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from alembic import op import sqlalchemy as sa def upgrade() -> None: # ### commands auto generated by Alembic - please adjust! ### op.add_column('block_run', sa.Column('metrics', sa.JSON(), nullable=True)) # ### end Alembic commands ###
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from alembic import op import sqlalchemy as sa def downgrade() -> None: # ### commands auto generated by Alembic - please adjust! ### op.drop_column('block_run', 'metrics') # ### end Alembic commands ###
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from alembic import op import sqlalchemy as sa def upgrade() -> None: # ### commands auto generated by Alembic - please adjust! ### op.create_table('backfill', sa.Column('id', sa.Integer(), autoincrement=True, nullable=False), sa.Column('block_uuid', sa.String(length=255), nullable=True), sa.Column...
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from alembic import op import sqlalchemy as sa def downgrade() -> None: # ### commands auto generated by Alembic - please adjust! ### with op.batch_alter_table('pipeline_run', schema=None) as batch_op: batch_op.drop_constraint('pipeline_run_backfill_id', type_='foreignkey') batch_op.drop_column...
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from alembic import op import sqlalchemy as sa def upgrade() -> None: # ### commands auto generated by Alembic - please adjust! ### op.create_table('tag', sa.Column('id', sa.Integer(), autoincrement=True, nullable=False), sa.Column('created_at', sa.DateTime(timezone=True), server_default=sa.text('(CURR...
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from alembic import op import sqlalchemy as sa def downgrade() -> None: # ### commands auto generated by Alembic - please adjust! ### with op.batch_alter_table('tag_association', schema=None) as batch_op: batch_op.drop_index(batch_op.f('ix_tag_association_taggable_id')) op.drop_table('tag_associat...
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from alembic import op import sqlalchemy as sa def upgrade() -> None: # ### commands auto generated by Alembic - please adjust! ### with op.batch_alter_table('oauth2_access_token', schema=None) as batch_op: batch_op.create_index(batch_op.f('ix_oauth2_access_token_token'), ['token'], unique=True) w...
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from alembic import op import sqlalchemy as sa def downgrade() -> None: # ### commands auto generated by Alembic - please adjust! ### with op.batch_alter_table('user', schema=None) as batch_op: batch_op.drop_index(batch_op.f('ix_user_username')) batch_op.drop_index(batch_op.f('ix_user_email')) ...
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from alembic import op import sqlalchemy as sa def upgrade() -> None: # ### commands auto generated by Alembic - please adjust! ### op.create_table('role_permission', sa.Column('id', sa.Integer(), autoincrement=True, nullable=False), sa.Column('created_at', sa.DateTime(timezone=True), server_default=sa...
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from alembic import op import sqlalchemy as sa def downgrade() -> None: # ### commands auto generated by Alembic - please adjust! ### op.drop_table('role_permission') # ### end Alembic commands ###
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from alembic import op import sqlalchemy as sa def upgrade() -> None: # ### commands auto generated by Alembic - please adjust! ### op.create_table('role', sa.Column('id', sa.Integer(), autoincrement=True, nullable=False), sa.Column('created_at', sa.DateTime(timezone=True), server_default=sa.text('(CUR...
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from alembic import op import sqlalchemy as sa def downgrade() -> None: # ### commands auto generated by Alembic - please adjust! ### op.drop_table('user_role') op.drop_table('permission') op.drop_table('role') # ### end Alembic commands ###
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from alembic import op import sqlalchemy as sa def upgrade() -> None: # ### commands auto generated by Alembic - please adjust! ### bind = op.get_bind() if bind.engine.name == 'postgresql': with op.get_context().autocommit_block(): op.execute("ALTER TYPE blockrunstatus ADD VALUE 'UPSTRE...
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from alembic import op import sqlalchemy as sa def downgrade() -> None: # ### commands auto generated by Alembic - please adjust! ### bind = op.get_bind() if bind.engine.name == 'postgresql': op.execute("ALTER TYPE blockrunstatus RENAME TO blockrunstatus_old") op.execute("CREATE TYPE blockr...
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import logging import os import sys from logging.config import fileConfig from alembic import context from sqlalchemy import engine_from_config, pool config = context.config if ( config.config_file_name is not None and config.attributes.get('configure_logger', True) ): fileConfig(config.config_file_name) im...
