Open Kubeflow and go to pipeline dashboard click experiments and open the run. You can see the pipeline graph which shows each step in our pipeline. As you can see all of your steps completed successfully.
During the training of your model, you are interested how your model loss and accuracy changes for each iteration. TensorBoard provides a visual presenation of iterations.
The logs of the training are uploaded to a Google Cloud Storage Bucket. TensorBoard automatically references this log location and displays the corresponding data.
The training component contains a TensorBoard visualization (TensorBoard viewer), which makes is comfortable to open the TensorBoard session for training jobs.
To open TensorBoard click on the training component in your experiment run. Located on the ride side is the artifact windows which shows a very handy button called (Open TensorBoard).
In order to use his visualizations, your pipeline component must write a JSON file. Kubeflow provides a good documenation on how visualizations are working and what types are available.
# write out TensorBoard viewer
metadata = {
'outputs' : [{
'type': 'tensorboard',
'source': args.input_job_dir,
}]
}
with open('/mlpipeline-ui-metadata.json', 'w') as f:
json.dump(metadata, f)
Your training component creates a metric (accuracy-score) which are displayed in the experiment UI. With those metrics, you can compare your different runs and model performance.
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