[Doc][Train] Add accelerator_type to Ray Train user guide
#44882
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Signed-off-by: Hongpeng Guo <[email protected]>
hongpeng-guo
requested review from
matthewdeng,
justinvyu,
woshiyyya and
a team
as code owners
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Tip if you haven't seen this already: we build the docs as part of the premerge CI, so you can take a look at your rendered docs: https://anyscale-ray--44882.com.readthedocs.build/en/44882/index.html 
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Nice tips! ty |
justinvyu
reviewed
Apr 22, 2024
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| Sometimes you might want to specify the accelerator type for a worker. For example, | ||
| you can specify `accelerator_type="A100"` in the `ScalingConfig` if you want to | ||
| assign the worker an NVIDIA A100 GPU. |
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Suggested change
| Sometimes you might want to specify the accelerator type for a worker. For example, | |
| you can specify `accelerator_type="A100"` in the `ScalingConfig` if you want to | |
| assign the worker an NVIDIA A100 GPU. | |
| Ray Train allows you to specify the accelerator type for each worker. | |
| This is useful if your model training has some GPU memory constraints that requires a specific type of GPU. | |
| In a heterogeneous Ray cluster, this means that your training workers will be forced to run on the specified GPU type, rather than on any arbitrary GPU node. | |
| For example, you can specify `accelerator_type="A100"` in the :class:`~ray.train.ScalingConfig` if you want to | |
| assign each worker a NVIDIA A100 GPU. |
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| import torch | ||
| from ray.train import ScalingConfig | ||
| from ray.train.torch import TorchTrainer, get_device | ||
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| def train_func(): | ||
| assert torch.cuda.is_available() | ||
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| device = get_device() | ||
| assert device == torch.device("cuda:0") | ||
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| trainer = TorchTrainer( | ||
| train_func, | ||
| scaling_config=ScalingConfig( | ||
| num_workers=1, | ||
| use_gpu=True, | ||
| accelerator_type="A100" | ||
| ) | ||
| ) | ||
| trainer.fit() |
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We can cut this down to just show the ScalingConfig.
justinvyu
reviewed
Apr 22, 2024
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| from ray.train import ScalingConfig | ||
| from ray.train.torch import TorchTrainer | ||
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| trainer = TorchTrainer( | ||
| train_func, | ||
| scaling_config=ScalingConfig( | ||
| num_workers=1, | ||
| use_gpu=True, | ||
| accelerator_type="A100" | ||
| ) | ||
| ) |
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Oh, for this one, I'm thinking of just showing:
ScalingConfig(...)
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| Ensure that your cluster has instances with the specified accelerator type | ||
| or is able to autoscale to fulfill the request. |
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We can make this a tip:
.. tip::
Ensure that your cluster has instances with the specified accelerator type
or is able to autoscale to fulfill the request.
Otherwise, your job will hang forever due to unsatisfiable pending resource requests.
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Oh nice tip structure. Will try.
Signed-off-by: Hongpeng Guo <[email protected]>
…accelerator-type
Signed-off-by: Hongpeng Guo <[email protected]>
woshiyyya
reviewed
Apr 22, 2024
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| ScalingConfig( | ||
| num_workers=1, | ||
| use_gpu=True, | ||
| accelerator_type="A100" | ||
| ) |
| Setting the GPU type | ||
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
| Ray Train allows you to specify the accelerator type for each worker. | ||
| This is useful if your model training has some GPU memory constraints that requires a specific type of GPU. |
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Users may want to use different accelerator types not only for GPU memory constraints, but also for e.g. compute power, cost efficiency, availability, etc.
Let's just say This is useful if you want to use a specific accelerator type for model training.
Signed-off-by: Hongpeng Guo <[email protected]>
Signed-off-by: Hongpeng Guo <[email protected]>
Signed-off-by: Hongpeng Guo <[email protected]>
justinvyu
changed the title
accelerator_type to Ray Train user guidesaccelerator_type to Ray Train user guide
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Why are these changes needed?
Our
ScalingConfig()function supports a new argumentaccelerator_type. This PR provides a user guide with example code to showcase the usage. The generated section of the user guide is appended below:Related issue number
"Closes #44763
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.