Run migrations in 'offline' mode. This configures the context with just a URL and not an Engine, though an Engine is acceptable here as well. By skipping the Engine creation we don't even need a DBAPI to be available. Calls to context.execute() here emit the given string to the script output.
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import logging import os import sys from logging.config import fileConfig from alembic import context from sqlalchemy import engine_from_config, pool config = context.config if ( config.config_file_name is not None and config.attributes.get('configure_logger', True) ): fileConfig(config.config_file_name) im...
Run migrations in 'online' mode. In this scenario we need to create an Engine and associate a connection with the context.
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import multiprocessing def start_session_and_run(*target_args, **kwargs): from mage_ai.orchestration.db import db_connection if len(target_args) == 0: return None target = target_args[0] args = target_args[1:] db_connection.start_session(force=True) try: results = target(*args) ...
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from mage_ai.orchestration.db import db_connection, safe_db_query from mage_ai.orchestration.db.models.schedules import ( Backfill, PipelineRun, PipelineSchedule, ) db_connection = DBConnection() class PipelineSchedule(PipelineScheduleProjectPlatformMixin, BaseModel): name = Column(String(255)) de...
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from datetime import datetime from typing import Dict, Optional from mage_ai.api.resources.PipelineScheduleResource import PipelineScheduleResource from mage_ai.data_preparation.models.pipeline import Pipeline from mage_ai.data_preparation.models.triggers import ScheduleStatus, ScheduleType from mage_ai.orchestration.d...
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import asyncio import json from datetime import datetime, timedelta, timezone from logging import Logger from time import sleep from typing import Dict, List, Optional from mage_ai.data_preparation.logging.logger import DictLogger from mage_ai.data_preparation.models.global_data_product import GlobalDataProduct from ma...
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import os import subprocess from typing import Tuple import psutil def get_compute() -> Tuple[float, float, float, float]: # Getting loadover15 minutes load1, load5, load15 = psutil.getloadavg() cpu_count = os.cpu_count() return load1, load5, load15, cpu_count
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import os import subprocess from typing import Tuple import psutil def get_memory() -> Tuple[float, float, float]: free_memory = None total_memory = None used_memory = None try: output = subprocess.check_output('free -t -m', shell=True).decode('utf-8') values = output.splitlines()[-1]....
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import pandas as pd def escape_quotes(line: str, single: bool = True, double: bool = True) -> str: new_line = str(line) if single: new_line = new_line.replace("'", "''") if double: new_line = new_line.replace('\"', '\\"') return new_line def format_value(value): if type(value) is no...
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from enum import Enum from typing import Callable, Dict, List, Mapping from pandas import DataFrame, Series from pandas.api.types import infer_dtype from mage_ai.shared.utils import clean_name The provided code snippet includes necessary dependencies for implementing the `infer_dtypes` function. Write a Python functio...
Fetches the internal pandas datatypes for the columns in the data frame. Args: df (DataFrame): Data frame to fetch dtypes from. Returns: Dict[str, str]: Map of column names to inferred dtypes
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from enum import Enum from typing import Callable, Dict, List, Mapping from pandas import DataFrame, Series from pandas.api.types import infer_dtype from mage_ai.shared.utils import clean_name The provided code snippet includes necessary dependencies for implementing the `clean_df_for_export` function. Write a Python ...
Cleans data frame with the appropriate steps to prepare loading the data frame to the target database. Args: df (DataFrame): Data frame to clean. column_mapper (Callable[[Series, str], str]): Function that cleans a column given the pandas data type. dtypes (Mapping[str, str]): Name of the new table to create Returns: s...
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from enum import Enum from typing import Callable, Dict, List, Mapping from pandas import DataFrame, Series from pandas.api.types import infer_dtype from mage_ai.shared.utils import clean_name def clean_name( name, allow_characters: List[str] = None, allow_number: bool = False, case_sensitive: bool = F...
Generates a database table creation query from a data frame. Args: dtypes (Mapping[str, str]): Database relative data types for each column of the data frame. schema_name (str): Name of schema to create new table in. table_name (str): Name of the new table to create. Returns: str: Table creation query for this table.
