Building LSTMs with PyTorch and Lightning AI Part 6: Analyzing Training with TensorBoard
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In the previous article , we trained our model and checked the outputs, In this article we will take that analysis a step further by using TensorBoard Launching TensorBoard First, install TensorBoard if you haven't already. pip install tensorboard Then start TensorBoard by running: tensorboard --logdir = lightning_logs/ We will be doing the analysis based on the lightning logs. Once the command runs, TensorBoard will start a local server, usually available at http://localhost:6006 . Open it in…
1Key Takeaways
- In the previous article , we trained our model and checked the outputs, In this article we will take that analysis a step further by using TensorBoard Launching TensorBoard First, install TensorBoard if you haven't already.
- pip install tensorboard Then start TensorBoard by running: tensorboard --logdir = lightning_logs/ We will be doing the analysis based on the lightning logs.
- Once the command runs, TensorBoard will start a local server, usually available at http://localhost:6006 .
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that in the previous article , we trained our model and checked the outputs, In this article we will take that analysis a step further by using TensorBoard Launching TensorBoard First, install TensorBoard if you haven't already.
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