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allow runtime selection of worker classes through training config (fixes
#300) (#532) * Rename worker_env.py to the more general worker_selection.py to accommodate upcoming runtime worker selection work * allow runtime selection of worker classes through training config This patch adds a new `worker-classes` section of the training config which allows for selecting the "class" of worker to use (currently GCP spot or GCP standard) by kind, with support for a default. This allows us quite flexible configuration, eg: using spot instances for translation and standard ones for training. We will also be able to add one or more classes for Snakepit machines when we bring those online. The default value is `{"default": "gcp-spot"}` - which means that we'll use all spot machines by default. (We can change the default in `config.prod.yml` if desired.) Most of this patch is quite a boring addition of the new `worker_selection` transform to the pipeline kinds. The most notable part otherwise is a fairly big rework of most things to do with workers in `config.yml`: * The taskgraph-required `workers.aliases` is now a very simple, straightforward list of all available worker types. * A new `local-worker-aliases` has been introduced. This maps the generic names like `b-largegpu` to concrete worker types `by-worker-class`. This removes the need for `-standard` variants for the generic names. * This necessitated a new transform function that looks up the concrete worker type in each kind before we hand off to the `task` transforms. (Previously, that transform simply looked up things like `b-largegpu` in the `workers.aliases` block. If we had the ability to feed it `worker-class` information we could have kept all our mappings there - but I couldn't come up with a reasonable way to do this upstream.)
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Original file line number | Diff line number | Diff line change |
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@@ -78,3 +78,5 @@ datasets: | |
target-stage: all | ||
taskcluster: | ||
split-chunks: 2 | ||
worker-classes: | ||
default: gcp-spot |
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