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import json import os from typing import Dict from google.api.launch_stage_pb2 import LaunchStage from google.api_core.exceptions import AlreadyExists from google.cloud import run_v2 from google.oauth2 import service_account from google.protobuf.duration_pb2 import Duration from mage_ai.server.logger import Logger from...
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import os import socket from enum import Enum from typing import List import requests from mage_ai.services.aws.emr.constants import SECURITY_GROUP_NAME_MASTER_DEFAULT from mage_ai.services.compute.aws.constants import ( CONNECTION_CREDENTIAL_AWS_ACCESS_KEY_ID, CONNECTION_CREDENTIAL_AWS_SECRET_ACCESS_KEY, ) fro...
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import os import socket from enum import Enum from typing import List import requests from mage_ai.services.aws.emr.constants import SECURITY_GROUP_NAME_MASTER_DEFAULT from mage_ai.services.compute.aws.constants import ( CONNECTION_CREDENTIAL_AWS_ACCESS_KEY_ID, CONNECTION_CREDENTIAL_AWS_SECRET_ACCESS_KEY, ) fro...
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import traceback import redis import redis def init_redis_client(redis_url): if not redis_url: return None try: redis_client = redis.Redis.from_url(url=redis_url, decode_responses=True) redis_client.ping() except Exception: traceback.print_exc() redis_client = None ...
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import json import os import socket from typing import Dict from sshtunnel import SSHTunnelForwarder from mage_ai.data_preparation.models.project import Project from mage_ai.services.compute.aws.models import Cluster from mage_ai.services.compute.constants import SSH_PORT from mage_ai.services.compute.models import Com...
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import json import os import socket from typing import Dict from sshtunnel import SSHTunnelForwarder from mage_ai.data_preparation.models.project import Project from mage_ai.services.compute.aws.models import Cluster from mage_ai.services.compute.constants import SSH_PORT from mage_ai.services.compute.models import Com...
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import os import time import traceback from azure.identity import DefaultAzureCredential from azure.mgmt.containerinstance import ContainerInstanceManagementClient from azure.mgmt.containerinstance.models import ( Container, ContainerGroup, ContainerGroupRestartPolicy, ResourceRequests, ResourceRequ...
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import logging from botocore.exceptions import ClientError logger = logging.getLogger(__name__) The provided code snippet includes necessary dependencies for implementing the `terminate_cluster` function. Write a Python function `def terminate_cluster(cluster_id, emr_client)` to solve the following problem: Terminates...
Terminates a cluster. This terminates all instances in the cluster and cannot be undone. Any data not saved elsewhere, such as in an Amazon S3 bucket, is lost. :param cluster_id: The ID of the cluster to terminate. :param emr_client: The Boto3 EMR client object.
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import logging from botocore.exceptions import ClientError logger = logging.getLogger(__name__) The provided code snippet includes necessary dependencies for implementing the `add_step` function. Write a Python function `def add_step(cluster_id, name, script_uri, script_args, emr_client)` to solve the following proble...
Adds a job step to the specified cluster. This example adds a Spark step, which is run by the cluster as soon as it is added. :param cluster_id: The ID of the cluster. :param name: The name of the step. :param script_uri: The URI where the Python script is stored. :param script_args: Arguments to pass to the Python scr...
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import logging from botocore.exceptions import ClientError logger = logging.getLogger(__name__) The provided code snippet includes necessary dependencies for implementing the `describe_step` function. Write a Python function `def describe_step(cluster_id, step_id, emr_client)` to solve the following problem: Gets deta...
Gets detailed information about the specified step, including the current state of the step. :param cluster_id: The ID of the cluster. :param step_id: The ID of the step. :param emr_client: The Boto3 EMR client object. :return: The retrieved information about the specified step.
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import json import logging import random import sys import time from datetime import datetime from typing import List from botocore.exceptions import ClientError from mage_ai.services.aws import get_aws_boto3_client from mage_ai.services.aws.emr import emr_basics from mage_ai.services.aws.emr.config import EmrConfig fr...
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import json import logging import random import sys import time from datetime import datetime from typing import List from botocore.exceptions import ClientError from mage_ai.services.aws import get_aws_boto3_client from mage_ai.services.aws.emr import emr_basics from mage_ai.services.aws.emr.config import EmrConfig fr...
