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To authenticate to the API, your autoscaling implementation can use an access token or a {% data variables.product.prodname\_dotcom %} app. Your access token will require the following scope: \* For private repositories, use an access token with the [`repo` scope](/apps/oauth-apps/building-oauth-apps/scopes-for-oauth-apps#available-scopes). \* For public repositories, use an access token with the [`public\_repo` scope](/apps/oauth-apps/building-oauth-apps/scopes-for-oauth-apps#available-scopes). \* For organizations, use an access token with the [`admin:org` scope](/apps/oauth-apps/building-oauth-apps/scopes-for-oauth-apps#available-scopes). To authenticate using a {% data variables.product.prodname\_dotcom %} App, it must be assigned the following permissions: \* For repositories, assign the `administration` permission. \* For organizations, assign the `organization\_self\_hosted\_runners` permission. You can register and delete enterprise self-hosted runners using [the API](/rest/actions/self-hosted-runners). To authenticate to the API, your autoscaling implementation can use an access token. Your access token will require the `manage\_runners:enterprise` scope. ## Communication Self-hosted runners connect to {% ifversion fpt or ghec %}{% data variables.product.github %}{% else %}{% data variables.location.product\_location\_enterprise %}{% endif %} to receive job assignments and download new versions of the runner application. {% data reusables.actions.runner-app-open-source %} {% ifversion fpt or ghec %} When a new version is released, the runner application automatically updates itself when a job is assigned to the runner, or within a week of release if the runner hasn't been assigned any jobs. {% else ifversion ghes %} When a new version is released, the runner application will automatically update within 24 hours.{% endif %} ### Requirements for communication{% ifversion fpt or ghec %} with {% data variables.product.github %}{% else %} with {% data variables.location.product\_location\_enterprise %}{% endif %} \* The self-hosted runner application must be running on the host machine to accept and run {% data variables.product.prodname\_actions %} jobs. {%- ifversion fpt or ghec %} \* The host machine must have appropriate network access with at least 70 kilobits per second upload and download speed. \* The host machine must be able to make outbound HTTPS connections over port 443. \* Depending on the function of the workflows assigned to your self-hosted runner, the host machine must be able to communicate with the {% data variables.product.github %} domains listed below. {% else %} \* {% data variables.product.prodname\_ghe\_server %} must accept inbound connections from your runners over HTTP(S) at {% data variables.location.product\_location\_enterprise %}'s hostname and API subdomain, and your runners must allow outbound connections over HTTP(S) to {% data variables.location.product\_location\_enterprise %}'s hostname and API subdomain. \* For caching to work, the runner must be able to communicate with, and directly download content from, blob storage. {% endif %} {% ifversion fpt or ghec %} ### Accessible domains by function {% data reusables.actions.domain-name-cname-recursive-firewall-rules %} {% data reusables.actions.runner-essential-communications %} In addition, your workflow may require access to other network resources. If you use an IP address allow list for your {% data variables.product.prodname\_dotcom %} organization or enterprise account, you must add your self-hosted runner's IP address to the allow list. See [Managing allowed IP addresses for your organization](/{% ifversion fpt %}enterprise-cloud@latest/{% endif %}/organizations/keeping-your-organization-secure/managing-allowed-ip-addresses-for-your-organization#using-github-actions-with-an-ip-allow-list) or [Enforcing policies for security settings in your enterprise](/{% ifversion fpt %}enterprise-cloud@latest/{% endif %}admin/policies/enforcing-policies-for-your-enterprise/enforcing-policies-for-security-settings-in-your-enterprise){% ifversion fpt %} in the {% data variables.product.prodname\_ghe\_cloud %} documentation.{% else %}.{% endif %} {% else %} ### Communication with {% data variables.product.prodname\_dotcom\_the\_website %} Self-hosted runners do not need to connect to {% data variables.product.prodname\_dotcom\_the\_website %} unless you have enabled automatic access to {% data variables.product.prodname\_dotcom\_the\_website %} actions for {% data variables.product.prodname\_ghe\_server %}. For more information, see [AUTOTITLE](/admin/github-actions/managing-access-to-actions-from-githubcom/about-using-actions-in-your-enterprise). If you want your runner to connect to {% data variables.product.prodname\_dotcom\_the\_website %}, the host machine must be able to make outbound HTTP connections over port 80, or HTTPS connections over port 443. To ensure connectivity over HTTPS, configure TLS for {% data variables.product.prodname\_ghe\_server %}. See [AUTOTITLE](/enterprise-server@latest/admin/configuration/hardening-security-for-your-enterprise/configuring-tls). If you
| null |
./vendor/github-actions/content/actions/reference/runners/self-hosted-runners.md
|
https://github.com/github/docs/blob/main//content/actions/reference/runners/self-hosted-runners.md
|
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|
see [AUTOTITLE](/admin/github-actions/managing-access-to-actions-from-githubcom/about-using-actions-in-your-enterprise). If you want your runner to connect to {% data variables.product.prodname\_dotcom\_the\_website %}, the host machine must be able to make outbound HTTP connections over port 80, or HTTPS connections over port 443. To ensure connectivity over HTTPS, configure TLS for {% data variables.product.prodname\_ghe\_server %}. See [AUTOTITLE](/enterprise-server@latest/admin/configuration/hardening-security-for-your-enterprise/configuring-tls). If you have enabled automatic access to {% data variables.product.prodname\_dotcom\_the\_website %} actions, then the self-hosted runner will connect directly to {% data variables.product.prodname\_dotcom\_the\_website %} to download actions. You must ensure that the machine has the appropriate network access to communicate with the {% data variables.product.prodname\_dotcom %} URLs listed below. ```shell copy github.com api.github.com codeload.github.com ``` You can use the REST API to get meta information about {% data variables.product.company\_short %}, including the IP addresses and domain details for {% data variables.product.company\_short %} services. The `actions\_inbound` section of the API supports both fully qualified and wildcard domains. Fully qualified domains specify a complete domain name (e.g., `example.github.com`), while wildcard domains use a `\*` to represent multiple possible subdomains (e.g., `\*.github.com`). An example of the self-hosted runner requirements using wildcard domains has been listed below. For more information, see [AUTOTITLE](/rest/meta/meta). ```shell copy github.com \*.github.com \*.githubusercontent.com ghcr.io ``` {% data reusables.actions.domain-name-cname-recursive-firewall-rules %} {% endif %}
| null |
./vendor/github-actions/content/actions/reference/runners/self-hosted-runners.md
|
https://github.com/github/docs/blob/main//content/actions/reference/runners/self-hosted-runners.md
|
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|
{% ifversion ghes %} {% data reusables.actions.enterprise-github-hosted-runners %} {% else %} ## Supported runners and hardware resources {% ifversion actions-hosted-runners %} Ranges of {% data variables.product.prodname\_dotcom %}-hosted runners are available for use in public and private repositories. For lists of available runners, see: \* [Standard runners for \*\*public\*\* repositories](#standard-github-hosted-runners-for-public-repositories) \* {% ifversion ghec %}[Standard runners for \*\*private\*\* and \*\*internal\*\* repositories](#standard-github-hosted-runners-for-internal-and-private-repositories){% else %}[Standard runners for \*\*private\*\* repositories](#standard-github-hosted-runners-for--private-repositories){% endif %} {% data variables.product.prodname\_dotcom %}-hosted Linux runners support hardware acceleration for Android SDK tools, which makes running Android tests much faster and consumes fewer minutes. For more information on Android hardware acceleration, see [Configure hardware acceleration for the Android Emulator](https://developer.android.com/studio/run/emulator-acceleration) in the Android Developers documentation. > [!NOTE] > The `-latest` runner images are the latest stable images that {% data variables.product.prodname\_dotcom %} provides, and might not be the most recent version of the operating system available from the operating system vendor. > [!WARNING] > Beta and Deprecated Images are provided "as-is", "with all faults" and "as available" and are excluded from the service level agreement and warranty. Beta Images may not be covered by customer support. {% endif %} {% data reusables.actions.supported-github-runners %} Workflow logs list the runner used to run a job. For more information, see [AUTOTITLE](/actions/monitoring-and-troubleshooting-workflows/viewing-workflow-run-history). ### Limitations for arm64 macOS runners {% data reusables.actions.macos-runner-limitations %} ### Single-CPU runners Single-CPU {% data variables.product.github %}-hosted runners are available in both public and private repositories. These runners—specified using the workflow label `ubuntu-slim`—offer a lower-cost option for running lightweight operations. This type of runner is optimized for automation tasks, issue operations and short-running jobs. They are not suitable for typical heavyweight CI/CD builds. `ubuntu-slim` runners execute Actions workflows in Ubuntu Linux, inside a container rather than a full VM instance. When the job begins, {% data variables.product.github %} automatically provisions a new container for that job. All steps in the job execute in the container, allowing the steps in that job to share information using the runner's file system. When the job has finished, the container is automatically decommissioned. Each container provides hypervisor level 2 isolation. A minimal set of tools is installed on the `ubuntu-slim` runner image, appropriate for lightweight tasks. For details on what software is installed on the `ubuntu-slim` image, see the [README file](https://github.com/actions/runner-images/blob/main/images/ubuntu-slim/ubuntu-slim-Readme.md) in the `actions/runner-images` repository. #### Usage limits Single-CPU runners follow the same concurrency model as other {% data variables.product.github %}-hosted standard runners. See [AUTOTITLE](/actions/reference/limits#job-concurrency-limits-for-github-hosted-runners). The concurrency for the runners is determined by your plan. The job timeout for single-CPU runners is 15 minutes. If a job reaches this limit, the job is terminated and fails. ### {% data variables.actions.hosted\_runner\_caps %}s {% data reusables.actions.about-larger-runners %} For more information, see [AUTOTITLE](/actions/using-github-hosted-runners/about-larger-runners). ## Administrative privileges The Linux and macOS virtual machines both run using passwordless `sudo`. When you need to execute commands or install tools that require more privileges than the current user, you can use `sudo` without needing to provide a password. For more information, see the [Sudo Manual](https://www.sudo.ws/man/1.8.27/sudo.man.html). Windows virtual machines are configured to run as administrators with User Account Control (UAC) disabled. For more information, see [How User Account Control works](https://docs.microsoft.com/windows/security/identity-protection/user-account-control/how-user-account-control-works) in the Windows documentation. ## IP addresses To get a list of IP address ranges that {% data variables.product.prodname\_actions %} uses for {% data variables.product.prodname\_dotcom %}-hosted runners, you can use the {% data variables.product.prodname\_dotcom %} REST API. For more information, see the `actions` key in the response of the `GET /meta` endpoint. For more information, see [AUTOTITLE](/rest/meta/meta#get-github-meta-information). Windows and Ubuntu runners are hosted in Azure and subsequently have the same IP address ranges as the Azure datacenters. macOS runners are hosted in {% data variables.product.prodname\_dotcom %}'s
| null |
./vendor/github-actions/content/actions/reference/runners/github-hosted-runners.md
|
https://github.com/github/docs/blob/main//content/actions/reference/runners/github-hosted-runners.md
|
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|
API. For more information, see the `actions` key in the response of the `GET /meta` endpoint. For more information, see [AUTOTITLE](/rest/meta/meta#get-github-meta-information). Windows and Ubuntu runners are hosted in Azure and subsequently have the same IP address ranges as the Azure datacenters. macOS runners are hosted in {% data variables.product.prodname\_dotcom %}'s own macOS cloud. Since there are so many IP address ranges for {% data variables.product.prodname\_dotcom %}-hosted runners, we do not recommend that you use these as allowlists for your internal resources. Instead, we recommend you use {% data variables.actions.hosted\_runner %}s with a static IP address range, or self-hosted runners. For more information, see [AUTOTITLE](/actions/using-github-hosted-runners/about-larger-runners) or [AUTOTITLE](/actions/hosting-your-own-runners/managing-self-hosted-runners/about-self-hosted-runners). The list of {% data variables.product.prodname\_actions %} IP addresses returned by the API is updated once a week. ## Communication requirements for {% data variables.product.prodname\_dotcom %}-hosted runners A {% data variables.product.github %}-hosted runner must establish connections to {% data variables.product.github %}-owned endpoints to perform essential communication operations. In addition, your runner may require access to additional networks that you specify or utilize within an action. To ensure proper communications for {% data variables.product.github %}-hosted runners between networks within your configuration, ensure that the following communications are allowed. {% data reusables.actions.domain-name-cname-recursive-firewall-rules %} {% data reusables.actions.runner-essential-communications %} ## File systems {% data variables.product.prodname\_dotcom %} executes actions and shell commands in specific directories on the virtual machine. The file paths on virtual machines are not static. Use the environment variables {% data variables.product.prodname\_dotcom %} provides to construct file paths for the `home`, `workspace`, and `workflow` directories. | Directory | Environment variable | Description | |-----------|----------------------|-------------| | `home` | `HOME` | Contains user-related data. For example, this directory could contain credentials from a login attempt. | | `workspace` | `GITHUB\_WORKSPACE` | Actions and shell commands execute in this directory. An action can modify the contents of this directory, which subsequent actions can access. | | `workflow/event.json` | `GITHUB\_EVENT\_PATH` | The `POST` payload of the webhook event that triggered the workflow. {% data variables.product.prodname\_dotcom %} rewrites this each time an action executes to isolate file content between actions. For a list of the environment variables {% data variables.product.prodname\_dotcom %} creates for each workflow, see [AUTOTITLE](/actions/learn-github-actions/variables#default-environment-variables). ### Docker container filesystem Actions that run in Docker containers have static directories under the `/github` path. However, we strongly recommend using the default environment variables to construct file paths in Docker containers. {% data variables.product.prodname\_dotcom %} reserves the `/github` path prefix and creates three directories for actions. \* `/github/home` \* `/github/workspace` - {% data reusables.repositories.action-root-user-required %} \* `/github/workflow` {% endif %}
