This API guide provides a quick overview of Vectice's simple Python API calls to help you get started auto-documenting your datasets, models, and notes.
# Install Vectice latest package
pip install vectice
# Install a specific version of the Vectice package
pip install vectice==<version number>
Connect to Vectice
Get started by connecting to the Vectice API and starting an iteration.
#import and connect to Vectice
import vectice
connect = vectice.connect(
api_token = 'your-api-key', # Paste your api key
host = 'https://app.vectice.com', # Paste your host
)
# Connect to your project phase using your phase ID
phase = connect.phase("PHA-XXX") #You can fetch the relevant phase ID from your chosen Vectice project in the app.
#Create an iteration
iteration = phase.create_or_get_current_iteration()
#import and connect to Vectice
import vectice
connect = vectice.connect(config="your-api-config.json") #Enter your API key JSON file
# Connect to your project phase using your phase ID
phase = connect.phase("PHA-XXX") #You can fetch the relevant phase ID from your chosen Vectice project in the app.
#Create an iteration
iteration = phase.create_or_get_current_iteration()
Auto-document your assets
Auto-document your notes, datasets, and models directly to Vectice.
Auto-document NOTES
# Auto-document your first comments or notes
iteration.log("this is a comment")
Auto-document DATASETS
For instructions on using these resources, refer to the Vectice API Reference guide's Resources section.
from vectice import FileResource, Dataset
# Auto-document your first dataset from a local file
file_resource = FileResource(paths="my/file/path", dataframe=your_df)
clean_dataset = Dataset.clean(resource=file_resource, name="your_dataset_name")
iteration.log(clean_dataset)
from vectice import BigQueryResource, Dataset
# Auto-document your first dataset from BigQuery
bq_resource = BigQueryResource(paths="your-bq-table", dataframes=your_df)
clean_dataset = Dataset.clean(resource=bq_resource, name="your_dataset_name")
iteration.log(clean_dataset)
from vectice import S3Resource, Dataset
# Auto-document your first dataset from S3
s3_resource = S3Resource(uris="s3://.../<file_path_inside_bucket>", dataframes=your_df)
clean_dataset = Dataset.clean(resource=s3_resource, name="your_dataset_name")
iteration.log(clean_dataset)
from vectice import GCSResource, Dataset
# Auto-document your first dataset from GCS
gcs_resource = GCSResource(uris="gs://.../<file_path_inside_bucket>", dataframes=your_df)
clean_dataset = Dataset.clean(gcs_resource, name, attachments)
iteration.log(clean_dataset)
from vectice import DatabricksTableResource, Dataset
# Auto-document your first dataset from Databricks
db_resource = DatabricksTableResource(paths="my-table", dataframes=your_df)
clean_dataset = Dataset.clean(resource=gcs_resource, name="your_dataset_name")
iteration.log(clean_dataset)
from vectice import SnowflakeResource, Dataset
# Auto-document your first dataset from Snowflake
connection_parameters = {
...
}
new_session = Session.builder.configs(connection_parameters).create()
sf_resource = SnowflakeResource(
dataframes=your_df
snowflake_client=new_session,
paths="SNOWFLAKE_SAMPLE_DATA.TPCH_SF10.PART",
)
clean_dataset = Dataset.clean(resource=sf_resource, name="your_dataset_name")
iteration.log(clean_dataset)
from vectice import SparkTableResource, Dataset
# Auto-document your first dataset from SparkTable
st_resource = SparkTableResource(
dataframes=your_df
spark_client=spark,
paths="my_table",
)
clean_dataset = Dataset.clean(resource=st_resource, name="your_dataset_name")
iteration.log(clean_dataset)
Auto-document MODELS
from vectice import Model
# Auto-document your first model
model = Model(metrics, properties, attachments, predictor)
iteration.log(model)
import mlflow
from mlflow.client import MlflowClient
from vectice import Model
# Auto-document your first model
model = Model.mlflow(
run_id="your-mlflow-run-id",
client=MlflowClient() #also work with client=mlflow
)
iteration.log(model)
Close your iteration
Once you are done logging your assets for an iteration, mark it complete.
# Completes and closes the current iteration once you are happy with it
iteration.complete()