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import json from typing import Dict, List, Union from mage_ai.services.aws import get_aws_boto3_client from mage_ai.services.aws.ecs.config import EcsConfig def get_aws_boto3_client(aws_service): import boto3 from botocore.config import Config config = Config(region_name=get_aws_region_name()) kwargs ...
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import json from typing import Dict, List, Union from mage_ai.services.aws import get_aws_boto3_client from mage_ai.services.aws.ecs.config import EcsConfig def get_aws_boto3_client(aws_service): import boto3 from botocore.config import Config config = Config(region_name=get_aws_region_name()) kwargs ...
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import json from typing import Dict, List, Union from mage_ai.services.aws import get_aws_boto3_client from mage_ai.services.aws.ecs.config import EcsConfig def get_aws_boto3_client(aws_service): def list_tasks(cluster) -> List[Dict]: ecs_client = get_aws_boto3_client('ecs') task_arns = ecs_client.list_tasks...
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import json from typing import Dict, List, Union from mage_ai.services.aws import get_aws_boto3_client from mage_ai.services.aws.ecs.config import EcsConfig def get_aws_boto3_client(aws_service): import boto3 from botocore.config import Config config = Config(region_name=get_aws_region_name()) kwargs ...
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import os import uuid from mage_ai.services.aws import get_aws_boto3_client EVENT_RULE_LIMIT = 100 def get_aws_boto3_client(aws_service): def get_all_event_rules(): client = get_aws_boto3_client('events') response = client.list_rules( Limit=EVENT_RULE_LIMIT ) formatted_rules = [ dict(...
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import os import uuid from mage_ai.services.aws import get_aws_boto3_client def get_aws_boto3_client(aws_service): import boto3 from botocore.config import Config config = Config(region_name=get_aws_region_name()) kwargs = dict() aws_access_key_id = get_aws_access_key_id() if aws_access_key_i...
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import os from typing import List from thefuzz import fuzz from mage_ai.cache.block_action_object import BlockActionObjectCache from mage_ai.cache.block_action_object.constants import ( OBJECT_TYPE_BLOCK_FILE, OBJECT_TYPE_MAGE_TEMPLATE, ) from mage_ai.shared.custom_logger import DX_PRINTER DEFAULT_RATIO = 50 de...
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import requests from mage_ai.services.discord.config import DiscordConfig class DiscordConfig(BaseConfig): webhook_url: str = None def is_valid(self) -> bool: return self.webhook_url is not None and self.webhook_url != 'None' def send_discord_message(config: DiscordConfig, message: str, title: str) -...
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import os from typing import List from mage_ai.services.spark.config import SparkConfig def has_same_spark_config(spark_session, spark_config: SparkConfig) -> bool: """ Checks if the spark session has the same configuration as the spark config. Args: spark_session (SparkSession): The spark session. ...
Gets a Spark session. If the given spark_config is None, then create a Spark session with the default configuration. If the given spark_config is not None, then check if the active Spark session has the same configuration as the given spark_config. If the active Spark session has the same configuration as the given spa...
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import os import time from typing import Dict, Union from kubernetes import client, config from kubernetes.client import V1Container, V1PodSpec from kubernetes.client.rest import ApiException from mage_ai.services.k8s.config import K8sExecutorConfig from mage_ai.services.k8s.constants import ( DEFAULT_NAMESPACE, ...
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import os import time from typing import Dict, Union from kubernetes import client, config from kubernetes.client import V1Container, V1PodSpec from kubernetes.client.rest import ApiException from mage_ai.services.k8s.config import K8sExecutorConfig from mage_ai.services.k8s.constants import ( DEFAULT_NAMESPACE, ...
Merge two V1Container objects. The merging process follows these rules: - For non-list and non-dict attributes, if the attribute in `left` is not None, use that; otherwise, use the attribute in `right`. - For list attributes (excluding 'command'), append non-duplicate elements from `right` to `left`. - For the 'command...
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from mage_ai.services.opsgenie.config import OpsgenieConfig import requests import json class OpsgenieConfig(BaseConfig): url: str = None api_key: str = None priority: str = "P3" responders: List[Dict] = field(default_factory=list) tags: List = field(default_factory=list) details: Dict = field(...