| null |
./vendor/github-actions/content/actions/reference/runners/github-hosted-runners.md
|
https://github.com/github/docs/blob/main//content/actions/reference/runners/github-hosted-runners.md
|
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] | 0.100392
|
## About custom transformers {% data variables.product.prodname\_actions\_importer %} offers the ability to extend its built-in mapping by creating custom transformers. Custom transformers can be used to: \* Convert items that {% data variables.product.prodname\_actions\_importer %} does not automatically convert, or modify how items are converted. For more information, see [Creating custom transformers for items](#creating-custom-transformers-for-items). \* Convert references to runners to use different runner labels. For more information, see [Creating custom transformers for runners](#creating-custom-transformers-for-runners). \* Convert environment variable values from your existing pipelines to {% data variables.product.prodname\_actions %} workflows. For more information, see [Creating custom transformers for environment variables](#creating-custom-transformers-for-environment-variables). ## Using custom transformers with {% data variables.product.prodname\_actions\_importer %} A custom transformer contains mapping logic that {% data variables.product.prodname\_actions\_importer %} can use to transform your plugins, tasks, runner labels, or environment variables to work with {% data variables.product.prodname\_actions %}. Custom transformers are written with a domain-specific language (DSL) built on top of Ruby, and are defined within a file with the `.rb` file extension. You can use the `--custom-transformers` CLI option to specify which custom transformer files to use with the `audit`, `dry-run`, and `migrate` commands. For example, if custom transformers are defined in a file named `transformers.rb`, you can use the following command to use them with {% data variables.product.prodname\_actions\_importer %}: ```shell gh actions-importer ... --custom-transformers transformers.rb ``` Alternatively, you can use the glob pattern syntax to specify multiple custom transformer files. For example, if multiple custom transformer files are within a directory named `transformers`, you can provide them all to {% data variables.product.prodname\_actions\_importer %} with the following command: ```shell gh actions-importer ... --custom-transformers transformers/\*.rb ``` > [!NOTE] > When you use custom transformers, the custom transformer files must reside in the same directory, or in subdirectories, from where the `gh actions-importer` command is run. ## Creating custom transformers for items You can create custom transformers that {% data variables.product.prodname\_actions\_importer %} will use when converting existing build steps or triggers to their equivalent in {% data variables.product.prodname\_actions %}. This is especially useful when: \* {% data variables.product.prodname\_actions\_importer %} doesn't automatically convert an item. \* You want to change how an item is converted by {% data variables.product.prodname\_actions\_importer %}. \* Your existing pipelines use custom or proprietary extensions, such as shared libraries in Jenkins, and you need to define how these steps should function in {% data variables.product.prodname\_actions %}. {% data variables.product.prodname\_actions\_importer %} uses custom transformers that are defined using a DSL built on top of Ruby. In order to create custom transformers for build steps and triggers: \* Each custom transformer file must contain at least one `transform` method. \* Each `transform` method must return a `Hash`, an array of `Hash`'s, or `nil`. This returned value will correspond to an action defined in YAML. For more information about actions, see [AUTOTITLE](/actions/learn-github-actions/understanding-github-actions). ### Example custom transformer for a build step The following example converts a build step that uses the "buildJavaScriptApp" identifier to run various `npm` commands: ```ruby copy transform "buildJavaScriptApp" do |item| command = ["build", "package", "deploy"].map do |script| "npm run #{script}" end { name: "build javascript app", run: command.join("\n") } end ``` The above example results in the following {% data variables.product.prodname\_actions %} workflow step. It is comprised of converted build steps that had a `buildJavaScriptApp` identifier: ```yaml - name: build javascript app run: | npm run build npm run package npm run deploy ``` The `transform` method uses the identifier of the build step from your source CI/CD instance in an argument. In this example, the identifier is `buildJavaScriptLibrary`. You can also use comma-separated values to pass multiple identifiers to the `transform` method. For example, `transform "buildJavaScriptApp", "buildTypeScriptApp" { |item|
| null |
./vendor/github-actions/content/actions/reference/github-actions-importer/custom-transformers.md
|
https://github.com/github/docs/blob/main//content/actions/reference/github-actions-importer/custom-transformers.md
|
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] | 0.054494
|
npm run deploy ``` The `transform` method uses the identifier of the build step from your source CI/CD instance in an argument. In this example, the identifier is `buildJavaScriptLibrary`. You can also use comma-separated values to pass multiple identifiers to the `transform` method. For example, `transform "buildJavaScriptApp", "buildTypeScriptApp" { |item| ... }`. > [!NOTE] > The data structure of `item` will be different depending on the CI/CD platform and the type of item being converted. ## Creating custom transformers for runners You can customize the mapping between runners in your source CI/CD instance and their equivalent {% data variables.product.prodname\_actions %} runners. {% data variables.product.prodname\_actions\_importer %} uses custom transformers that are defined using a DSL built on top of Ruby. To create custom transformers for runners: \* The custom transformer file must have at least one `runner` method. \* The `runner` method accepts two parameters. The first parameter is the source CI/CD instance's runner label, and the second parameter is the corresponding {% data variables.product.prodname\_actions %} runner label. For more information on {% data variables.product.prodname\_actions %} runners, see [AUTOTITLE](/actions/using-github-hosted-runners/about-github-hosted-runners#supported-runners-and-hardware-resources). ### Example custom transformers for runners The following example shows a `runner` method that converts one runner label to one {% data variables.product.prodname\_actions %} runner label in the resulting workflow. ```ruby copy runner "linux", "ubuntu-latest" ``` You can also use the `runner` method to convert one runner label to multiple {% data variables.product.prodname\_actions %} runner labels in the resulting workflow. ```ruby copy runner "big-agent", ["self-hosted", "xl", "linux"] ``` {% data variables.product.prodname\_actions\_importer %} attempts to map the runner label as best it can. In cases where it cannot do this, the `ubuntu-latest` runner label is used as a default. You can use a special keyword with the `runner` method to control this default value. For example, the following custom transformer instructs {% data variables.product.prodname\_actions\_importer %} to use `macos-latest` as the default runner instead of `ubuntu-latest`. ```ruby copy runner :default, "macos-latest" ``` ## Creating custom transformers for environment variables You can customize the mapping between environment variables in your source CI/CD pipelines to their values in {% data variables.product.prodname\_actions %}. {% data variables.product.prodname\_actions\_importer %} uses custom transformers that are defined using a DSL built on top of Ruby. To create custom transformers for environment variables: \* The custom transformer file must have at least one `env` method. \* The `env` method accepts two parameters. The first parameter is the name of the environment variable in the original pipeline, and the second parameter is the updated value for the environment variable for {% data variables.product.prodname\_actions %}. For more information about {% data variables.product.prodname\_actions %} environment variables, see [AUTOTITLE](/actions/learn-github-actions/variables). ### Example custom transformers for environment variables There are several ways you can set up custom transformers to map your environment variables. \* The following example sets the value of any existing environment variables named `OCTO`, to `CAT` when transforming a pipeline. ```ruby copy env "OCTO", "CAT" ``` You can also remove all instances of a specific environment variable so they are not transformed to an {% data variables.product.prodname\_actions %} workflow. The following example removes all environment variables with the name `MONA\_LISA`. ```ruby copy env "MONA\_LISA", nil ``` \* You can also map your existing environment variables to secrets. For example, the following `env` method maps an environment variable named `MONALISA` to a secret named `OCTOCAT`. ```ruby copy env "MONALISA", secret("OCTOCAT") ``` This will set up a reference to a secret named `OCTOCAT` in the transformed workflow. For the secret to work, you will need to create the secret in your GitHub repository. For more information, see [AUTOTITLE](/actions/security-guides/using-secrets-in-github-actions#creating-secrets-for-a-repository). \* You can also use regular expressions to update
| null |
./vendor/github-actions/content/actions/reference/github-actions-importer/custom-transformers.md
|
https://github.com/github/docs/blob/main//content/actions/reference/github-actions-importer/custom-transformers.md
|
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] | 0.131075
|
copy env "MONALISA", secret("OCTOCAT") ``` This will set up a reference to a secret named `OCTOCAT` in the transformed workflow. For the secret to work, you will need to create the secret in your GitHub repository. For more information, see [AUTOTITLE](/actions/security-guides/using-secrets-in-github-actions#creating-secrets-for-a-repository). \* You can also use regular expressions to update the values of multiple environment variables at once. For example, the following custom transformer removes all environment variables from the converted workflow: ```ruby copy env /.\*/, nil ``` The following example uses a regular expression match group to transform environment variable values to dynamically generated secrets. ```ruby copy env /^(.+)\_SSH\_KEY/, secret("%s\_SSH\_KEY) ``` > [!NOTE] > The order in which `env` methods are defined matters when using regular expressions. The first `env` transformer that matches an environment variable name takes precedence over subsequent `env` methods. You should define your most specific environment variable transformers first. ## Legal notice {% data reusables.actions.actions-importer-legal-notice %}
| null |
./vendor/github-actions/content/actions/reference/github-actions-importer/custom-transformers.md
|
https://github.com/github/docs/blob/main//content/actions/reference/github-actions-importer/custom-transformers.md
|
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This article provides general information for configuring {% data variables.product.prodname\_actions\_importer %}'s supplemental arguments and settings, such as optional parameters, path arguments, and network settings. ## Optional parameters {% data variables.product.prodname\_actions\_importer %} has several optional parameters that you can use to customize the migration process. ### Limiting allowed actions The following options can be used to limit which actions are allowed in converted workflows. When used in combination, these options expand the list of allowed actions. If none of these options are supplied, then all actions are allowed. \* `--allowed-actions` specifies a list of actions to allow in converted workflows. Wildcards are supported. Any other actions other than those provided will be disallowed. For example: ```shell --allowed-actions {% data reusables.actions.action-checkout %} actions/upload-artifact@\* my-org/\* ``` You can provide an empty list to disallow all actions. For example, `--allowed-actions=`. \* `--allow-verified-actions` specifies that all actions from verified creators are allowed. \* `--allow-github-created-actions` specifies that actions published from the `github` or `actions` organizations are allowed. For example, such actions include `github/super-linter` and `actions/checkout`. This option is equivalent to `--allowed-actions actions/\* github/\*`. ### Using a credentials file for authentication The `--credentials-file` parameter specifies the path to a file containing credentials for different servers that {% data variables.product.prodname\_actions\_importer %} can authenticate to. This is useful when build scripts (such as `.travis.yml` or `jenkinsfile`) are stored in multiple {% data variables.product.prodname\_ghe\_server %} instances. A credentials file must be a YAML file containing a list of server and access token combinations. {% data variables.product.prodname\_actions\_importer %} uses the credentials for the URL that most closely matches the network request being made. For example: ```yaml - url: https://github.com access\_token: ghp\_mygeneraltoken - url: https://github.com/specific\_org/ access\_token: ghp\_myorgspecifictoken - url: https://jenkins.org access\_token: abc123 username: marty\_mcfly ``` For the above credentials file, {% data variables.product.prodname\_actions\_importer %} uses the access token `ghp\_mygeneraltoken` to authenticate all network requests to `https://github.com`, \_unless\_ the network request