What is a pipeline revision? A pipeline revision is a version of the pipeline that contains a sequence of steps . There are three states of a pipeline revision:
Published : The pipeline revision is published and can be used on the next executionEditing : The pipeline revision is in draft mode and can be edited.Archived : The pipeline revision is archived for future reference.
You can edit the pipeline revision with two methods:
Web Console : Edit the pipeline revision using the web console.YAML manifest : Apply the pipeline revision using the YAML manifest.
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:
Toolbox : Quick menus 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.
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.
See the Pipeline steps page for more details.
Pipeline variables Pipeline variables are working similarly to the environment variables in the pipeline execution. You can define the pipeline variables in the Variables section and use them in the pipeline steps.
See the Pipeline variables page for more details.
Once you finish editing the pipeline revision, click the blue Publish button on the top right corner to publish the revision.
Edit revision using YAML manifest You can also use the YAML manifest to edit the pipeline revision. By managing pipeline revisions with YAML, you can easily track the changes, apply the changes to the pipeline revision or share the workflow definition with others.
To edit the pipeline revision using the YAML manifest, click Import YAML on the editing revision list.
A dialog will appear where you can paste the YAML manifest. After you paste the YAML manifest, click Import to apply the changes.
Sample YAML manifest Here is the sample YAML manifest for fine-tuning a LLM(Large Language Model) and uploading the quantized model to the Huggingface 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 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- gcp- oregon
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- gcp- oregon
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- gcp- oregon
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- gcp- oregon
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- gcp- oregon
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- gcp- oregon
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
