#ask-the-community, Hello All, I am trying to pass python dicts between tasks and i am getting the below error Failed to convert outputs of task 'train_model_task.prepare_training_data' at position 0: Object of type ndarray is not JSON serializable Can dicts not be passed between tasks, I am having no issues passing ndarrays and strings but this is causing issues now

What are the key and value types? Are you annotating the expected key and value types? As the message states, these types need to be json serializable.

Would it be please possible to share an example of such dicts being passed with keys and values defined in the function signatures

How big is your array?

Out[18]:

dict_values([array([[6.4, 2.8, 5.6, 2.2],
       [6.3, 2.7, 4.9, 1.8],
       [6.5, 3. , 5.8, 2.2],
       [5. , 3.2, 1.2, 0.2],
       [4.9, 3. , 1.4, 0.2],
       [7.6, 3. , 6.6, 2.1],
       [6.3, 2.8, 5.1, 1.5],
       [5.6, 3. , 4.5, 1.5],
       [6.9, 3.1, 5.4, 2.1],
       [6.7, 3. , 5.2, 2.3],
       [4.6, 3.1, 1.5, 0.2],
       [7.2, 3.6, 6.1, 2.5],
       [4.3, 3. , 1.1, 0.1],
       [5.4, 3. , 4.5, 1.5],
       [5.5, 2.5, 4. , 1.3],
       [7.9, 3.8, 6.4, 2. ],
       [7.2, 3.2, 6. , 1.8],
       [5.3, 3.7, 1.5, 0.2],
       [6.2, 2.8, 4.8, 1.8],
       [5.1, 3.4, 1.5, 0.2],
       [4.7, 3.2, 1.6, 0.2],
       [5.5, 2.4, 3.8, 1.1],
       [5.7, 4.4, 1.5, 0.4],
       [4.8, 3. , 1.4, 0.3],
       [6.2, 2.9, 4.3, 1.3],
       [5.6, 2.7, 4.2, 1.3],
       [5.7, 2.8, 4.5, 1.3],
       [5.5, 4.2, 1.4, 0.2],
       [6.7, 3. , 5. , 1.7],
       [5.9, 3.2, 4.8, 1.8],
       [6. , 2.2, 4. , 1. ],
       [5.1, 3.8, 1.5, 0.3],
       [5.8, 2.7, 3.9, 1.2],
       [4.9, 3.6, 1.4, 0.1],
       [6.8, 3.2, 5.9, 2.3],
       [6.3, 2.9, 5.6, 1.8],
       [5.8, 2.8, 5.1, 2.4],
       [7.3, 2.9, 6.3, 1.8],
       [5.5, 2.3, 4. , 1.3],
       [6.1, 2.8, 4. , 1.3],
       [5. , 3. , 1.6, 0.2],
       [6.5, 3. , 5.5, 1.8],
       [7.4, 2.8, 6.1, 1.9],
       [5.2, 3.5, 1.5, 0.2],
       [4.9, 3.1, 1.5, 0.1],
       [4.9, 2.4, 3.3, 1. ],
       [5.4, 3.4, 1.7, 0.2],
       [4.4, 2.9, 1.4, 0.2],
       [6.6, 3. , 4.4, 1.4],
       [6.3, 2.5, 5. , 1.9],
       [5. , 2. , 3.5, 1. ],
       [6.4, 2.7, 5.3, 1.9],
       [5.4, 3.9, 1.3, 0.4],
       [6.7, 3.3, 5.7, 2.1],
       [5.4, 3.4, 1.5, 0.4],
       [5. , 3.5, 1.3, 0.3],
       [7.2, 3. , 5.8, 1.6],
       [6.1, 3. , 4.9, 1.8],
       [6.9, 3.1, 5.1, 2.3],
       [7.1, 3. , 5.9, 2.1],
       [5.1, 3.5, 1.4, 0.2],
