Dataset Integration

This section covers the integration of the imdb dataset into Tensorleap. We'll later use this dataset with a classification model.

Dataset Script

Below is the full dataset script to be used in the integration. More information about the structure of this script can be found under Dataset Script.
from typing import List, Optional, Callable, Tuple, Dict
import json, os, re, string
from os.path import basename, dirname, join
import pandas as pd
import numpy as np
from google.auth.credentials import AnonymousCredentials
from import storage
from import Bucket
from keras_preprocessing.text import Tokenizer as TokenizerType
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import tokenizer_from_json
from pandas.core.frame import DataFrame as DataFrameType
# Tensorleap imports
from code_loader import leap_binder
from code_loader.contract.datasetclasses import PreprocessResponse
from code_loader.contract.enums import DatasetMetadataType, LeapDataType, Metric
from code_loader.contract.visualizer_classes import LeapText
BUCKET_NAME = 'example-datasets-47ml982d'
PROJECT_ID = 'example-dev-project-nmrksf0o'
### Helper Functions: ###
def _connect_to_gcs() -> Bucket:
gcs_client = storage.Client(project=PROJECT_ID, credentials=AnonymousCredentials())
return gcs_client.bucket(BUCKET_NAME)
def _download(cloud_file_path: str, local_file_path: Optional[str] = None) -> str:
BASE_PATH = "imdb"
cloud_file_path = join(BASE_PATH, cloud_file_path)
# if local_file_path is not specified saving in home dir
if local_file_path is None:
home_dir = os.getenv("HOME")
assert home_dir is not None
local_file_path = os.path.join(home_dir, "Tensorleap_data", BUCKET_NAME, cloud_file_path)
# check if file already exists
if os.path.exists(local_file_path):
return local_file_path
bucket = _connect_to_gcs()
dir_path = os.path.dirname(local_file_path)
os.makedirs(dir_path, exist_ok=True)
blob = bucket.blob(cloud_file_path)
return local_file_path
def load_tokenizer(tokenizer_path: str) -> TokenizerType:
with open(tokenizer_path, 'r') as f:
data = json.load(f)
tokenizer = tokenizer_from_json(data)
return tokenizer
def download_load_assets() -> Tuple[TokenizerType, DataFrameType]:
cloud_path = join("assets", "imdb.csv")
local_path = _download(cloud_path)
df = pd.read_csv(local_path)
cloud_path = join("assets", "tokenizer_v2.json")
local_path = _download(cloud_path)
tokenizer = load_tokenizer(local_path)
return tokenizer, df
# Preprocess Function
def preprocess_func() -> List[PreprocessResponse]:
tokenizer, df = download_load_assets()
train_label_size = int(0.9 * NUMBER_OF_SAMPLES / 2)
val_label_size = int(0.1 * NUMBER_OF_SAMPLES / 2)
df = df[df['subset'] == 'train']
train_df = pd.concat([df[df['gt'] == 'pos'][:train_label_size], df[df['gt'] == 'neg'][:train_label_size]], ignore_index=True)
val_df = pd.concat([df[df['gt'] == 'pos'][train_label_size:train_label_size + val_label_size], df[df['gt'] == 'neg'][train_label_size:train_label_size + val_label_size]], ignore_index=True)
ohe = {"pos": [1.0, 0.], "neg": [0., 1.0]}
# Generate a PreprocessResponse for each data slice, to later be read by the encoders.
# The length of each data slice is provided, along with the data dictionary.
# In this example we pass `images` and `labels` that later are encoded into the inputs and outputs
train = PreprocessResponse(length=2 * train_label_size, data={"df": train_df, "tokenizer": tokenizer, "ohe": ohe})
val = PreprocessResponse(length=2 * val_label_size, data={"df": val_df, "tokenizer": tokenizer, "ohe": ohe})
response = [train, val]
# Adding custom data to leap_binder for later usage within the visualizer function
leap_binder.custom_tokenizer = tokenizer
return response
# Input Encoder Helper Functions
def standardize(comment: str) -> str:
lowercase = comment.lower()
html_stripped = re.sub('<br />', ' ', lowercase)
punctuation_stripped = re.sub('[%s]' % re.escape(string.punctuation), '', html_stripped)
return punctuation_stripped
def prepare_input(tokanizer: TokenizerType, input_text: str, sequence_length: int = 250) -> np.ndarray:
standard_text = standardize(input_text)
tokanized_input = tokanizer.texts_to_sequences([standard_text])
padded_input = pad_sequences(tokanized_input, maxlen=sequence_length)
return padded_input[0, ...]
# Input Encoder - fetches the text with the index `idx` from the `paths` array set in
# the PreprocessResponse's data. Returns a numpy array containing padded tokenized input.
def input_tokens(idx: int, preprocess: PreprocessResponse) -> np.ndarray:
comment_path =['df']['paths'][idx]
local_path = _download(comment_path)
with open(local_path, 'r') as f:
comment =
tokenizer =['tokenizer']
padded_input = prepare_input(tokenizer, comment)
return padded_input
# Ground Truth Encoder - fetches the label with the index `idx` from the `gt` array set in
# the PreprocessResponse's data. Returns a numpy array containing a hot vector label correlated with the sample.
def gt_sentiment(idx: int, preprocess: PreprocessResponse) -> List[float]:
gt_str =['df']['gt'][idx]
# Metadata functions allow to add extra data for a later use in analysis.
# This metadata adds the ground truth of each sample (not a hot vector).
def gt_metadata(idx: int, preprocess: PreprocessResponse) -> str:
if['df']['gt'][idx] == "pos":
return "positive"
return "negative"
# Visualizer functions define how to interpet the data and visualize it.
# In this example we define a tokens-to-text visualizer.
def text_visualizer_func(data: np.ndarray) -> LeapText:
tokenizer = leap_binder.custom_tokenizer
texts = tokenizer.sequences_to_texts([data])
return LeapText(texts[0].split(' '))
# Binders
leap_binder.set_input(function=input_tokens, name='tokens')
leap_binder.set_ground_truth(function=gt_sentiment, name='sentiment')
leap_binder.set_metadata(function=gt_metadata, metadata_type=DatasetMetadataType.string, name='gt')
leap_binder.set_visualizer(function=text_visualizer_func, visualizer_type=LeapDataType.Text, name='text_from_token')
leap_binder.add_prediction(name='sentiment', labels=['positive','negative'])