Opens an alert in Opsgenie with the given message and description. Args: config (OpsgenieConfig): Opsgenie config dataclass message (str): The title of the alert. description (str): The message body of the alert.
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from mage_ai.services.teams.config import TeamsConfig import requests class TeamsConfig(BaseConfig): webhook_url: str = None def is_valid(self) -> bool: return self.webhook_url is not None and self.webhook_url != 'None' def send_teams_message( config: TeamsConfig, message: str, title: str...
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import json import requests from mage_ai.services.slack.config import SlackConfig class SlackConfig(BaseConfig): webhook_url: str = None def is_valid(self) -> bool: return self.webhook_url is not None and self.webhook_url != 'None' def send_slack_message(config: SlackConfig, message: str, title: str ...
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from mage_ai.services.google_chat.config import GoogleChatConfig import requests class GoogleChatConfig(BaseConfig): def is_valid(self) -> bool: def send_google_chat_message( config: GoogleChatConfig, message: str, title: str = 'Mage pipeline run status logs', ) -> None: requests.post( ur...
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import requests from mage_ai.services.telegram.config import TelegramConfig class TelegramConfig(BaseConfig): webhook_url: str = None def is_valid(self) -> bool: return self.webhook_url is not None and self.webhook_url != 'None' def send_telegram_message(config: TelegramConfig, message: str, title: s...
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import smtplib from email.message import EmailMessage from mage_ai.services.email.config import EmailConfig class EmailConfig(BaseConfig): smtp_host: str smtp_mail_from: str smtp_user: str = None smtp_password: str = None smtp_starttls: bool = True smtp_ssl: bool = False smtp_port: int = 58...
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import os from typing import Dict, List, Tuple from mage_ai.data_preparation.models.constants import ( PIPELINE_CONFIG_FILE, PIPELINES_FOLDER, ) from mage_ai.settings.platform import ( build_repo_path_for_all_projects, get_repo_paths_for_file_path, project_platform_activated, repo_path_from_data...
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import os from typing import Dict, List, Tuple from mage_ai.data_preparation.models.constants import ( PIPELINE_CONFIG_FILE, PIPELINES_FOLDER, ) from mage_ai.settings.platform import ( build_repo_path_for_all_projects, get_repo_paths_for_file_path, project_platform_activated, repo_path_from_data...
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import secrets import string def generate_jwt_secret(length=64): characters = string.ascii_letters + string.digits return ''.join(secrets.choice(characters) for _ in range(length))
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import os from .secret_generation import generate_jwt_secret try: DISABLE_NOTEBOOK_EDIT_ACCESS = int(os.getenv('DISABLE_NOTEBOOK_EDIT_ACCESS', 0)) except ValueError: DISABLE_NOTEBOOK_EDIT_ACCESS = 1 if os.getenv('DISABLE_NOTEBOOK_EDIT_ACCESS') else 0 def is_disable_pipeline_edit_access( disable_notebook_ed...
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import os from .secret_generation import generate_jwt_secret The provided code snippet includes necessary dependencies for implementing the `get_bool_value` function. Write a Python function `def get_bool_value(value: str) -> bool` to solve the following problem: Converts a string environment variable to a bool value....
Converts a string environment variable to a bool value. Returns True if the value is 'true', '1', or 't' (case insensitive). Otherwise, False
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import asyncio import os from dataclasses import dataclass from enum import Enum from pathlib import Path from typing import Dict, Union import aiofiles import yaml from jinja2 import Template from mage_ai.authentication.permissions.constants import EntityName from mage_ai.data_preparation.models.constants import Block...
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import os from datetime import datetime from typing import Dict, List from mage_ai.cluster_manager.constants import ( ECS_CLUSTER_NAME, GCP_PATH_TO_KEYFILE, GCP_PROJECT_ID, GCP_REGION, KUBE_NAMESPACE, ClusterType, ) from mage_ai.cluster_manager.workspace.base import Workspace from mage_ai.data_p...
Check and potentially terminate idle workspaces in a given cluster. Currently, this is only supported for Kubernetes clusters. This function is responsible for checking and, if necessary, terminating idle workspaces in a specified cluster. 1. Retrieve a list of workspaces based on the cluster_type by calling get_worksp...
