pixeltable
Pixeltable — AI Data infrastructure providing a declarative, incremental approach for multimodal workloads.
Stars: 143
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 providing a declarative interface for multimodal data (text, images, audio, video). It features built-in versioning, lineage tracking, and incremental updates, enabling users to store, transform, index, and iterate on data for their ML workflows.
Data transformations, model inference, and custom logic are embedded as computed columns.
- Load/Query all data types: Interact with video data at the frame level and documents at the chunk level
- Incremental updates for data transformation: Maintain an embedding index colocated with your data
- Lazy evaluation and cache management: Eliminates the need for manual frame extraction
- Integrates with any Python libraries: Use built-in and custom functions (UDFs) without complex pipelines
- Data format agnostic and extensibility: Access tables as Parquet files, PyTorch datasets, or COCO annotations
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 | |
| Incremental Prompt Engineering | |
Working with External Files | |
| Integrating with Label Studio |  |
Audio/Video Transcript Indexing | |
| Multimodal Application |  |
Document Indexing and RAG | |
| Context-Aware Discord Bot |  |
Image/Text Similarity Search |  |
import pixeltable as pxt
v = pxt.create_table('external_data.videos', {'video': pxt.Video})
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.Image})
# 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.add_computed_column(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.String})
# 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.add_computed_column(
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.add_computed_column(
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.
import pixeltable as pxt
from pixeltable.functions.huggingface import clip
from pixeltable.iterators import FrameIterator
import PIL.Image
video_table = pxt.create_table('videos', {'video': pxt.Video})
video_table.insert([{'video': '/video.mp4'}])
frames_view = pxt.create_view(
'frames', video_table, iterator=FrameIterator.create(video=video_table.video))
# Create an index on the 'frame' column that allows text and image search
frames_view.add_embedding_index('frame', embed=clip.using('openai/clip-vit-base-patch32'))
# Now we will retrieve images based on a sample image
sample_image = '/image.jpeg'
sim = frames_view.frame.similarity(sample_image)
frames_view.order_by(sim, asc=False).limit(5).select(frames_view.frame, sim=sim).collect()
# Now we will retrieve images based on a string
sample_text = 'red truck'
sim = frames_view.frame.similarity(sample_text)
frames_view.order_by(sim, asc=False).limit(5).select(frames_view.frame, sim=sim).collect()Learn how to work with Embedding and Vector Indexes.
| Requirement | Traditional | Pixeltable |
|---|---|---|
| Frame Extraction | ffmpeg + custom code | Automatic via FrameIterator |
| Object Detection | Multiple scripts + caching | Single computed column |
| Video Indexing | Custom pipelines + Vector DB | Native similarity search |
| Annotation Management | Separate tools + custom code | Label Studio integration |
| Model Evaluation | Custom metrics pipeline | Built-in mAP computation |
| Requirement | Traditional | Pixeltable |
|---|---|---|
| Document Chunking | Tool + custom code | Native DocumentSplitter |
| Embedding Generation | Separate pipeline + caching | Computed columns |
| Vector Search | External vector DB | Built-in vector indexing |
| Prompt Management | Custom tracking solution | Version-controlled columns |
| Chain Management | Tool + custom code | Computed column DAGs |
| Requirement | Traditional | Pixeltable |
|---|---|---|
| Data Types | Multiple storage systems | Unified table interface |
| Cross-Modal Search | Complex integration | Native similarity support |
| Pipeline Orchestration | Multiple tools (Airflow, etc.) | Single declarative interface |
| Asset Management | Custom tracking system | Automatic lineage |
| Quality Control | Multiple validation tools | Computed validation columns |
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.
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 lets ML Engineers and Data Scientists focus on exploration, modeling, and app development without dealing with the customary data plumbing.
- 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.
We're excited to welcome contributions from the community! Here's how you can get involved:
- Found a bug? Open an issue
- Include steps to reproduce and environment details
- Fork the repository
- Create a feature branch
- Submit a pull request
- See our Contributing Guide for detailed instructions
- Have questions? Start a Discussion
- Share your Pixeltable projects and use cases
- Help others in the community
- Suggest examples and tutorials
- Propose improvements
This library is licensed under the Apache 2.0 License.
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