ViewKit

ViewKit

Browser-based dataset viewer for ML researchers with full client-side processing

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About ViewKit

ViewKit is a browser-based data file viewer built for machine learning researchers and data scientists who need to inspect datasets without uploading them anywhere. The entire application runs client-side, which means your data never leaves your machine. You open a file, it processes in your browser, and that's it. No server, no upload, no third party ever sees your work.

The problem it solves is one that anyone working with research data knows well. You have an HDF5 file or a Parquet dataset sitting on your machine, and you just want to look at it. The traditional path is to fire up a Jupyter notebook, import the right libraries, and write a few lines of code to peek at the structure. ViewKit skips all of that. You drag the file into the browser, and you're looking at the data in seconds.

Format support is broad and clearly aimed at the ML ecosystem. It handles HDF5, Parquet, Zarr in both v2 and v3, CSV, TSV, Excel, JSON Lines, Apache Arrow, Feather, NumPy arrays in npy and npz form, safetensors, TFRecord, WebDataset tar archives, and a range of audio files including WAV, MP3, FLAC, OGG, and M4A. That coverage means you can open most of the common artifacts from a training pipeline without switching tools or installing anything locally.

The audience is anyone who touches datasets in a research or production ML context. If you're debugging a data pipeline, checking the shape of tensors before a training run, or just exploring a dataset you received from a collaborator, ViewKit lets you do that in the browser. There's no account to create, no installation to manage, and no data leaving your control.

What sets it apart is the combination of breadth and simplicity. Plenty of tools can open a CSV, but fewer handle the more specialized formats like safetensors or TFRecord without requiring local setup. And because everything runs in the browser with client-side processing, you can use it on a locked-down work machine where you can't install arbitrary software. The privacy story is strong because there's nothing to trust beyond your own browser.

ViewKit is free with no paid tier mentioned. You visit the site, open your file, and inspect your data. There's light and dark mode support that persists between sessions, but the core value is the instant, zero-friction inspection of datasets in a format-aware viewer that never phones home.

Key Features

  • Full client-side data processing
  • Support for HDF5, Parquet, Zarr, safetensors, and more
  • NumPy array inspection for npy and npz files
  • Audio file preview for WAV, MP3, FLAC, OGG, M4A
  • No installation or account required
  • Light and dark mode with persistence

Pros & Cons

What we like

  • Data never leaves your machine because everything runs in the browser
  • Supports over a dozen ML and data science file formats
  • No setup or installation needed on any operating system
  • Completely free with no paywalls or usage limits

Room for improvement

  • Performance depends on browser and local hardware for large files
  • No editing or transformation capabilities, read-only inspection only
  • Newer tool with a smaller community and fewer resources
  • Limited documentation for advanced use cases

Frequently Asked Questions

What is ViewKit?
ViewKit is a browser-based data file viewer designed for ML researchers and data scientists. It lets you open and inspect datasets in formats like HDF5, Parquet, Zarr, safetensors, and NumPy arrays directly in your browser without uploading anything.
Does ViewKit upload my data anywhere?
No. ViewKit runs entirely client-side in your browser. Your files are processed locally, and no data is ever sent to a server. The privacy model is built around keeping everything on your machine.
Is ViewKit free?
Yes, ViewKit is completely free. There's no paid tier, no account required, and no usage limits. You just visit the site and open your files.
What file formats does ViewKit support?
ViewKit supports HDF5, Parquet, Zarr v2 and v3, CSV, TSV, Excel, JSON Lines, Apache Arrow, Feather, NumPy npy and npz, safetensors, TFRecord, WebDataset tar, and audio files including WAV, MP3, FLAC, OGG, and M4A.

Best For

Inspecting HDF5 or Parquet datasets before a training runPreviewing safetensors model weights without PythonChecking the structure of a dataset received from a collaboratorDebugging data pipeline outputs on a locked-down work machine

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