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Revisions dashboard

The Revisions dashboard offers an overview of the revision statuses. It enables you to modify a specific version of a pipeline that consists of a series of steps.

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Revision states

There are three states to revise each step:
  • Published: The pipeline revision is published and available for use in the next execution.
  • Drafts (Editable): The pipeline revision is currently in an editable state.
  • Archived: The pipeline revision has been archived for future reference.

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Revision editor — actions on revisions

You can edit the pipeline revision with two methods:
  • Web console
  • YAML manifest

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Edit revision using web console

To edit the pipeline revision using the web console, click edit icon on the editing revision list.
When you navigate to the revision editor, you can see the page with the following components. Here is the description of the components on the revision editor page:
  • Quick menu: The tool box where you can expand or collapse the tools such as Step List and Pipeline Variables.
  • Step list: The list of steps that define each action of the pipeline. You can drag-and-drop the steps to the canvas.
  • Dependency graph: The connection of each step and dependency of the editing revision. You can click each step on the graph to edit the details of the step.
  • Pipeline variables: The list of pipeline variables that can be injected in the pipeline steps.

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Pipeline steps

You can drag and drop the steps from the Step list to the Dependency graph to define the pipeline steps. You can also connect the steps by dragging the arrow from the output of the step to the input of the next step.

Learn more about Pipeline steps

Manage your pipeline steps

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Pipeline variables

Pipeline variables are working similarly to the environment variables in the pipeline execution. You can define the pipeline variables in the Variables tab and use them in the pipeline steps.

Learn more about Pipeline variables

Manage your pipeline environment
Once you finish editing the pipeline revision, click the Publish button on the top right corner to publish the revision.

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Edit revision using YAML manifest

Managing pipeline revisions with a YAML manifest allows for easy tracking, applying updates, and sharing workflow definitions with others. In Pipeline revisions, there are two ways to import a YAML manifest:
  • Import YAML file
  • Paste a YAML configuration
Try out the demo below:

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Sample YAML manifest

Here is the sample YAML manifest for fine-tuning an LLM and uploading the quantized model to the Hugging Face model registry:
Replace the following variables on the sample YAML manifest with your values:
  • {HUGGINGFACE_TOKEN}: Your Huggingface token to access the model registry
  • {LORA_ADAPTER_NAME}: Huggingface Repository name of QLoRA adapter to upload
  • {QUANTIZED_MODEL_NAME}: Huggingface Repository name of quantized model
llm-finetuning.yaml
api_version: v1
variables:
  HF_TOKEN:
    description: Hugginface token to access to model registry
    required: true
    default: {HUGGINGFACE_TOKEN}
  BASE_MODEL_NAME:
    description: Base LLM name to fine-tune
    options:
      - meta-llama/Meta-Llama-3.1-8B-Instruct
      - mistralai/Mistral-Large-Instruct-2407
    required: true
    default: meta-llama/Meta-Llama-3.1-8B-Instruct
  ADAPTER_NAME:
    description: Repository name of QLoRA adapter to upload
    required: true
    default: {LORA_ADAPTER_NAME}
  QUANTIZE_MODEL:
    description: Whether to quantize trained model for production usage
    options:
      - "yes"
      - "no"
    required: true
    default: "yes"
  QUANTIZED_MODEL_NAME:
    description: Repository name of quantized model
    required: true
    default: {QUANTIZED_MODEL_NAME}
steps:
  - key: Fine-tune-with-QLoRA
    title: Fine-tune with QLoRA
    type: v1/run
    description: Fine-tune model with QLoRA
    spec:
      input_variables:
        BASE_MODEL_NAME:
          type: pipeline_variable
          value: BASE_MODEL_NAME
        HF_TOKEN:
          type: pipeline_variable
          value: HF_TOKEN
      run_spec:
        name: Fine-tune with QLoRA
        description: Fine-tune model with QLoRA
        resources:
          cluster: vessl-oci-sanjose
          preset: gpu-l4-small-spot
        image: quay.io/vessl-ai/cuda:12.4-r4
        import:
          /code/:
            git:
              url: github.com/vessl-ai/examples.git
              ref: main
        export:
          /root/output/: vessl-artifact://
        run: |-
          cd /code/pipelines/pipelines-quickstart/llm-finetuning