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import time from collections.abc import Iterable from dataclasses import dataclass from typing import Dict, List from influxdb_client import InfluxDBClient, WriteOptions, WritePrecision from mage_ai.shared.config import BaseConfig from mage_ai.streaming.constants import DEFAULT_BATCH_SIZE, DEFAULT_TIMEOUT_MS from mage_...
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import datetime import time from dataclasses import dataclass from typing import Callable, Tuple from influxdb_client import InfluxDBClient, QueryApi from influxdb_client.client.flux_table import TableList from mage_ai.shared.config import BaseConfig from mage_ai.streaming.constants import DEFAULT_BATCH_SIZE from mage_...
Build flux query for data in the range (start_time - time_delay, stop_time - time_delay) Args: bucket (str): Bucket to query from. stop_time (float): Time range stop timestamp. time_delay (str): Flux duration. E.g. 3d12h4m25s for 3 days, 12 hours, 4 minutes, and 25 seconds. Documentation: https://docs.influxdata.com/fl...
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import datetime import time from dataclasses import dataclass from typing import Callable, Tuple from influxdb_client import InfluxDBClient, QueryApi from influxdb_client.client.flux_table import TableList from mage_ai.shared.config import BaseConfig from mage_ai.streaming.constants import DEFAULT_BATCH_SIZE from mage_...
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import time import uuid from dataclasses import dataclass from typing import Callable import stomp from stomp.exception import ConnectFailedException from mage_ai.shared.config import BaseConfig from mage_ai.streaming.sources.base import BaseSource def messageProcessingFunction(message, handler): print('Recieved m...
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import multiprocessing import time import traceback from contextlib import nullcontext from enum import Enum import newrelic.agent import sentry_sdk from mage_ai.orchestration.db.database_manager import database_manager from mage_ai.orchestration.db.process import create_process from mage_ai.server.logger import Logger...
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import multiprocessing import time import traceback from contextlib import nullcontext from enum import Enum import newrelic.agent import sentry_sdk from mage_ai.orchestration.db.database_manager import database_manager from mage_ai.orchestration.db.process import create_process from mage_ai.server.logger import Logger...
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import importlib import json import subprocess import traceback from typing import Dict, List from mage_ai.data_integrations.utils.settings import get_uuid from mage_ai.data_preparation.models.constants import PYTHON_COMMAND from mage_ai.server.logger import Logger def build_integration_module_info(key: str, option: Di...
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from typing import Dict from jupyter_client import KernelClient, KernelManager from jupyter_client.kernelspec import NoSuchKernel from mage_ai.data_preparation.models.project import Project from mage_ai.data_preparation.models.project.constants import FeatureUUID from mage_ai.server.kernels import DEFAULT_KERNEL_NAME, ...
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from typing import Dict from jupyter_client import KernelClient, KernelManager from jupyter_client.kernelspec import NoSuchKernel from mage_ai.data_preparation.models.project import Project from mage_ai.data_preparation.models.project.constants import FeatureUUID from mage_ai.server.kernels import DEFAULT_KERNEL_NAME, ...
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from typing import Dict from jupyter_client import KernelClient, KernelManager from jupyter_client.kernelspec import NoSuchKernel from mage_ai.data_preparation.models.project import Project from mage_ai.data_preparation.models.project.constants import FeatureUUID from mage_ai.server.kernels import DEFAULT_KERNEL_NAME, ...
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from datetime import datetime from mage_ai.server.active_kernel import get_active_kernel_client from mage_ai.server.logger import Logger logger = Logger().new_server_logger(__name__) def get_active_kernel_client() -> KernelClient: def get_messages(callback=None): now = datetime.utcnow() while True: t...
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import json from jupyter_client import KernelClient from mage_ai.api.errors import ApiError from mage_ai.api.utils import authenticate_client_and_token from mage_ai.orchestration.db.models.oauth import Oauth2Application from mage_ai.server.kernel_output_parser import DataType from mage_ai.server.websockets.constants im...
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import json from jupyter_client import KernelClient from mage_ai.api.errors import ApiError from mage_ai.api.utils import authenticate_client_and_token from mage_ai.orchestration.db.models.oauth import Oauth2Application from mage_ai.server.kernel_output_parser import DataType from mage_ai.server.websockets.constants im...
