Could not find host server definition

[egormcobakaster]

when i run pipline from ui appears error: clearml_agent: ERROR: Could not find host server definition (missing ~/clearml.conf or Environment CLEARML_API_HOST) To get started with ClearML: setup your own clearml-server, or create a free account at https://app.clear.ml and run clearml-agent init

docker-compose.yaml:

version: "3.6" services:

apiserver: command:

• apiserver container_name: clearml-apiserver image: allegroai/clearml:latest restart: unless-stopped volumes:
• ./logs:/var/log/clearml
• ./config:/opt/clearml/config
• ./data/fileserver:/mnt/fileserver depends_on:
  • redis
  • mongo
  • elasticsearch
  • fileserver environment: CLEARML_ELASTIC_SERVICE_HOST: elasticsearch CLEARML_ELASTIC_SERVICE_PORT: 9200 CLEARML_ELASTIC_SERVICE_PASSWORD: ${ELASTIC_PASSWORD} CLEARML_MONGODB_SERVICE_HOST: mongo CLEARML_MONGODB_SERVICE_PORT: 27017 CLEARML_REDIS_SERVICE_HOST: redis CLEARML_REDIS_SERVICE_PORT: 6379 CLEARML_SERVER_DEPLOYMENT_TYPE: ${CLEARML_SERVER_DEPLOYMENT_TYPE:-linux} CLEARMLapiserverpre_populateenabled: "true" CLEARMLapiserverpre_populate__zip_files: "/opt/clearml/db-pre-populate" CLEARMLapiserverpre_populate__artifacts_path: "/mnt/fileserver" CLEARMLservicesasync_urls_deleteenabled: "true" CLEARMLservicesasync_urls_deletefileserverurl_prefixes: "[${CLEARML_FILES_HOST:-}]" ports:

• "8008:8008" networks:

  • backend
  • frontend

  elasticsearch: networks:

  • backend container_name: clearml-elastic environment: ES_JAVA_OPTS: -Xms2g -Xmx2g -Dlog4j2.formatMsgNoLookups=true ELASTIC_PASSWORD: ${ELASTIC_PASSWORD} bootstrap.memory_lock: "true" cluster.name: clearml cluster.routing.allocation.node_initial_primaries_recoveries: "500" cluster.routing.allocation.disk.watermark.low: 500mb cluster.routing.allocation.disk.watermark.high: 500mb cluster.routing.allocation.disk.watermark.flood_stage: 500mb discovery.zen.minimum_master_nodes: "1" discovery.type: "single-node" http.compression_level: "7" node.ingest: "true" node.name: clearml reindex.remote.whitelist: '.' xpack.monitoring.enabled: "false" xpack.security.enabled: "false" ulimits: memlock: soft: -1 hard: -1 nofile: soft: 65536 hard: 65536 image: docker.elastic.co/elasticsearch/elasticsearch:7.17.7 restart: unless-stopped volumes:
  • ./data/elastic_7:/usr/share/elasticsearch/data
  • /usr/share/elasticsearch/logs

  fileserver: networks:

  • backend
  • frontend command:
• fileserver container_name: clearml-fileserver image: allegroai/clearml:latest environment: CLEARMLfileserverdelete__allow_batch: "true" restart: unless-stopped volumes:
• ./logs:/var/log/clearml
• ./data/fileserver:/mnt/fileserver
• ./config:/opt/clearml/config ports:

• "8081:8081"

  mongo: networks:

  • backend container_name: clearml-mongo image: mongo:4.4.9 restart: unless-stopped command: --setParameter internalQueryMaxBlockingSortMemoryUsageBytes=196100200 volumes:
• ./data/mongo_4/db:/data/db

• ./data/mongo_4/configdb:/data/configdb

  redis: networks:

  • backend container_name: clearml-redis image: redis:5.0 restart: unless-stopped volumes:

• ./data/redis:/data

  webserver: command:

• webserver container_name: clearml-webserver

  environment:

  CLEARML_SERVER_SUB_PATH : clearml-web # Allow Clearml to be served with a URL path prefix.

  image: allegroai/clearml:latest restart: unless-stopped depends_on:

  • apiserver ports:

• "8080:80" networks:

  • backend
  • frontend

  async_delete: depends_on:

  • apiserver
  • redis
  • mongo
  • elasticsearch
  • fileserver container_name: async_delete image: allegroai/clearml:latest networks:
  • backend restart: unless-stopped environment: CLEARML_ELASTIC_SERVICE_HOST: elasticsearch CLEARML_ELASTIC_SERVICE_PORT: 9200 CLEARML_ELASTIC_SERVICE_PASSWORD: ${ELASTIC_PASSWORD} CLEARML_MONGODB_SERVICE_HOST: mongo CLEARML_MONGODB_SERVICE_PORT: 27017 CLEARML_REDIS_SERVICE_HOST: redis CLEARML_REDIS_SERVICE_PORT: 6379 PYTHONPATH: /opt/clearml/apiserver CLEARMLservicesasync_urls_deletefileserverurl_prefixes: "[${CLEARML_FILES_HOST:-}]" entrypoint:
  • python3
  • -m
  • jobs.async_urls_delete
  • --fileserver-host
  • http://fileserver:8081 volumes:
  • ./logs:/var/log/clearml

  agent-services: networks:

  • backend container_name: clearml-agent-services image: allegroai/clearml-agent-services:latest deploy: restart_policy: condition: on-failure privileged: true environment: CLEARML_HOST_IP: ${CLEARML_HOST_IP} CLEARML_WEB_HOST: ${CLEARML_WEB_HOST:-} CLEARML_API_HOST: http://apiserver:8008 CLEARML_FILES_HOST: ${CLEARML_FILES_HOST:-} CLEARML_API_ACCESS_KEY: KN8KN262M335YBUKM5UH CLEARML_API_SECRET_KEY: X5slLqO7Lnq5IfRpt1rwqOBAWekipI9GC1e3LjtcG1DT1geDI0 CLEARML_AGENT_GIT_USER: ${CLEARML_AGENT_GIT_USER} CLEARML_AGENT_GIT_PASS: ${CLEARML_AGENT_GIT_PASS} CLEARML_AGENT_UPDATE_VERSION: ${CLEARML_AGENT_UPDATE_VERSION:->=0.17.0} CLEARML_AGENT_DEFAULT_BASE_DOCKER: "ubuntu:18.04" AWS_ACCESS_KEY_ID: ${AWS_ACCESS_KEY_ID:-} AWS_SECRET_ACCESS_KEY: ${AWS_SECRET_ACCESS_KEY:-} AWS_DEFAULT_REGION: ${AWS_DEFAULT_REGION:-} AZURE_STORAGE_ACCOUNT: ${AZURE_STORAGE_ACCOUNT:-} AZURE_STORAGE_KEY: ${AZURE_STORAGE_KEY:-} GOOGLE_APPLICATION_CREDENTIALS: ${GOOGLE_APPLICATION_CREDENTIALS:-} CLEARML_WORKER_ID: "clearml-services" CLEARML_AGENT_DOCKER_HOST_MOUNT: "/opt/clearml/agent:/root/.clearml" SHUTDOWN_IF_NO_ACCESS_KEY: 1 volumes:
  • /var/run/docker.sock:/var/run/docker.sock
  • ./agent:/root/.clearml depends_on:
  • apiserver entrypoint: > bash -c "curl --retry 10 --retry-delay 10 --retry-connrefused 'http://apiserver:8008/debug.ping' && /usr/agent/entrypoint.sh"

networks: backend: driver: bridge frontend: driver: bridge

[ainoam]

@egormcobakaster This seems to indicate the environment in which the clearml-agent running your pipeline is deployed is not properly configured. Where are you running this clearml-agent? Did you complete clearml-agent init properly?