is for a repository in the `specific\_org` organization. In that case, the `ghp\_myorgspecifictoken` token is used to authenticate instead. #### Alternative source code providers {% data variables.product.prodname\_actions\_importer %} can automatically fetch source code from non-{% data variables.product.prodname\_dotcom %} repositories. A credentials file can specify the `provider`, the provider URL, and the credentials needed to retrieve the source code. For example: ```yaml - url: https://gitlab.com access\_token: super\_secret\_token provider: gitlab ``` For the above example, {% data variables.product.prodname\_actions\_importer %} uses the token `super\_secret\_token` to retrieve any source code that is hosted on `https://gitlab.com`. Supported values for `provider` are: \* `github` (default) \* `gitlab` \* `bitbucket\_server` \* `azure\_devops` ### Controlling optional features You can use the `--features` option to limit the features used in workflows that {% data variables.product.prodname\_actions\_importer %} creates. This is useful for excluding newer {% data variables.product.prodname\_actions %} syntax from workflows when migrating to an older {% data variables.product.prodname\_ghe\_server %} instance. When using the `--features` option, you must specify the version of {% data variables.product.prodname\_ghe\_server %} that you are migrating to. For example: ```shell gh actions-importer dry-run ... --features ghes-3.3 ``` The supported values for `--features` are: \* `all` (default value) \* `ghes-latest` \* `ghes-`, where `` is the version of {% data variables.product.prodname\_ghe\_server %}, `3.0` or later. For example, `ghes-3.3`. You can view the list of available feature flags by {% data variables.product.prodname\_actions\_importer %} by running the `list-features` command. For example: ```shell copy gh actions-importer list-features ``` You should see an output similar to the following. ```shell Available feature flags: actions/cache (disabled): Control usage of actions/cache inside of workflows. Outputs a comment if not enabled. GitHub Enterprise Server >= ghes-3.5 required. composite-actions (enabled): Minimizes resulting workflow complexity through the use of composite actions. See https://docs.github.com/en/actions/creating-actions/creating-a-composite-action for more information. GitHub Enterprise Server
| null |
./vendor/github-actions/content/actions/reference/github-actions-importer/supplemental-arguments-and-settings.md
|
https://github.com/github/docs/blob/main//content/actions/reference/github-actions-importer/supplemental-arguments-and-settings.md
|
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] | 0.044756
|
output similar to the following. ```shell Available feature flags: actions/cache (disabled): Control usage of actions/cache inside of workflows. Outputs a comment if not enabled. GitHub Enterprise Server >= ghes-3.5 required. composite-actions (enabled): Minimizes resulting workflow complexity through the use of composite actions. See https://docs.github.com/en/actions/creating-actions/creating-a-composite-action for more information. GitHub Enterprise Server >= ghes-3.4 required. reusable-workflows (disabled): Avoid duplication by re-using existing workflows. See https://docs.github.com/en/actions/using-workflows/reusing-workflows for more information. GitHub Enterprise Server >= ghes-3.4 required. workflow-concurrency-option-allowed (enabled): Allows the use of the `concurrency` option in workflows. See https://docs.github.com/en/actions/reference/workflow-syntax-for-github-actions#concurrency for more information. GitHub Enterprise Server >= ghes-3.2 required. Enable features by passing --enable-features feature-1 feature-2 Disable features by passing --disable-features feature-1 feature-2 ``` To toggle feature flags, you can use either of the following methods: \* Use the `--enable-features` and `--disable-features` options when running a `gh actions-importer` command. \* Use an environment variable for each feature flag. You can use the `--enable-features` and `--disable-features` options to select specific features to enable or disable for the duration of the command. For example, the following command disables use of `actions/cache` and `composite-actions`: ```shell gh actions-importer dry-run ... --disable-features=composite-actions actions/cache ``` You can use the `configure --features` command to interactively configure feature flags and automatically write them to your environment: ```shell $ gh actions-importer configure --features ✔ Which features would you like to configure?: actions/cache, reusable-workflows ✔ actions/cache (disabled): Enable ? reusable-workflows (disabled): › Enable Disable ``` ### Disabling network response caching By default, {% data variables.product.prodname\_actions\_importer %} caches responses from network requests to reduce network load and reduce run time. You can use the `--no-http-cache` option to disable the network cache. For example: ```shell gh actions-importer forecast ... --no-http-cache ``` ## Path arguments When running {% data variables.product.prodname\_actions\_importer %}, path arguments are relative to the container's disk, so absolute paths relative to the container's host machine are not supported. When {% data variables.product.prodname\_actions\_importer %} is run, the container's `/data` directory is mounted to the directory where {% data variables.product.prodname\_actions\_importer %} is run. For example, the following command, when used in the `/Users/mona` directory, outputs the {% data variables.product.prodname\_actions\_importer %} audit summary to the `/Users/mona/out` directory: ```shell gh actions-importer audit --output-dir /data/out ``` ## Using a proxy To access servers that are configured with a HTTP proxy, you must set the following environment variables with the proxy's URL: \* `OCTOKIT\_PROXY`: for any {% data variables.product.prodname\_dotcom %} server. \* `HTTP\_PROXY` (or `HTTPS\_PROXY`): for any other servers. For example: ```shell export OCTOKIT\_PROXY=https://proxy.example.com:8443 export HTTPS\_PROXY=$OCTOKIT\_PROXY ``` If the proxy requires authentication, a username and password must be included in the proxy URL. For example, `https://username:password@proxy.url:port`. ## Disabling SSL certificate verification By default, {% data variables.product.prodname\_actions\_importer %} verifies SSL certificates when making network requests. You can disable SSL certificate verification with the `--no-ssl-verify` option. For example: ```shell gh actions-importer audit --output-dir ./output --no-ssl-verify ``` ## Legal notice {% data reusables.actions.actions-importer-legal-notice %}
| null |
./vendor/github-actions/content/actions/reference/github-actions-importer/supplemental-arguments-and-settings.md
|
https://github.com/github/docs/blob/main//content/actions/reference/github-actions-importer/supplemental-arguments-and-settings.md
|
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] | 0.163363
|
{% data reusables.actions.enterprise-github-hosted-runners %} ## Overview {% data reusables.actions.about-actions %} You can create workflows that build and test every pull request to your repository, or deploy merged pull requests to production. {% data variables.product.prodname\_actions %} goes beyond just DevOps and lets you run workflows when other events happen in your repository. For example, you can run a workflow to automatically add the appropriate labels whenever someone creates a new issue in your repository. {% ifversion fpt or ghec %} {% data variables.product.prodname\_dotcom %} provides Linux, Windows, and macOS virtual machines to run your workflows, or you can host your own self-hosted runners in your own data center or cloud infrastructure. {% elsif ghes %} You must host your own Linux, Windows, or macOS virtual machines to run workflows for {% data variables.location.product\_location %}. {% endif %} {% ifversion ghec or ghes %} For more information about introducing {% data variables.product.prodname\_actions %} to your enterprise, see [AUTOTITLE](/admin/github-actions/getting-started-with-github-actions-for-your-enterprise/introducing-github-actions-to-your-enterprise). {% endif %} ## The components of {% data variables.product.prodname\_actions %} You can configure a {% data variables.product.prodname\_actions %} \*\*workflow\*\* to be triggered when an \*\*event\*\* occurs in your repository, such as a pull request being opened or an issue being created. Your workflow contains one or more \*\*jobs\*\* which can run in sequential order or in parallel. Each job will run inside its own virtual machine \*\*runner\*\*, or inside a container, and has one or more \*\*steps\*\* that either run a script that you define or run an \*\*action\*\*, which is a reusable extension that can simplify your workflow.  ### Workflows {% data reusables.actions.about-workflows-long %} You can reference a workflow within another workflow. For more information, see [AUTOTITLE](/actions/using-workflows/reusing-workflows). For more information, see [AUTOTITLE](/actions/using-workflows). ### Events An \*\*event\*\* is a specific activity in a repository that triggers a \*\*workflow\*\* run. For example, an activity can originate from {% data variables.product.prodname\_dotcom %} when someone creates a pull request, opens an issue, or pushes a commit to a repository. You can also trigger a workflow to run on a [schedule](/actions/using-workflows/events-that-trigger-workflows#schedule), by [posting to a REST API](/rest/repos/repos#create-a-repository-dispatch-event), or manually. For a complete list of events that can be used to trigger workflows, see [Events that trigger workflows](/actions/using-workflows/events-that-trigger-workflows). ### Jobs A \*\*job\*\* is a set of \*\*steps\*\* in a workflow that is executed on the same \*\*runner\*\*. Each step is either a shell script that will be executed, or an \*\*action\*\* that will be run. Steps are executed in order and are dependent on each other. Since each step is executed on the same runner, you can share data from one step to another. For example, you can have a step that builds your application followed by a step that tests the application that was built. You can configure a job's dependencies with other jobs; by default, jobs have no dependencies and run in parallel. When a job takes a dependency on another job, it waits for the dependent job to complete before running. You can also use a \*\*matrix\*\* to run the same job multiple times, each with a different combination of variables—like operating systems or language versions. For example, you might configure multiple build jobs for different architectures without any job dependencies and a packaging job that depends on those builds. The build jobs run in parallel, and once they complete successfully, the packaging job runs. For more information, see [AUTOTITLE](/actions/using-jobs). ### Actions An \*\*action\*\* is a pre-defined, reusable set of jobs or code that performs specific tasks
| null |
./vendor/github-actions/content/actions/get-started/understand-github-actions.md
|
https://github.com/github/docs/blob/main//content/actions/get-started/understand-github-actions.md
|
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|
without any job dependencies and a packaging job that depends on those builds. The build jobs run in parallel, and once they complete successfully, the packaging job runs. For more information, see [AUTOTITLE](/actions/using-jobs). ### Actions An \*\*action\*\* is a pre-defined, reusable set of jobs or code that performs specific tasks within a \*\*workflow\*\*, reducing the amount of repetitive code you write in your workflow files. Actions can perform tasks such as: \* Pulling your Git repository from {% data variables.product.prodname\_dotcom %} \* Setting up the correct toolchain for your build environment \* Setting up authentication to your cloud provider You can write your own actions, or you can find actions to use in your workflows in the {% data variables.product.prodname\_marketplace %}. {% data reusables.actions.internal-actions-summary %} For more information on actions, see [AUTOTITLE](/actions/creating-actions). ### Runners A \*\*runner\*\* is a server that runs your workflows when they're triggered. Each runner can run a single \*\*job\*\* at a time. {% ifversion ghes %} You must host your own runners for {% data variables.product.prodname\_ghe\_server %}. {% elsif fpt or ghec %}{% data variables.product.company\_short %} provides Ubuntu Linux, Microsoft Windows, and macOS runners to run your \*\*workflows\*\*. Each workflow run executes in a fresh, newly-provisioned virtual machine. {% ifversion actions-hosted-runners %} {% data variables.product.prodname\_dotcom %} also offers {% data variables.actions.hosted\_runner %}s, which are available in larger configurations. For more information, see [AUTOTITLE](/actions/using-github-hosted-runners/using-larger-runners). {% endif %} If you need a different operating system or require a specific hardware configuration, you can host your own runners. {% endif %} For more information{% ifversion fpt or ghec %} about self-hosted runners{% endif %}, see [AUTOTITLE](/actions/how-tos/managing-self-hosted-runners). ## Next steps {% data reusables.actions.onboarding-next-steps %} {% ifversion ghec or ghes %} ## Further reading \* [AUTOTITLE](/admin/github-actions/getting-started-with-github-actions-for-your-enterprise/about-github-actions-for-enterprises) {% endif %}
| null |
./vendor/github-actions/content/actions/get-started/understand-github-actions.md
|
https://github.com/github/docs/blob/main//content/actions/get-started/understand-github-actions.md
|
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|
