[Doc] Convert list tables to MyST (#11594)
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
GitHub
parent
4fb8e329fd
commit
32b4c63f02
@ -197,4 +197,4 @@ if __name__ == '__main__':
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## Known Issues
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- 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).
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- 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) .
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- 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) .
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@ -141,26 +141,25 @@ Gaudi2 devices. Configurations that are not listed may or may not work.
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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.
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```{eval-rst}
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.. list-table:: vLLM execution modes
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:widths: 25 25 50
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:header-rows: 1
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```{list-table} vLLM execution modes
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:widths: 25 25 50
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:header-rows: 1
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* - ``PT_HPU_LAZY_MODE``
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- ``enforce_eager``
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- execution mode
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* - 0
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- 0
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- torch.compile
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* - 0
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- 1
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- PyTorch eager mode
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* - 1
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- 0
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- HPU Graphs
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* - 1
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- 1
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- PyTorch lazy mode
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* - `PT_HPU_LAZY_MODE`
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- `enforce_eager`
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- execution mode
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* - 0
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- 0
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- torch.compile
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* - 0
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- 1
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- PyTorch eager mode
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* - 1
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- 0
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- HPU Graphs
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* - 1
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- 1
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- PyTorch lazy mode
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```
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```{warning}
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@ -68,33 +68,32 @@ gcloud alpha compute tpus queued-resources create QUEUED_RESOURCE_ID \
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--service-account SERVICE_ACCOUNT
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```
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```{eval-rst}
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.. list-table:: Parameter descriptions
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:header-rows: 1
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```{list-table} Parameter descriptions
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:header-rows: 1
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* - Parameter name
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- Description
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* - QUEUED_RESOURCE_ID
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- The user-assigned ID of the queued resource request.
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* - TPU_NAME
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- The user-assigned name of the TPU which is created when the queued
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resource request is allocated.
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* - PROJECT_ID
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- Your Google Cloud project
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* - ZONE
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- The GCP zone where you want to create your Cloud TPU. The value you use
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depends on the version of TPUs you are using. For more information, see
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`TPU regions and zones <https://cloud.google.com/tpu/docs/regions-zones>`_
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* - ACCELERATOR_TYPE
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- The TPU version you want to use. Specify the TPU version, for example
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`v5litepod-4` specifies a v5e TPU with 4 cores. For more information,
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see `TPU versions <https://cloud.devsite.corp.google.com/tpu/docs/system-architecture-tpu-vm#versions>`_.
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* - RUNTIME_VERSION
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- The TPU VM runtime version to use. For more information see `TPU VM images <https://cloud.google.com/tpu/docs/runtimes>`_.
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* - SERVICE_ACCOUNT
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- The email address for your service account. You can find it in the IAM
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Cloud Console under *Service Accounts*. For example:
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`tpu-service-account@<your_project_ID>.iam.gserviceaccount.com`
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* - Parameter name
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- Description
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* - QUEUED_RESOURCE_ID
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- The user-assigned ID of the queued resource request.
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* - TPU_NAME
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- The user-assigned name of the TPU which is created when the queued
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resource request is allocated.
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* - PROJECT_ID
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- Your Google Cloud project
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* - ZONE
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- The GCP zone where you want to create your Cloud TPU. The value you use
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depends on the version of TPUs you are using. For more information, see
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`TPU regions and zones <https://cloud.google.com/tpu/docs/regions-zones>`_
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* - ACCELERATOR_TYPE
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- The TPU version you want to use. Specify the TPU version, for example
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`v5litepod-4` specifies a v5e TPU with 4 cores. For more information,
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see `TPU versions <https://cloud.devsite.corp.google.com/tpu/docs/system-architecture-tpu-vm#versions>`_.
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* - RUNTIME_VERSION
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- The TPU VM runtime version to use. For more information see `TPU VM images <https://cloud.google.com/tpu/docs/runtimes>`_.
