[train/docs] Extend resource guide (training backend + choosing resources) #39202
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added 6 commits
September 1, 2023 12:13
Signed-off-by: Kai Fricke <kai@anyscale.com>
Signed-off-by: Kai Fricke <kai@anyscale.com>
Signed-off-by: Kai Fricke <kai@anyscale.com>
Signed-off-by: Kai Fricke <kai@anyscale.com>
Signed-off-by: Kai Fricke <kai@anyscale.com>
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Sep 5, 2023
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| .. _train-resource-guide: | ||
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| How many nodes, workers, and resources should I use? |
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This is good information but I feel like it's really scattered right now and as a user it would be very hard to figure out what to concretely do. It's generally not clear to me if this is intended to be more catered to people who are starting distributed training, or people who already have a job and are facing concrete bottlenecks (maybe we want to apply this to both categories).
Sorry if this comment isn't super concrete, maybe we can separate this from the PR for now and think more how to architect this information.
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September 6, 2023 15:52
Signed-off-by: Kai Fricke <kai@anyscale.com>
…oc/train/resource-guide
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Sep 6, 2023
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Sep 6, 2023
| The :class:`Trainer <ray.train.trainer.BaseTrainer>` object you instantiate in the | ||
| training script contains the settings to run your training. When you call | ||
| :meth:`Trainer.fit() <ray.train.trainer.BaseTrainer.fit>`, it will be scheduled | ||
| as a :ref:`Ray Actor <actor-key-concept>`. It can then also use resources. | ||
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Not able to leave the comment on the exact lines since it was not edited in this PR, but I feel like the code snippet should just be moved to an example in the API reference. 1. I feel like we're missing an example in the API reference. 2. I don't think we should include more advanced confgs like placement_strategy in the introduction of this guide.
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September 7, 2023 14:05
Signed-off-by: Kai Fricke <kai@anyscale.com>
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Sep 8, 2023
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Scaling/GPUs guide looks good after the suggested changes. Will let @justinvyu take another pass for the other pages!
…e-guide # Conflicts: # doc/source/train/api/api.rst # python/ray/air/config.py
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…rces) (ray-project#39202) Signed-off-by: Kai Fricke <kai@anyscale.com> Co-authored-by: matthewdeng <matthew.j.deng@gmail.com>
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…39468) * [train] Fix issues in migration of tune_cifar_torch_pbt_example (#39158) Resolves three issues that come up when migrating the `tune_cifar_torch_pbt_example` from Ray 2.6 to Ray 2.7: 1. There is a warning message because PBT uses the `_schedule_trial_save` interface. This is added to the white list attributes so it doesn't come up anymore. 2. PBT malfunctions in Python 2.7, so instead of silently failing, we raise an error and ask users to migrate 3. When users use old `ray.air.Checkpoint` APIs on `ray.train.Checkpoint`, we should raise an actionable error message. Signed-off-by: Kai Fricke <kai@anyscale.com> * [tune] Make Trainable.save/restore developer APIs (#39391) Signed-off-by: Kai Fricke <kai@anyscale.com> * [Telemetry] Add Telemetry for Ray Train Utilities (#39363) Signed-off-by: woshiyyya <xiaoyunxuan1998@gmail.com> * [train] update Train API references & annotations (#39294) Signed-off-by: Matthew Deng <matt@anyscale.com> * [2.7] Cleanup all LightningTrainer Mentions in Ray Doc (#39406) Signed-off-by: woshiyyya <xiaoyunxuan1998@gmail.com> * [train] remove _max_cpu_fraction_per_node (#39412) Signed-off-by: Matthew Deng <matt@anyscale.com> * [train] Legacy interface cleanup (`air.Checkpoint`, `LegacyExperimentAnalysis`) (#39289) Signed-off-by: Justin Yu <justinvyu@anyscale.com> Co-authored-by: matthewdeng <matt@anyscale.com> * [Train][Telemetry] Limit the usage of `ray.train.torch.get_device`. (#39432) Signed-off-by: woshiyyya <xiaoyunxuan1998@gmail.com> * [train-ci] Fix Train examples with authentication buildkite commands. (#39387) * [train-ci] fix Train examples with authentication buildkite commands. Signed-off-by: xwjiang2010 <xwjiang2010@gmail.com> * [train][doc] Remove preprocessor reference in tune+train user guide (#39442) Signed-off-by: Justin Yu <justinvyu@anyscale.com> * [train/docs] Extend resource guide (training backend + choosing resources) (#39202) Signed-off-by: Kai Fricke <kai@anyscale.com> Co-authored-by: matthewdeng <matthew.j.deng@gmail.com> * fix docs Signed-off-by: Matthew Deng <matt@anyscale.com> * [Minor] Remove remaining LightningTrainer Mentions (#39441) Signed-off-by: woshiyyya <xiaoyunxuan1998@gmail.com> --------- Signed-off-by: Kai Fricke <kai@anyscale.com> Signed-off-by: woshiyyya <xiaoyunxuan1998@gmail.com> Signed-off-by: Matthew Deng <matt@anyscale.com> Signed-off-by: Justin Yu <justinvyu@anyscale.com> Signed-off-by: xwjiang2010 <xwjiang2010@gmail.com> Co-authored-by: Kai Fricke <krfricke@users.noreply.github.com> Co-authored-by: Yunxuan Xiao <yunxuanx@anyscale.com> Co-authored-by: Justin Yu <justinvyu@anyscale.com> Co-authored-by: xwjiang2010 <87673679+xwjiang2010@users.noreply.github.com>
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…rces) (ray-project#39202) Signed-off-by: Kai Fricke <kai@anyscale.com> Co-authored-by: matthewdeng <matthew.j.deng@gmail.com> Signed-off-by: Jim Thompson <jimthompson5802@gmail.com>
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…rces) (ray-project#39202) Signed-off-by: Kai Fricke <kai@anyscale.com> Co-authored-by: matthewdeng <matthew.j.deng@gmail.com> Signed-off-by: Victor <vctr.y.m@example.com>
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Why are these changes needed?
This docs update adds a comprehensive guide to choosing resources for distributed training. Additionally, it touches setting the distributed communication backend in torch, configuring persistence storage via an environment variable, and expands on checkpoint restoration. The PR also fixes a few references within existing checkpointing docs.
Related issue number
Checks
git commit -s) in this PR.scripts/format.shto lint the changes in this PR.method in Tune, I've added it in
doc/source/tune/api/under thecorresponding
.rstfile.