{% data reusables.actions.enterprise-github-hosted-runners %} ## Introduction {% data reusables.actions.about-actions %} You can create workflows that run tests whenever you push a change to your repository, or that deploy merged pull requests to production. This quickstart guide shows you how to use the user interface of {% data variables.product.github %} to add a workflow that demonstrates some of the essential features of {% data variables.product.prodname\_actions %}. {% data reusables.actions.workflow-templates-for-more-information %} For an overview of {% data variables.product.prodname\_actions %} workflows, see [AUTOTITLE](/actions/using-workflows/about-workflows). If you want to learn about the various components that make up {% data variables.product.prodname\_actions %}, see [AUTOTITLE](/actions/learn-github-actions/understanding-github-actions). ## Using workflow templates {% data reusables.actions.workflow-template-overview %} {% data reusables.actions.workflow-templates-repo-link %} ## Prerequisites This guide assumes that: \* You have at least a basic knowledge of how to use {% data variables.product.prodname\_dotcom %}. If you don't, you'll find it helpful to read some of the articles in the documentation for repositories and pull requests first. For example, see [AUTOTITLE](/repositories/creating-and-managing-repositories/quickstart-for-repositories), [AUTOTITLE](/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/about-branches), and [AUTOTITLE](/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/about-pull-requests). \* You have a repository on {% data variables.product.github %} where you can add files. \* You have access to {% data variables.product.prodname\_actions %}. > [!NOTE] If the \*\*{% octicon "play" aria-hidden="true" aria-label="play" %} Actions\*\* tab is not displayed under the name of your repository on {% data variables.product.prodname\_dotcom %}, it may be because Actions is disabled for the repository. For more information, see [AUTOTITLE](/repositories/managing-your-repositorys-settings-and-features/enabling-features-for-your-repository/managing-github-actions-settings-for-a-repository). ## Creating your first workflow 1. In your repository on {% data variables.product.github %}, create a workflow file called `github-actions-demo.yml` in the `.github/workflows` directory. To do this: \* If the `.github/workflows` directory already exists, navigate to that directory on {% data variables.product.prodname\_dotcom %}, click \*\*Add file\*\*, then click \*\*Create new file\*\*, and name the file `github-actions-demo.yml`. \* If your repository doesn't have a `.github/workflows` directory, go to the main page of the repository on {% data variables.product.prodname\_dotcom %}, click \*\*Add file\*\*, then click \*\*Create new file\*\*, and name the file `.github/workflows/github-actions-demo.yml`. This creates the `.github` and `workflows` directories and the `github-actions-demo.yml` file in a single step. > [!NOTE] > For {% data variables.product.prodname\_dotcom %} to discover any {% data variables.product.prodname\_actions %} workflows in your repository, you must save the workflow files in a directory called `.github/workflows`. > > You can give the workflow file any name you like, but you must use `.yml` or `.yaml` as the file name extension. YAML is a markup language that's commonly used for configuration files. 1. Copy the following YAML contents into the `github-actions-demo.yml` file: ```yaml copy name: GitHub Actions Demo run-name: {% raw %}${{ github.actor }}{% endraw %} is testing out GitHub Actions 🚀 on: [push] jobs: Explore-GitHub-Actions: runs-on: ubuntu-latest steps: - run: echo "🎉 The job was automatically triggered by a {% raw %}${{ github.event\_name }}{% endraw %} event." - run: echo "🐧 This job is now running on a {% raw %}${{ runner.os }}{% endraw %} server hosted by GitHub!" - run: echo "🔎 The name of your branch is {% raw %}${{ github.ref }}{% endraw %} and your repository is {% raw %}${{ github.repository }}{% endraw %}." - name: Check out repository code uses: {% data reusables.actions.action-checkout %} - run: echo "💡 The {% raw %}${{ github.repository }}{% endraw %} repository has been cloned to the runner." - run: echo "🖥️ The workflow is now ready to test your code on the runner." - name: List files in the repository run: | ls {% raw %}${{ github.workspace }}{% endraw %} - run: echo "🍏 This job's status is {% raw %}${{ job.status }}{% endraw %}." ``` At this stage you don't need to understand the details of this workflow. For now, you can just
| null |
./vendor/github-actions/content/actions/get-started/quickstart.md
|
https://github.com/github/docs/blob/main//content/actions/get-started/quickstart.md
|
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] | 0.079212
|
name: List files in the repository run: | ls {% raw %}${{ github.workspace }}{% endraw %} - run: echo "🍏 This job's status is {% raw %}${{ job.status }}{% endraw %}." ``` At this stage you don't need to understand the details of this workflow. For now, you can just copy and paste the contents into the file. After completing this quickstart guide, you can learn about the syntax of workflow files in [AUTOTITLE](/actions/using-workflows/about-workflows#understanding-the-workflow-file), and for an explanation of {% data variables.product.prodname\_actions %} contexts, such as `{% raw %}${{ github.actor }}{% endraw %}` and `{% raw %}${{ github.event\_name }}{% endraw %}`, see [AUTOTITLE](/actions/learn-github-actions/contexts). 1. Click \*\*Commit changes\*\*. 1. In the "Propose changes" dialog, select either the option to commit to the default branch or the option to create a new branch and start a pull request. Then click \*\*Commit changes\*\* or \*\*Propose changes\*\*.  Committing the workflow file to a branch in your repository triggers the `push` event and runs your workflow. If you chose to start a pull request, you can continue and create the pull request, but this is not necessary for the purposes of this quickstart because the commit has still been made to a branch and will trigger the new workflow. ## Viewing your workflow results {% data reusables.repositories.navigate-to-repo %} {% data reusables.repositories.actions-tab %} 1. In the left sidebar, click the workflow you want to display, in this example "GitHub Actions Demo."  1. From the list of workflow runs, click the name of the run you want to see, in this example "USERNAME is testing out GitHub Actions." 1. In the left sidebar of the workflow run page, under \*\*Jobs\*\*, click the \*\*Explore-GitHub-Actions\*\* job.  1. The log shows you how each of the steps was processed. Expand any of the steps to view its details.  For example, you can see the list of files in your repository:  The example workflow you just added is triggered each time code is pushed to the branch, and shows you how {% data variables.product.prodname\_actions %} can work with the contents of your repository. For an in-depth tutorial, see [AUTOTITLE](/actions/learn-github-actions/understanding-github-actions). ## Next steps {% data reusables.actions.onboarding-next-steps %}
| null |
./vendor/github-actions/content/actions/get-started/quickstart.md
|
https://github.com/github/docs/blob/main//content/actions/get-started/quickstart.md
|
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|
{% data reusables.actions.enterprise-github-hosted-runners %} ## About continuous deployment \_Continuous deployment\_ (CD) is the practice of using automation to publish and deploy software updates. As part of the typical CD process, the code is automatically built and tested before deployment. Continuous deployment is often coupled with continuous integration. For more information about continuous integration, see [AUTOTITLE](/actions/automating-builds-and-tests/about-continuous-integration). ## About continuous deployment using {% data variables.product.prodname\_actions %} You can set up a {% data variables.product.prodname\_actions %} workflow to deploy your software product. To verify that your product works as expected, your workflow can build the code in your repository and run your tests before deploying. You can configure your CD workflow to run when an event occurs (for example, when new code is pushed to the default branch of your repository), on a set schedule, manually, or when an external event occurs using the repository dispatch webhook. For more information about when your workflow can run, see [AUTOTITLE](/actions/using-workflows/events-that-trigger-workflows). {% data variables.product.prodname\_actions %} provides features that give you more control over deployments. For example, you can use environments to require approval for a job to proceed, restrict which branches can trigger a workflow, or limit access to secrets. You can use concurrency to limit your CD pipeline to a maximum of one in-progress deployment and one pending deployment. For more information about these features, see [AUTOTITLE](/actions/deployment/about-deployments/deploying-with-github-actions) and [AUTOTITLE](/actions/deployment/targeting-different-environments/managing-environments-for-deployment). ## Workflow templates and third-party actions {% data reusables.actions.cd-templates-actions %} ## Next steps If your {% data variables.product.prodname\_actions %} workflows need to access resources from a cloud provider that supports OpenID Connect (OIDC), you can configure your workflows to authenticate directly to the cloud provider. This will let you stop storing these credentials as long-lived secrets and provide other security benefits. For more information, see [AUTOTITLE](/actions/deployment/security-hardening-your-deployments/about-security-hardening-with-openid-connect).
| null |
./vendor/github-actions/content/actions/get-started/continuous-deployment.md
|
https://github.com/github/docs/blob/main//content/actions/get-started/continuous-deployment.md
|
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] | 0.156555
|
{% data variables.product.prodname\_marketplace %} offers both {% data variables.product.prodname\_actions %} and {% data variables.product.prodname\_github\_apps %}, each of which can be valuable automation and workflow tools. Understanding the differences and the benefits of each option will allow you to select the best fit for your job. {% data variables.product.prodname\_github\_apps %}: \* Run persistently and can react to events quickly. \* Work great when persistent data is needed. \* Work best with API requests that aren't time consuming. \* Run on a server or compute infrastructure that you provide. {% data variables.product.prodname\_actions %}: \* Provide automation that can perform continuous integration and continuous deployment. \* Can run directly on runner machines or in Docker containers. \* Can include access to a clone of your repository, enabling deployment and publishing tools, code formatters, and command line tools to access your code. \* Don't require you to deploy code or serve an app. \* Have a simple interface to create and use secrets, which enables actions to interact with third-party services without needing to store the credentials of the person using the action.
| null |
./vendor/github-actions/content/actions/get-started/actions-vs-apps.md
|
https://github.com/github/docs/blob/main//content/actions/get-started/actions-vs-apps.md
|
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] | 0.124278
|
{% data reusables.actions.enterprise-github-hosted-runners %} ## About continuous integration Continuous integration (CI) is a software practice that requires frequently committing code to a shared repository. Committing code more often detects errors sooner and reduces the amount of code a developer needs to debug when finding the source of an error. Frequent code updates also make it easier to merge changes from different members of a software development team. This is great for developers, who can spend more time writing code and less time debugging errors or resolving merge conflicts. When you commit code to your repository, you can continuously build and test the code to make sure that the commit doesn't introduce errors. Your tests can include code linters (which check style formatting), security checks, code coverage, functional tests, and other custom checks. Building and testing your code requires a server. You can build and test updates locally before pushing code to a repository, or you can use a CI server that checks for new code commits in a repository. ## About continuous integration using {% data variables.product.prodname\_actions %} CI using {% data variables.product.prodname\_actions %} offers workflows that can build the code in your repository and run your tests. Workflows can run on {% data variables.product.prodname\_dotcom %}-hosted virtual machines, or on machines that you host yourself. For more information, see [AUTOTITLE](/actions/using-github-hosted-runners/about-github-hosted-runners) and [AUTOTITLE](/actions/hosting-your-own-runners/managing-self-hosted-runners/about-self-hosted-runners). You can configure your CI workflow to run when a {% data variables.product.prodname\_dotcom %} event occurs (for example, when new code is pushed to your repository), on a set schedule, or when an external event occurs using the repository dispatch webhook. {% data variables.product.github %} runs your CI tests and provides the results of each test in the pull request, so you can see whether the change in your branch introduces an error. When all CI tests in a workflow pass, the changes you pushed are ready to be reviewed by a team member or merged. When a test fails, one of your changes may have caused the failure. When you set up CI in your repository, {% data variables.product.github %} analyzes the code in your repository and recommends CI workflows based on the language and framework in your repository. For example, if you use [Node.js](https://nodejs.org/en/), {% data variables.product.github %} will suggest a workflow template that installs your Node.js packages and runs your tests. You can use the CI workflow template suggested by {% data variables.product.github %}, customize the suggested workflow template, or create your own custom workflow file to run your CI tests. In addition to helping you set up CI workflows for your project, you can use {% data variables.product.prodname\_actions %} to create workflows across the full software development life cycle. For example, you can use actions to deploy, package, or release your project. For more information, see [AUTOTITLE](/actions/learn-github-actions). For a definition of common terms, see [AUTOTITLE](/actions/learn-github-actions/understanding-github-actions). ## Next steps {% data variables.product.github %} offers CI workflow templates for a variety of languages and frameworks. For tutorials on setting up continuous integration with these templates, see [AUTOTITLE](/actions/use-cases-and-examples/building-and-testing).
| null |
./vendor/github-actions/content/actions/get-started/continuous-integration.md
|
https://github.com/github/docs/blob/main//content/actions/get-started/continuous-integration.md
|
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] | 0.115691
|
# Get started with Streamlit This Get Started guide explains how Streamlit works, how to install Streamlit on your preferred operating system, and how to create your first Streamlit app! helps you set up your development environment. Walk through installing Streamlit on Windows, macOS, or Linux. Alternatively, code right in your browser with GitHub Codespaces or Streamlit in Snowflake. introduces you to Streamlit's data model and development flow. You'll learn what makes Streamlit the most powerful way to build data apps, including the ability to display and style data, draw charts and maps, add interactive widgets, customize app layouts, cache computation, and define themes. walks you through creating apps using core features to fetch and cache data, draw charts, plot information on a map, and use interactive widgets to filter results. if you want to skip past local installation and code right in your browser. This guide uses Streamlit Community Cloud to help you automatically configure a codespace.
