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Finetuning_VSCodeaitoolkit.md

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Maligayang Pagdating sa AI Toolkit para sa VS Code

AI Toolkit para sa VS Code ay nagdadala ng iba't ibang modelo mula sa Azure AI Studio Catalog at iba pang katalogo tulad ng Hugging Face. Ang toolkit ay nagpapadali sa mga karaniwang gawain sa pagbuo ng AI apps gamit ang generative AI tools at mga modelo sa pamamagitan ng:

  • Pagsisimula sa pagdiskubre ng mga modelo at playground.
  • Fine-tuning at inference ng modelo gamit ang lokal na mga computing resources.
  • Remote na fine-tuning at inference gamit ang Azure resources.

Install AI Toolkit para sa VSCode

AIToolkit FineTuning

[Pribadong Preview] Isang-click na provisioning para sa Azure Container Apps upang magpatakbo ng fine-tuning at inference ng modelo sa cloud.

Ngayon, simulan na natin ang iyong AI app development:

Lokal na Pag-unlad

Mga Paghahanda

  1. Siguraduhing naka-install ang NVIDIA driver sa host.
  2. Patakbuhin ang huggingface-cli login kung gumagamit ka ng HF para sa paggamit ng dataset.
  3. Olive mga paliwanag sa mga key settings para sa anumang nagbabago ng paggamit ng memorya.

I-activate ang Conda

Dahil gumagamit tayo ng WSL environment at ito ay shared, kailangan mong manu-manong i-activate ang conda environment. Pagkatapos ng hakbang na ito, maaari mong patakbuhin ang fine-tuning o inference.

conda activate [conda-env-name]

Fine-tuning para sa Base Model Lamang

Upang subukan lamang ang base model nang walang fine-tuning, maaari mong patakbuhin ang command na ito pagkatapos i-activate ang conda.

cd inference

# Web browser interface allows to adjust a few parameters like max new token length, temperature and so on.
# User has to manually open the link (e.g. http://0.0.0.0:7860) in a browser after gradio initiates the connections.
python gradio_chat.py --baseonly

Fine-tuning at Inferencing ng Modelo

Kapag ang workspace ay nabuksan na sa isang dev container, magbukas ng terminal (ang default na path ay project root), pagkatapos ay patakbuhin ang command sa ibaba upang mag-fine-tune ng LLM sa napiling dataset.

python finetuning/invoke_olive.py

Ang mga checkpoints at ang final na modelo ay mase-save sa models folder.

Next run inferencing with the fune-tuned model through chats in a console, web browser or prompt flow.

cd inference

# Console interface.
python console_chat.py

# Web browser interface allows to adjust a few parameters like max new token length, temperature and so on.
# User has to manually open the link (e.g. http://127.0.0.1:7860) in a browser after gradio initiates the connections.
python gradio_chat.py

Upang magamit ang prompt flow in VS Code, please refer to this Quick Start.

Model Fine-tuning

Next, download the following model depending on the availability of a GPU on your device.

To initiate the local fine-tuning session using QLoRA, select a model you want to fine-tune from our catalog.

Platform(s) GPU available Model name Size (GB)
Windows Yes Phi-3-mini-4k-directml-int4-awq-block-128-onnx 2.13GB
Linux Yes Phi-3-mini-4k-cuda-int4-onnx 2.30GB
Windows
Linux		 No Phi-3-mini-4k-cpu-int4-rtn-block-32-acc-level-4-onnx 2.72GB

Note You do not need an Azure Account to download the models

The Phi3-mini (int4) model is approximately 2GB-3GB in size. Depending on your network speed, it could take a few minutes to download.

Start by selecting a project name and location. Next, select a model from the model catalog. You will be prompted to download the project template. You can then click "Configure Project" to adjust various settings.

Microsoft Olive

We use Olive to run QLoRA fine-tuning on a PyTorch model from our catalog. All of the settings are preset with the default values to optimize to run the fine-tuning process locally with optimized use of memory, but it can be adjusted for your scenario.

Fine Tuning Samples and Resoures

[Private Preview] Remote Development

Prerequisites

  1. To run the model fine-tuning in your remote Azure Container App Environment, make sure your subscription has enough GPU capacity. Submit a support ticket to request the required capacity for your application. Get More Info about GPU capacity
  2. If you are using private dataset on HuggingFace, make sure you have a HuggingFace account and generate an access token
  3. Enable Remote Fine-tuning and Inference feature flag in the AI Toolkit for VS Code
    1. Open the VS Code Settings by selecting File -> Preferences -> Settings.
    2. Navigate to Extensions and select AI Toolkit.
    3. Select the "Enable Remote Fine-tuning And Inference" option.
    4. Reload VS Code to take effect.

