How I Run Multiple Tensorboard Instances to Compare Deep Learning Experiments Side by Side

2 minute read

If you’re deep into model training and experiment tracking like me, you’ve probably faced the need to monitor different runs side by side. At one point, I was juggling multiple folders of logs and manually switching directories, which felt clunky.

Eventually, I found a better way: running multiple instances of TensorBoard at the same time — each on a different port. This small change made my debugging and comparison process much more efficient.

Here’s how I do it.

Step-by-Step: Run Multiple TensorBoard Instances

✅ Step 1: Open CMD in Your Project Folder

Make sure you’re inside the folder where your training script or log directory lives.

cd path/to/your/project

✅ Step 2: Activate Your Conda Environment

conda activate your_env_name

✅ Step 3: Launch TensorBoard

For the default log directory:

tensorboard --logdir=logs

Or to run on a specific port (e.g., 6007):

tensorboard --logdir=logs --port=6007

Running Two TensorBoard Instances in Parallel

Let’s say you have two different training runs, each saving logs in different folders. Here’s what to do:

🖥️ Terminal Window 1:

tensorboard --logdir=path/to/first/logdir --port=6006

🖥️ Terminal Window 2:

tensorboard --logdir=path/to/second/logdir --port=6007

Access in Browser:

Visit http://localhost:6006 for the first instance.
Visit http://localhost:6007 for the second instance.

Why This Helped Me

When I was tuning hyperparameters for a reinforcement learning project, comparing reward curves across multiple runs in one interface just wasn’t cutting it. By running parallel TensorBoard instances, I could visually scan and compare experiments without jumping between folders or restarting logs.

Final Thoughts

If you’re training deep learning models regularly, learning how to run multiple TensorBoard sessions is a productivity boost. It’s one of those small things that pay off big over time.

👋 About Me

Hi, I’m Shuvangkar Das, a power systems researcher with a Ph.D. in Electrical Engineering from Clarkson University. I work at the intersection of power electronics, DER, IBR, and AI — building greener, smarter, and more stable grids. Currently, I’m a Research Engineer at EPRI (though everything I share here reflects my personal experience, not my employer’s views).

Over the years, I’ve worked on real-world projects involving large scale EMT simulation and firmware development for grid-forming and grid following inverter and reinforcement learning (RL). I also publish technical content and share hands-on insights with the goal of making complex ideas accessible to engineers and researchers.

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