| null |
./vendor/streamlit/content/get-started/_index.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/_index.md
|
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] | 0.057812
|
# App model summary Now that you know a little more about all the individual pieces, let's close the loop and review how it works together: 1. Streamlit apps are Python scripts that run from top to bottom. 1. Every time a user opens a browser tab pointing to your app, the script is executed and a new session starts. 1. As the script executes, Streamlit draws its output live in a browser. 1. Every time a user interacts with a widget, your script is re-executed and Streamlit redraws its output in the browser. - The output value of that widget matches the new value during that rerun. 1. Scripts use the Streamlit cache to avoid recomputing expensive functions, so updates happen very fast. 1. Session State lets you save information that persists between reruns when you need more than a simple widget. 1. Streamlit apps can contain multiple pages, which are defined in separate `.py` files in a `pages` folder. 
| null |
./vendor/streamlit/content/get-started/fundamentals/summary.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/fundamentals/summary.md
|
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] | 0.060603
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# Basic concepts of Streamlit Working with Streamlit is simple. First you sprinkle a few Streamlit commands into a normal Python script, then you run it with `streamlit run`: ```bash streamlit run your\_script.py [-- script args] ``` As soon as you run the script as shown above, a local Streamlit server will spin up and your app will open in a new tab in your default web browser. The app is your canvas, where you'll draw charts, text, widgets, tables, and more. What gets drawn in the app is up to you. For example [`st.text`](/develop/api-reference/text/st.text) writes raw text to your app, and [`st.line\_chart`](/develop/api-reference/charts/st.line\_chart) draws — you guessed it — a line chart. Refer to our [API documentation](/develop/api-reference) to see all commands that are available to you. When passing your script some custom arguments, they must be passed after two dashes. Otherwise the arguments get interpreted as arguments to Streamlit itself. Another way of running Streamlit is to run it as a Python module. This can be useful when configuring an IDE like PyCharm to work with Streamlit: ```bash # Running python -m streamlit run your\_script.py # is equivalent to: streamlit run your\_script.py ``` You can also pass a URL to `streamlit run`! This is great when combined with GitHub Gists. For example: ```bash streamlit run https://raw.githubusercontent.com/streamlit/demo-uber-nyc-pickups/master/streamlit\_app.py ``` ## Development flow Every time you want to update your app, save the source file. When you do that, Streamlit detects if there is a change and asks you whether you want to rerun your app. Choose "Always rerun" at the top-right of your screen to automatically update your app every time you change its source code. This allows you to work in a fast interactive loop: you type some code, save it, try it out live, then type some more code, save it, try it out, and so on until you're happy with the results. This tight loop between coding and viewing results live is one of the ways Streamlit makes your life easier. While developing a Streamlit app, it's recommended to lay out your editor and browser windows side by side, so the code and the app can be seen at the same time. Give it a try! As of Streamlit version 1.10.0 and higher, Streamlit apps cannot be run from the root directory of Linux distributions. If you try to run a Streamlit app from the root directory, Streamlit will throw a `FileNotFoundError: [Errno 2] No such file or directory` error. For more information, see GitHub issue [#5239](https://github.com/streamlit/streamlit/issues/5239). If you are using Streamlit version 1.10.0 or higher, your main script should live in a directory other than the root directory. When using Docker, you can use the `WORKDIR` command to specify the directory where your main script lives. For an example of how to do this, read [Create a Dockerfile](/deploy/tutorials/docker#create-a-dockerfile). ## Data flow Streamlit's architecture allows you to write apps the same way you write plain Python scripts. To unlock this, Streamlit apps have a unique data flow: any time something must be updated on the screen, Streamlit reruns your entire Python script from top to bottom. This can happen in two situations: - Whenever you modify your app's source code. - Whenever a user interacts with widgets in the app. For example, when dragging a slider, entering text in an input box, or clicking a button. Whenever a callback is passed to a widget via the `on\_change` (or `on\_click`) parameter, the callback will always run before the rest of your script. For details on the Callbacks API, please refer to our [Session State API Reference Guide](/develop/api-reference/caching-and-state/st.session\_state#use-callbacks-to-update-session-state). And to make
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https://github.com/streamlit/docs/blob/main//content/get-started/fundamentals/main-concepts.md
|
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an input box, or clicking a button. Whenever a callback is passed to a widget via the `on\_change` (or `on\_click`) parameter, the callback will always run before the rest of your script. For details on the Callbacks API, please refer to our [Session State API Reference Guide](/develop/api-reference/caching-and-state/st.session\_state#use-callbacks-to-update-session-state). And to make all of this fast and seamless, Streamlit does some heavy lifting for you behind the scenes. A big player in this story is the [`@st.cache\_data`](#caching) decorator, which allows developers to skip certain costly computations when their apps rerun. We'll cover caching later in this page. ## Display and style data There are a few ways to display data (tables, arrays, data frames) in Streamlit apps. [Below](#use-magic), you will be introduced to \_magic\_ and [`st.write()`](/develop/api-reference/write-magic/st.write), which can be used to write anything from text to tables. After that, let's take a look at methods designed specifically for visualizing data. ### Use magic You can also write to your app without calling any Streamlit methods. Streamlit supports "[magic commands](/develop/api-reference/write-magic/magic)," which means you don't have to use [`st.write()`](/develop/api-reference/write-magic/st.write) at all! To see this in action try this snippet: ```python try """ # My first app Here's our first attempt at using data to create a table: """ import streamlit as st import pandas as pd df = pd.DataFrame({ 'first column': [1, 2, 3, 4], 'second column': [10, 20, 30, 40] }) df ``` Any time that Streamlit sees a variable or a literal value on its own line, it automatically writes that to your app using [`st.write()`](/develop/api-reference/write-magic/st.write). For more information, refer to the documentation on [magic commands](/develop/api-reference/write-magic/magic). ### Write a data frame Along with [magic commands](/develop/api-reference/write-magic/magic), [`st.write()`](/develop/api-reference/write-magic/st.write) is Streamlit's "Swiss Army knife". You can pass almost anything to [`st.write()`](/develop/api-reference/write-magic/st.write): text, data, Matplotlib figures, Altair charts, and more. Don't worry, Streamlit will figure it out and render things the right way. ```python import streamlit as st import pandas as pd st.write("Here's our first attempt at using data to create a table:") st.write(pd.DataFrame({ 'first column': [1, 2, 3, 4], 'second column': [10, 20, 30, 40] })) ``` There are other data specific functions like [`st.dataframe()`](/develop/api-reference/data/st.dataframe) and [`st.table()`](/develop/api-reference/data/st.table) that you can also use for displaying data. Let's understand when to use these features and how to add colors and styling to your data frames. You might be asking yourself, "why wouldn't I always use `st.write()`?" There are a few reasons: 1. \_Magic\_ and [`st.write()`](/develop/api-reference/write-magic/st.write) inspect the type of data that you've passed in, and then decide how to best render it in the app. Sometimes you want to draw it another way. For example, instead of drawing a dataframe as an interactive table, you may want to draw it as a static table by using `st.table(df)`. 2. The second reason is that other methods return an object that can be used and modified, either by adding data to it or replacing it. 3. Finally, if you use a more specific Streamlit method you can pass additional arguments to customize its behavior. For example, let's create a data frame and change its formatting with a Pandas `Styler` object. In this example, you'll use Numpy to generate a random sample, and the [`st.dataframe()`](/develop/api-reference/data/st.dataframe) method to draw an interactive table. This example uses Numpy to generate a random sample, but you can use Pandas DataFrames, Numpy arrays, or plain Python arrays. ```python import streamlit as st import numpy as np dataframe = np.random.randn(10, 20) st.dataframe(dataframe) ``` Let's expand on the first example using the Pandas `Styler` object to highlight some elements in the interactive table. ```python import streamlit as st import numpy as np import pandas as pd dataframe
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|
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|
Python arrays. ```python import streamlit as st import numpy as np dataframe = np.random.randn(10, 20) st.dataframe(dataframe) ``` Let's expand on the first example using the Pandas `Styler` object to highlight some elements in the interactive table. ```python import streamlit as st import numpy as np import pandas as pd dataframe = pd.DataFrame( np.random.randn(10, 20), columns=('col %d' % i for i in range(20))) st.dataframe(dataframe.style.highlight\_max(axis=0)) ``` Streamlit also has a method for static table generation: [`st.table()`](/develop/api-reference/data/st.table). ```python import streamlit as st import numpy as np import pandas as pd dataframe = pd.DataFrame( np.random.randn(10, 20), columns=('col %d' % i for i in range(20))) st.table(dataframe) ``` ### Draw charts and maps Streamlit supports several popular data charting libraries like [Matplotlib, Altair, deck.gl, and more](/develop/api-reference#chart-elements). In this section, you'll add a bar chart, line chart, and a map to your app. ### Draw a line chart You can easily add a line chart to your app with [`st.line\_chart()`](/develop/api-reference/charts/st.line\_chart). We'll generate a random sample using Numpy and then chart it. ```python import streamlit as st import numpy as np import pandas as pd chart\_data = pd.DataFrame( np.random.randn(20, 3), columns=['a', 'b', 'c']) st.line\_chart(chart\_data) ``` ### Plot a map With [`st.map()`](/develop/api-reference/charts/st.map) you can display data points on a map. Let's use Numpy to generate some sample data and plot it on a map of San Francisco. ```python import streamlit as st import numpy as np import pandas as pd map\_data = pd.DataFrame( np.random.randn(1000, 2) / [50, 50] + [37.76, -122.4], columns=['lat', 'lon']) st.map(map\_data) ``` ## Widgets When you've got the data or model into the state that you want to explore, you can add in widgets like [`st.slider()`](/develop/api-reference/widgets/st.slider), [`st.button()`](/develop/api-reference/widgets/st.button) or [`st.selectbox()`](/develop/api-reference/widgets/st.selectbox). It's really straightforward — treat widgets as variables: ```python import streamlit as st x = st.slider('x') # 👈 this is a widget st.write(x, 'squared is', x \* x) ``` On first run, the app above should output the text "0 squared is 0". Then every time a user interacts with a widget, Streamlit simply reruns your script from top to bottom, assigning the current state of the widget to your variable in the process. For example, if the user moves the slider to position `10`, Streamlit will rerun the code above and set `x` to `10` accordingly. So now you should see the text "10 squared is 100". Widgets can also be accessed by key, if you choose to specify a string to use as the unique key for the widget: ```python import streamlit as st st.text\_input("Your name", key="name") # You can access the value at any point with: st.session\_state.name ``` Every widget with a key is automatically added to Session State. For more information about Session State, its association with widget state, and its limitations, see [Session State API Reference Guide](/develop/api-reference/caching-and-state/st.session\_state). ### Use checkboxes to show/hide data One use case for checkboxes is to hide or show a specific chart or section in an app. [`st.checkbox()`](/develop/api-reference/widgets/st.checkbox) takes a single argument, which is the widget label. In this sample, the checkbox is used to toggle a conditional statement. ```python import streamlit as st import numpy as np import pandas as pd if st.checkbox('Show dataframe'): chart\_data = pd.DataFrame( np.random.randn(20, 3), columns=['a', 'b', 'c']) chart\_data ``` ### Use a selectbox for options Use [`st.selectbox`](/develop/api-reference/widgets/st.selectbox) to choose from a series. You can write in the options you want, or pass through an array or data frame column. Let's use the `df` data frame we created earlier. ```python import streamlit as st import pandas as pd df = pd.DataFrame({ 'first column': [1, 2, 3, 4], 'second column': [10, 20, 30, 40] }) option = st.selectbox( 'Which number do you like best?',
| null |
./vendor/streamlit/content/get-started/fundamentals/main-concepts.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/fundamentals/main-concepts.md
|
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through an array or data frame column. Let's use the `df` data frame we created earlier. ```python import streamlit as st import pandas as pd df = pd.DataFrame({ 'first column': [1, 2, 3, 4], 'second column': [10, 20, 30, 40] }) option = st.selectbox( 'Which number do you like best?', df['first column']) 'You selected: ', option ``` ## Layout Streamlit makes it easy to organize your widgets in a left panel sidebar with [`st.sidebar`](/develop/api-reference/layout/st.sidebar). Each element that's passed to [`st.sidebar`](/develop/api-reference/layout/st.sidebar) is pinned to the left, allowing users to focus on the content in your app while still having access to UI controls. For example, if you want to add a selectbox and a slider to a sidebar, use `st.sidebar.slider` and `st.sidebar.selectbox` instead of `st.slider` and `st.selectbox`: ```python import streamlit as st # Add a selectbox to the sidebar: add\_selectbox = st.sidebar.selectbox( 'How would you like to be contacted?', ('Email', 'Home phone', 'Mobile phone') ) # Add a slider to the sidebar: add\_slider = st.sidebar.slider( 'Select a range of values', 0.0, 100.0, (25.0, 75.0) ) ``` Beyond the sidebar, Streamlit offers several other ways to control the layout of your app. [`st.columns`](/develop/api-reference/layout/st.columns) lets you place widgets side-by-side, and [`st.expander`](/develop/api-reference/layout/st.expander) lets you conserve space by hiding away large content. ```python import streamlit as st left\_column, right\_column = st.columns(2) # You can use a column just like st.sidebar: left\_column.button('Press me!') # Or even better, call Streamlit functions inside a "with" block: with right\_column: chosen = st.radio( 'Sorting hat', ("Gryffindor", "Ravenclaw", "Hufflepuff", "Slytherin")) st.write(f"You are in {chosen} house!") ``` `st.echo` and `st.spinner` are not currently supported inside the sidebar or layout options. Rest assured, though, we're currently working on adding support for those too! ### Show progress When adding long running computations to an app, you can use [`st.progress()`](/develop/api-reference/status/st.progress) to display status in real time. First, let's import time. We're going to use the `time.sleep()` method to simulate a long running computation: ```python import time ``` Now, let's create a progress bar: ```python import streamlit as st import time 'Starting a long computation...' # Add a placeholder latest\_iteration = st.empty() bar = st.progress(0) for i in range(100): # Update the progress bar with each iteration. latest\_iteration.text(f'Iteration {i+1}') bar.progress(i + 1) time.sleep(0.1) '...and now we\'re done!' ```
| null |
./vendor/streamlit/content/get-started/fundamentals/main-concepts.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/fundamentals/main-concepts.md
|