Add Integration Script


Add a Dataset Using UI

To add a new Dataset:
  1. 1.
    Navigate to the Resources Management and click on the
  2. 2.
    In the Dataset Editor, enter these properties:
    • Dataset Name: imdb
    • Script: copy and paste the script from the Dataset Script above
  3. 3.
    Click Save.
Dataset Instance
After saving the imdb dataset, the platform will automatically parse the database script. This process evaluates the script and ensures that all its functions, including the ability to successfully read the data, are working as expected.
Upon successful parsing, the details of the MNIST dataset will be displayed on the right. In case of unsuccessful parsing, errors will be shown instead.

Initial CLI Setup

Verify that leapcli is installed. For more informaton, see Installing Leap CLI.

Project Folder Setup

  1. 1.
    Create a folder for our imdb project.
    mkdir imdb
    cd imdb
  2. 2.
    Initialize and synchronize the created folder with the Tensorleap platform by running a command that will set up the .tensorleap folder within the project folder. The command leap init (PROJECT) (DATASET) (--h5/--onnx) should be set with the following parameters:
    • PROJECT = IMDB (project name)
    • DATASET = imdb (dataset name)
    • (--h5/--onnx) = model format, --h5 for Tensorflow (H5) and --onnx for PyTorch (ONNX)
    leap init IMDB myorg imdb --h5
  3. 3.
    Next, we need to set your credentials to leap CLI by running the following command:
    leap login [API_ID] [API_KEY]
The API_ID , API_KEY and the ORIGIN, along with the full command, can easily be found by clicking the
button within the Resources Management view.

Push Dataset

When using the CLI, the Dataset Script is defined within the .tensorleap/ file, and the Dataset Instance is created/updated upon performing leap push.
  1. 1.
    By default, the .tensorleap/ file has a sample template. Let's replace it with our Dataset Script above. One way to do it is withvim:
    rm .tensorleap/
    cat > .tensorleap/
    << paste the dataset script above + CTRL-D >>
  2. 2.
    Let's test our dataset script using leap check:
    leap check --dataset
  3. 3.
    Next, we'll push our dataset to the Tensorleap platform using the following command:
    leap push --dataset
It should print out:
New dataset detected. Dataset name: imdb Push command successfully complete
Congrats! You have successfully created the imdb Dataset Instance and integrated the Dataset Script. You can view it in the UI in the Resources Management view.

Up Next - Model Integration

The purpose of this section was to help you define a dataset script and create a dataset instance in Tensorleap.
Now that the imdb dataset has been integrated into Tensorleap, we can use it with a classification model. That's what we'll do in the next section, where we'll build a classification model.
When ready, move on to Model Integration.