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* - SERVICE_ACCOUNT
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- The email address for your service account. You can find it in the IAM
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Cloud Console under *Service Accounts*. For example:
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`tpu-service-account@<your_project_ID>.iam.gserviceaccount.com`
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```
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Connect to your TPU using SSH:
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File diff suppressed because it is too large
Load Diff
@ -4,121 +4,120 @@
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The table below shows the compatibility of various quantization implementations with different hardware platforms in vLLM:
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```{eval-rst}
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.. list-table::
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:header-rows: 1
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:widths: 20 8 8 8 8 8 8 8 8 8 8
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```{list-table}
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:header-rows: 1
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:widths: 20 8 8 8 8 8 8 8 8 8 8
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* - Implementation
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- Volta
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- Turing
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- Ampere
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- Ada
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- Hopper
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- AMD GPU
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- Intel GPU
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- x86 CPU
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- AWS Inferentia
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- Google TPU
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* - AWQ
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- ✗
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- ✅︎
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- ✅︎
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- ✅︎
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- ✅︎
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- ✗
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- ✅︎
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- ✅︎
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- ✗
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- ✗
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* - GPTQ
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- ✅︎
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- ✅︎
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- ✅︎
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- ✅︎
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- ✅︎
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- ✗
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- ✅︎
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- ✅︎
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- ✗
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- ✗
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* - Marlin (GPTQ/AWQ/FP8)
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- ✗
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- ✗
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- ✅︎
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- ✅︎
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- ✅︎
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- ✗
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- ✗
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- ✗
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- ✗
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- ✗
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* - INT8 (W8A8)
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- ✗
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- ✅︎
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- ✅︎
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- ✅︎
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- ✅︎
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- ✗
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- ✗
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- ✅︎
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- ✗
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- ✗
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* - FP8 (W8A8)
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- ✗
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- ✗
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- ✗
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- ✅︎
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- ✅︎
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- ✅︎
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- ✗
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- ✗
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- ✗
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- ✗
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* - AQLM
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- ✅︎
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- ✅︎
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- ✅︎
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- ✅︎
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- ✅︎
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- ✗
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- ✗
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- ✗
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- ✗
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- ✗
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* - bitsandbytes
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- ✅︎
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- ✅︎
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- ✅︎
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- ✅︎
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- ✅︎
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- ✗
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- ✗
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- ✗
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- ✗
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- ✗
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* - DeepSpeedFP
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- ✅︎
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- ✅︎
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- ✅︎
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- ✅︎
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- ✅︎
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- ✗
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- ✗
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- ✗
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- ✗
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- ✗
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* - GGUF
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- ✅︎
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- ✅︎
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- ✅︎
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- ✅︎
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- ✅︎
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- ✗
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- ✗
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- ✗
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- ✗
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- ✗
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* - Implementation
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- Volta
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- Turing
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- Ampere
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- Ada
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- Hopper
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- AMD GPU
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- Intel GPU
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- x86 CPU
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- AWS Inferentia
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- Google TPU
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* - AWQ
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- ✗
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- ✅︎
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- ✅︎
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- ✅︎
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- ✅︎
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- ✗
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- ✅︎
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- ✅︎
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- ✗
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- ✗
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* - GPTQ
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- ✅︎
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- ✅︎
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- ✅︎
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- ✅︎
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- ✅︎
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- ✗
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- ✅︎
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- ✅︎
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- ✗
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- ✗
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* - Marlin (GPTQ/AWQ/FP8)
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- ✗
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- ✗
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- ✅︎
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- ✅︎
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- ✅︎
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- ✗
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- ✗
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- ✗
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- ✗
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- ✗
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* - INT8 (W8A8)
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- ✗
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- ✅︎
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- ✅︎
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- ✅︎
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- ✅︎
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- ✗
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- ✗
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- ✅︎
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- ✗
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- ✗
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* - FP8 (W8A8)
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- ✗
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- ✗
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- ✗
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- ✅︎
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- ✅︎
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- ✅︎
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- ✗
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- ✗
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- ✗
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- ✗
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* - AQLM
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- ✅︎
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- ✅︎
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- ✅︎
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- ✅︎
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- ✅︎
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- ✗
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- ✗
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- ✗
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- ✗
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- ✗
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* - bitsandbytes
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- ✅︎
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- ✅︎
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- ✅︎
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- ✅︎
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- ✅︎
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- ✗
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- ✗
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- ✗
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- ✗
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- ✗
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* - DeepSpeedFP
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- ✅︎
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- ✅︎
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- ✅︎
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- ✅︎
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- ✅︎
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- ✗
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- ✗
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- ✗
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- ✗
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- ✗
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* - GGUF
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- ✅︎
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- ✅︎
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- ✅︎
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- ✅︎
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- ✅︎
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- ✗
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- ✗
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- ✗
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- ✗
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- ✗
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```
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## Notes:
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@ -43,209 +43,208 @@ chart **including persistent volumes** and deletes the release.
