Jayant Parashar
Jayant Parashar is an open source contributor and ML engineer. He's a GSoC 2026 contributor at FOSSASIA, where he's building native autologging for Visdom — extending it across PyTorch, Keras, PyTorch Lightning, XGBoost, and scikit-learn so training metrics stream to the dashboard without manual logging code.
Sessions
Set up Visdom
Training loops shouldn't need boilerplate just to see a loss curve. As part of my GSoC 2026 work with FOSSASIA on Visdom, I built VisdomLogger — a native logging API that streams metrics straight to Visdom's live dashboard. It shipped in Visdom v0.3.0 and is already usable today.
VisdomLogger's design keeps Visdom's philosophy intact it receives values through a simple log(name, value) call and never instruments training itself. That boundary is what makes true autologging possible on top of it — and in this talk I'll show the real thing a Keras callback that hooks into model.fit() and streams loss, accuracy, and any tracked metric to Visdom automatically, with zero manual logging calls in the training loop.
In this lightning talk, I'll run a live Keras training script with no logging boilerplate and watch the dashboard populate in real time, then briefly show the same pattern working for PyTorch. I'll close with what's next extending true autologging to PyTorch Lightning, XGBoost, and scikit-learn on a shared backend.
Every 12 seconds, Ethereum produces a new block full of transactions, fees and smart-contract activity. Most people only see this data as a list of hashes on a block explorer. In this hands-on workshop, you will turn it into a live dashboard of your own.
We'll use Visdom and Web3.py to connect to an Ethereum node and stream blockchain data into charts. By the end of the session, each participant will have a working dashboard that updates with every new block, showing:
base fee and priority fee over time
transactions per block
legacy vs. EIP-1559 transaction types
the distribution of ETH transferred
a network overview panel with the latest headline figures
You'll learn how to arrange and save dashboard layouts, and how to set up separate environments for mainnet, testnets or individual contracts. We'll also look at next steps: smart-contract analytics, DeFi and NFT tracking, node health monitoring, and visualizing machine-learning models trained on on-chain data, such as anomaly or bot detection.
No blockchain experience is needed. Basic Python is enough.
What participants bring: A laptop with Python 3 installed, and a free Ethereum RPC endpoint from any provider. It would help to share setup instructions in advance so no workshop time goes to installs.