[Doc] Convert list tables to MyST (#11594)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

docs/source/getting_started/debugging.md

@@ -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) .

docs/source/getting_started/gaudi-installation.md

@@ -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}

docs/source/getting_started/tpu-installation.md

@@ -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:

docs/source/quantization/supported_hardware.md

@@ -4,121 +4,120 @@
 
 The table below shows the compatibility of various quantization implementations with different hardware platforms in vLLM:
 
-```{eval-rst}
-.. list-table::
-   :header-rows: 1
-   :widths: 20 8 8 8 8 8 8 8 8 8 8
+```{list-table}
+:header-rows: 1
+:widths: 20 8 8 8 8 8 8 8 8 8 8
 
-   * - Implementation
-     - Volta
-     - Turing
-     - Ampere
-     - Ada
-     - Hopper
-     - AMD GPU
-     - Intel GPU
-     - x86 CPU
-     - AWS Inferentia
-     - Google TPU
-   * - AWQ
-     - ✗
-     - ✅︎
-     - ✅︎
-     - ✅︎
-     - ✅︎
-     - ✗
-     - ✅︎
-     - ✅︎
-     - ✗
-     - ✗
-   * - GPTQ
-     - ✅︎
-     - ✅︎
-     - ✅︎
-     - ✅︎
-     - ✅︎
-     - ✗
-     - ✅︎
-     - ✅︎
-     - ✗
-     - ✗
-   * - Marlin (GPTQ/AWQ/FP8)
-     - ✗
-     - ✗
-     - ✅︎
-     - ✅︎
-     - ✅︎
-     - ✗
-     - ✗
-     - ✗
-     - ✗
-     - ✗
-   * - INT8 (W8A8)
-     - ✗
-     - ✅︎
-     - ✅︎
-     - ✅︎
-     - ✅︎
-     - ✗
-     - ✗
-     - ✅︎
-     - ✗
-     - ✗
-   * - FP8 (W8A8)
-     - ✗
-     - ✗
-     - ✗
-     - ✅︎
-     - ✅︎
-     - ✅︎
-     - ✗
-     - ✗
-     - ✗
-     - ✗
-   * - AQLM
-     - ✅︎
-     - ✅︎
-     - ✅︎
-     - ✅︎
-     - ✅︎
-     - ✗
-     - ✗
-     - ✗
-     - ✗
-     - ✗
-   * - bitsandbytes
-     - ✅︎
-     - ✅︎
-     - ✅︎
-     - ✅︎
-     - ✅︎
-     - ✗
-     - ✗
-     - ✗
-     - ✗
-     - ✗
-   * - DeepSpeedFP
-     - ✅︎
-     - ✅︎
-     - ✅︎
-     - ✅︎
-     - ✅︎
-     - ✗
-     - ✗
-     - ✗
-     - ✗
-     - ✗
-   * - GGUF
-     - ✅︎
-     - ✅︎
-     - ✅︎
-     - ✅︎
-     - ✅︎
-     - ✗
-     - ✗
-     - ✗
-     - ✗
-     - ✗
+* - Implementation
+  - Volta
+  - Turing
+  - Ampere
+  - Ada
+  - Hopper
+  - AMD GPU
+  - Intel GPU
+  - x86 CPU
+  - AWS Inferentia
+  - Google TPU
+* - AWQ
+  - ✗
+  - ✅︎
+  - ✅︎
+  - ✅︎
+  - ✅︎
+  - ✗
+  - ✅︎
+  - ✅︎
+  - ✗
+  - ✗
+* - GPTQ
+  - ✅︎
+  - ✅︎
+  - ✅︎
+  - ✅︎
+  - ✅︎
+  - ✗
+  - ✅︎
+  - ✅︎
+  - ✗
+  - ✗
+* - Marlin (GPTQ/AWQ/FP8)
+  - ✗
+  - ✗
+  - ✅︎
+  - ✅︎
+  - ✅︎
+  - ✗
+  - ✗
+  - ✗
+  - ✗
+  - ✗
+* - INT8 (W8A8)
+  - ✗
+  - ✅︎
+  - ✅︎
+  - ✅︎
+  - ✅︎
+  - ✗
+  - ✗
+  - ✅︎
+  - ✗
+  - ✗
+* - FP8 (W8A8)
+  - ✗
+  - ✗
+  - ✗
+  - ✅︎
+  - ✅︎
+  - ✅︎
+  - ✗
+  - ✗
+  - ✗
+  - ✗
+* - AQLM
+  - ✅︎
+  - ✅︎
+  - ✅︎
+  - ✅︎
+  - ✅︎
+  - ✗
+  - ✗
+  - ✗
+  - ✗
+  - ✗
+* - bitsandbytes
+  - ✅︎
+  - ✅︎
+  - ✅︎
+  - ✅︎
+  - ✅︎
+  - ✗
+  - ✗
+  - ✗
+  - ✗
+  - ✗
+* - DeepSpeedFP
+  - ✅︎
+  - ✅︎
+  - ✅︎
+  - ✅︎
+  - ✅︎
+  - ✗
+  - ✗
+  - ✗
+  - ✗
+  - ✗
+* - GGUF
+  - ✅︎
+  - ✅︎
+  - ✅︎
+  - ✅︎
+  - ✅︎
+  - ✗
+  - ✗
+  - ✗
+  - ✗
+  - ✗
 ```
 
 ## Notes:

docs/source/serving/deploying_with_helm.md

@@ -43,209 +43,208 @@ chart **including persistent volumes** and deletes the release.
 
 ## Values
 
-```{eval-rst}
-.. list-table:: Values
-   :widths: 25 25 25 25
-   :header-rows: 1
-
-   * - 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
+```{list-table}
+:widths: 25 25 25 25
+:header-rows: 1
+
+* - 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
 ```