Skip to main content

Clusterer

Learn how to train a clustering model using Clarifai SDKs

Clusterer models are algorithms used in machine learning and data analysis to group similar data points together into clusters or clusters. These models aim to find patterns and structures within datasets by organizing the data into groups based on similarities in their features. You can learn more about Clusterer here.

App Creation​

The first part of model training includes the creation of an app under which the training process takes place. Here we are creating an app with the app id as “demo_train” and the base workflow is set as “Universal”. You can change the base workflows to Empty, Universal, Language Understanding, and General according to your use case.

from clarifai.client.user import User
#replace your "user_id"
client = User(user_id="user_id")
app = client.create_app(app_id="demo_train", base_workflow="Universal")

Dataset Upload​

The next step involves dataset upload. You can upload the dataset to your app so that the model accepts the data directly from the platform. The data used for training in this tutorial is available in the examples repository you have cloned.

#importing load_module_dataloader for calling the dataloader object in dataset.py in the local data folder
from clarifai.datasets.upload.utils import load_module_dataloader


# Construct the path to the dataset folder
CSV_PATH = os.path.join(os.getcwd().split('/models/model_train')[0],'datasets/upload/data/imdb.csv')


# Create a Clarifai dataset with the specified dataset_id 
dataset = app.create_dataset(dataset_id="text_dataset")
# Upload the dataset using the provided dataloader and get the upload status
dataset.upload_from_csv(csv_path=CSV_PATH,input_type='text',csv_type='raw', labels=True)

If you have followed the steps correctly you should receive an output that looks like this,

Output

Choose The Model Type​

First let's list the all available trainable model types in the platform,

print(app.list_trainable_model_types())
Output
['visual-classifier',
 'visual-detector',
 'visual-segmenter',
 'visual-embedder',
 'clusterer',
 'text-classifier',
 'embedding-classifier',
 'text-to-text']

Click here to know more about Clarifai Model Types.

Model Creation​

From the above list of model types we are going to choose clusterer as it is similar to our use case. Now let's create a model with the above model type.

MODEL_ID = "model_clusterer"
MODEL_TYPE_ID = "clusterer"

# Create a model by passing the model name and model type as parameter
model = app.create_model(model_id=MODEL_ID, model_type_id=MODEL_TYPE_ID)
Output

Patch Model​

After creating a model, you can perform patch operations on it by merging, removing, or overwriting data. By default, all actions support overwriting, but they handle lists of objects in specific ways.

  • The merge action updates a key:value pair with key:new_value or appends to an existing list. For dictionaries, it merges entries that share the same id field.
  • The remove action is only used to delete the model's cover image on the platform UI.
  • The overwrite action completely replaces an existing object with a new one.

Below is an example of performing patch operations on a model, such as updating its description and notes.

from clarifai.client.app import App

app = App(app_id="YOUR_APP_ID_HERE", user_id="YOUR_USER_ID_HERE", pat="YOUR_PAT_HERE")

# Update the details of the model
app.patch_model(model_id="model_clusterer", action="merge", description="description", notes="notes", toolkits=["OpenAI"], use_cases=["llm"], languages=["en"], image_url="https://samples.clarifai.com/metro-north.jpg")

# Update the model's image by specifying the 'remove' action
app.patch_model(model_id='model_clusterer', action='remove', image_url='https://samples.clarifai.com/metro-north.jpg')

Template Selection​

Inside the Clarifai platform there is a template feature. Templates give you the control to choose the specific architecture used by your neural network, as well as define a set of hyperparameters you can use to fine-tune the way your model learns. But when it comes to clustering there is only one default template available.

Setup Model Parameters​

You can update the model params to your need before initiating training.

# Get the params for the selected template
model_params = model.get_params()
print(model_params)
Output
{'train_params': {'base_embed_model': None,
  'coarse_clusters': 32.0,
  'eval_holdout_fraction': 0.2,
  'query_holdout_fraction': 0.1,
  'to_be_indexed_queries_fraction': 0.25,
  'max_num_query_embeddings': 100.0,
  'num_results_per_query': [1.0, 5.0, 10.0, 20.0],
  'max_visited': 32.0,
  'quota': 1000.0,
  'beta': 1.0}}

Initiate Model Training​

We can initiate the model training by calling the model.train() method. The Clarifai SDKs also offers features like showing training status and saving training logs in a local file.

note

If the status code is 'MODEL-TRAINED', then the user can know the Model is Trained and ready to use._

import time
#Starting the training
model_version_id = model.train()

#Checking the status of training
while True:
    status = model.training_status(version_id=model_version_id,training_logs=False)
    if status.code == 21106: #MODEL_TRAINING_FAILED
        print(status)
        break
    elif status.code == 21100: #MODEL_TRAINED
        print(status)
        break
    else:
        print("Current Status:",status)
        print("Waiting---")
        time.sleep(120)
Output

Model Prediction​

Since the model is trained and ready let’s run some predictions to view the model performance,

TEXT = b"This is a great place to work"

# get the predictions
model_prediction = model.predict_by_bytes(TEXT, input_type="text")

print(model_prediction.outputs[0].data.clusters)
Output
[id: "22_5"

projection: 0.010116016492247581

projection: -0.035988882184028625

]
© 2024 Clarifai, Inc. All rights reserved