Common Errors Guide

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↔️ Galileo API errors

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400: bad request

• Message: Missing required field: 'model_id'
• Interpretation: A required field in the request body is missing or malformed.
• Troubleshooting:
  • Validate your JSON payload structure.
  • Review the Galileo API for required fields.

{
  "dataset": "review_data"
}

{
  "model_id": "bert-v1",
  "dataset": "review_data"
}

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401: unauthorized

• Message: Authentication credentials were not provided.
• Interpretation: The request was made without a valid API key.
• Troubleshooting:
  • Ensure the Authorization header is set.
  • Visit your Galileo account settings to confirm the API key is valid and not expired.

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403: forbidden

• Message: You do not have permission to access this resource.
• Interpretation: The API key is valid, but access to the specified resource is denied.
• Troubleshooting:
  • Double-check your project, workspace, or dataset permissions.
  • Visit your Galileo account settings to confirm your API key has the correct scope.

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404: not found

• Message: Project ID 'xyz' not found.
• Interpretation: The specified resource doesn’t exist.
• Troubleshooting:
  • Verify the resource ID exists in your Galileo Console.
  • Double-check for typos in the endpoint or parameter.

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405: method not allowed

• Message: POST method not supported for this endpoint.
• Interpretation: The wrong HTTP method was used.
• Troubleshooting:
  • Consult the API documentation to confirm the correct method (GET, POST, PUT, DELETE).

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422: unprocessable entity

• Message: Field 'logits' must be an array of floats matching number of classes.
• Interpretation: The data format is incorrect even though the syntax is valid.
• Troubleshooting:
  • Ensure arrays like logits, tokens, or labels match expected dimensions.
  • Refer to the model type’s required input structure.

{
  "model_id": "bert-v1",
  "dataset": "reviews",
  "inputs": ["This movie was amazing!"],
  "logits": [[1.2, 0.8]], // ❌ Only 2 values, should be 3
  "labels": [2]
}

{
  "model_id": "bert-v1",
  "dataset": "reviews",
  "inputs": ["This movie was amazing!"],
  "logits": [[1.2, 0.8, -0.3]], // ✅ 3 values
  "labels": [2]
}

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501: not implemented

• Message: This endpoint is not available.
• Interpretation: The endpoint exists but is not supported or publicly accessible.
• Troubleshooting:
  • Do not use undocumented endpoints.
  • Contact support if access to the endpoint is expected.

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📁 Galileo SDK errors

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Missing or invalid API key

• Error Message: ValueError: No API key found. Please set your GALILEO_API_KEY.
• Interpretation: The SDK cannot authenticate with the Galileo API.
• Troubleshooting:
  • Set your API key using an environment variable:
    Copy

    Ask AI

    export GALILEO_API_KEY="your-api-key"
    

  • Or programmatically:
    Copy

    Ask AI

    import galileo
    galileo.set_api_key("your-api-key")
    

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Invalid input shape

• Error Message: ValueError: Inputs and logits must have the same batch size.
• Interpretation: The arrays passed to log_model_outputs() are mismatched.
• Troubleshooting:
  • Ensure the number of entries in inputs, logits, and labels is the same.

inputs = ["text1", "text2"]
logits = [[0.1, 0.9]]  # Only one set of logits
labels = [1, 0]
galileo.log_model_outputs(inputs=inputs, logits=logits, labels=labels)

logits = [[0.1, 0.9], [0.8, 0.2]]  # Now matches batch size

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Unsupported model type

• Error Message: UnsupportedModelTypeError: Model type 'regression' is not supported.
• Interpretation: The SDK currently supports a limited set of model types.
• Troubleshooting:
  • Use supported types like classification, token_classification, or text_generation.
  • Review Galileo model compatibility.

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Token mismatch error

• Error Message: ValueError: Token offsets do not match number of tokens.
• Interpretation: The offsets passed during token classification are misaligned.
• Troubleshooting:
  • Validate token offset array lengths match token arrays.
  • Use the Galileo Tokenizer helpers when possible.

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Dataset not found

• Error Message: DatasetNotFoundError: Dataset 'reviews_v2' not found.
• Interpretation: The specified dataset was not found in the project or workspace.
• Troubleshooting:
  • Confirm dataset name and project association in the Galileo Console.
  • Run galileo.get_datasets() to verify available datasets.

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Experiment creation failed

• Error Message: ExperimentError: Cannot create experiment. Missing metadata.
• Interpretation: Required metadata fields were not logged before calling create_experiment().
• Troubleshooting:
  • Ensure you’ve logged inputs, outputs, and model predictions.
  • Call log_model_outputs() before create_experiment().

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Network or timeout issues

• Error Message: requests.exceptions.ConnectionError
• Interpretation: SDK could not connect to the Galileo API.
• Troubleshooting:
  • Check your internet connection.
  • Retry after a few moments.
  • Ensure the API base URL is not overridden or misconfigured.

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🖥️ Galileo Console errors

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Access denied

• Error Message: You do not have permission to view this project.
• Interpretation: Your account lacks access to a resource within a workspace or project.
• Troubleshooting:
  • Ask your admin to verify your permissions.
  • Ensure you’re logged into the correct workspace.

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Project not found

• Error Message: Project does not exist or has been deleted.
• Interpretation: The requested project ID or slug is invalid.
• Troubleshooting:
  • Refresh the Console and check your project list.
  • Ask a teammate to verify the project is still active.

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Dataset load failed

• Error Message: Failed to load dataset. Please try again.
• Interpretation: The dataset may not have been logged properly, or it failed to sync.
• Troubleshooting:
  • Verify that the dataset was successfully logged via SDK or API.
  • Try refreshing the browser or logging out and back in.
  • If persistent, check SDK logs for any upload errors.

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Experiment failed to run

• Error Message: Unable to create experiment. Check that all required data is available.
• Interpretation: The backend did not receive sufficient input data.
• Troubleshooting:
  • Ensure model inputs, outputs, and predictions were logged.
  • Confirm the correct dataset is selected.
  • Re-run log_model_outputs() before retrying in the Console.

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Visualization error

• Error Message: Visualization failed to render.
• Interpretation: The plot component encountered unexpected data or rendering issues.
• Troubleshooting:
  • Try changing filters, date range, or toggling options.
  • If you uploaded custom metadata, ensure it follows the schema.
  • Try a different browser or clear cache.

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File upload failed

• Error Message: File type not supported. or Upload failed. Please retry.
• Interpretation: Invalid file type or temporary server-side issue.
• Troubleshooting:
  • Check that the file format is supported (e.g., CSV, JSONL).
  • Rename the file and try again.
  • If issue persists, contact support.

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Export unavailable

• Error Message: Export failed. No records available for download.
• Interpretation: Attempting to export an empty or invalid dataset or experiment.
• Troubleshooting:
  • Ensure filters are not excluding all records.
  • Re-run the experiment to regenerate results.

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General tips

• Refresh the Console often when data changes.
• Use the latest version of a modern browser (Chrome, Edge, Firefox).
• Ensure cookies and local storage are enabled for session persistence.