          pip install torch==2.2.0
          pip install -r requirements.txt

          python finetune.py \
            --base-model-name $BASE_MODEL_NAME \
            --checkpoint-path /root/output/checkpoints \
            --output-model-name /root/output/adapter \
            --train-epochs 4 \
            --lora-rank 16
        env:
          BASE_MODEL_NAME:
            source: inject
          HF_TOKEN:
            source: inject
  - key: Quantize-Model
    title: Quantize Model?
    type: v1/if
    depends_on:
      - Fine-tune-with-QLoRA
    spec:
      condition: ${QUANTIZE_MODEL} == yes
  - key: Upload-Adapter
    title: Upload Adapter
    type: v1/run
    depends_on:
      - Fine-tune-with-QLoRA
    spec:
      input_variables:
        ADAPTER_NAME:
          type: pipeline_variable
          value: ADAPTER_NAME
        HF_TOKEN:
          type: pipeline_variable
          value: HF_TOKEN
      volumes:
        /model/:
          source: pipeline-step
          source_step:
            step_key: Fine-tune-with-QLoRA
            volume_claim_name: /root/output/
      run_spec:
        name: Upload Adapter
        description: ""
        resources:
          cluster: vessl-oci-sanjose
          preset: cpu-medium-spot
        image: quay.io/vessl-ai/python:3.10-r18
        import:
          /model/: vessl-artifact://
        run: |-
          pip install -U "huggingface_hub[cli]"

          huggingface-cli upload $ADAPTER_NAME /model/adapter .
        env:
          ADAPTER_NAME:
            source: inject
          HF_TOKEN:
            source: inject
  - key: AWQ-Marlin-Quantization
    title: AWQ+Marlin Quantization
    type: v1/run
    depends_on:
      - Quantize-Model:yes
    spec:
      input_variables:
        ADAPTER_NAME:
          type: pipeline_variable
          value: ADAPTER_NAME
        BASE_MODEL_NAME:
          type: pipeline_variable
          value: BASE_MODEL_NAME
        HF_TOKEN:
          type: pipeline_variable
          value: HF_TOKEN
      run_spec:
        name: AWQ+Marlin Quantization
        description: ""
        resources:
          cluster: vessl-oci-sanjose
          preset: gpu-l4-small-spot
        image: quay.io/vessl-ai/cuda:12.4-r4
        import:
          /code/:
            git:
              url: github.com/vessl-ai/examples.git
              ref: main
        export:
          /root/output/: vessl-artifact://
        run: |-
          cd /code/pipelines/pipelines-quickstart/llm-quantization

          pip install torch==2.3.1
          pip install -r requirements.txt

          python merge_and_quantize.py \
            --base-model-name $BASE_MODEL_NAME \
            --adapter-name $ADAPTER_NAME \
            --quantized-model-name /root/output
        env:
          ADAPTER_NAME:
            source: inject
          BASE_MODEL_NAME:
            source: inject
          HF_TOKEN:
            source: inject
  - key: Evaluate-Quantized-Model
    title: Evaluate Quantized Model
    type: v1/run
    depends_on:
      - AWQ-Marlin-Quantization
    spec:
      input_variables:
        HF_TOKEN:
          type: pipeline_variable
          value: HF_TOKEN
        QUANTIZED_MODEL_NAME:
          type: pipeline_variable
          value: QUANTIZED_MODEL_NAME
      volumes:
        /model/:
          source: pipeline-step
          source_step:
            step_key: AWQ-Marlin-Quantization
            volume_claim_name: /root/output/
      run_spec:
        name: Evaluate Quantized Model
        description: ""
        resources:
          cluster: vessl-oci-sanjose
          preset: gpu-l4-small-spot
        image: quay.io/vessl-ai/cuda:12.4-r4
        import:
          /code/:
            git:
              url: github.com/vessl-ai/examples.git
              ref: main
          /model/: vessl-artifact://
        run: |-
          cd /code/pipelines/pipelines-quickstart/llm-finetuning-eval

