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Import Model
Learn how to import models into Tensorleap
Tensorleap supports importing models exported to various standard formats, including standard TensorFlow (SaveModel/H5/JSON) and PyTorch (ONNX).
The JSON format serializes the model layers, their properties and connectivity. It does not hold the state (weights) of the model.
Below is a code snippet for saving a TensorFlow model as a JSON file:
json_file = open("model.json", "w")
json_file.write(model.to_json())
json_file.close()
The
h5 format serializes the model and model state (weights) as a single h5 file.Below is a code snippet for loading an exported
h5 file:model.save('model.h5')
The
onnx format is commonly used in PyTorch for serializing the model's layers and state (weights).Below is a code snippet for exporting a model to an
onnx file in PyTorch:import torch
import torchvision
dummy_input = torch.randn(10, 3, 224, 224, device="cuda")
model = torchvision.models.alexnet(pretrained=True).cuda()
input_names = [ "actual_input_1" ] + [ "learned_%d" % i for i in range(16) ]
output_names = [ "output1" ]
torch.onnx.export(model, dummy_input, "alexnet.onnx", verbose=True, input_names=input_names, output_names=output_names)
Uses the TensorFlow 2
SaveModel to export a folder with files containing the serialized model layers and state.Below is a code snippet to load the model from the extracted folder:
model.save('model_folder')
The serialized data is stored to a folder with this directory structure:
assets/ (folder)
variables/ (folder)
saved_model.pb
When importing the folder generated by the
model.save() format from Tensorleap, the exported folder needs to be contained in a tar.gz file. One way to do it is by using
tar:tar -zcvf model_folder.tar.gz model_folder
To import a model:
- 1.On the Network view, clickto open the Import Model panel.
- 2.Enter the revision name and model name, and select the import file format from the list.
- 3.Click and select the import file from your system.
- 4.
If your model contains Custom Layers this step is mandatory. You must first add these to the dataset script and select the dataset before moving to the next step.
5. Click Import.
Setting up the import
7. Position your cursor over the version and click 
, then Open Commit.
8. Back on the Network view, point the Dataset Block to the model's Dataset Instance and connect it to the first layer in the network.
Once a dataset has been integrated with Tensorleap, it becomes available for use with your models. More info about Dataset Integration can be found at Dataset.
Importing a model and pointing its Dataset Block to a dataset instance
Last modified 1yr ago