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Copy file name to clipboardExpand all lines: docs/features/runs/17_yaml-configs/2_yaml-api.md
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@@ -10,7 +10,7 @@ The following YML file contains a commented version of every YML key. You need t
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Use either [cluster context](../../../platform/2_Custom%20Cloud%20Credentials/5_grid-cluster-context.md) or make sure to place your cluster ID in the `cluster` field, replacing the **XXXXXX** placeholder.
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:::
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```text
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<!--```text
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# Main compute configuration.
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compute:
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# your params here
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--foo: 1
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-bar: 2
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```
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```-->
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:::note
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Flags in grid YML files have a 1-to-1 mapping with `grid run` flags
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:::
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In addition to the parameters above, you can also run arbitrary commands on different stages of you training operation.
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Config can be used to specify arguments that will be used to run experiments.
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Apart from parametrization config allows to define custom `actions` executed in given moments of the experitment lifecycle, and pass environment variables.
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You don't have to define more parameters than you actually want to override, missing ones from config we have the default CLI values.
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To check the defaults please refer to the CLI docs [here](../../cli.md).
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```text
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# Main compute configuration. Defines parameters for `grid run` command.
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compute:
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# For BYOC users, we can specify directly which cluster to run experiments on.
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provider:
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cluster: prod-2 # [str] --cluster
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# Train section defines the training environment each experiement eg. instance type, datastore access, custom actions.
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train:
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framework: lightning # [enum] --framework
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environment:
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MY_ENVIRONMENT_VARIABLE: "example" # Pass in environment variables
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dependency_file_info:
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path: null # [str] --dependency_file
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instance: m5a.large # [enum] --instance_type
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use_spot: False # [str] --use_spot
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# We should ideally remove those are we hope to depricate gpus/cpus
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# cpus: int 1 # --cpus - cannot be more than available in the `instance`
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# gpus: int 0 # --gpus - cannot be more than available in the `instance`
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storage_gb: 100 # [int] --memory [should be greater than 100Gb]
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