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Update Neural Solution Docs (#1026)
Signed-off-by: yiliu30 <yi4.liu@intel.com>
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‎.azure-pipelines/scripts/codeScan/pyspelling/inc_dict.txt

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‎neural_solution/README.md

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# Why Neural Solution?
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- Efficiency: Neural Solution accelerates the optimization process by seamlessly parallelizing the tuning across multiple nodes.
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- Task Parallelism: Neural Solution automatically schedules the optimization task queue by coordinating available resources and allows execution of multiple optimization tasks simultaneously.
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- Tuning Parallelism: Neural Solution accelerates the optimization process by seamlessly parallelizing the tuning across multiple nodes.
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- APIs Support: Neural Solution supports both RESTful and gRPC APIs, enabling users to conveniently submit optimization tasks.
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- Code Less: When working with Hugging Face models, Neural Solution seamlessly integrates the functionality of the [Neural Coder](https://github.com/intel/neural-compressor/tree/master/neural_coder), eliminating the need for any code modifications during the optimization process.
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# Get Started
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## Installation
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<details>
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<summary>Prerequisites</summary>
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### Prerequisites
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- Install [Anaconda](https://docs.anaconda.com/free/anaconda/install/)
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- Install [Open MPI](https://www.open-mpi.org/faq/?category=building#easy-build)
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- Python 3.8 or later
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</details>
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There are two ways to install the neural solution:
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### Method 1. Using pip:

‎neural_solution/docs/source/README.md

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## Design Doc for Optimization as a Service [WIP]
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### Contents
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- [Design Doc for Optimization as a Service \[WIP\]](#design-doc-for-optimization-as-a-service-wip)
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- [Contents](#contents)
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- [Overview](#overview)
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- [Workflow of OaaS](#workflow-of-oaas)
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- [Class definition diagram](#class-definition-diagram)
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- [Extensibility](#extensibility)
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### Overview
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Optimization as a service(OaaS) is a platform that enables users to submit quantization tasks for their models and automatically dispatches these tasks to one or multiple nodes for accuracy-aware tuning. OaaS is designed to parallelize the tuning process in two levels: tuning and model. At the tuning level, OaaS execute the tuning process across multiple nodes for one model. At the model level, OaaS allocate free nodes to incoming requests automatically.
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### Workflow of OaaS
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```mermaid
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sequenceDiagram
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participant Studio
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participant TaskMonitor
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participant Scheduler
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participant Cluster
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participant TaskLauncher
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participant ResultMonitor
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Par receive task
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Studio ->> TaskMonitor: P1-1. Post quantization Request
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TaskMonitor ->> TaskMonitor: P1-2. Add task to task DB
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TaskMonitor ->> Studio: P1-3. Task received notification
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and Schedule task
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loop
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Scheduler ->> Scheduler: P2-1. Pop task from task DB
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Scheduler ->> Cluster: P2-2. Apply for resources
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Note over Scheduler, Cluster: the number of Nodes
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Cluster ->> Cluster: P2-3. Check the status of nodes in cluster
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Cluster ->> Scheduler: P2-4. Resources info
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Note over Scheduler, Cluster: host:socket list
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Scheduler ->> TaskLauncher: P2-5. Dispatch task
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end
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and Run task
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TaskLauncher ->> TaskLauncher: P3-1. Run task
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Note over TaskLauncher, TaskLauncher: mpirun -np 4 -hostfile hostfile python main.py
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TaskLauncher ->> TaskLauncher: P3-2. Wait task to finish...
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TaskLauncher ->> Cluster: P3-3. Free resource
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TaskLauncher ->> ResultMonitor: P3-4. Report the Acc and Perf
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ResultMonitor ->> Studio: P3-5. Post result to Studio
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and Query task status
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Studio ->> ResultMonitor: P4-1. Query the status of the submitted task
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ResultMonitor ->> Studio: P4-2. Post the status of queried task
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End
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# Get started
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- [Get started](#get-started)
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- [Install Neural Solution](#install-neural-solution)
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- [Prerequisites](#prerequisites)
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- [Method 1. Using pip](#method-1-using-pip)
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- [Method 2. Building from source](#method-2-building-from-source)
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- [Start service](#start-service)
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- [Submit task](#submit-task)
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- [Query task status](#query-task-status)
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- [Stop service](#stop-service)
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- [Inspect logs](#inspect-logs)
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## Install Neural Solution
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### Prerequisites
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- Install [Anaconda](https://docs.anaconda.com/free/anaconda/install/)
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- Install [Open MPI](https://www.open-mpi.org/faq/?category=building#easy-build)
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- Python 3.8 or later
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There are two ways to install the neural solution:
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### Method 1. Using pip
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```
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pip install neural-solution
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```
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### Method 2. Building from source
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The optimization process is divided into four parts, each executed in separate threads.
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- Part 1. Posting new quantization task. (P1-1 -> P1-2 -> P1-3)
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- Part 2. Resource allocation and scheduling. (P2-1 -> P2-2 -> P2-3 -> P2-4 -> P2-5)
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- Part 3. Task execution and reporting. (P3-1 -> P3-2 -> P3-3 -> P3-4 -> P3-5)
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```shell
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# get source code
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git clone https://github.com/intel/neural-compressor
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cd neural-compressor
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- Part 4. Updating the status. (P4-1 -> P4-2)
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# install neural compressor
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pip install -r requirements.txt
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python setup.py install
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### Class definition diagram
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# install neural solution
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pip install -r neural_solution/requirements.txt
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python setup.py neural_solution install
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```
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## Start service
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```shell
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# Start neural solution service with custom configuration
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neural_solution start --task_monitor_port=22222 --result_monitor_port=33333 --restful_api_port=8001
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# Help Manual
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neural_solution -h
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# Help output
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usage: neural_solution {start,stop} [-h] [--hostfile HOSTFILE] [--restful_api_port RESTFUL_API_PORT] [--grpc_api_port GRPC_API_PORT]
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[--result_monitor_port RESULT_MONITOR_PORT] [--task_monitor_port TASK_MONITOR_PORT] [--api_type API_TYPE]
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[--workspace WORKSPACE] [--conda_env CONDA_ENV] [--upload_path UPLOAD_PATH]
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Neural Solution
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positional arguments:
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{start,stop} start/stop service
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optional arguments:
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-h, --help show this help message and exit
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--hostfile HOSTFILE start backend serve host file which contains all available nodes
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--restful_api_port RESTFUL_API_PORT
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start restful serve with {restful_api_port}, default 8000
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--grpc_api_port GRPC_API_PORT
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start gRPC with {restful_api_port}, default 8000
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--result_monitor_port RESULT_MONITOR_PORT
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start serve for result monitor at {result_monitor_port}, default 3333
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--task_monitor_port TASK_MONITOR_PORT
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start serve for task monitor at {task_monitor_port}, default 2222
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--api_type API_TYPE start web serve with all/grpc/restful, default all
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--workspace WORKSPACE
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neural solution workspace, default "./ns_workspace"
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--conda_env CONDA_ENV
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specify the running environment for the task
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--upload_path UPLOAD_PATH
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specify the file path for the tasks
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```
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```mermaid
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classDiagram
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## Submit task
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- For RESTful API: `[user@server hf_model]$ curl -H "Content-Type: application/json" --data @./task.json http://localhost:8000/task/submit/`
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- For gRPC API: `python -m neural_solution.frontend.gRPC.client submit --request="test.json"`
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> For more details, please reference the [API description](./description_api.md) and [examples](../../examples/README.md).
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TaskDB "1" --> "*" Task
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TaskMonitor --> TaskDB
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ResultMonitor --> TaskDB
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Scheduler --> TaskDB
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Scheduler --> Cluster
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## Query task status
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Query the task status and result according to the `task_id`.
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class Task{
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+ status
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+ get_status()
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+ update_status()
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}
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- For RESTful API: `[user@server hf_model]$ curl -X GET http://localhost:8000/task/status/{task_id}`
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- For gRPC API: `python -m neural_solution.frontend.gRPC.client query --task_id={task_id}`
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class TaskDB{
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- task_collections
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+ append_task()
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+ get_all_pending_tasks()
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+ update_task_status()
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}
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class TaskMonitor{
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- task_db
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+ wait_new_task()
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}
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class Scheduler{
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- task_db
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- cluster
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+ schedule_tasks()
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+ dispatch_task()
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+ launch_task()
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}
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> For more details, please reference the [API description](./description_api.md) and [examples](../../examples/README.md).
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class ResultMonitor{
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- task_db
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+ query_task_status()
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}
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class Cluster{
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- node_list
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+ free()
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+ reserve_resource()
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+ get_node_status()
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}
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## Stop service
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```shell
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# Stop neural solution service with default configuration
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neural_solution stop
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```
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## Inspect logs
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The default logs locate in `./ns_workspace/`. Users can specify a custom workspace by using `neural_solution ---workspace=/path/to/custom/workspace`.
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There are several logs under workspace:
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```shell
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(ns) [username@servers ns_workspace]$ tree
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.
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├── db
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│ └── task.db # database to save the task-related information
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├── serve_log # service running log
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│ ├── backend.log # backend log
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│ ├── frontend_grpc.log # grpc frontend log
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│ └── frontend.log # HTTP/RESTful frontend log
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├── task_log # overall log for each task
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│ ├── task_bdf0bd1b2cc14bc19bce12d4f9b333c7.txt # task log
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│ └── ...
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└── task_workspace # the log for each task
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...
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├── bdf0bd1b2cc14bc19bce12d4f9b333c7 # task_id
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...
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### Extensibility
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- The service can be deployed on various resource pool, including a set of worker nodes, such as a local cluster or cloud cluster (AWS and GCP).
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```
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‎neural_solution/docs/source/description_api.md

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# Neural Solution OaaS API Documentation
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# Neural Solution API
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Welcome to Neural Solution OaaS API documentation. This API documentation provides a detailed description of all the endpoints available in Neural Solution OaaS API.
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‎neural_solution/docs/source/get_started.md

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‎neural_solution/docs/source/imgs/NS-OaaS-Intro.png

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