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[Doc] Convert list tables to MyST #11594

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2 changes: 1 addition & 1 deletion docs/source/getting_started/debugging.md
Original file line number Diff line number Diff line change
Expand Up @@ -197,4 +197,4 @@ if __name__ == '__main__':
## Known Issues

- In `v0.5.2`, `v0.5.3`, and `v0.5.3.post1`, there is a bug caused by [zmq](https://github.com/zeromq/pyzmq/issues/2000) , which can occasionally cause vLLM to hang depending on the machine configuration. The solution is to upgrade to the latest version of `vllm` to include the [fix](gh-pr:6759).
- To circumvent a NCCL [bug](https://github.com/NVIDIA/nccl/issues/1234) , all vLLM processes will set an environment variable ``NCCL_CUMEM_ENABLE=0`` to disable NCCL's ``cuMem`` allocator. It does not affect performance but only gives memory benefits. When external processes want to set up a NCCL connection with vLLM's processes, they should also set this environment variable, otherwise, inconsistent environment setup will cause NCCL to hang or crash, as observed in the [RLHF integration](https://github.com/OpenRLHF/OpenRLHF/pull/604) and the [discussion](gh-issue:5723#issuecomment-2554389656) .
- To circumvent a NCCL [bug](https://github.com/NVIDIA/nccl/issues/1234) , all vLLM processes will set an environment variable `NCCL_CUMEM_ENABLE=0` to disable NCCL's `cuMem` allocator. It does not affect performance but only gives memory benefits. When external processes want to set up a NCCL connection with vLLM's processes, they should also set this environment variable, otherwise, inconsistent environment setup will cause NCCL to hang or crash, as observed in the [RLHF integration](https://github.com/OpenRLHF/OpenRLHF/pull/604) and the [discussion](gh-issue:5723#issuecomment-2554389656) .
39 changes: 19 additions & 20 deletions docs/source/getting_started/gaudi-installation.md
Original file line number Diff line number Diff line change
Expand Up @@ -141,26 +141,25 @@ Gaudi2 devices. Configurations that are not listed may or may not work.

Currently in vLLM for HPU we support four execution modes, depending on selected HPU PyTorch Bridge backend (via `PT_HPU_LAZY_MODE` environment variable), and `--enforce-eager` flag.

```{eval-rst}
.. list-table:: vLLM execution modes
:widths: 25 25 50
:header-rows: 1

* - ``PT_HPU_LAZY_MODE``
- ``enforce_eager``
- execution mode
* - 0
- 0
- torch.compile
* - 0
- 1
- PyTorch eager mode
* - 1
- 0
- HPU Graphs
* - 1
- 1
- PyTorch lazy mode
```{list-table} vLLM execution modes
:widths: 25 25 50
:header-rows: 1

* - `PT_HPU_LAZY_MODE`
- `enforce_eager`
- execution mode
* - 0
- 0
- torch.compile
* - 0
- 1
- PyTorch eager mode
* - 1
- 0
- HPU Graphs
* - 1
- 1
- PyTorch lazy mode
```

```{warning}
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53 changes: 26 additions & 27 deletions docs/source/getting_started/tpu-installation.md
Original file line number Diff line number Diff line change
Expand Up @@ -68,33 +68,32 @@ gcloud alpha compute tpus queued-resources create QUEUED_RESOURCE_ID \
--service-account SERVICE_ACCOUNT
```

```{eval-rst}
.. list-table:: Parameter descriptions
:header-rows: 1

* - Parameter name
- Description
* - QUEUED_RESOURCE_ID
- The user-assigned ID of the queued resource request.
* - TPU_NAME
- The user-assigned name of the TPU which is created when the queued
resource request is allocated.
* - PROJECT_ID
- Your Google Cloud project
* - ZONE
- The GCP zone where you want to create your Cloud TPU. The value you use
depends on the version of TPUs you are using. For more information, see
`TPU regions and zones <https://cloud.google.com/tpu/docs/regions-zones>`_
* - ACCELERATOR_TYPE
- The TPU version you want to use. Specify the TPU version, for example
`v5litepod-4` specifies a v5e TPU with 4 cores. For more information,
see `TPU versions <https://cloud.devsite.corp.google.com/tpu/docs/system-architecture-tpu-vm#versions>`_.
* - RUNTIME_VERSION
- The TPU VM runtime version to use. For more information see `TPU VM images <https://cloud.google.com/tpu/docs/runtimes>`_.
* - SERVICE_ACCOUNT
- The email address for your service account. You can find it in the IAM
Cloud Console under *Service Accounts*. For example:
`tpu-service-account@<your_project_ID>.iam.gserviceaccount.com`
```{list-table} Parameter descriptions
:header-rows: 1

* - Parameter name
- Description
* - QUEUED_RESOURCE_ID
- The user-assigned ID of the queued resource request.
* - TPU_NAME
- The user-assigned name of the TPU which is created when the queued
resource request is allocated.
* - PROJECT_ID
- Your Google Cloud project
* - ZONE
- The GCP zone where you want to create your Cloud TPU. The value you use
depends on the version of TPUs you are using. For more information, see
`TPU regions and zones <https://cloud.google.com/tpu/docs/regions-zones>`_
* - ACCELERATOR_TYPE
- The TPU version you want to use. Specify the TPU version, for example
`v5litepod-4` specifies a v5e TPU with 4 cores. For more information,
see `TPU versions <https://cloud.devsite.corp.google.com/tpu/docs/system-architecture-tpu-vm#versions>`_.
* - RUNTIME_VERSION
- The TPU VM runtime version to use. For more information see `TPU VM images <https://cloud.google.com/tpu/docs/runtimes>`_.
* - SERVICE_ACCOUNT
- The email address for your service account. You can find it in the IAM
Cloud Console under *Service Accounts*. For example:
`tpu-service-account@<your_project_ID>.iam.gserviceaccount.com`
```

Connect to your TPU using SSH:
Expand Down
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