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import json from jupyter_client import KernelClient from mage_ai.api.errors import ApiError from mage_ai.api.utils import authenticate_client_and_token from mage_ai.orchestration.db.models.oauth import Oauth2Application from mage_ai.server.kernel_output_parser import DataType from mage_ai.server.websockets.constants im...
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import json from jupyter_client import KernelClient from mage_ai.api.errors import ApiError from mage_ai.api.utils import authenticate_client_and_token from mage_ai.orchestration.db.models.oauth import Oauth2Application from mage_ai.server.kernel_output_parser import DataType from mage_ai.server.websockets.constants im...
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import uuid from dataclasses import dataclass from typing import Dict, List, Union from jupyter_client import KernelClient from mage_ai.server.active_kernel import ( get_active_kernel_client, get_active_kernel_name, switch_active_kernel, ) from mage_ai.server.kernel_output_parser import DataType from mage_a...
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from typing import List The provided code snippet includes necessary dependencies for implementing the `build_color_commands` function. Write a Python function `def build_color_commands(uuid: str = None) -> List[str]` to solve the following problem: Enter this in your terminal to view available colors: Here is the fu...
Enter this in your terminal to view available colors:
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import argparse import asyncio import json import os import shutil import stat import traceback import webbrowser from datetime import datetime from time import sleep from typing import Optional, Union import pytz import tornado.ioloop import tornado.web from tornado import autoreload from tornado.ioloop import Periodi...
This function will create the BASE_PATH_EXPORTS_FOLDER and replace all the occurrences of CLOUD_NOTEBOOK_BASE_PATH_PLACEHOLDER_ with the base_path parameter. Args: base_path (str): The base path to replace the placeholder with. Returns: str: The path of the frontend static export folder with the replaced base paths.
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import argparse import asyncio import json import os import shutil import stat import traceback import webbrowser from datetime import datetime from time import sleep from typing import Optional, Union import pytz import tornado.ioloop import tornado.web from tornado import autoreload from tornado.ioloop import Periodi...
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import argparse import asyncio import json import os import shutil import stat import traceback import webbrowser from datetime import datetime from time import sleep from typing import Optional, Union import pytz import tornado.ioloop import tornado.web from tornado import autoreload from tornado.ioloop import Periodi...
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from distutils.file_util import copy_file from mage_ai.data_preparation.models.constants import PIPELINE_CONFIG_FILE from mage_ai.data_preparation.models.pipeline import Pipeline from typing import Callable import asyncio import multiprocessing import os import shutil pipeline_execution = PipelineExecution() class Pip...
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from distutils.file_util import copy_file from mage_ai.data_preparation.models.constants import PIPELINE_CONFIG_FILE from mage_ai.data_preparation.models.pipeline import Pipeline from typing import Callable import asyncio import multiprocessing import os import shutil pipeline_execution = PipelineExecution() The provi...
Set the process that the current pipeline execution is running in.
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from distutils.file_util import copy_file from mage_ai.data_preparation.models.constants import PIPELINE_CONFIG_FILE from mage_ai.data_preparation.models.pipeline import Pipeline from typing import Callable import asyncio import multiprocessing import os import shutil pipeline_execution = PipelineExecution() The provi...
Set the task that current is processing messages from execution process.
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from distutils.file_util import copy_file from mage_ai.data_preparation.models.constants import PIPELINE_CONFIG_FILE from mage_ai.data_preparation.models.pipeline import Pipeline from typing import Callable import asyncio import multiprocessing import os import shutil pipeline_execution = PipelineExecution() def delete...
Cancel the current pipeline execution running in the saved process if the process is alive.
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from distutils.file_util import copy_file from mage_ai.data_preparation.models.constants import PIPELINE_CONFIG_FILE from mage_ai.data_preparation.models.pipeline import Pipeline from typing import Callable import asyncio import multiprocessing import os import shutil pipeline_execution = PipelineExecution() The provi...
Reset state on the execution manager.
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from distutils.file_util import copy_file from mage_ai.data_preparation.models.constants import PIPELINE_CONFIG_FILE from mage_ai.data_preparation.models.pipeline import Pipeline from typing import Callable import asyncio import multiprocessing import os import shutil pipeline_execution = PipelineExecution() The provi...