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# Additional Streamlit features So you've read all about Streamlit's [Basic concepts](/get-started/fundamentals/main-concepts) and gotten a taste of caching and Session State in [Advanced concepts](/get-started/fundamentals/advanced-concepts). But what about the bells and whistles? Here's a quick look at some extra features to take your app to the next level. ## Theming Streamlit supports Light and Dark themes out of the box. Streamlit will first check if the user viewing an app has a Light or Dark mode preference set by their operating system and browser. If so, then that preference will be used. Otherwise, the Light theme is applied by default. You can also change the active theme from "⋮" → "Settings".  Want to add your own theme to an app? The "Settings" menu has a theme editor accessible by clicking on "Edit active theme". You can use this editor to try out different colors and see your app update live.  When you're happy with your work, themes can be saved by [setting config options](/develop/concepts/configuration) in the `[theme]` config section. After you've defined a theme for your app, it will appear as "Custom Theme" in the theme selector and will be applied by default instead of the included Light and Dark themes. More information about the options available when defining a theme can be found in the [theme option documentation](/develop/concepts/configuration/theming). The theme editor menu is available only in local development. If you've deployed your app using Streamlit Community Cloud, the "Edit active theme" button will no longer be displayed in the "Settings" menu. Another way to experiment with different theme colors is to turn on the "Run on save" option, edit your config.toml file, and watch as your app reruns with the new theme colors applied. ## Pages As apps grow large, it becomes useful to organize them into multiple pages. This makes the app easier to manage as a developer and easier to navigate as a user. Streamlit provides a powerful way to create multipage apps using [`st.Page`](/develop/api-reference/navigation/st.page) and [`st.navigation`](/develop/api-reference/navigation/st.navigation). Just create your pages and connect them with navigation as follows: 1. Create an entry point script that defines and connects your pages 2. Create separate Python files for each page's content 3. Use [`st.Page`](/develop/api-reference/navigation/st.page) to define your pages and [`st.navigation`](/develop/api-reference/navigation/st.navigation) to connect them Here's an example of a three-page app: `streamlit_app.py` ```python import streamlit as st # Define the pages main\_page = st.Page("main\_page.py", title="Main Page", icon="🎈") page\_2 = st.Page("page\_2.py", title="Page 2", icon="❄️") page\_3 = st.Page("page\_3.py", title="Page 3", icon="🎉") # Set up navigation pg = st.navigation([main\_page, page\_2, page\_3]) # Run the selected page pg.run() ``` `main_page.py` ```python import streamlit as st # Main page content st.markdown("# Main page 🎈") st.sidebar.markdown("# Main page 🎈") ``` `page_2.py` ```python import streamlit as st st.markdown("# Page 2 ❄️") st.sidebar.markdown("# Page 2 ❄️") ``` `page_3.py` ```python import streamlit as st st.markdown("# Page 3 🎉") st.sidebar.markdown("# Page 3 🎉") ``` Now run `streamlit run streamlit\_app.py` and view your shiny new multipage app! The navigation menu will automatically appear, allowing users to switch between pages. Our documentation on [Multipage apps](/develop/concepts/multipage-apps) teaches you how to add pages to your app, including how to define pages, structure and run multipage apps, and navigate between pages. Once you understand the basics, [create your first multipage app](/get-started/tutorials/create-a-multipage-app)! ## Custom components If you can't find the right component within the Streamlit library, try out custom components to extend Streamlit's built-in functionality. Explore and browse through popular, community-created components in the [Components gallery](https://streamlit.io/components). If you dabble in frontend development, you can build your own custom component with Streamlit's [components API](/develop/concepts/custom-components/intro). ## Static file serving As you learned in Streamlit fundamentals,
| null |
./vendor/streamlit/content/get-started/fundamentals/additional-features.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/fundamentals/additional-features.md
|
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the Streamlit library, try out custom components to extend Streamlit's built-in functionality. Explore and browse through popular, community-created components in the [Components gallery](https://streamlit.io/components). If you dabble in frontend development, you can build your own custom component with Streamlit's [components API](/develop/concepts/custom-components/intro). ## Static file serving As you learned in Streamlit fundamentals, Streamlit runs a server that clients connect to. That means viewers of your app don't have direct access to the files which are local to your app. Most of the time, this doesn't matter because Streamlt commands handle that for you. When you use `st.image()` your Streamlit server will access the file and handle the necessary hosting so your app viewers can see it. However, if you want a direct URL to an image or file you'll need to host it. This requires setting the correct configuration and placing your hosted files in a directory named `static`. For example, your project could look like: ```bash your-project/ ├── static/ │ └── my\_hosted-image.png └── streamlit\_app.py ``` To learn more, read our guide on [Static file serving](/develop/concepts/configuration/serving-static-files). ## App testing Good development hygiene includes testing your code. Automated testing allows you to write higher quality code, faster! Streamlit has a built-in testing framework that let's you build tests easily. Use your favorite testing framework to run your tests. We like [`pytest`](https://pypi.org/project/pytest/). When you test a Streamlit app, you simulate running the app, declare user input, and inspect the results. You can use GitHub workflows to automate your tests and get instant alerts about breaking changes. Learn more in our guide to [App testing](/develop/concepts/app-testing).
| null |
./vendor/streamlit/content/get-started/fundamentals/additional-features.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/fundamentals/additional-features.md
|
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] | -0.023657
|
# Fundamental concepts Are you new to Streamlit and want the grand tour? If so, you're in the right place! Learn the fundamental concepts of Streamlit. How is a Streamlit app structured? How does it run? How does it magically get your data on a webpage? After you understand the rerun logic of Streamlit, learn how to make efficient and dynamic apps with caching and Session State. Get introduced to handling database connections. Learn about Streamlit's additional features. You don't need to know these concepts for your first app, but check it out to know what's available.
| null |
./vendor/streamlit/content/get-started/fundamentals/_index.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/fundamentals/_index.md
|
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|
# Advanced concepts of Streamlit Now that you know how a Streamlit app runs and handles data, let's talk about being efficient. Caching allows you to save the output of a function so you can skip over it on rerun. Session State lets you save information for each user that is preserved between reruns. This not only allows you to avoid unecessary recalculation, but also allows you to create dynamic pages and handle progressive processes. ## Caching Caching allows your app to stay performant even when loading data from the web, manipulating large datasets, or performing expensive computations. The basic idea behind caching is to store the results of expensive function calls and return the cached result when the same inputs occur again. This avoids repeated execution of a function with the same input values. To cache a function in Streamlit, you need to apply a caching decorator to it. You have two choices: - `st.cache\_data` is the recommended way to cache computations that return data. Use `st.cache\_data` when you use a function that returns a serializable data object (e.g. str, int, float, DataFrame, dict, list). \*\*It creates a new copy of the data at each function call\*\*, making it safe against [mutations and race conditions](/develop/concepts/architecture/caching#mutation-and-concurrency-issues). The behavior of `st.cache\_data` is what you want in most cases – so if you're unsure, start with `st.cache\_data` and see if it works! - `st.cache\_resource` is the recommended way to cache global resources like ML models or database connections. Use `st.cache\_resource` when your function returns unserializable objects that you don’t want to load multiple times. \*\*It returns the cached object itself\*\*, which is shared across all reruns and sessions without copying or duplication. If you mutate an object that is cached using `st.cache\_resource`, that mutation will exist across all reruns and sessions. Example: ```python @st.cache\_data def long\_running\_function(param1, param2): return … ``` In the above example, `long\_running\_function` is decorated with `@st.cache\_data`. As a result, Streamlit notes the following: - The name of the function (`"long\_running\_function"`). - The value of the inputs (`param1`, `param2`). - The code within the function. Before running the code within `long\_running\_function`, Streamlit checks its cache for a previously saved result. If it finds a cached result for the given function and input values, it will return that cached result and not rerun function's code. Otherwise, Streamlit executes the function, saves the result in its cache, and proceeds with the script run. During development, the cache updates automatically as the function code changes, ensuring that the latest changes are reflected in the cache. For more information about the Streamlit caching decorators, their configuration parameters, and their limitations, see [Caching](/develop/concepts/architecture/caching). ## Session State Session State provides a dictionary-like interface where you can save information that is preserved between script reruns. Use `st.session\_state` with key or attribute notation to store and recall values. For example, `st.session\_state["my\_key"]` or `st.session\_state.my\_key`. Remember that widgets handle their statefulness all by themselves, so you won't always need to use Session State! ### What is a session? A session is a single instance of viewing an app. If you view an app from two different tabs in your browser, each tab will have its own session. So each viewer of an app will have a Session State tied to their specific view. Streamlit maintains this session as the user interacts with the app. If the user refreshes their browser page or reloads the URL to the app, their Session State resets and they begin again with a new session. ### Examples of using Session State Here's a simple app that counts the number of times the page has been
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https://github.com/streamlit/docs/blob/main//content/get-started/fundamentals/advanced-concepts.md
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interacts with the app. If the user refreshes their browser page or reloads the URL to the app, their Session State resets and they begin again with a new session. ### Examples of using Session State Here's a simple app that counts the number of times the page has been run. Every time you click the button, the script will rerun. ```python import streamlit as st if "counter" not in st.session\_state: st.session\_state.counter = 0 st.session\_state.counter += 1 st.header(f"This page has run {st.session\_state.counter} times.") st.button("Run it again") ``` - \*\*First run:\*\* The first time the app runs for each user, Session State is empty. Therefore, a key-value pair is created (`"counter":0`). As the script continues, the counter is immediately incremented (`"counter":1`) and the result is displayed: "This page has run 1 times." When the page has fully rendered, the script has finished and the Streamlit server waits for the user to do something. When that user clicks the button, a rerun begins. - \*\*Second run:\*\* Since "counter" is already a key in Session State, it is not reinitialized. As the script continues, the counter is incremented (`"counter":2`) and the result is displayed: "This page has run 2 times." There are a few common scenarios where Session State is helpful. As demonstrated above, Session State is used when you have a progressive process that you want to build upon from one rerun to the next. Session State can also be used to prevent recalculation, similar to caching. However, the differences are important: - Caching associates stored values to specific functions and inputs. Cached values are accessible to all users across all sessions. - Session State associates stored values to keys (strings). Values in session state are only available in the single session where it was saved. If you have random number generation in your app, you'd likely use Session State. Here's an example where data is generated randomly at the beginning of each session. By saving this random information in Session State, each user gets different random data when they open the app but it won't keep changing on them as they interact with it. If you select different colors with the picker you'll see that the data does not get re-randomized with each rerun. (If you open the app in a new tab to start a new session, you'll see different data!) ```python import streamlit as st import pandas as pd import numpy as np if "df" not in st.session\_state: st.session\_state.df = pd.DataFrame(np.random.randn(20, 2), columns=["x", "y"]) st.header("Choose a datapoint color") color = st.color\_picker("Color", "#FF0000") st.divider() st.scatter\_chart(st.session\_state.df, x="x", y="y", color=color) ``` If you are pulling the same data for all users, you'd likely cache a function that retrieves that data. On the other hand, if you pull data specific to a user, such as querying their personal information, you may want to save that in Session State. That way, the queried data is only available in that one session. As mentioned in [Basic concepts](/get-started/fundamentals/main-concepts#widgets), Session State is also related to widgets. Widgets are magical and handle statefulness quietly on their own. As an advanced feature however, you can manipulate the value of widgets within your code by assigning keys to them. Any key assigned to a widget becomes a key in Session State tied to the value of the widget. This can be used to manipulate the widget. After you finish understanding the basics of Streamlit, check out our guide on [Widget behavior](/develop/concepts/architecture/widget-behavior) to dig in the details if you're interested. ## Connections As hinted above, you can use `@st.cache\_resource` to cache connections. This is the most general solution which allows you
| null |
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https://github.com/streamlit/docs/blob/main//content/get-started/fundamentals/advanced-concepts.md
|
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|
be used to manipulate the widget. After you finish understanding the basics of Streamlit, check out our guide on [Widget behavior](/develop/concepts/architecture/widget-behavior) to dig in the details if you're interested. ## Connections As hinted above, you can use `@st.cache\_resource` to cache connections. This is the most general solution which allows you to use almost any connection from any Python library. However, Streamlit also offers a convenient way to handle some of the most popular connections, like SQL! `st.connection` takes care of the caching for you so you can enjoy fewer lines of code. Getting data from your database can be as easy as: ```python import streamlit as st conn = st.connection("my\_database") df = conn.query("select \* from my\_table") st.dataframe(df) ``` Of course, you may be wondering where your username and password go. Streamlit has a convenient mechanism for [Secrets management](/develop/concepts/connections/secrets-management). For now, let's just see how `st.connection` works very nicely with secrets. In your local project directory, you can save a `.streamlit/secrets.toml` file. You save your secrets in the toml file and `st.connection` just uses them! For example, if you have an app file `streamlit\_app.py` your project directory may look like this: ```none hideHeader your-LOCAL-repository/ ├── .streamlit/ │ └── secrets.toml # Make sure to gitignore this! └── streamlit\_app.py ``` For the above SQL example, your `secrets.toml` file might look like the following: ```toml [connections.my\_database] type="sql" dialect="mysql" username="xxx" password="xxx" host="example.com" # IP or URL port=3306 # Port number database="mydb" # Database name ``` Since you don't want to commit your `secrets.toml` file to your repository, you'll need to learn how your host handles secrets when you're ready to publish your app. Each host platform may have a different way for you to pass your secrets. If you use Streamlit Community Cloud for example, each deployed app has a settings menu where you can load your secrets. After you've written an app and are ready to deploy, you can read all about how to [Deploy your app](/deploy/streamlit-community-cloud/deploy-your-app) on Community Cloud.
