Configure common layers for a function - Function Compute

Common layers supported by Function Compute

Function Compute supports the following types of common layers. For more information about common layers, see Common layers.

Custom runtimes

Custom runtimes, such as Python 3.10 and Node.js 17, are included. In addition, collections of the dependency libraries for some custom runtimes are provided. For example, the common layer Python310-Package-Collection contains some dependency libraries commonly used in Python.


Common layer	Compatible runtime	Description
Python310	Custom	Python 3.10.5 runtime layer
Python310-Package-Collection	Custom	A collection of common dependency libraries for the Python 3.10 runtime
Python39	Custom	Python 3.9.13 runtime layer
Python39-Package-Collection	Custom	A collection of common dependency libraries for the Python 3.9 runtime
Python38	Custom	Python 3.8.13 runtime layer
Python38-Package-Collection	Custom	A collection of common dependency libraries for the Python 3.8.13 runtime
Python36	Custom	Python 3.6.15 runtime layer
Python36-Package-Collection	Custom	A collection of common dependency libraries for the Python 3.6.15 runtime
Dotnet6	Custom	ASP.NET 6.0.5 runtime layer
PHP81	Custom	PHP 8.1 runtime layer
PHP80	Custom	PHP 8.0 runtime layer
PHP72	Custom	PHP 7.2 runtime layer
Java11	Custom	Java 11 runtime layer
Java17	Custom	Java 17 runtime layer
Nodejs17	Custom	Node.js 17 runtime layer
Nodejs16	Custom	Node.js 16 runtime layer
Nodejs14	Custom	Node.js 14 runtime layer
Nodejs12	Custom	Node.js 12 runtime layer

Common dependency libraries


Common layer	Compatible runtime	Description	Version (the version number of the core library)
Python39-SciPy1x	Python 3.9 Custom	An open source scientific computing library	scipy==1.9.0 numpy==1.23.2
Python39-PyTorch1x	Python 3.9 Custom	An open source machine learning framework (CPU edition)	torch==1.12.1+cpu torchaudio==0.12.1+cpu torchvision==0.13.1+cpu
Python39-Pandas1x	Python 3.9 Custom	An open source data analysis and processing tool based on NumPy	pandas==1.4.3 numpy==1.23.2
Nodejs-Puppeteer17x	Node.js 14 Custom	A Headless Chrome tool	puppeteer-v17.1.0

Alibaba Cloud SDKs


Common layer	Compatible runtime	Description	Version
Aliyun-DataX	Python 2.7 Python 3 Python 3.9 Custom	Open source edition of Alibaba Cloud DataWorks Data Integration	datax_v202205

Only part of the common layers are listed. If you fail to find the layer that you want to use, Contact Us.

Configure common layers by using the console

Prerequisites

Create a function

Procedure

Log on to the Function Compute console. In the left-side navigation pane, click Services & Functions.
In the top navigation bar, select a region. On the Services page, click the desired service.
On the Functions page, find the desired function and click Configure in the Actions column.
In the Layers section, click + Add Layer and select Add Official Common Layer from the drop-down list.
In the Official Common Layer drop-down list, view the layer description and license information and select a common layer. Select a layer version from the Layer Version drop-down list, and then click Save.

Note

A function can be configured with a maximum of five layers, including custom layers and public layers.
When multiple layers are configured for a function, the content of these layers is merged and stored in the /opt directory in reverse order. If layers contain files with the same name, the files in the first configured layer overwrites the files with the same names in the later configured layer.

Configure common layers by using Serverless Devs

Prerequisites

Procedure

Run the following command to find the custom layers in the specified region:
```
s cli fc layer list --official --region cn-hangzhou
```
After the execution is successful, return to the list of common layers. Obtain and record the new ARN (arnV2) of the desired layer.
```
-
  layerName:         Aliyun-DataX
  arn:
  arnV2:             acs:fc:cn-hangzhou:official:layers/Aliyun-DataX/versions/1
  version:           1
  acl:               1
  description:       Aliyun DataX for python (datax_v202205)
  compatibleRuntime:
    - custom
    - python2.7
    - python3
    - python3.9
-
  layerName:         Python36-SciPy1x
  arn:
  arnV2:             acs:fc:cn-hangzhou:official:layers/Python36-SciPy1x/versions/1
  version:           1
  acl:               1
  description:       SciPy Layer for python36(scipy-1.5.4,numpy-1.19.5)
  compatibleRuntime:
    - custom
    - python3
```
The following content describes the code snippets in the sample code:
- layerName: the name of the layer.
- version: the version of the layer.
- arn: the original ARN of the layer. The original ARN can be used only in custom layers.
- arnV2: the new ARN of the layer. The new ARN can be used only in public layers.
- acl: the permissions of the layer. The digit 0 specifies that the layer is private and the digit 1 specifies that the layer is public. By default, the public layers are public. The custom layers can be set to private or public.
- description: the description of the layer.
- compatibleRuntime: the list of compatible runtimes.

Create the s.yaml file in any directory and enter the new ARN of the layer.

Example:

edition: 1.0.0          #  The version of the YAML syntax. The version complies with the semantic versioning specification.
name: fcDeployApp       #  The name of the project.
access: "default"  #  The alias of the key.

services:
  fc-deploy-test: #  The name of the service.
    component: fc  # The name of the component.
    props: # The property value of the component.
      region: cn-hangzhou
      service:
        name: fctest
        description: 'test'
        internetAccess: true
      function:
        name: emoji # The name of the function.
        description: this is a layer demo
        runtime: python3.9
        codeUri: ./
        handler: index.handler
        memorySize: 128
        timeout: 6
        layers:    # The layers bound to the function. The values are the ARNs of the layers.
          - acs:fc:cn-hangzhou:official:layers/Aliyun-DataX/versions/1
          - acs:fc:cn-hangzhou:official:layers/Python39-SciPy1x/versions/1

Run the following command in the directory in which the s.yaml file is stored to deploy the function and configure the layers for the function:
```
s deploy
```

Additional information

You can also use API operations or SDKs to manage and configure layers. You can configure layers by calling the following API operations:

You can use OpenAPI Explorer to call API operations and use SDKs.