | Probability: 0.6692667603492737
Class-id: 14
| Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Prediction of the model and explain | explain(input_data) | Anchor: b3009 <= 1.00 AND 0.40 < b3004 <= 1.00 AND 0.39 < b3002 <= 1.00 AND b3012 <= 1.00 AND b3011 <= 1.00 AND b3013 <= 1.00 AND b3006 <= 1.00 AND b3003 <= 1.00 AND b3010 <= 1.00 AND b3005 <= 1.00 AND b3001 <= 1.00 AND b3007 <= 1.00 AND b3008 <= 1.00
Coverage: 0.48
| Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Clean Up Delete a gateway | custom_api.delete_namespaced_custom_object(group="networking.istio.io", version="v1alpha3", namespace=namespace, plural="gateways", name=gateway_spec["metadata"]["name"],body=k8s_client.V1DeleteOptions()) | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Delete Seldon Serving Deployment | custom_api.delete_namespaced_custom_object(group="machinelearning.seldon.io", version="v1alpha2", namespace=namespace, plural="seldondeployments", name=seldon_deploy_spec["metadata"]["name"], body=k8s_client.V1DeleteOptions()) | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Delete model and explainer folders from notebook | !rm -rf $EXPLAINER_PATH
!rm -rf $TF_MODEL_DIR | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
MobileCoin Example WalletThis is an example python client that interacts with `mobilecoind` to manage a MobileCoin wallet.You must start the `mobilecoind` daemon in order to run a wallet. See the mobilecoind README for more information.To run this notebook, make sure you have the requirements installed, and that you h... | from mobilecoin import Client | _____no_output_____ | Apache-2.0 | mobilecoind/clients/python/jupyter/wallet.ipynb | MCrank/mobilecoin |
Start the Mob ClientThe client talks to your local mobilecoind. See the mobilecoind/README.md for information on how to set it up. | client = Client("localhost:4444", ssl=False) | _____no_output_____ | Apache-2.0 | mobilecoind/clients/python/jupyter/wallet.ipynb | MCrank/mobilecoin |
Input Root Entropy for AccountNote: The root entropy is sensitive material. It is used as the seed to create your account keys. Anyone with your root entropy can steal your MobileCoin. | entropy = "4ec2c081e764f4189afba528956c05804a448f55f24cc3d04c9ef7e807a93bcd"
credentials_response = client.get_account_key(bytes.fromhex(entropy)) | _____no_output_____ | Apache-2.0 | mobilecoind/clients/python/jupyter/wallet.ipynb | MCrank/mobilecoin |
Monitor your AccountMonitoring an account means that mobilecoind will persist the transactions that belong to you to a local database. This allows you to retrieve your funds and calculate your balance, as well as to construct and submit transactions.Note: MobileCoin uses accounts and subaddresses for managing funds. Y... | monitor_id_response = client.add_monitor(credentials_response.account_key) | _____no_output_____ | Apache-2.0 | mobilecoind/clients/python/jupyter/wallet.ipynb | MCrank/mobilecoin |
Check BalanceYou will need to provide a subaddress index. Most people will only use one subaddress, and can default to 0. Exchanges or users who want to generate lots of new public addresses may use multiple subaddresses. | subaddress_index = 0
client.get_balance(monitor_id_response.monitor_id, subaddress_index) | _____no_output_____ | Apache-2.0 | mobilecoind/clients/python/jupyter/wallet.ipynb | MCrank/mobilecoin |
Send a TransactionMobileCoin uses "request codes" to wrap public addresses. See below for how to generate request codes. | address_code = "2nTy8m2VE5UMtfqRf12gEjZmFHKNTDEtNufQZNvE713ytYvdu2kqpbcncHJUSLwmgTCkB56Li9fsGwJF9LRYEQvoQCDzqVQEJETDNQKLzqHCzd"
target_address_response = client.parse_request_code(address_code)
# Construct the transaction
txo_list_response = client.get_unspent_tx_output_list(monitor_id_response.monitor_id, subaddress_... | _____no_output_____ | Apache-2.0 | mobilecoind/clients/python/jupyter/wallet.ipynb | MCrank/mobilecoin |
Public Address (Request Code) | public_address_response = client.get_public_address(monitor_id_response.monitor_id, subaddress_index)
request_code_response = client.create_request_code(public_address_response.public_address)
print(f"Request code = {request_code_response}") | _____no_output_____ | Apache-2.0 | mobilecoind/clients/python/jupyter/wallet.ipynb | MCrank/mobilecoin |
Show me the first lines of the original file | df = pd.read_excel('/tmp/gonzalo_test/aseg.xls')
df.head() | _____no_output_____ | MIT | notebooks/Miscellaneous/Reshaping an Excel table.ipynb | xgrg/alfa |
