pixeltable
Pixeltable — Data, models, and orchestration in a unified declarative interface⚡
Stars: 53
Pixeltable is a Python library designed for ML Engineers and Data Scientists to focus on exploration, modeling, and app development without the need to handle data plumbing. It provides a declarative interface for working with text, images, embeddings, and video, enabling users to store, transform, index, and iterate on data within a single table interface. Pixeltable is persistent, acting as a database unlike in-memory Python libraries such as Pandas. It offers features like data storage and versioning, combined data and model lineage, indexing, orchestration of multimodal workloads, incremental updates, and automatic production-ready code generation. The tool emphasizes transparency, reproducibility, cost-saving through incremental data changes, and seamless integration with existing Python code and libraries.
README:
Pixeltable is a Python library that lets ML Engineers and Data Scientists focus on exploration, modeling, and app development without dealing with the customary data plumbing.
Today's solutions for AI app development require extensive custom coding and infrastructure plumbing. Tracking lineage and versions between and across data transformations, models, and deployments is cumbersome. Pixeltable provides a declarative interface for working with text, images, embeddings, and even video, enabling users to store, transform, index, and iterate on data within a single table interface.
pip install pixeltablePixeltable is persistent. Unlike in-memory Python libraries such as Pandas, Pixeltable is a database.
Learn how to create tables, populate them with data, and enhance them with built-in or user-defined transformations.
| Topic | Notebook | Topic | Notebook |
|---|---|---|---|
| 10-Minute Tour of Pixeltable |  |
Tables and Data Operations | |
| User-Defined Functions (UDFs) | |
Object Detection Models | |
| Experimenting with Chunking (RAG) | |
Working with External Files | |
| Integrating with Label Studio |  |
Audio/Video Transcript Indexing | |
import pixeltable as pxt
v = pxt.create_table('external_data.videos', {'video': pxt.VideoType()})
prefix = 's3://multimedia-commons/'
paths = [
'data/videos/mp4/ffe/ffb/ffeffbef41bbc269810b2a1a888de.mp4',
'data/videos/mp4/ffe/feb/ffefebb41485539f964760e6115fbc44.mp4',
'data/videos/mp4/ffe/f73/ffef7384d698b5f70d411c696247169.mp4'
]
v.insert({'video': prefix + p} for p in paths)Learn how to work with data in Pixeltable.
import pixeltable as pxt
from pixeltable.functions import huggingface
# Create a table to store data persistently
t = pxt.create_table('image', {'image': pxt.ImageType()})
# Insert some images
prefix = 'https://upload.wikimedia.org/wikipedia/commons'
paths = [
'/1/15/Cat_August_2010-4.jpg',
'/e/e1/Example_of_a_Dog.jpg',
'/thumb/b/bf/Bird_Diversity_2013.png/300px-Bird_Diversity_2013.png'
]
t.insert({'image': prefix + p} for p in paths)
# Add a computed column for image classification
t['classification'] = huggingface.detr_for_object_detection(
(t.image), model_id='facebook/detr-resnet-50'
)
# Retrieve the rows where cats have been identified
t.select(animal = t.image,
classification = t.classification.label_text[0]) \
.where(t.classification.label_text[0]=='cat').head()Learn about computed columns and object detection: Comparing object detection models.
@pxt.udf
def draw_boxes(img: PIL.Image.Image, boxes: list[list[float]]) -> PIL.Image.Image:
result = img.copy() # Create a copy of `img`
d = PIL.ImageDraw.Draw(result)
for box in boxes:
d.rectangle(box, width=3) # Draw bounding box rectangles on the copied image
return resultLearn more about user-defined functions: UDFs in Pixeltable.
# In this example, the view is defined by iteration over the chunks of a DocumentSplitter
chunks_table = pxt.create_view(
'rag_demo.chunks',
documents_table,
iterator=DocumentSplitter.create(
document=documents_table.document,
separators='token_limit', limit=300)
)Learn how to leverage views to build your RAG workflow.
# The computation of the mAP metric can become a query over the evaluation output
frames_view.select(mean_ap(frames_view.eval_yolox_tiny), mean_ap(frames_view.eval_yolox_m)).show()Learn how to leverage Pixeltable for Model analytics.
chat_table = pxt.create_table('together_demo.chat', {'input': pxt.StringType()})
# The chat-completions API expects JSON-formatted input:
messages = [{'role': 'user', 'content': chat_table.input}]
# This example shows how additional parameters from the Together API can be used in Pixeltable
chat_table['output'] = chat_completions(
messages=messages,
model='mistralai/Mixtral-8x7B-Instruct-v0.1',
max_tokens=300,
stop=['\n'],
temperature=0.7,
top_p=0.9,
top_k=40,
repetition_penalty=1.1,
logprobs=1,
echo=True
)
chat_table['response'] = chat_table.output.choices[0].message.content
# Start a conversation
chat_table.insert([
{'input': 'How many species of felids have been classified?'},
{'input': 'Can you make me a coffee?'}
])
chat_table.select(chat_table.input, chat_table.response).head()Learn how to interact with inference services such as Together AI in Pixeltable.
Pixeltable unifies data storage, versioning, and indexing with orchestration and model versioning under a declarative table interface, with transformations, model inference, and custom logic represented as computed columns.
- Data storage and versioning
- Combined Data and Model Lineage
- Indexing (e.g. embedding vectors) and Data Retrieval
- Orchestration of multimodal workloads
- Incremental updates
- Code is automatically production-ready
-
It gives you transparency and reproducibility
- All generated data is automatically recorded and versioned
- You will never need to re-run a workload because you lost track of the input data
-
It saves you money
- All data changes are automatically incremental
- You never need to re-run pipelines from scratch because you’re adding data
-
It integrates with any existing Python code or libraries
- Bring your ever-changing code and workloads
- You choose the models, tools, and AI practices (e.g., your embedding model for a vector index); Pixeltable orchestrates the data
- Pixeltable is not a low-code, prescriptive AI solution. We empower you to use the best frameworks and techniques for your specific needs.
- We do not aim to replace your existing AI toolkit, but rather enhance it by streamlining the underlying data infrastructure and orchestration.
[!TIP] Check out the Integrations section, and feel free to submit a request for additional ones.
Are you experiencing issues or bugs with Pixeltable? File an Issue. Do you want to contribute? Feel free to open a PR.
This library is licensed under the Apache 2.0 License.
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