[Example] PyTorch distributed training with minGPT #4464
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| ### Using normal `torchrun` | ||
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| The following command spawn 2 nodes with 2 L4 GPU each. |
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| The following command spawn 2 nodes with 2 L4 GPU each. | |
| The following command will spawn 2 nodes with 2 L4 GPU each: |
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| The main difference between the two for fixed-size distributed training is that `rdvz` backend automatically handles the rank for each node, while `torchrun` requires the rank to be set manually. | ||
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| SkyPilot offers easy built-in environment variables to help you start distributed training easily. |
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nit
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| SkyPilot offers easy built-in environment variables to help you start distributed training easily. | |
| SkyPilot offers convinient built-in environment variables to help you start distributed training easily. |
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| The following command spawn 2 nodes with 2 L4 GPU each. | ||
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| `sky launch -c train.yaml` |
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Missing cluster name? Also might be nice to put in a code block
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| `sky launch -c train.yaml` | |
| \``` | |
| sky launch -c train train.yaml | |
| \``` |
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| `sky launch -c train.yaml` | ||
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| In the [train.yaml](./train.yaml), we use `torchrun` to launch the training and set the arguments for distributed training using environment variables provided by SkyPilot. |
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Suggested change
| In the [train.yaml](./train.yaml), we use `torchrun` to launch the training and set the arguments for distributed training using environment variables provided by SkyPilot. | |
| In [train.yaml](./train.yaml), we use `torchrun` to launch the training and set the arguments for distributed training using [environment variables](https://docs.skypilot.co/en/latest/running-jobs/environment-variables.html#skypilot-environment-variables) provided by SkyPilot. |
| `rdvz` is an alternative backend for distributed training: | ||
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| ``` | ||
| sky launch -c train-rdzv.yaml |
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| sky launch -c train-rdzv.yaml | |
| sky launch -c train-rdzv train-rdzv.yaml |
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| ### Using `rdvz` backend |
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| ### Using `rdvz` backend | |
| ### Using `rdzv` backend |
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| ### Using `rdvz` backend | ||
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| `rdvz` is an alternative backend for distributed training: |
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Suggested change
| `rdvz` is an alternative backend for distributed training: | |
| `rdzv` is an alternative backend for distributed training: |
| sky launch -c train-rdzv.yaml | ||
| ``` | ||
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| In the [train-rdzv.yaml](./train-rdzv.yaml), we use `torchrun` to launch the training and set the arguments for distributed training using environment variables provided by SkyPilot. |
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Suggested change
| In the [train-rdzv.yaml](./train-rdzv.yaml), we use `torchrun` to launch the training and set the arguments for distributed training using environment variables provided by SkyPilot. | |
| In [train-rdzv.yaml](./train-rdzv.yaml), we use `torchrun` to launch the training and set the arguments for distributed training using [environment variables](https://docs.skypilot.co/en/latest/running-jobs/environment-variables.html#skypilot-environment-variables) provided by SkyPilot. |
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| For example, the following command will spawn 4 nodes with 4 L4 GPUs each. | ||
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| `sky launch -c train.yaml --num-nodes 2 --gpus L4:2 --cpus 8+` |
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change to num nodes 4 and L4:4
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| `sky launch -c train.yaml --num-nodes 2 --gpus L4:2 --cpus 8+` | |
| \``` | |
| sky launch -c train.yaml --num-nodes 4 --gpus L4:4 --cpus 8+ | |
| \``` |
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This PR adds a more modern distributed training example.
TODOs:
Tested (run the relevant ones):
bash format.shpytest tests/test_smoke.pypytest tests/test_smoke.py::test_fill_in_the_nameconda deactivate; bash -i tests/backward_compatibility_tests.sh