Clarifai/pipeline-examples

Clarifai/pipeline-examples

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Created: 2026-01-15T15:46:39Z

Pushed: 2026-06-11T00:39:51Z

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README:

Clarifai Pipeline Templates

This repository contains pipeline templates for training machine learning models on the Clarifai platform.

Quick Start Guide

Step 1: Set Up Your Environment

pip install clarifai
clarifai login # interactive: paste your Personal Access Token when prompted

Alternatively, set the PAT non-interactively:

export CLARIFAI_PAT=

Step 2: Browse Available Templates

clarifai pipelinetemplate ls

Step 3: Initialize a Pipeline from Template

clarifai pipeline init --template=classifier-pipeline-resnet-quick-start

This creates a new folder named after the template. `cd` into that folder before running any of the subsequent `clarifai pipeline ...` commands — they read the local config.yaml / config-lock.yaml:

cd classifier-pipeline-resnet-quick-start

Optional — override defaults at init time (different user/app from your clarifai login, a custom pipeline ID, or a model parameter default):

clarifai pipeline init --template=classifier-pipeline-resnet-quick-start \
--user_id MY_CUSTOM_USER_ID --app_id MY_CUSTOM_APP_ID \
--set id=MY_CUSTOM_PIPELINE_ID --set num_epochs=20

Step 4: Upload and Run the Pipeline

Make sure you are inside the generated pipeline folder (e.g. classifier-pipeline-resnet-quick-start/) from Step 3, then upload:

clarifai pipeline upload

Then run the pipeline using one of the two compute options:

# (a) Simplest — auto-create or reuse compute from an instance type
clarifai pipeline run --instance=g6e.xlarge

# (b) Use your existing nodepool + compute cluster (both flags required)
clarifai pipeline run \
--nodepool_id= \
--compute_cluster_id=

To override pipeline parameters at run time, repeat --set key=value:

clarifai pipeline run --instance=g6e.xlarge --set num_epochs=20 --set batch_size=32

Step 5: Monitor Your Pipeline

Go to https://clarifai.com/YOUR_USER_ID/YOUR_APP_ID, check the Pipelines tab to monitor your pipeline and check the Models tab to find your model once training is done.

Available Templates

Quick-Start Pipelines — Try These First!

Quick-start pipelines come with default public datasets pre-configured, so you can launch them right away to see an end-to-end training run — no data preparation needed.

| Template | Description | |----------|-------------| | classifier-pipeline-resnet-quick-start | Image classification with ResNet and sample dataset | | detector-pipeline-yolof-quick-start | Object detection with YOLOF and sample dataset | | detector-pipeline-eval-yolof-quick-start | Evaluation pipeline for a pretrained YOLOF detector — runs inference on a dataset and reports COCO detection metrics (no training) | | lora-pipeline-unsloth-quick-start | LLM LoRA fine-tuning with Unsloth and sample dataset |

Other Pipeline Examples

These are diverse pipelines (some of them may require additional setting up, e.g. a Clarifai dataset as a prerequisite).

| Template | Description | |----------|-------------| | classifier-pipeline-resnet | ResNet-based image classifier | | detector-pipeline-yolof | YOLOF-based object detector | | detector-pipeline-dfine | D-FINE-based object detector |