| null |
./vendor/streamlit/content/get-started/fundamentals/advanced-concepts.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/fundamentals/advanced-concepts.md
|
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# Create an app If you've made it this far, chances are you've [installed Streamlit](/get-started/installation) and run through the basics in [Basic concepts](/get-started/fundamentals/main-concepts) and [Advanced concepts](/get-started/fundamentals/advanced-concepts). If not, now is a good time to take a look. The easiest way to learn how to use Streamlit is to try things out yourself. As you read through this guide, test each method. As long as your app is running, every time you add a new element to your script and save, Streamlit's UI will ask if you'd like to rerun the app and view the changes. This allows you to work in a fast interactive loop: you write some code, save it, review the output, write some more, and so on, until you're happy with the results. The goal is to use Streamlit to create an interactive app for your data or model and along the way to use Streamlit to review, debug, perfect, and share your code. In this guide, you're going to use Streamlit's core features to create an interactive app; exploring a public Uber dataset for pickups and drop-offs in New York City. When you're finished, you'll know how to fetch and cache data, draw charts, plot information on a map, and use interactive widgets, like a slider, to filter results. If you'd like to skip ahead and see everything at once, the [complete script is available below](#lets-put-it-all-together). ## Create your first app Streamlit is more than just a way to make data apps, it’s also a community of creators that share their apps and ideas and help each other make their work better. Please come join us on the community forum. We love to hear your questions, ideas, and help you work through your bugs — stop by today! 1. The first step is to create a new Python script. Let's call it `uber\_pickups.py`. 2. Open `uber\_pickups.py` in your favorite IDE or text editor, then add these lines: ```python import streamlit as st import pandas as pd import numpy as np ``` 3. Every good app has a title, so let's add one: ```python st.title('Uber pickups in NYC') ``` 4. Now it's time to run Streamlit from the command line: ```bash streamlit run uber\_pickups.py ``` Running a Streamlit app is no different than any other Python script. Whenever you need to view the app, you can use this command. Did you know you can also pass a URL to `streamlit run`? This is great when combined with GitHub Gists. For example: ```bash streamlit run https://raw.githubusercontent.com/streamlit/demo-uber-nyc-pickups/master/streamlit\_app.py ``` 5. As usual, the app should automatically open in a new tab in your browser. ## Fetch some data Now that you have an app, the next thing you'll need to do is fetch the Uber dataset for pickups and drop-offs in New York City. 1. Let's start by writing a function to load the data. Add this code to your script: ```python DATE\_COLUMN = 'date/time' DATA\_URL = ('https://s3-us-west-2.amazonaws.com/' 'streamlit-demo-data/uber-raw-data-sep14.csv.gz') def load\_data(nrows): data = pd.read\_csv(DATA\_URL, nrows=nrows) lowercase = lambda x: str(x).lower() data.rename(lowercase, axis='columns', inplace=True) data[DATE\_COLUMN] = pd.to\_datetime(data[DATE\_COLUMN]) return data ``` You'll notice that `load\_data` is a plain old function that downloads some data, puts it in a Pandas dataframe, and converts the date column from text to datetime. The function accepts a single parameter (`nrows`), which specifies the number of rows that you want to load into the dataframe. 2. Now let's test the function and review the output. Below your function, add these lines: ```python # Create a text element and let the reader know the data is loading. data\_load\_state = st.text('Loading data...') # Load 10,000 rows of data into
| null |
./vendor/streamlit/content/get-started/fundamentals/tutorials/create-an-app.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/fundamentals/tutorials/create-an-app.md
|
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that you want to load into the dataframe. 2. Now let's test the function and review the output. Below your function, add these lines: ```python # Create a text element and let the reader know the data is loading. data\_load\_state = st.text('Loading data...') # Load 10,000 rows of data into the dataframe. data = load\_data(10000) # Notify the reader that the data was successfully loaded. data\_load\_state.text('Loading data...done!') ``` You'll see a few buttons in the upper-right corner of your app asking if you'd like to rerun the app. Choose \*\*Always rerun\*\*, and you'll see your changes automatically each time you save. Ok, that's underwhelming... It turns out that it takes a long time to download data, and load 10,000 lines into a dataframe. Converting the date column into datetime isn’t a quick job either. You don’t want to reload the data each time the app is updated – luckily Streamlit allows you to cache the data. ## Effortless caching 1. Try adding `@st.cache\_data` before the `load\_data` declaration: ```python @st.cache\_data def load\_data(nrows): ``` 2. Then save the script, and Streamlit will automatically rerun your app. Since this is the first time you’re running the script with `@st.cache\_data`, you won't see anything change. Let’s tweak your file a little bit more so that you can see the power of caching. 3. Replace the line `data\_load\_state.text('Loading data...done!')` with this: ```python data\_load\_state.text("Done! (using st.cache\_data)") ``` 4. Now save. See how the line you added appeared immediately? If you take a step back for a second, this is actually quite amazing. Something magical is happening behind the scenes, and it only takes one line of code to activate it. ### How's it work? Let's take a few minutes to discuss how `@st.cache\_data` actually works. When you mark a function with Streamlit’s cache annotation, it tells Streamlit that whenever the function is called that it should check two things: 1. The input parameters you used for the function call. 2. The code inside the function. If this is the first time Streamlit has seen both these items, with these exact values, and in this exact combination, it runs the function and stores the result in a local cache. The next time the function is called, if the two values haven't changed, then Streamlit knows it can skip executing the function altogether. Instead, it reads the output from the local cache and passes it on to the caller -- like magic. "But, wait a second," you’re saying to yourself, "this sounds too good to be true. What are the limitations of all this awesomesauce?" Well, there are a few: 1. Streamlit will only check for changes within the current working directory. If you upgrade a Python library, Streamlit's cache will only notice this if that library is installed inside your working directory. 2. If your function is not deterministic (that is, its output depends on random numbers), or if it pulls data from an external time-varying source (for example, a live stock market ticker service) the cached value will be none-the-wiser. 3. Lastly, you should avoid mutating the output of a function cached with `st.cache\_data` since cached values are stored by reference. While these limitations are important to keep in mind, they tend not to be an issue a surprising amount of the time. Those times, this cache is really transformational. Whenever you have a long-running computation in your code, consider refactoring it so you can use `@st.cache\_data`, if possible. Please read [Caching](/develop/concepts/architecture/caching) for more details. Now that you know how caching with Streamlit works, let’s get back to the Uber pickup data. ## Inspect the raw
| null |
./vendor/streamlit/content/get-started/fundamentals/tutorials/create-an-app.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/fundamentals/tutorials/create-an-app.md
|
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|
cache is really transformational. Whenever you have a long-running computation in your code, consider refactoring it so you can use `@st.cache\_data`, if possible. Please read [Caching](/develop/concepts/architecture/caching) for more details. Now that you know how caching with Streamlit works, let’s get back to the Uber pickup data. ## Inspect the raw data It's always a good idea to take a look at the raw data you're working with before you start working with it. Let's add a subheader and a printout of the raw data to the app: ```python st.subheader('Raw data') st.write(data) ``` In the [Basic concepts](/get-started/fundamentals/main-concepts) guide you learned that [`st.write`](/develop/api-reference/write-magic/st.write) will render almost anything you pass to it. In this case, you're passing in a dataframe and it's rendering as an interactive table. [`st.write`](/develop/api-reference/write-magic/st.write) tries to do the right thing based on the data type of the input. If it isn't doing what you expect you can use a specialized command like [`st.dataframe`](/develop/api-reference/data/st.dataframe) instead. For a full list, see [API reference](/develop/api-reference). ## Draw a histogram Now that you've had a chance to take a look at the dataset and observe what's available, let's take things a step further and draw a histogram to see what Uber's busiest hours are in New York City. 1. To start, let's add a subheader just below the raw data section: ```python st.subheader('Number of pickups by hour') ``` 2. Use NumPy to generate a histogram that breaks down pickup times binned by hour: ```python hist\_values = np.histogram( data[DATE\_COLUMN].dt.hour, bins=24, range=(0,24))[0] ``` 3. Now, let's use Streamlit's [`st.bar\_chart()`](/develop/api-reference/charts/st.bar\_chart) method to draw this histogram. ```python st.bar\_chart(hist\_values) ``` 4. Save your script. This histogram should show up in your app right away. After a quick review, it looks like the busiest time is 17:00 (5 P.M.). To draw this diagram we used Streamlit's native `bar\_chart()` method, but it's important to know that Streamlit supports more complex charting libraries like Altair, Bokeh, Plotly, Matplotlib and more. For a full list, see [supported charting libraries](/develop/api-reference/charts). ## Plot data on a map Using a histogram with Uber's dataset helped us determine what the busiest times are for pickups, but what if we wanted to figure out where pickups were concentrated throughout the city. While you could use a bar chart to show this data, it wouldn't be easy to interpret unless you were intimately familiar with latitudinal and longitudinal coordinates in the city. To show pickup concentration, let's use Streamlit [`st.map()`](/develop/api-reference/charts/st.map) function to overlay the data on a map of New York City. 1. Add a subheader for the section: ```python st.subheader('Map of all pickups') ``` 2. Use the `st.map()` function to plot the data: ```python st.map(data) ``` 3. Save your script. The map is fully interactive. Give it a try by panning or zooming in a bit. After drawing your histogram, you determined that the busiest hour for Uber pickups was 17:00. Let's redraw the map to show the concentration of pickups at 17:00. 1. Locate the following code snippet: ```python st.subheader('Map of all pickups') st.map(data) ``` 2. Replace it with: ```python hour\_to\_filter = 17 filtered\_data = data[data[DATE\_COLUMN].dt.hour == hour\_to\_filter] st.subheader(f'Map of all pickups at {hour\_to\_filter}:00') st.map(filtered\_data) ``` 3. You should see the data update instantly. To draw this map we used the [`st.map`](/develop/api-reference/charts/st.map) function that's built into Streamlit, but if you'd like to visualize complex map data, we encourage you to take a look at the [`st.pydeck\_chart`](/develop/api-reference/charts/st.pydeck\_chart). ## Filter results with a slider In the last section, when you drew the map, the time used to filter results was hardcoded into the script, but what if we wanted to let a reader dynamically filter the data in real