Show me the region names containing 'Vent' or 'WM' or 'Hippo' | names = set([each for each in df['StructName'].tolist() \
if 'WM' in each
or 'Vent' in each
or 'Hippo' in each])
names | _____no_output_____ | MIT | notebooks/Miscellaneous/Reshaping an Excel table.ipynb | xgrg/alfa |
Reshape the table and show me the first lines | df = pd.DataFrame(df[df['StructName'].isin(names)], columns=['subject', 'StructName', 'Volume_mm3'])
df = df.pivot(index='subject', columns='StructName', values='Volume_mm3')
df.head() | _____no_output_____ | MIT | notebooks/Miscellaneous/Reshaping an Excel table.ipynb | xgrg/alfa |
Save it and success ! | df.to_excel('/tmp/gonzalo_test/aseg_pivot.xls')
from IPython.display import Image
Image(url='http://s2.quickmeme.com/img/c3/c37a6cc5f88867e5387b8787aaf67afc350b3f37f357ed0a3088241488063bce.jpg') | _____no_output_____ | MIT | notebooks/Miscellaneous/Reshaping an Excel table.ipynb | xgrg/alfa |
The effect of temperature and reaction time affects the %yield. Develop a model for %yield in terms of temperature and time | import pandas as mypanda
import numpy as np
from scipy import stats as mystats
import matplotlib.pyplot as myplot
from pandas.plotting import scatter_matrix
from statsmodels.formula.api import ols as myols
from statsmodels.stats.anova import anova_lm
myData=mypanda.read_csv('datasets/Mult_Reg_Yield.csv')
myData
tmp=myD... | _____no_output_____ | Apache-2.0 | Regression_Analysis_Chemical_Process.ipynb | mohan-mj/Regression_Analysis |
check for relationship now | scatter_matrix(myData)
myplot.show() | C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: 'pandas.tools.plotting.scatter_matrix' is deprecated, import 'pandas.plotting.scatter_matrix' instead.
"""Entry point for launching an IPython kernel.
| Apache-2.0 | Regression_Analysis_Chemical_Process.ipynb | mohan-mj/Regression_Analysis |
correlation between xs and y should be high | np.corrcoef(tmp,yld)
np.corrcoef(time,yld)
np.corrcoef(time,tmp)
mymodel=myols("yld ~ time + tmp",myData)
mymodel=mymodel.fit()
mymodel.summary() | C:\ProgramData\Anaconda3\lib\site-packages\scipy\stats\stats.py:1334: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=16
"anyway, n=%i" % int(n))
| Apache-2.0 | Regression_Analysis_Chemical_Process.ipynb | mohan-mj/Regression_Analysis |
check p value ==> only time is related to yield | mymodel=myols("yld ~ time ",myData).fit()
mymodel.summary()
pred=mymodel.predict()
res=yld-pred
res
#print(yld, res)
myplot.scatter(yld,pred)
myplot.show()
mystats.probplot(res,plot=myplot)
myplot.show()
mystats.normaltest(res) | C:\ProgramData\Anaconda3\lib\site-packages\scipy\stats\stats.py:1334: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=16
"anyway, n=%i" % int(n))
| Apache-2.0 | Regression_Analysis_Chemical_Process.ipynb | mohan-mj/Regression_Analysis |
Implies it is normal | myplot.scatter(time,res)
myplot.show()
myplot.scatter(pred,res)
myplot.show() | _____no_output_____ | Apache-2.0 | Regression_Analysis_Chemical_Process.ipynb | mohan-mj/Regression_Analysis |
**Análise de Dados com Python e Pandas** | # Monta o drive no ambiente virtual permitindo acesso aos arquivos do drive
from google.colab import drive
drive.mount('/content/drive')
# Permite escolher um arquivo da máquina para upload no colab
from google.colab import files
arq = files.upload()
from google.colab import drive
drive.mount('/content/drive') | Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
| MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
*Importando a biblioteca Pandas* | #importando a biblioteca Pandas
import pandas as pd | _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
*Lendo arquivos* | #Lendo CSV
df = pd.read_csv("/content/drive/MyDrive/Datasets/Gapminder.csv", error_bad_lines=False, sep=";")
#Visualizando as 5 primeiras linhas
df.head() | _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
*Renomeando Colunas* | df = df.rename(columns={'country':'Country', 'continent':'Continent', 'year':'Year', 'lifeExp':'LifeExp', 'pop':'Population', 'gdpPercap':'PIB'})
df.head() | _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
*Trabalhando com Linhas e Colunas do arquivo* | #Quantidade de linhas e colunas dentro do arquivo
df.shape
#Nome das colunas
df.columns | _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
#Tipo de dado em ccada coluna
df.dtypes
#Últimas cindo linhas por padrao do arquivo (df.tail(10) → Últimas 10 linhas...)