       [5.8, 2.7, 5.1, 1.9],
       [6.7, 3.1, 4.4, 1.4],
       [6.1, 2.6, 5.6, 1.4],
       [6.2, 2.2, 4.5, 1.5],
       [6.4, 3.1, 5.5, 1.8],
       [5.8, 2.7, 4.1, 1. ],
       [5.7, 2.5, 5. , 2. ],
       [7.7, 3.8, 6.7, 2.2],
       [5.5, 2.6, 4.4, 1.2],
       [6.3, 2.5, 4.9, 1.5],
       [6.1, 2.9, 4.7, 1.4],
       [5.8, 2.7, 5.1, 1.9],
       [6.7, 3.1, 5.6, 2.4],
       [6.1, 3. , 4.6, 1.4],
       [4.6, 3.4, 1.4, 0.3],
       [6. , 2.9, 4.5, 1.5],
       [4.9, 2.5, 4.5, 1.7],
       [4.7, 3.2, 1.3, 0.2],
       [5.1, 3.8, 1.6, 0.2],
       [5.4, 3.7, 1.5, 0.2],
       [5.1, 3.7, 1.5, 0.4],
       [5. , 3.3, 1.4, 0.2],
       [4.6, 3.2, 1.4, 0.2],
       [5.5, 3.5, 1.3, 0.2],
       [6.7, 3.1, 4.7, 1.5],
       [5.2, 2.7, 3.9, 1.4],
       [5.1, 2.5, 3. , 1.1],
       [6.9, 3.1, 4.9, 1.5],
       [6.1, 2.8, 4.7, 1.2],
       [6.3, 3.3, 4.7, 1.6],
       [5. , 3.6, 1.4, 0.2],
       [6.2, 3.4, 5.4, 2.3],
       [7.7, 2.8, 6.7, 2. ],
       [5.7, 3. , 4.2, 1.2],
       [6.4, 3.2, 5.3, 2.3],
       [5.7, 2.6, 3.5, 1. ],
       [5.9, 3. , 5.1, 1.8],
       [6.9, 3.2, 5.7, 2.3],
       [5.2, 4.1, 1.5, 0.1],
       [5.6, 2.9, 3.6, 1.3],
       [6.5, 3. , 5.2, 2. ],
       [4.8, 3.4, 1.6, 0.2],
       [5. , 3.5, 1.6, 0.6],
       [6.7, 2.5, 5.8, 1.8],
       [4.8, 3. , 1.4, 0.1],
       [6.3, 2.3, 4.4, 1.3],
       [6. , 3. , 4.8, 1.8],
       [5.6, 3. , 4.1, 1.3],
       [4.4, 3. , 1.3, 0.2],
       [6.3, 3.3, 6. , 2.5],
       [5.1, 3.8, 1.9, 0.4],
       [6.8, 3. , 5.5, 2.1],
       [4.6, 3.6, 1. , 0.2],
       [5.6, 2.5, 3.9, 1.1],
       [6.5, 2.8, 4.6, 1.5],
       [6.7, 3.3, 5.7, 2.5],
       [6.8, 2.8, 4.8, 1.4],
       [6.4, 3.2, 4.5, 1.5],
       [5. , 3.4, 1.5, 0.2]]), array([2, 2, 2, 0, 0, 2, 2, 1, 2, 2, 0, 2, 0, 1, 1, 2, 2, 0, 2, 0, 0, 1,
       0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 2, 2, 2, 2, 1, 1, 0, 2, 2, 0,
       0, 1, 0, 0, 1, 2, 1, 2, 0, 2, 0, 0, 2, 2, 2, 2, 0, 2, 1, 2, 1, 2,
       1, 2, 2, 1, 1, 1, 2, 2, 1, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
       1, 1, 1, 0, 2, 2, 1, 2, 1, 2, 2, 0, 1, 2, 0, 0, 2, 0, 1, 2, 1, 0,
       2, 0, 2, 0, 1, 1, 2, 1, 1, 0])])

About a 100

Gotcha. Yeah, if each example is 1d, you could consider using pandas since that is automatically handled by flyte

Thanks for your inputs. I will explore this

Check out this example for passing dataframes: https://docs.flyte.org/projects/cookbook/en/stable/auto_examples/pima_diabetes/diabetes.html