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## Values
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```{eval-rst}
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.. list-table:: Values
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:widths: 25 25 25 25
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:header-rows: 1
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```{list-table}
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:widths: 25 25 25 25
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:header-rows: 1
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* - Key
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- Type
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- Default
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- Description
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* - autoscaling
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- object
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- {"enabled":false,"maxReplicas":100,"minReplicas":1,"targetCPUUtilizationPercentage":80}
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- Autoscaling configuration
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* - autoscaling.enabled
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- bool
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- false
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- Enable autoscaling
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* - autoscaling.maxReplicas
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- int
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- 100
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- Maximum replicas
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* - autoscaling.minReplicas
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- int
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- 1
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- Minimum replicas
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* - autoscaling.targetCPUUtilizationPercentage
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- int
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- 80
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- Target CPU utilization for autoscaling
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* - configs
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- object
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- {}
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- Configmap
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* - containerPort
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- int
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- 8000
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- Container port
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* - customObjects
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- list
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- []
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- Custom Objects configuration
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* - deploymentStrategy
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- object
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- {}
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- Deployment strategy configuration
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* - externalConfigs
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- list
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- []
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- External configuration
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* - extraContainers
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- list
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- []
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- Additional containers configuration
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* - extraInit
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- object
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- {"pvcStorage":"1Gi","s3modelpath":"relative_s3_model_path/opt-125m", "awsEc2MetadataDisabled": true}
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- Additional configuration for the init container
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* - extraInit.pvcStorage
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- string
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- "50Gi"
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- Storage size of the s3
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* - extraInit.s3modelpath
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- string
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- "relative_s3_model_path/opt-125m"
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- Path of the model on the s3 which hosts model weights and config files
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* - extraInit.awsEc2MetadataDisabled
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- boolean
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- true
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- Disables the use of the Amazon EC2 instance metadata service
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* - extraPorts
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- list
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- []
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- Additional ports configuration
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* - gpuModels
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- list
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- ["TYPE_GPU_USED"]
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- Type of gpu used
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* - image
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- object
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- {"command":["vllm","serve","/data/","--served-model-name","opt-125m","--host","0.0.0.0","--port","8000"],"repository":"vllm/vllm-openai","tag":"latest"}
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- Image configuration
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* - image.command
