Function Compute provides common layers. You can configure the layer feature for your functions without providing custom layers. This topic describes how to configure common layers for a function.

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)
  • Python 3.9
  • Custom
An open source scientific computing library
  • scipy==1.9.0
  • numpy==1.23.2
  • 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
  • Python 3.9
  • Custom
An open source data analysis and processing tool based on NumPy
  • pandas==1.4.3
  • numpy==1.23.2
  • Node.js 14
  • Custom
A Headless Chrome tool puppeteer-v17.1.0

Alibaba Cloud SDKs

Common layer Compatible runtime Description Version
  • 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


Create a function


  1. Log on to the Function Compute console. In the left-side navigation pane, click Services & Functions.
  2. In the top navigation bar, select a region. On the Services page, click the desired service.
  3. On the Functions page, find the desired function and click Configure in the Actions column.
  4. In the Layers section, click + Add Layer and select Add Official Common Layer from the drop-down list.
  5. 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.
  • 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



  1. 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
      arnV2:             acs:fc:cn-hangzhou:official:layers/Aliyun-DataX/versions/1
      version:           1
      acl:               1
      description:       Aliyun DataX for python (datax_v202205)
        - custom
        - python2.7
        - python3
        - python3.9
      layerName:         Python36-SciPy1x
      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)
        - 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.
  2. Create the s.yaml file in any directory and enter the new ARN of the layer.
    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.
      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
            name: fctest
            description: 'test'
            internetAccess: true
            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
  3. 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.