          pip install torch==2.3.1
          pip install -r requirements.txt
          pip install "git+https://github.com/IST-DASLab/marlin"

          python evaluate.py \
            --model-name /model \
            --quantization awq \
            --prompts "What is the capital of France?" "How does a transformer model work?"
        env:
          HF_TOKEN:
            source: inject
          QUANTIZED_MODEL_NAME:
            source: inject
  - key: Accept-Evaluation
    title: Accept Evaluation?
    type: v1/manual_judgment
    description: See the logs from "Evaluate Model" and decide whether to push the fine-tuned model to production.
    depends_on:
      - Evaluate-Quantized-Model
      - Evaluate-Original-Model
    spec:
      assignee_email_addresses:
        - sample_email@foo.bar
  - key: Fail
    title: Fail
    type: v1/fail
    depends_on:
      - Accept-Evaluation:no
    spec: {}
  - key: Upload-Quantized-Model
    title: Upload Quantized Model
    type: v1/run
    depends_on:
      - Evaluated-Quantized-Model:yes
    spec:
      input_variables:
        HF_TOKEN:
          type: pipeline_variable
          value: HF_TOKEN
        QUANTIZED_MODEL_NAME:
          type: pipeline_variable
          value: QUANTIZED_MODEL_NAME
      volumes:
        /model/:
          source: pipeline-step
          source_step:
            step_key: AWQ-Marlin-Quantization
            volume_claim_name: /root/output/
      run_spec:
        name: Upload Quantized Model
        description: ""
        resources:
          cluster: vessl-oci-sanjose
          preset: cpu-medium-spot
        image: quay.io/vessl-ai/python:3.10-r18
        import:
          /model/: vessl-artifact://
        run: |-
          pip install -U "huggingface_hub[cli]"

          huggingface-cli upload $QUANTIZED_MODEL_NAME /model .
        env:
          HF_TOKEN:
            source: inject
          QUANTIZED_MODEL_NAME:
            source: inject
  - key: Notify-Fine-tuning-Completion
    title: Notify Fine-tuning Completion
    type: v1/notification
    depends_on:
      - Evaluated-Quantized-Model:no
      - Upload-Quantized-Model
    spec:
      email_addresses:
        - sample_email@foo.bar
      email_subject: '[llm-finetuning] Fine tuning job has finished'
      email_contents: '😉 Fine-tuning has successfully completed. Please check the logs!'
  - key: Notify-Fine-tuning-has-failed
    title: Notify Fine-tuning has failed
    type: v1/notification
    depends_on:
      - Fail
    spec:
      email_addresses:
        - sample_email@foo.bar
      email_subject: '[llm-finetuning] Fine-tuning has failed'
      email_contents: '😱 Fine-tuning has failed. Please check the logs.'
  - key: Evaluate-Original-Model
    title: Evaluate Original Model
    type: v1/run
    depends_on:
      - Quantize-Model:no
    spec:
      input_variables:
        ADAPTER_NAME:
          type: pipeline_variable
          value: ADAPTER_NAME
        HF_TOKEN:
          type: pipeline_variable
          value: HF_TOKEN
      run_spec:
        name: Evaluate Original Model
        description: ""
        resources:
          cluster: vessl-oci-sanjose
          preset: gpu-l4-small-spot
        image: quay.io/vessl-ai/cuda:12.4-r4
        import:
          /code/:
            git:
              url: github.com/vessl-ai/examples.git
              ref: main
        run: |-
          cd /code/pipelines/pipelines-quickstart/llm-finetuning-eval

          pip install torch==2.3.1
          pip install -r requirements.txt

          python evaluate.py \
            --model-name $ADAPTER_NAME \
            --prompts "What is the capital of France?" "How does a transformer model work?"
        env:
          ADAPTER_NAME:
            source: inject
          HF_TOKEN:
            source: inject
  - key: Evaluated-Quantized-Model
    title: Evaluated Quantized Model?
    type: v1/if
    depends_on:
      - Accept-Evaluation:yes
    spec:
      condition: ${QUANTIZE_MODEL} == yes