docker compose on server:

  apiserver:
    command:
    - apiserver
    container_name: clearml-apiserver
    image: allegroai/clearml:latest
    privileged: true
    restart: unless-stopped
    volumes:
    - ${LOGS_DIR}:/var/log/clearml
    - /opt/clearml/config:/opt/clearml/config
    - ${FILESERVER_DATA_DIR}:/mnt/fileserver
    depends_on:
      - redis
      - mongo
      - elasticsearch
      - fileserver
    environment:
      CLEARML_ELASTIC_SERVICE_HOST: elasticsearch
      C...

which logs are helpful? console output, fileserver, or api?

my main issue is that i can see that the model artefacts are here file:///root/.clearml/venvs-builds/3.11/task_repository/my_awesome_facility_model/checkpoint-33/scheduler.pt
which i believe is not persistent/retrievable with an artefact id

this is how im initializing before calling my training function. this is inside my training_script:

task = Task.init(project_name=project_name, task_name=task_name, output_uri=True) 

I was away for a week, was anyone able to come up with nay solutions for this?

This issue was solved by adding task.output_uri = "fileserver" in the scheduling script, but for some reason this does not work when setting in task.create call in the same script but needs to be set after. it also doesnt work when being set in the training script, so there must have been some unknown overriding

it is reachable to get data for training as image data is saved on the file server

this task is currently running, i obfuscated some personal info

local conf

api {
    # Notice: 'host' is the api server (default port 8008), not the web server.
    api_server: 

    web_server: 

    files_server: 

    # Credentials are generated using the webapp, 

    # Override with os environment: CLEARML_API_ACCESS_KEY / CLEARML_API_SECRET_KEY
    credentials {"access_key": "somekey", "secret_key": "somekey"}
}

 # Default Task output_uri. if output_uri is not provided to Task.init, default_outp...

way im attempting to access with an id

cl_model_id = config.get("model_id")
    #model = Model(model_id=cl_model_id)
    model = InputModel(model_id=cl_model_id)
    checkpoint = model.get_local_copy()

further context. it saves the optimizer.pt , scheduler.pt , rng_state_path, and training_args.bin. but i cant locate the model.safetensors or meta jsons

None ?

you can see here its missing some files

console output:

clearml.Task - INFO - Completed model upload to file_server/training.e5f99149b9b/models/optimizer.pt
clearml.Task - INFO - Completed model upload to file_server/training.e5f99149b9b/models/scheduler.pt
clearml.Task - INFO - Completed model upload to file_server/training.e5f99149b9b/models/rng_state.pth
save_model
somemodel/checkpoint-198
clearml.Task - INFO - Completed model upload to file_server/training.e5f99149b9b/models/training_args.bin

called with:

task = Task.init(
    project_name=project_name, task_name=task_name, output_uri="fileserver_address"
)


task.connect(config)


checkpoint = config.get("model_path")

image_processor = AutoImageProcessor.from_pretrained(
        checkpoint,
        num_labels=config.get("class_number"),
    )

best_model = training(checkpoint, image_processor)

queued with:

task = Task.create(
    project_name="name",
    task_name="training",
    repo="repo",
    branch="branch",
    script="training_script",
    packages=package_list,
    docker="docker_gpu_image",
    docker_args=["--network=host"],
)
task.output_uri = "filer_server"
task.enqueue(task, "training")

default_outputdir in the conf is set to the filerserver address same as above pointing

training function:

def training(checkpoint, image_processor):

    data_test_train, labels, label_to_id, id_to_label = pre_process()

    model = AutoModelForImageClassification.from_pretrained(
        checkpoint,
        num_labels=len(labels),
        id2label=id_to_label,
        label2id=label_to_id,
        ),
    )
    def metrics(eval_pred):
        metric_val = config.get("eval_metric")
        metric = evaluate.load(metric_val)
        predictions, labels = eval_pred
        ...

need to work out if i need to reconfigure something and re-train or are my files (actual model tensor) recoverable

hmm i can probably provide snippets

it would seem they are related but i cant see the further details of this bug. Either doing a manual artefact upload with task or turning tensor board tracking off in the hugging face trainer both seemed to enable json tracking within the checkpoints. But I would have thought the tensorboard behavior wasnt desired.

So we have managed to get whole checkpoint files to save by removing the save_total_limit from training, this seems to save checkpoint folders with all files in it. however now we have a ballooning server.

did discover this None
and wondering if there's some nuance in autotracking that needs to be circumvented

so turning report_to="tensorboard", off seemed to solve the issue...as in the training run saves checkpoints as you would expect. That doesnt seem like desired behavior..

UI, in the dashboard. I know I could create my own custom plot and track it but it seems odd not to have epoch as configurable option

can you attach the log of the task where ClearML SDK fails to resolve the output_uri?

the console output? or?

sure!

So this is how im queuing the job:

task = Task.create(
    project_name="multiclass-classifier",
    task_name="training",
    repo="reponame_url",
    branch="branch_name",
    script='training_script_name',
    packages=package_list,
    docker="python:3.11",
    docker_args="--privileged"

)
task.enqueue(task, "services")  # services queue is the one with a remote worker

I ran into trouble with this, i found for saving data you need to have it specified in the conf. even though as far as im aware setting it as par tof a task is supposed to overwrite this.

further i found that the server wasnt able to resolve itself as a destination without providing an alias to the server name in the server side docker.

finally when it comes to saving artifacts it seems this had to be set in task.output_uri and not in the create or init :man-shrugging: