[Doc] Installation of ucm

docs/source/getting-started/installation.md

@@ -1 +1,56 @@
 # Installation
+This document describes how to install unified-cache-management.
+
+## Requirements
+- OS: Linux
+- Python: >= 3.9, < 3.12
+- GPU: compute capability 8.0 or higher (e.g., V100, T4, RTX20xx, A100, L4, H100, etc.)
+- CUDA 12.8+
+
+You have 2 ways to install for now:
+- Setup from code: First, prepare vLLM environment, then install unified-cache-management from source code.
+- Setup from docker: use the unified-cache-management docker image directly.
+
+## Setup from code
+
+### Prepare vLLM Environment
+For the sake of environment isolation and simplicity, we recommend preparing the vLLM environment by pulling the official, pre-built vLLM Docker image.
+```bash
+docker pull vllm/vllm-openai:v0.9.2
+```
+Use the following command to run your own container:
+```bash
+# Use `--ipc=host` to make sure the shared memory is large enough.
+docker run \
+    --gpus all \
+    --network=host \
+    --ipc=host \
+    -v <path_to_your_models>:/app/model \
+    -v <path_to_your_storage>:/app/storage \
+    --name <name_of_your_container> \
+    -it vllm/vllm-openai:v0.9.2 bash
+```
+Refer to [Set up using docker](https://docs.vllm.ai/en/latest/getting_started/installation/gpu.html#set-up-using-docker) for more information to run your own vLLM container. After installation, please refer to this [issue](https://github.com/vllm-project/vllm/issues/21702) in order to use the uc_connector provided by unified-cache-management.
+
+### Build from source code
+Follow commands below to install unified-cache-management:
+```bash
+git clone --depth 1 --branch develop https://github.com/ModelEngine-Group/unified-cache-management.git
+cd unified-cache-management
+pip install -v -e .
+cd ..
+```
+
+## Setup from docker
+Download the pre-built docker image provided. Then run your container using following command. You can add or remove Docker parameters as needed.
+```bash
+# Use `--ipc=host` to make sure the shared memory is large enough.
+docker run --rm \
+    --gpus all \
+    --network=host \
+    --ipc=host \
+    -v <path_to_your_models>:/app/model \
+    -v <path_to_your_storage>:/app/storage \
+    --name <name_of_your_container> \
+    -it <image_id>
+```

docs/source/getting-started/installation_npu.md

@@ -1 +1,72 @@
 # Installation NPU
+This document describes how to install unified-cache-management when using Ascend NPU manually.
+
+## Requirements
+- OS: Linux
+- Python: >= 3.9, < 3.12
+- A hardware with Ascend NPU. It’s usually the Atlas 800 A2 series.
+
+The current version of unified-cache-management based on vLLM-Ascend v0.9.2rc1, refer to [vLLM-Ascend Installation Requirements](https://vllm-ascend.readthedocs.io/en/latest/installation.html#requirements) to meet the requirements.
+
+You have 2 ways to install for now:
+- Setup from code: First, prepare vLLM-Ascend environment, then install unified-cache-management from source code.
+- Setup from docker: use the unified-cache-management docker image directly.
+
+## Setup from code
+
+### Prepare vLLM-Ascend Environment
+For the sake of environment isolation and simplicity, we recommend preparing the vLLM-Ascend environment by pulling the official, pre-built vLLM-Ascend Docker image.
+```bash
+docker pull quay.io/ascend/vllm-ascend:v0.9.2rc1
+```
+Use the following command to run your own container:
+```bash
+# Update DEVICE according to your device (/dev/davinci[0-7])
+export DEVICE=/dev/davinci7
+# Update the vllm-ascend image
+export IMAGE=quay.io/ascend/vllm-ascend:v0.9.2rc1
+docker run --rm \
+    --name vllm-ascend-env \
+    --device $DEVICE \
+    --device /dev/davinci_manager \
+    --device /dev/devmm_svm \
+    --device /dev/hisi_hdc \
+    -v /usr/local/dcmi:/usr/local/dcmi \
+    -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
+    -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
+    -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
+    -v /etc/ascend_install.info:/etc/ascend_install.info \
+    -v /root/.cache:/root/.cache \
+    -it $IMAGE bash
+```
+Codes of vLLM and vLLM Ascend are placed in /vllm-workspace, you can refer to [vLLM-Ascend Installation](https://vllm-ascend.readthedocs.io/en/latest/installation.html) for more information. After installation, please refer to these [vllm-issue](https://github.com/vllm-project/vllm/issues/21702) and [vllm-ascend-issue](https://github.com/vllm-project/vllm-ascend/issues/2057) in order to use the uc_connector provided by unified-cache-management.
+
+### Build from source code
+Follow commands below to install unified-cache-management:
+```bash
+git clone --depth 1 --branch develop https://github.com/ModelEngine-Group/unified-cache-management.git
+cd unified-cache-management
+pip install -v -e .
+cd ..
+```
+
+## Setup from docker
+Download the pre-built docker image provided. Then run your container using following command. You can add or remove Docker parameters as needed.
+```bash
+# Use `--ipc=host` to make sure the shared memory is large enough.
+docker run --rm \
+    --device $DEVICE \
+    --device /dev/davinci_manager \
+    --device /dev/devmm_svm \
+    --device /dev/hisi_hdc \
+    -v /usr/local/dcmi:/usr/local/dcmi \
+    -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
+    -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
+    -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
+    -v /etc/ascend_install.info:/etc/ascend_install.info \
+    -v /root/.cache:/root/.cache \
+    -v <path_to_your_models>:/app/model \
+    -v <path_to_your_storage>:/app/storage \
+    --name <name_of_your_container> \
+    -it <image_id> bash
+```