These notebooks show how to fine-tune an NLP model on AzureML. They are intended to be cloned to and executed on an AzureML compute instance within a Jupyter environment. They go through the process of creating a DeepSpeed enabled training environment, creating a compute target (if there isn't one already), preparing and registering datasets, fine tuning a model on those data sets, and registering the resulting output model. This is configured and supported by only a few outside files in the src directory.

• Clone this repo into an interactive session on a fresh AzureML compute instance
• From the command line, install the requirements.txt into the local AzureML_Py3.8 conda environment via conda activate azureml_py38 && pip install -r requirements.txt.
• Follow the notebooks in numerical order
  • 01 Create compute ensures requirements are installed and compute cluster is accessible
  • 02 Prepare environment creates an AzureML environment that supports DeepSpeed training
  • 03 Prepare data downloads, preprocesses, and registers a dataset for versioned and reproducible training
  • 04 Train model launches a distributed fine-tuning job using the outputs of the prior notebooks