Finetuning on concatenated datasets #165
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There are several other changes: - Upgrade beaker-py to 2.x (queues are not supported in older version). - Update helios.Dockerfile to use pytorch 2.7.0 to reduce build time. - Update one_off_projects/convert_satlas_webmercator_to_rslearn/lib/__init__.py for new rslearn VectorFormat.encode_vector API. - Remove manage_scratch_dir_on_data_disk option since it's not needed anymore (since the Docker volumes are now on the big /data disk across all Beaker nodes).
I also updated the launcher code to accept specifying a list of configs. This enables reducing duplication between some of the config files, although makes it more complicated to start the experiments since you need to specify a list of configs to get the right combination (this is documented in the README files within each task dir in data/helios though).
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Mostly just applying changes from rslearn (allenai/rslearn#207)
New configs in
data/helios/v3_multitaskanddata/helios/v3_perf_benchmarkare for multi-dataset training. These are the majority of the edits, plus a bunch of scripts for early evals I did (kind of messy, I don't mind deleting them since they're not really useful for anyone else). Onlymake_multidataset_config.pyshould really be used with any frequency, to create multi-dataset training configs.Also, made some improvements to
launch_finetune. It now supports aprofilerflag, ado_evalflag (run on validation set and save metrics, useful for eval sweeps), and alocalflag (useful for debugging finetuning in the current Beaker session). Seedata/helios/v3_multitask/README.mdfor docs on how to run a multi-dataset job.BREAKING: When building docker images, please place
rslearnandheliosin./docker_build/rslearnand./docker_build/helios, instead of at the repository root. This is to avoid linter issues, where the linter thinks thatrslearnandheliosare local packages instead of standardpipinstalls.