df.tail()
#Média entre os dados das respectivas linhas e colunas
df.describe() | _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO | |
*Trabalhando com Filtros* | df['Continent'].unique()
Oceania = df.loc[df['Continent'] == 'Oceania']
Oceania.head()
Oceania['Continent'].unique()
df.groupby('Continent')['Country'].nunique()
df.groupby('Year')['LifeExp'].mean()
df['PIB'].mean()
df['PIB'].sum() | _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
**Trabalhando com Planilhas de Excel** *Leitura dos Arquivos* | df1 = pd.read_excel("/content/drive/MyDrive/Datasets/Aracaju.xlsx")
df2 = pd.read_excel("/content/drive/MyDrive/Datasets/Fortaleza.xlsx")
df3 = pd.read_excel("/content/drive/MyDrive/Datasets/Natal.xlsx")
df4 = pd.read_excel("/content/drive/MyDrive/Datasets/Recife.xlsx")
df5 = pd.read_excel("/content/drive/MyDrive/Datas... | _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
***Tratando valores faltantes*** | #Consultando linhas com valores faltantes
df.isnull().sum()
#Apagando as linhas com valores nulos
df.dropna(inplace=True)
#Apagando as linhas com valores nulos com base apenas em 1 coluna
df.dropna(subset=['Vendas'], inplace=True)
#Removendo linhas que estejam com valores faltantes em todas as colunas
df.dropna(how='al... | _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
***Criando novas colunas*** | #Criando a coluna de receita
df['Receita'] = df['Vendas'].mul(df['Qtde'])
df.head()
df.tail()
df['Receita/Venda'] = df['Receita'] / df['Vendas']
df.head()
#Retornando maior receita
df['Receita'].max()
#Retornando a menor receita
df['Receita'].min()
#nlargest
df.nlargest(3,'Receita')
#nsmallest
df.nsmallest(3, 'Receita'... | _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
***Trabalhando com datas*** | #Transfomando a coluna de dataa em tipo inteiro
df['Data'] = df['Data'].astype('int64')
#Verificando o tipo de dado de cada coluna
df.dtypes
#Transformando a coluna de Data em Data
df['Data'] = pd.to_datetime(df['Data'])
df.dtypes
#Agrupamento por ano
df.groupby(df['Data'].dt.year)['Receita'].sum()
#Criado uma nova col... | _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
**Visualizacao de Dados** | df['LojaID'].value_counts(ascending=False) | _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
***Gráficos*** | #Gráfico de barras
df['LojaID'].value_counts(ascending=False).plot.bar();
#Gráfico de barras horizontais
df['LojaID'].value_counts().plot.barh();
#Gráfco de barras horizonatal
df['LojaID'].value_counts(ascending=True).plot.barh();
#Gráfico de Pizza
df.groupby(df['Data'].dt.year)['Receita'].sum().plot.pie();
#Total de v... | _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
**Análise Exploratória** | plt.style.use('seaborn')
#Upload de arquivo
from google.colab import files
arq = files.upload()
#Criando nosso DataFrame
df = pd.read_excel("/content/drive/MyDrive/Datasets/AdventureWorks.xlsx")
df.head()
#Quantidade de linhas e colunas
df.shape
#Verificando os tipos de dados
df.dtypes
#Qual a Receita total?
