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Small edits (#425)
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@ -16,7 +16,7 @@ but can be overridden by command-line arguments.
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|Name| Description |
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|**CLEARML_LOG_ENVIRONMENT** | List of Environment variable names. These environment variables will be logged in the ClearML task’s configuration hyperparameters `Environment` section. When executed by a ClearML agent, these values will be set in the task’s execution environment. |
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|**CLEARML_TASK_NO_REUSE** | Boolean. <br/> When set to `true`, a new task is created for every execution (see Task [reuse](../clearml_sdk/task_sdk#task_reuse). |
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|**CLEARML_TASK_NO_REUSE** | Boolean. <br/> When set to `true`, a new task is created for every execution (see Task [reuse](../clearml_sdk/task_sdk#task-reuse). |
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|**CLEARML_CACHE_DIR** | Set the path for the ClearML cache directory, where ClearML stores all downloaded content. |
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|**CLEARML_DOCKER_IMAGE** | Sets the default docker image to use when running an agent in [Docker mode](../clearml_agent.md#docker-mode). |
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|**CLEARML_LOG_LEVEL** | Sets the ClearML package's log verbosity. Log levels adhere to [Python log levels](https://docs.python.org/3/library/logging.config.html#configuration-file-format): CRITICAL, ERROR, WARNING, INFO, DEBUG, NOTSET |
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@ -70,7 +70,7 @@ it to you later in the UI, we have a nice and clear overview of all of the diffe
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I'll add some dataset statistics that's also something you can do and ClearML is just add some, for example, class
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distribution or other kind of plots that could be interesting, and then I'm actually building the ClearML dataset here.
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Also, an an extra thing that is really, really useful if you use ClearML datasets is you can actually share it as well.
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Also, an extra thing that is really, really useful if you use ClearML datasets is you can actually share it as well.
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So not only with colleagues and friends, for example. You can share the data with them, and they can add to the data, and
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always you will always have the latest version, you will always know what happened before that.
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