Finetuner

Cloud service to finetune embeddings on domain specific data

Finetuner makes fine-tuning simple and fast. Moving the process into the cloud, Finetuner handles all related complexity and infrastructure, making models performant and production ready.

Last-mile performance tuning

Fine-tuning deep neural networks (DNNs) significantly improves performance on domain specific neural search tasks. However, fine-tuning for neural search is not trivial, as it requires a combination of expertise in ML and Information Retrieval.

Performance boost

Finetuner significantly increases the performance of pretrained models on domain specific neural search applications.

Simple yet powerful

Interacting with Finetuner is simple and supports selections of different loss functions.

Fine-tune in the cloud

Never again worry about provisioning cloud resources! Finetuner handles all related complexity and infrastructure.

How does it work?

    
    
    
    
    
import finetuner
from docarray import DocumentArray

# Login to Jina ecosystem
finetuner.login()

# Prepare training data
train_data = DocumentArray(...)

# Fine-tune in the cloud
run = finetuner.fit(
    model='resnet50', train_data=train_data, epochs=5, batch_size=128,
)

print(run.name)
print(run.logs())

# When ready
run.save_artifact(directory='experiment')
    
  

Training data

Load your training data: text, image, video, audio, matrix, tensor, or any Jina-compatible data format.

Choosing your model

Select a backbone model from our supported DNNs that you’d like to fine-ture.

Select hyper-parameters

Configure the number of epochs, batch size and other configurable hyper-parameters.

Monitor the logs

At any time, retrieve your experiments and runs and monitor their logs.

Finetuned neural network

A turned DNN that gives much better search relevance, which can be directly used as an Executor in Jina.

Ready to get started?

Easily finetune your ML models with domain specific data.