# How to register a model in Azure Machine Learning when the model is a self made class encapsulating a tensorflow model ?

Hello,

I'm training a tensor flow model locally, and using it in a self made class to add pre/post code to the TF model prediction.

I can't find how to register the model because a tensor flow model (ans thus my class containing it) can't be pickled (and apparently is not advised) so I can't use

`Model.register(workspace, model_name=..., model_path=..., tags=..., description=...)`

As I train my model locally and don't use experiments, it seems I can't either use `run.register(...)`

as I don't have a run.

Also, I've seen from the documentation that I can add `model_framework`

and `model_framework_version`

parameters to the register method but I'm not sure what I should put in these parameters as I guess the framework is not just tensorflow but my self made class ?

Here is the structure of my class in case it can help:

```
class DNN():
def __init__(self, dim, tokenizer, max_len):
"""
Initialize Neural Network recommender.
Parameters
----------
dim : int Dimension of the output Embedding layer.
tokenizer : The tokenize() generator
max_len : int Maximum number of elements for each list.
"""
self.dim = dim
self.tokenizer = tokenizer
self.max_len = max_len
def fit(self, X, Y):
self.model = Sequential()
self.model.add(...)
self.model.compile(...)
self.model.fit(x=X, y=Y, ...)
return self
def predict(self, x):
apply_preprocessing(x)
pred = self.model.predict(padded_order)[0]
res = apply_postprocessing(pred )
return res
```

Thank you for your help !