[egormcobakaster]

@egormcobakaster This seems to indicate the environment in which the clearml-agent running your pipeline is deployed is not properly configured. Where are you running this clearml-agent? Did you complete clearml-agent init properly?

i am running clearml-agent on the same machine as the clearml-server.

when I start a new agent with a new queue:

clearml-agent daemon --queue 6c86514d67014415967bc1d319f03fac

this error disappears and individual tasks are launched from the ui, but when I start pipline, the first task gets queued and does not leave the queue

[jkhenning]

Hi @egormcobakaster, Can you share the log of the pipeline task and your pipeline code?

Also, do you only have a single clearml-agent running? and what is the queue name it listens to?

[egormcobakaster]

Hi @jkhenning, pipeline log:

Environment setup completed successfully Starting Task Execution: ClearML results page: http://172.21.0.98:8080/projects/6072ec75526e493f917e5e770f24319d/experiments/abf2370a46bc4844984d98643e995ff4/output/log ClearML pipeline page: http://172.21.0.98:8080/pipelines/6072ec75526e493f917e5e770f24319d/experiments/abf2370a46bc4844984d98643e995ff4 2023-12-11 10:03:05,217 - clearml.util - WARNING - 2 task found when searching for {'project_name': 'data process', 'task_name': 'Pipeline step 2 create clearml dataset', 'include_archived': True, 'task_filter': {'status': ['created', 'queued', 'in_progress', 'published', 'stopped', 'completed', 'closed']}} 2023-12-11 10:03:05,217 - clearml.util - WARNING - Selected task Pipeline step 2 create clearml dataset (id=adad180edd364cb1b8cedcb77e0a7712) Launching the next 1 steps Launching step [anotation] Cloning Task id=8e7aac5e6f004730a0a3088f6fb0e327 with parameters: {'General/dataset_path': '/mnt/ext2/datasets/DataSet/Casia_images'} Launching step: anotation Parameters: {'General/dataset_path': '${pipeline.path}'} Configurations: {} Overrides: {}

pipeline code:
from clearml import Dataset
import argparse
import sys
from clearml import Task
from clearml.automation import PipelineController

def pre_execute_callback_example(a_pipeline, a_node, current_param_override):
    # type (PipelineController, PipelineController.Node, dict) -> bool
    print(
        "Cloning Task id={} with parameters: {}".format(
            a_node.base_task_id, current_param_override
        )
    )
    # if we want to skip this node (and subtree of this node) we return False
    # return True to continue DAG execution
    return True

def post_execute_callback_example(a_pipeline, a_node):
    # type (PipelineController, PipelineController.Node) -> None
    print("Completed Task id={}".format(a_node.executed))
    # if we need the actual executed Task: Task.get_task(task_id=a_node.executed)
    return

parser = argparse.ArgumentParser()
parser.add_argument('--path', default='', action='store',
                    help='path to dataset')
args = parser.parse_args()
if args.path == '':
    print("empty path to dataset")
    sys.exit()

pipe = PipelineController(
    name="Pipeline demo", project="data process", version="0.0.1", add_pipeline_tags=False
)

pipe.add_parameter(
    "path",
    args.path,
    "path_to_dataset",
)

pipe.set_default_execution_queue("default")

pipe.add_step(
    name="anotation",
    base_task_project="data process",
    base_task_name="Pipeline step 1 create anotation",
    parameter_override={"General/dataset_path": "${pipeline.path}"},
    pre_execute_callback=pre_execute_callback_example,
    post_execute_callback=post_execute_callback_example,
)

pipe.add_step(
    name="create dataset",
    parents=["anotation"],
    base_task_project="data process",
    base_task_name="Pipeline step 2 create clearml dataset",
    parameter_override={
        "General/dataset_path": "${pipeline.path}",
    },
    pre_execute_callback=pre_execute_callback_example,
    post_execute_callback=post_execute_callback_example,
)

pipe.start()

print("done")

the first task only gets queued and is not executed:

[egormcobakaster]

@jkhenning , @ainoam Thanks for the answers, it helped me to create another queue. one for the pipeline and the other for tasks