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- list
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- ["vllm","serve","/data/","--served-model-name","opt-125m","--host","0.0.0.0","--port","8000"]
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- Container launch command
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* - image.repository
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- string
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- "vllm/vllm-openai"
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- Image repository
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* - image.tag
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- string
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- "latest"
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- Image tag
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* - livenessProbe
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- object
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- {"failureThreshold":3,"httpGet":{"path":"/health","port":8000},"initialDelaySeconds":15,"periodSeconds":10}
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- Liveness probe configuration
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* - livenessProbe.failureThreshold
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- int
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- 3
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- Number of times after which if a probe fails in a row, Kubernetes considers that the overall check has failed: the container is not alive
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* - livenessProbe.httpGet
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- object
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- {"path":"/health","port":8000}
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- Configuration of the Kubelet http request on the server
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* - livenessProbe.httpGet.path
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- string
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- "/health"
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- Path to access on the HTTP server
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* - livenessProbe.httpGet.port
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- int
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- 8000
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- Name or number of the port to access on the container, on which the server is listening
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* - livenessProbe.initialDelaySeconds
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- int
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- 15
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- Number of seconds after the container has started before liveness probe is initiated
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* - livenessProbe.periodSeconds
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- int
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- 10
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- How often (in seconds) to perform the liveness probe
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* - maxUnavailablePodDisruptionBudget
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- string
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- ""
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- Disruption Budget Configuration
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* - readinessProbe
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- object
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- {"failureThreshold":3,"httpGet":{"path":"/health","port":8000},"initialDelaySeconds":5,"periodSeconds":5}
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- Readiness probe configuration
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* - readinessProbe.failureThreshold
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- int
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- 3
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- Number of times after which if a probe fails in a row, Kubernetes considers that the overall check has failed: the container is not ready
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* - readinessProbe.httpGet
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- object
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- {"path":"/health","port":8000}
|
||||
- Configuration of the Kubelet http request on the server
|
||||
* - readinessProbe.httpGet.path
|
||||
- string
|
||||
- "/health"
|
||||
- Path to access on the HTTP server
|
||||
* - readinessProbe.httpGet.port
|
||||
- int
|
||||
- 8000
|
||||
- Name or number of the port to access on the container, on which the server is listening
|
||||
* - readinessProbe.initialDelaySeconds
|
||||
- int
|
||||
- 5
|
||||
- Number of seconds after the container has started before readiness probe is initiated
|
||||
* - readinessProbe.periodSeconds
|
||||
- int
|
||||
- 5
|
||||
- How often (in seconds) to perform the readiness probe
|
||||
* - replicaCount
|
||||
- int
|
||||
- 1
|
||||
- Number of replicas
|
||||
* - resources
|
||||
- object
|
||||
- {"limits":{"cpu":4,"memory":"16Gi","nvidia.com/gpu":1},"requests":{"cpu":4,"memory":"16Gi","nvidia.com/gpu":1}}
|
||||
- Resource configuration
|
||||
* - resources.limits."nvidia.com/gpu"
|
||||
- int
|
||||
- 1
|
||||
- Number of gpus used
|
||||
* - resources.limits.cpu
|
||||
- int
|
||||
- 4
|
||||
- Number of CPUs
|
||||
* - resources.limits.memory
|
||||
- string
|
||||
- "16Gi"
|
||||
- CPU memory configuration
|
||||
* - resources.requests."nvidia.com/gpu"
|
||||
- int
|
||||
- 1
|
||||
- Number of gpus used
|
||||
* - resources.requests.cpu
|
||||
- int
|
||||
- 4
|
||||
- Number of CPUs
|
||||
* - resources.requests.memory
|
||||
- string
|
||||
- "16Gi"
|
||||
- CPU memory configuration
|
||||
* - secrets
|
||||
- object
|
||||
- {}
|
||||
- Secrets configuration
|
||||
* - serviceName
|
||||
- string
|
||||
-
|
||||
- Service name
|
||||
* - servicePort
|
||||
- int
|
||||
- 80
|
||||
- Service port
|
||||
* - labels.environment
|
||||
- string