df['Valor ... | _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
Aerospike Java Client – Advanced Collection Data Types*Last updated: June 22, 2021*The goal of this tutorial is to highlight the power of working with [collection data types (CDTs)]("https://docs.aerospike.com/docs/guide/cdt.html") in Aerospike. It covers the following topics:1. Setting [contexts (CTXs)]("https://docs... | import io.github.spencerpark.ijava.IJava;
import io.github.spencerpark.jupyter.kernel.magic.common.Shell;
IJava.getKernelInstance().getMagics().registerMagics(Shell.class); | _____no_output_____ | MIT | notebooks/java/java-advanced_collection_data_types.ipynb | markprincely/interactive-notebooks |
Start AerospikeEnsure Aerospike Database is running locally. | %sh asd | _____no_output_____ | MIT | notebooks/java/java-advanced_collection_data_types.ipynb | markprincely/interactive-notebooks |
Download the Aerospike Java ClientAsk Maven to download and install the project object model (POM) of the Aerospike Java Client. | %%loadFromPOM
<dependencies>
<dependency>
<groupId>com.aerospike</groupId>
<artifactId>aerospike-client</artifactId>
<version>5.0.0</version>
</dependency>
</dependencies> | _____no_output_____ | MIT | notebooks/java/java-advanced_collection_data_types.ipynb | markprincely/interactive-notebooks |
Start the Aerospike Java Client and ConnectCreate an instance of the Aerospike Java Client, and connect to the demo cluster.The default cluster location for the Docker container is *localhost* port *3000*. If your cluster is not running on your local machine, modify *localhost* and *3000* to the values for your Aerosp... | import com.aerospike.client.AerospikeClient;
AerospikeClient client = new AerospikeClient("localhost", 3000);
System.out.println("Initialized the client and connected to the cluster."); | Initialized the client and connected to the cluster.
| MIT | notebooks/java/java-advanced_collection_data_types.ipynb | markprincely/interactive-notebooks |
Create CDT Data, Put into Aerospike, and Print It | import com.aerospike.client.Key;
import com.aerospike.client.Bin;
import com.aerospike.client.policy.ClientPolicy;
import com.aerospike.client.Record;
import com.aerospike.client.Operation;
import com.aerospike.client.Value;
import com.aerospike.client.cdt.ListOperation;
import com.aerospike.client.cdt.ListPolicy;
impo... | listwhalebin: [[1420, beluga whale, Beaufort Sea, Bering Sea], [13988, gray whale, Baja California, Chukchi Sea], [1278, north pacific right whale, Japan, Sea of Okhotsk], [5100, humpback whale, Columbia, Antarctic Peninsula], [3100, southern hemisphere blue whale, Corcovado Gulf, The Galapagos]]
mapobsbin: {13456={la... | MIT | notebooks/java/java-advanced_collection_data_types.ipynb | markprincely/interactive-notebooks |
Using Contexts (CTXs) to work with Nested CDTsWhat are Nested CDTs and CTXs? What is a Nested CDT?The primary use case of Key-Value Stores, like Aerospike Database, is to store document-oriented data, like a JSON map. As document-oriented data grows organically, it is common for one CDT (list or map) to contain anoth... | import com.aerospike.client.cdt.CTX;
import com.aerospike.client.cdt.MapReturnType;
Integer lookupMapKey = 14567;
String latKeyName = "lat";
Record whaleSightings = client.operate(client.writePolicyDefault, whaleKey,
MapOperation.getByKey(mapObsBinName, Value.get(latKeyName), MapReturnType.VALUE, CTX.mapKey(Valu... | mapobsbin: {13456={lat=-25, long=-50}, 14567={lat=35, long=30}, 12345={lat=-85, long=-130}}
The lat of sighting at timestamp 14567: 35
| MIT | notebooks/java/java-advanced_collection_data_types.ipynb | markprincely/interactive-notebooks |
Drill down into a List or MapHere are the options to drill down into a CDT.Drilling down to a CTX in a List:* `listIndex`: Lookup list by index offset.* `listRank`: Lookup list by rank.* `listValue`: Lookup list by value.Drilling down to a CTX in a Map: * `mapIndex`: Lookup map by index offset.* `mapRank`: Lookup map ... | import com.aerospike.client.cdt.ListReturnType;
// CDT Drilldown Values
Integer drilldownIndex = 2;
Integer drilldownRank = 1;
Value listDrilldownValue = Value.get(whaleMigration1);
Value mapDrilldownValue = Value.get(mapCoords0);
// Variables to access parts of the selected CDT.
Integer getIndex = 1;
Record theRe... | The whale migration list is: [[1420, beluga whale, Beaufort Sea, Bering Sea], [13988, gray whale, Baja California, Chukchi Sea], [1278, north pacific right whale, Japan, Sea of Okhotsk], [5100, humpback whale, Columbia, Antarctic Peninsula], [3100, southern hemisphere blue whale, Corcovado Gulf, The Galapagos]]
The wh... | MIT | notebooks/java/java-advanced_collection_data_types.ipynb | markprincely/interactive-notebooks |
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