Setting Up a Remote Development Project

  1. Execute the command palette AI Toolkit: Focus on Resource View.
  2. Navigate to Model Fine-tuning to access the model catalog. Assign a name to your project and select its location on your machine. Then, hit the "Configure Project" button.
  3. Project Configuration
    1. Avoid enabling the "Fine-tune locally" option.
    2. The Olive configuration settings will appear with pre-set default values. Please adjust and fill in these configurations as required.
    3. Move on to Generate Project. This stage leverages WSL and involves setting up a new Conda environment, preparing for future updates that include Dev Containers.
  4. Click on "Relaunch Window In Workspace" to open your remote development project.

Note: The project currently works either locally or remotely within the AI Toolkit for VS Code. If you choose "Fine-tune locally" during project creation, it will operate exclusively in WSL without remote development capabilities. On the other hand, if you forego enabling "Fine-tune locally", the project will be restricted to the remote Azure Container App environment.

Provision Azure Resources

To get started, you need to provision the Azure Resource for remote fine-tuning. Do this by running the AI Toolkit: Provision Azure Container Apps job para sa fine-tuning from the command palette.

Monitor the progress of the provision through the link displayed in the output channel.

[Optional] Add Huggingface Token to the Azure Container App Secret

If you're using private HuggingFace dataset, set your HuggingFace token as an environment variable to avoid the need for manual login on the Hugging Face Hub. You can do this using the AI Toolkit: Add Azure Container Apps Job secret para sa fine-tuning command. With this command, you can set the secret name as HF_TOKEN and use your Hugging Face token as the secret value.

Run Fine-tuning

To start the remote fine-tuning job, execute the AI Toolkit: Patakbuhin ang fine-tuning command.

To view the system and console logs, you can visit the Azure portal using the link in the output panel (more steps at View and Query Logs on Azure). Or, you can view the console logs directly in the VSCode output panel by running the command AI Toolkit: Ipakita ang tumatakbong fine-tuning job streaming logs.

Note: The job might be queued due to insufficient resources. If the log is not displayed, execute the AI Toolkit: Ipakita ang tumatakbong fine-tuning job streaming logs command, wait for a while and then execute the command again to re-connect to the streaming log.

During this process, QLoRA will be used for fine-tuning, and will create LoRA adapters for the model to use during inference. The results of the fine-tuning will be stored in the Azure Files.

Provision Inference Endpoint

After the adapters are trained in the remote environment, use a simple Gradio application to interact with the model. Similar to the fine-tuning process, you need to set up the Azure Resources for remote inference by executing the AI Toolkit: Provision Azure Container Apps para sa inference from the command palette.

By default, the subscription and the resource group for inference should match those used for fine-tuning. The inference will use the same Azure Container App Environment and access the model and model adapter stored in Azure Files, which were generated during the fine-tuning step.

Deploy the Inference Endpoint

If you wish to revise the inference code or reload the inference model, please execute the AI Toolkit: I-deploy para sa inference command. This will synchronize your latest code with Azure Container App and restart the replica.

Once deployment is successfully completed, you can access the inference API by clicking on the "Go to Inference Endpoint" button displayed in the VSCode notification. Or, the web API endpoint can be found under ACA_APP_ENDPOINT in ./infra/inference.config.json at sa output panel. Handa ka nang suriin ang modelo gamit ang endpoint na ito.

Advanced na Paggamit

Para sa karagdagang impormasyon sa remote development gamit ang AI Toolkit, sumangguni sa Fine-Tuning models remotely at Inferencing with the fine-tuned model na dokumentasyon.

Paunawa:
Ang dokumentong ito ay isinalin gamit ang mga serbisyo ng AI na batay sa makina. Bagamat sinisikap namin ang pagiging tumpak, pakitandaan na ang mga awtomatikong pagsasalin ay maaaring maglaman ng mga pagkakamali o hindi pagkakatugma. Ang orihinal na dokumento sa sariling wika nito ang dapat ituring na opisyal na sanggunian. Para sa mahalagang impormasyon, inirerekomenda ang propesyonal na pagsasalin ng tao. Hindi kami mananagot sa anumang hindi pagkakaunawaan o maling interpretasyon na dulot ng paggamit ng pagsasaling ito.