Organization Activity Heatmap - AI Research Tools Tool

Overview

Organization Activity Heatmap is a Hugging Face Space that visualizes an organization’s model release activity by fetching metadata from the Hugging Face Docker repository and rendering it as a calendar-style heatmap. The Space provides a dashboard-style view that helps maintainers, researchers, and stakeholders quickly assess when and how frequently models—or Docker-based model releases—are being published or updated across an organization. The tool is intended for monitoring release cadence and spotting temporal patterns in organizational activity (for example, bursts of releases around major events or sustained slow periods). The Space page lists community engagement metrics: according to the Hugging Face Space page, the project has 139 likes and 0 downloads as of the referenced page (source: https://huggingface.co/spaces/cfahlgren1/model-release-heatmap). Implementation and operational details (frameworks, exact data fields visualized) are not documented on the Space page, so users seeking integration details should consult the Space repository or contact the maintainer directly via the Hugging Face Space.

Model Statistics

• Likes: 139

Model Details

This Space is a visualization/dashboard application rather than a machine learning model. Its primary capability is to fetch metadata from the Hugging Face Docker repository for a chosen organization and render that metadata as a heatmap to show temporal release patterns. The Space page does not publish a model architecture, parameter counts, or pipeline information; those fields are unknown for this app. Technical specifics about the implementation (web framework used on the Space, exact API endpoints or metadata fields consumed, and whether it uses server-side caching) are not documented on the public Space page. Functionally, the app operates by querying Hugging Face repository metadata (organization-scoped repositories or Docker images) and aggregating timestamps to produce a time-series heatmap for easy inspection. For reproducible local workflows, the Hugging Face Python client (huggingface_hub) can be used to list organization models and metadata, which can then be aggregated and plotted with standard Python visualization libraries.

Key Features

• Fetches repository metadata from the Hugging Face Docker repository for an organization
• Renders temporal activity as a calendar-style heatmap for quick pattern recognition
• Dashboard view suitable for monitoring model release cadence across an organization
• Aggregates release timestamps to reveal bursts and lulls in publishing activity
• Lightweight visual interface hosted as a Hugging Face Space for easy sharing

Example Usage

Example (python):

from huggingface_hub import HfApi
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Replace 'my-org' with the organization name you want to analyze
ORG = "my-org"
api = HfApi()

# List models for the organization (may include Docker-backed repos tagged as models)
models = api.list_models(author=ORG)

# Collect timestamps (fall back to multiple possible attributes safely)
rows = []
for m in models:
    ts = None
    # ModelInfo objects may expose different timestamp attributes depending on API version
    for attr in ("lastModified", "last_modified", "lastUpdated", "last_updated", "sha"):
        ts = getattr(m, attr, None)
        if ts:
            break
    # If timestamp is unavailable, skip that entry
    if not ts:
        continue
    # Ensure a datetime object
    ts = pd.to_datetime(ts)
    rows.append({"id": m.modelId if hasattr(m, "modelId") else getattr(m, "id", None), "ts": ts})

if not rows:
    print("No timestamped repositories found for this organization.")
else:
    df = pd.DataFrame(rows)
    df["year"] = df["ts"].dt.year
    df["month"] = df["ts"].dt.month

    # Aggregate counts by year-month
    pivot = df.groupby(["year", "month"]).size().unstack(fill_value=0)

    # Plot a heatmap of releases per month by year
    plt.figure(figsize=(10, 4))
    sns.heatmap(pivot, annot=True, fmt="d", cmap="YlGnBu")
    plt.title(f"Release activity heatmap for {ORG}")
    plt.ylabel("Year")
    plt.xlabel("Month")
    plt.show()

Benchmarks

Hugging Face likes: 139 (Source: https://huggingface.co/spaces/cfahlgren1/model-release-heatmap)

Hugging Face downloads: 0 (Source: https://huggingface.co/spaces/cfahlgren1/model-release-heatmap)

Key Information

• Category: Research Tools
• Type: AI Research Tools Tool