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Add nniignore example link in training service docs #2660

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Add nniignore example link in training service docs #2660

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2 changes: 1 addition & 1 deletion docs/en_US/TrainingService/Overview.md
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Expand Up @@ -12,7 +12,7 @@ If the computing resource customers try to use is not listed above, NNI provides

Training service needs to be chosen and configured properly in experiment configuration YAML file. Users could refer to the document of each training service for how to write the configuration. Also, [reference](../Tutorial/ExperimentConfig) provides more details on the specification of the experiment configuration file.

Next, users should prepare code directory, which is specified as `codeDir` in config file. Please note that in non-local mode, the code directory will be uploaded to remote or cluster before the experiment. Therefore, we limit the number of files to 2000 and total size to 300MB. If the code directory contains too many files, users can choose which files and subfolders should be excluded by adding a `.nniignore` file that works like a `.gitignore` file. For more details on how to write this file, see the [git documentation](https://git-scm.com/docs/gitignore#_pattern_format).
Next, users should prepare code directory, which is specified as `codeDir` in config file. Please note that in non-local mode, the code directory will be uploaded to remote or cluster before the experiment. Therefore, we limit the number of files to 2000 and total size to 300MB. If the code directory contains too many files, users can choose which files and subfolders should be excluded by adding a `.nniignore` file that works like a `.gitignore` file. For more details on how to write this file, see [this example](https://github.com/Microsoft/nni/tree/master/examples/trials/mnist-tfv1/.nniignore) and the [git documentation](https://git-scm.com/docs/gitignore#_pattern_format).

In case users intend to use large files in their experiment (like large-scaled datasets) and they are not using local mode, they can either: 1) download the data before each trial launches by putting it into trial command; or 2) use a shared storage that is accessible to worker nodes. Usually, training platforms are equipped with shared storage, and NNI allows users to easily use them. Refer to docs of each built-in training service for details.

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