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allegroai 2022-12-13 16:07:51 +02:00
parent dd6e4f1598
commit 47ea919ddc
2 changed files with 2 additions and 3 deletions
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clearml/binding/frameworks/tensorflow_bind.py
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@ -729,7 +729,6 @@ class EventTrainsWriter(object):
# unlike other frameworks, tensorflow already accounts for the iteration number
# when continuing the training. we substract the smallest iteration such that we
# don't increment the step twice number
step_before = step
if EventTrainsWriter._current_task:
step -= EventTrainsWriter._current_task.get_initial_iteration()
# there can be a few metrics getting reported again, so the step can be negative

4
examples/hyperdatasets/README.md
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@ -2,9 +2,9 @@
Hyper-Datasets is a data management system that’s designed for unstructured data such as text, audio, or visual data. It is part of the ClearML enterprise offering, which means it includes quite a few upgrades over the open source clearml-data.
The main conceptual difference between the two is that Hyper-Datasets decouple the metadata from the raw data files. This allows you to manipulate the metadata in all kinds of ways, while abstracting away the logistics of having to deal with large amounts of data.
The main conceptual difference between the two is that Hyper-Datasets decouples the metadata from the raw data files. This allows you to manipulate the metadata in all kinds of ways, while abstracting away the logistics of having to deal with large amounts of data.
To leverage Hyper-Dataset power, users define Dataviews which are sophisticated queries connecting specific data from one or more datasets to an experiment in the Experiment Manager. Essentially it creates and manages local views of remote Datasets.
To leverage Hyper-Datasets power, users define Dataviews which are sophisticated queries connecting specific data from one or more datasets to an experiment in the Experiment Manager. Essentially it creates and manages local views of remote Datasets.
![Dataview in the UI](../../docs/screenshots/hpd.png)
## Examples Overview ##