|
||||
- test
|
||||
- Environment name
|
||||
* - labels.release
|
||||
- string
|
||||
- test
|
||||
- Release name
|
||||
* - Key
|
||||
- Type
|
||||
- Default
|
||||
- Description
|
||||
* - autoscaling
|
||||
- object
|
||||
- {"enabled":false,"maxReplicas":100,"minReplicas":1,"targetCPUUtilizationPercentage":80}
|
||||
- Autoscaling configuration
|
||||
* - autoscaling.enabled
|
||||
- bool
|
||||
- false
|
||||
- Enable autoscaling
|
||||
* - autoscaling.maxReplicas
|
||||
- int
|
||||
- 100
|
||||
- Maximum replicas
|
||||
* - autoscaling.minReplicas
|
||||
- int
|
||||
- 1
|
||||
- Minimum replicas
|
||||
* - autoscaling.targetCPUUtilizationPercentage
|
||||
- int
|
||||
- 80
|
||||
- Target CPU utilization for autoscaling
|
||||
* - configs
|
||||
- object
|
||||
- {}
|
||||
- Configmap
|
||||
* - containerPort
|
||||
- int
|
||||
- 8000
|
||||
- Container port
|
||||
* - customObjects
|
||||
- list
|
||||
- []
|
||||
- Custom Objects configuration
|
||||
* - deploymentStrategy
|
||||
- object
|
||||
- {}
|
||||
- Deployment strategy configuration
|
||||
* - externalConfigs
|
||||
- list
|
||||
- []
|
||||
- External configuration
|
||||
* - extraContainers
|
||||
- list
|
||||
- []
|
||||
- Additional containers configuration
|
||||
* - extraInit
|
||||
- object
|
||||
- {"pvcStorage":"1Gi","s3modelpath":"relative_s3_model_path/opt-125m", "awsEc2MetadataDisabled": true}
|
||||
- Additional configuration for the init container
|
||||
* - extraInit.pvcStorage
|
||||
- string
|
||||
- "50Gi"
|
||||
- Storage size of the s3
|
||||
* - extraInit.s3modelpath
|
||||
- string
|
||||
- "relative_s3_model_path/opt-125m"
|
||||
- Path of the model on the s3 which hosts model weights and config files
|
||||
* - extraInit.awsEc2MetadataDisabled
|
||||
- boolean
|
||||
- true
|
||||
- Disables the use of the Amazon EC2 instance metadata service
|
||||
* - extraPorts
|
||||
- list
|
||||
- []
|
||||
- Additional ports configuration
|
||||
* - gpuModels
|
||||
- list
|
||||
- ["TYPE_GPU_USED"]
|
||||
- Type of gpu used
|
||||
* - image
|
||||
- object
|
||||
- {"command":["vllm","serve","/data/","--served-model-name","opt-125m","--host","0.0.0.0","--port","8000"],"repository":"vllm/vllm-openai","tag":"latest"}
|
||||
- Image configuration
|
||||
* - image.command
|
||||
- list
|
||||
- ["vllm","serve","/data/","--served-model-name","opt-125m","--host","0.0.0.0","--port","8000"]
|
||||
- Container launch command
|
||||
* - image.repository
|
||||
- string
|
||||
- "vllm/vllm-openai"
|
||||
- Image repository
|
||||
* - image.tag
|
||||
- string
|
||||
- "latest"
|
||||
- Image tag
|
||||
* - livenessProbe
|
||||
- object
|
||||
- {"failureThreshold":3,"httpGet":{"path":"/health","port":8000},"initialDelaySeconds":15,"periodSeconds":10}
|
||||
- Liveness probe configuration
|
||||
* - livenessProbe.failureThreshold
|
||||
- int
|
||||
- 3
|
||||
- Number of times after which if a probe fails in a row, Kubernetes considers that the overall check has failed: the container is not alive
|
||||
* - livenessProbe.httpGet
|
||||
- object
|
||||
- {"path":"/health","port":8000}
|
||||
- Configuration of the Kubelet http request on the server
|
||||
* - livenessProbe.httpGet.path
|
||||
- string
|
||||
- "/health"
|
||||
- Path to access on the HTTP server
|
||||
* - livenessProbe.httpGet.port
|
||||
- int
|
||||
- 8000
|
||||
- Name or number of the port to access on the container, on which the server is listening
|
||||
* - livenessProbe.initialDelaySeconds
|
||||
- int
|
||||
- 15
|
||||
- Number of seconds after the container has started before liveness probe is initiated
|
||||
* - livenessProbe.periodSeconds
|
||||
- int
|
||||
- 10
|
||||
- How often (in seconds) to perform the liveness probe
|
||||
* - maxUnavailablePodDisruptionBudget
|
||||
- string
|
||||
- ""
|
||||
- Disruption Budget Configuration
|
||||
* - readinessProbe
|
||||
- object
|
||||
- {"failureThreshold":3,"httpGet":{"path":"/health","port":8000},"initialDelaySeconds":5,"periodSeconds":5}
|
||||
- Readiness probe configuration
|
||||
* - readinessProbe.failureThreshold
|
||||
- int
|
||||
- 3
|
||||
- Number of times after which if a probe fails in a row, Kubernetes considers that the overall check has failed: the container is not ready
|
||||
* - readinessProbe.httpGet
|
||||
- object
|
||||
- {"path":"/health","port":8000}
|
||||
- Configuration of the Kubelet http request on the server
|
||||
* - readinessProbe.httpGet.path
|
||||
- string
|
||||
- "/health"
|
||||
- Path to access on the HTTP server
|
||||
* - readinessProbe.httpGet.port
|
||||
- int
|
||||
- 8000
|
||||
- Name or number of the port to access on the container, on which the server is listening
|
||||
* - readinessProbe.initialDelaySeconds
|
||||
- int
|
||||
- 5
|
||||
- Number of seconds after the container has started before readiness probe is initiated
|
||||
* - readinessProbe.periodSeconds
|
||||
- int
|
||||
- 5
|
||||
- How often (in seconds) to perform the readiness probe
|
||||
* - replicaCount
|
||||
- int
|
||||
- 1
|
||||
- Number of replicas
|
||||
* - resources
|
||||
- object
|
||||
- {"limits":{"cpu":4,"memory":"16Gi","nvidia.com/gpu":1},"requests":{"cpu":4,"memory":"16Gi","nvidia.com/gpu":1}}
|
||||
- Resource configuration
|
||||
* - resources.limits."nvidia.com/gpu"
|
||||
- int
|
||||
- 1
|
||||
- Number of gpus used
|
||||
* - resources.limits.cpu
|
||||
- int
|
||||
- 4
|
||||
- Number of CPUs
|
||||
* - resources.limits.memory
|
||||
- string
|
||||
- "16Gi"
|
||||
- CPU memory configuration
|
||||
* - resources.requests."nvidia.com/gpu"
|
||||
- int
|
||||
- 1
|
||||
- Number of gpus used
|
||||
* - resources.requests.cpu
|
||||
- int
|
||||
- 4
|
||||
- Number of CPUs
|
||||
* - resources.requests.memory
|
||||
- string
|
||||
- "16Gi"
|
||||
- CPU memory configuration
|
||||
* - secrets
|
||||
- object
|
||||
- {}
|
||||
- Secrets configuration
|
||||
* - serviceName
|
||||
- string
|
||||
-
|
||||
- Service name
|
||||
* - servicePort
|
||||
- int
|
||||
- 80
|
||||
- Service port
|
||||
* - labels.environment
|
||||
- string
|
||||
- test
|
||||
- Environment name
|
||||
* - labels.release
|
||||
- string
|
||||
- test
|
||||
- Release name
|
||||
```
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user