Save the path where we save the copy of the pipeline config before running the execution.
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from enum import Enum from mage_ai.data_preparation.models.constants import MAX_PRINT_OUTPUT_LINES class DataType(str, Enum): DATA_FRAME = 'data_frame' IMAGE_PNG = 'image/png' PROGRESS = 'progress' TABLE = 'table' TEXT = 'text' TEXT_HTML = 'text/html' TEXT_PLAIN = 'text/plain' COMMS_MESSAGE_...
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import asyncio import json import multiprocessing import os import re import traceback import uuid from datetime import datetime, timedelta from distutils.file_util import copy_file from typing import Dict, List import tornado.websocket from jupyter_client import KernelClient from mage_ai.api.errors import ApiError fro...
Execute pipeline synchronously. This function is meant to be run in a separate process, and will write status messages to the passed in multiprocessing queue.
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import asyncio import json import multiprocessing import os import re import traceback import uuid from datetime import datetime, timedelta from distutils.file_util import copy_file from typing import Dict, List import tornado.websocket from jupyter_client import KernelClient from mage_ai.api.errors import ApiError fro...
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from IPython import get_ipython from IPython.display import IFrame, Javascript, display from enum import Enum from mage_ai.server.app import ( server_config, ) from mage_ai.server.constants import SERVER_PORT from mage_ai.server.logger import Logger import os class NotebookType(str, Enum): DATABRICKS = 'databri...
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from IPython import get_ipython from IPython.display import IFrame, Javascript, display from enum import Enum from mage_ai.server.app import ( server_config, ) from mage_ai.server.constants import SERVER_PORT from mage_ai.server.logger import Logger import os IFRAME_HEIGHT = 1000 class NotebookType(str, Enum): ...
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from IPython import get_ipython from IPython.display import IFrame, Javascript, display from enum import Enum from mage_ai.server.app import ( server_config, ) from mage_ai.server.constants import SERVER_PORT from mage_ai.server.logger import Logger import os logger = Logger().new_server_logger(__name__) class Note...
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import json import re from typing import Dict, List from mage_ai.data_preparation.models.block.dynamic.utils import ( has_reduce_output_from_upstreams, is_dynamic_block, is_dynamic_block_child, ) from mage_ai.data_preparation.models.constants import ( DATAFRAME_ANALYSIS_MAX_COLUMNS, DATAFRAME_SAMPLE...
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import json import re from typing import Dict, List from mage_ai.data_preparation.models.block.dynamic.utils import ( has_reduce_output_from_upstreams, is_dynamic_block, is_dynamic_block_child, ) from mage_ai.data_preparation.models.constants import ( DATAFRAME_ANALYSIS_MAX_COLUMNS, DATAFRAME_SAMPLE...
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import json import re from typing import Dict, List from mage_ai.data_preparation.models.block.dynamic.utils import ( has_reduce_output_from_upstreams, is_dynamic_block, is_dynamic_block_child, ) from mage_ai.data_preparation.models.constants import ( DATAFRAME_ANALYSIS_MAX_COLUMNS, DATAFRAME_SAMPLE...
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import json import re from typing import Dict, List from mage_ai.data_preparation.models.block.dynamic.utils import ( has_reduce_output_from_upstreams, is_dynamic_block, is_dynamic_block_child, ) from mage_ai.data_preparation.models.constants import ( DATAFRAME_ANALYSIS_MAX_COLUMNS, DATAFRAME_SAMPLE...
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import importlib import re from typing import Tuple from mage_ai.autocomplete.utils import extract_all_imports from mage_ai.settings.repo import get_repo_path def extract_decorated_function(code: str, decorated_function_name: str) -> Tuple: spans = [] span_current_start = None span_current_def = None ...
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import json from typing import Dict, List from mage_integrations.destinations.constants import ( COLUMN_FORMAT_DATETIME, COLUMN_TYPE_ARRAY, COLUMN_TYPE_OBJECT, COLUMN_TYPE_STRING, ) from mage_integrations.destinations.snowflake.constants import ( SNOWFLAKE_COLUMN_TYPE_VARIANT, ) from mage_integratio...
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