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The default directory path is `/var/log/katib/tfevent/`.
@@ -110,10 +113,10 @@ To define the pull-based metrics collector for your Experiment:
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## Push-based Metrics Collector
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Your training code needs to call [`report_metrics()`](https://github.com/kubeflow/katib/blob/e251a07cb9491e2d892db306d925dddf51cb0930/sdk/python/v1beta1/kubeflow/katib/api/report_metrics.py#L26) function in Python SDK to record metrics.
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The `report_metrics()` function works by parsing the metrics in `metrics` field into a gRPC request, automatically adding the current timestamp for users, and sending the request to Katib DB Manager.
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Your training code needs to call [`report_metrics()`](https://github.com/kubeflow/katib/blob/e251a07cb9491e2d892db306d925dddf51cb0930/sdk/python/v1beta1/kubeflow/katib/api/report_metrics.py#L26) function in Python SDK to record metrics.
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The `report_metrics()` function works by parsing the metrics in `metrics` field into a gRPC request, automatically adding the current timestamp for users, and sending the request to Katib DB Manager.
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But before that, `kubeflow-katib` package should be installed in your training container.
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But before that, `kubeflow-katib` package should be installed in your training container.
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To define the push-based metrics collector for your Experiment, you have two options:
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@@ -146,7 +149,7 @@ To define the push-based metrics collector for your Experiment, you have two opt
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max_trial_count=2,
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metrics_collector_config={"kind": "Push"},
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# When SDK is released, replace it with packages_to_install=["kubeflow-katib==0.18.0"].
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# Currently, the training container should have `git` package to install this SDK.
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# Currently, the training container should have `git` package to install this SDK.
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