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Here you can find information about common problems users encounter and how to solve them. If you can't find your problem here, reach out to us on our Discord Channel and let us know.
Users might get an error when trying to push files to DagsHub while pulling files might work.
!!!danger Error
ERROR: unexpected error - 401 Client Error: Unauthorized for url: http://<remote_url>
Root cause 1: You don’t have write permissions for the repository. Either ask the maintainers to add you as a collaborator or fork the repository, make the changes, and create a pull request to the original project.
Root cause 2: You didn't configure your DVC remote authentication. You can either set DagsHub as the remote Storage or use an S3 compatible as the remote storage.
When trying to push files using Git, the operation fails.
!!!danger Error
fatal: could not read Username for 'https://dagshub.com': No such device or address error: failed to push some refs to 'https://dagshub.com/<path>.git'
Root cause 1: You don’t have your Git credentials for DagsHub set on the machine you’re working on. Please use the following command structure to push files:
git push https://{user}:{token}@dagshub.com/{repo-owner}/{repo-name}
DagsHub supports working with DVC in a monorepo architecture. This means that you can have multiple .dvc/
folders in subdirectories. Files would still be scanned and visualized properly. However, to detect that a monorepo has a DVC integration, the root must contain a .dvc/config
file as well. This config file can be empty, but it needs to appear in order for scanning to work properly.
When trying to push files using DVC, the operation fails.
!!!danger Error
ERROR: failed to push data to the cloud - '503 Service Temporarily Unavailable'
!!!danger Error
ERROR: failed to push data to the cloud - 1 files failed to upload
!!!danger Error
ERROR: failed to transfer 'md5: ...' - 400, message='Bad Request', url=URL('http...')
Root cause 1: There are a few versions of DVC with bugs that could cause these errors. The safe versions of DVC are earlier than 2.3.0, 2.8.1, or 2.18.0 and up to the latest version. The latest version is preferred. Upgrade to the latest DVC version by:
pip3 install --upgrade dvc
To downgrade or install a specific version, run:
pip3 install --upgrade dvc==2.8.1
When trying to load a Label Studio project from DagsHub Annotations, it fails with the following error:
!!!danger Error
Runtime error. Load failed
Root cause 1: Your user tier doesn't have access to DagsHub Annotations. Community tier users can only use DagsHub annotations for open source projects. You can either make the repository public or upgrade your user tier.
When trying to log an mlflow.pyfunc
model, you get:
!!!danger Error
yaml.representer.RepresenterError: ('cannot represent an object', <__main__.MarioModelWrapper object at 0x105772f70>)
Root cause 1: Check that your conda_env
parameter is properly set when calling mlflow.pyfunc.log_model
and is a type that can be converted to YAML
If when trying to interact with the MLflow Model Registry, you get the error:
!!!danger Error
mlflow.exceptions.MlflowException: Model Registry features are not supported by the store with URI: 'file:///Users/***/repos/mario_vs_wario_102/mlruns'. Stores with the following URI schemes are supported: ['databricks', 'http', 'https', 'postgresql', 'mysql', 'sqlite', 'mssql'].
Root cause 1: Ensure that you've set your tracking URI or have saved a model locally
model_uri
While using an MLflow command that takes a model_uri
as a parameter, you see:
!!!danger Error
mlflow.exceptions.RestException: RESOURCE_DOES_NOT_EXIST: Response: {'error_code': 'RESOURCE_DOES_NOT_EXIST'}
or:
Root cause 1: Either the model name is incorrect or the version number doesn't exist.
!!!danger Error
mlflow.exceptions.MlflowException: API request to endpoint /api/2.0/mlflow/runs/get failed with error code 400 != 200. Response body: '"repo not associated with run"'
Root cause 1: This can happen if you use a run_id
that doesn't exist.
If you get the following error:
!!!danger Error
The repository with name 'mlflow-pyfunc' does not exist in the registry with id '***'
Root cause 1: Ensure you have run mlflow sagemaker build-and-push-container
at least once first.
Root cause 2: Specify the SageMaker execution role in your command.
or:
!!!danger Error
The role with name *** cannot be found.
Root cause 3: You may need to specify the region name.
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Are you sure you want to delete this access key?