<jupyter_start><jupyter_text>Client Integration (scikit-learn) This example takes scikit-learn's [MNIST SVM example](https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html) and incorportates Verta's Client integration.<jupyter_code>HOST = "app.verta.ai"

PROJECT_NAME = "MNIST Multiclassification"
EXPERIMENT_NAME = "SVM"
# import os
# os.environ['VERTA_EMAIL'] = 
# os.environ['VERTA_DEV_KEY'] = 
from verta import Client

client = Client(HOST)
proj = client.set_project(PROJECT_NAME)
expt = client.set_experiment(EXPERIMENT_NAME)<jupyter_output><empty_output><jupyter_text>Imports<jupyter_code>from __future__ import print_function

from sklearn import datasets, svm, metrics
from sklearn.model_selection import train_test_split<jupyter_output><empty_output><jupyter_text>---  Log Workflow  Prepare Data<jupyter_code>digits = datasets.load_digits()
n_samples = len(digits.images)
data = digits.images.reshape((n_samples, -1))

X_train, X_test, y_train, y_test = train_test_split(
    data, digits.target, test_size=0.5, shuffle=False)<jupyter_output><empty_output><jupyter_text>Define Model<jupyter_code>classifier = svm.SVC(gamma=0.001)<jupyter_output><empty_output><jupyter_text>Run and Log Training<jupyter_code>run = client.set_experiment_run()
import verta.integrations.sklearn


classifier.fit(X_train, y_train, run=run)
run<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11, 'fraction_numerical': 0.005, 'mean_word_length': 8.662068965517241, 'pattern_counts': {'":': 0, '<': 21, '<?xml version=': 0, '>': 21, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '240115', 'license': 'Apache-2.0', 'n_tokens_mistral': 528, 'n_tokens_neox': 509, 'n_words': 79, 'path': 'client/workflows/examples/sklearn-integration.ipynb', 'repo_name': 'CaptEmulation/modeldb', 'size': '1400'}