| null |
./vendor/streamlit/content/get-started/fundamentals/tutorials/create-an-app.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/fundamentals/tutorials/create-an-app.md
|
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|
encourage you to take a look at the [`st.pydeck\_chart`](/develop/api-reference/charts/st.pydeck\_chart). ## Filter results with a slider In the last section, when you drew the map, the time used to filter results was hardcoded into the script, but what if we wanted to let a reader dynamically filter the data in real time? Using Streamlit's widgets you can. Let's add a slider to the app with the `st.slider()` method. 1. Locate `hour\_to\_filter` and replace it with this code snippet: ```python hour\_to\_filter = st.slider('hour', 0, 23, 17) # min: 0h, max: 23h, default: 17h ``` 2. Use the slider and watch the map update in real time. ## Use a button to toggle data Sliders are just one way to dynamically change the composition of your app. Let's use the [`st.checkbox`](/develop/api-reference/widgets/st.checkbox) function to add a checkbox to your app. We'll use this checkbox to show/hide the raw data table at the top of your app. 1. Locate these lines: ```python st.subheader('Raw data') st.write(data) ``` 2. Replace these lines with the following code: ```python if st.checkbox('Show raw data'): st.subheader('Raw data') st.write(data) ``` We're sure you've got your own ideas. When you're done with this tutorial, check out all the widgets that Streamlit exposes in our [API Reference](/develop/api-reference). ## Let's put it all together That's it, you've made it to the end. Here's the complete script for our interactive app. If you've skipped ahead, after you've created your script, the command to run Streamlit is `streamlit run [app name]`. ```python import streamlit as st import pandas as pd import numpy as np st.title('Uber pickups in NYC') DATE\_COLUMN = 'date/time' DATA\_URL = ('https://s3-us-west-2.amazonaws.com/' 'streamlit-demo-data/uber-raw-data-sep14.csv.gz') @st.cache\_data def load\_data(nrows): data = pd.read\_csv(DATA\_URL, nrows=nrows) lowercase = lambda x: str(x).lower() data.rename(lowercase, axis='columns', inplace=True) data[DATE\_COLUMN] = pd.to\_datetime(data[DATE\_COLUMN]) return data data\_load\_state = st.text('Loading data...') data = load\_data(10000) data\_load\_state.text("Done! (using st.cache\_data)") if st.checkbox('Show raw data'): st.subheader('Raw data') st.write(data) st.subheader('Number of pickups by hour') hist\_values = np.histogram(data[DATE\_COLUMN].dt.hour, bins=24, range=(0,24))[0] st.bar\_chart(hist\_values) # Some number in the range 0-23 hour\_to\_filter = st.slider('hour', 0, 23, 17) filtered\_data = data[data[DATE\_COLUMN].dt.hour == hour\_to\_filter] st.subheader('Map of all pickups at %s:00' % hour\_to\_filter) st.map(filtered\_data) ``` ## Share your app After you’ve built a Streamlit app, it's time to share it! To show it off to the world you can use \*\*Streamlit Community Cloud\*\* to deploy, manage, and share your app for free. It works in 3 simple steps: 1. Put your app in a public GitHub repo (and make sure it has a requirements.txt!) 2. Sign into [share.streamlit.io](https://share.streamlit.io) 3. Click 'Deploy an app' and then paste in your GitHub URL That's it! 🎈 You now have a publicly deployed app that you can share with the world. Click to learn more about [how to use Streamlit Community Cloud](/deploy/streamlit-community-cloud). ## Get help That's it for getting started, now you can go and build your own apps! If you run into difficulties here are a few things you can do. - Check out our [community forum](https://discuss.streamlit.io/) and post a question - Quick help from command line with `streamlit help` - Go through our [Knowledge Base](/knowledge-base) for tips, step-by-step tutorials, and articles that answer your questions about creating and deploying Streamlit apps. - Read more documentation! Check out: - [Concepts](/develop/concepts) for things like caching, theming, and adding statefulness to apps. - [API reference](/develop/api-reference/) for examples of every Streamlit command.
| null |
./vendor/streamlit/content/get-started/fundamentals/tutorials/create-an-app.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/fundamentals/tutorials/create-an-app.md
|
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# Create a multipage app In [Additional features](/get-started/fundamentals/additional-features), we introduced multipage apps, including how to define pages, structure and run multipage apps, and navigate between pages in the user interface. You can read more details in our guide to [Multipage apps](/develop/concepts/multipage-apps) In this guide, let’s put our understanding of multipage apps to use by converting the previous version of our `streamlit hello` app to a multipage app! ## Motivation Before Streamlit 1.10.0, the streamlit hello command was a large single-page app. As there was no support for multiple pages, we resorted to splitting the app's content using `st.selectbox` in the sidebar to choose what content to run. The content is comprised of three demos for plotting, mapping, and dataframes. Here's what the code and single-page app looked like: **`hello.py`** (👈 Toggle to expand) ```python import streamlit as st def intro(): import streamlit as st st.write("# Welcome to Streamlit! 👋") st.sidebar.success("Select a demo above.") st.markdown( """ Streamlit is an open-source app framework built specifically for Machine Learning and Data Science projects. \*\*👈 Select a demo from the dropdown on the left\*\* to see some examples of what Streamlit can do! ### Want to learn more? - Check out [streamlit.io](https://streamlit.io) - Jump into our [documentation](https://docs.streamlit.io) - Ask a question in our [community forums](https://discuss.streamlit.io) ### See more complex demos - Use a neural net to [analyze the Udacity Self-driving Car Image Dataset](https://github.com/streamlit/demo-self-driving) - Explore a [New York City rideshare dataset](https://github.com/streamlit/demo-uber-nyc-pickups) """ ) def mapping\_demo(): import streamlit as st import pandas as pd import pydeck as pdk from urllib.error import URLError st.markdown(f"# {list(page\_names\_to\_funcs.keys())[2]}") st.write( """ This demo shows how to use [`st.pydeck\_chart`](https://docs.streamlit.io/develop/api-reference/charts/st.pydeck\_chart) to display geospatial data. """ ) @st.cache\_data def from\_data\_file(filename): url = ( "http://raw.githubusercontent.com/streamlit/" "example-data/master/hello/v1/%s" % filename ) return pd.read\_json(url) try: ALL\_LAYERS = { "Bike Rentals": pdk.Layer( "HexagonLayer", data=from\_data\_file("bike\_rental\_stats.json"), get\_position=["lon", "lat"], radius=200, elevation\_scale=4, elevation\_range=[0, 1000], extruded=True, ), "Bart Stop Exits": pdk.Layer( "ScatterplotLayer", data=from\_data\_file("bart\_stop\_stats.json"), get\_position=["lon", "lat"], get\_color=[200, 30, 0, 160], get\_radius="[exits]", radius\_scale=0.05, ), "Bart Stop Names": pdk.Layer( "TextLayer", data=from\_data\_file("bart\_stop\_stats.json"), get\_position=["lon", "lat"], get\_text="name", get\_color=[0, 0, 0, 200], get\_size=15, get\_alignment\_baseline="'bottom'", ), "Outbound Flow": pdk.Layer( "ArcLayer", data=from\_data\_file("bart\_path\_stats.json"), get\_source\_position=["lon", "lat"], get\_target\_position=["lon2", "lat2"], get\_source\_color=[200, 30, 0, 160], get\_target\_color=[200, 30, 0, 160], auto\_highlight=True, width\_scale=0.0001, get\_width="outbound", width\_min\_pixels=3, width\_max\_pixels=30, ), } st.sidebar.markdown("### Map Layers") selected\_layers = [ layer for layer\_name, layer in ALL\_LAYERS.items() if st.sidebar.checkbox(layer\_name, True) ] if selected\_layers: st.pydeck\_chart( pdk.Deck( map\_style="mapbox://styles/mapbox/light-v9", initial\_view\_state={ "latitude": 37.76, "longitude": -122.4, "zoom": 11, "pitch": 50, }, layers=selected\_layers, ) ) else: st.error("Please choose at least one layer above.") except URLError as e: st.error( """ \*\*This demo requires internet access.\*\* Connection error: %s """ % e.reason ) def plotting\_demo(): import streamlit as st import time import numpy as np st.markdown(f'# {list(page\_names\_to\_funcs.keys())[1]}') st.write( """ This demo illustrates a combination of plotting and animation with Streamlit. We're generating a bunch of random numbers in a loop for around 5 seconds. Enjoy! """ ) progress\_bar = st.sidebar.progress(0) status\_text = st.sidebar.empty() last\_rows = np.random.randn(1, 1) chart = st.line\_chart(last\_rows) for i in range(1, 101): new\_rows = last\_rows[-1, :] + np.random.randn(5, 1).cumsum(axis=0) status\_text.text("%i%% Complete" % i) chart.add\_rows(new\_rows) progress\_bar.progress(i) last\_rows = new\_rows time.sleep(0.05) progress\_bar.empty() # Streamlit widgets automatically run the script from top to bottom. Since # this button is not connected to any other logic, it just causes a plain # rerun. st.button("Re-run") def data\_frame\_demo(): import streamlit as st import pandas as pd import altair as alt from urllib.error import URLError st.markdown(f"# {list(page\_names\_to\_funcs.keys())[3]}") st.write( """ This demo shows how to use `st.write` to visualize Pandas DataFrames. (Data courtesy of the [UN Data Explorer](http://data.un.org/Explorer.aspx).) """ ) @st.cache\_data def get\_UN\_data(): AWS\_BUCKET\_URL = "http://streamlit-demo-data.s3-us-west-2.amazonaws.com" df = pd.read\_csv(AWS\_BUCKET\_URL + "/agri.csv.gz") return df.set\_index("Region") try: df = get\_UN\_data() countries = st.multiselect( "Choose countries", list(df.index), ["China", "United States of America"] )
| null |
./vendor/streamlit/content/get-started/fundamentals/tutorials/create-a-multi-page-app.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/fundamentals/tutorials/create-a-multi-page-app.md
|
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""" This demo shows how to use `st.write` to visualize Pandas DataFrames. (Data courtesy of the [UN Data Explorer](http://data.un.org/Explorer.aspx).) """ ) @st.cache\_data def get\_UN\_data(): AWS\_BUCKET\_URL = "http://streamlit-demo-data.s3-us-west-2.amazonaws.com" df = pd.read\_csv(AWS\_BUCKET\_URL + "/agri.csv.gz") return df.set\_index("Region") try: df = get\_UN\_data() countries = st.multiselect( "Choose countries", list(df.index), ["China", "United States of America"] ) if not countries: st.error("Please select at least one country.") else: data = df.loc[countries] data /= 1000000.0 st.write("### Gross Agricultural Production ($B)", data.sort\_index()) data = data.T.reset\_index() data = pd.melt(data, id\_vars=["index"]).rename( columns={"index": "year", "value": "Gross Agricultural Product ($B)"} ) chart = ( alt.Chart(data) .mark\_area(opacity=0.3) .encode( x="year:T", y=alt.Y("Gross Agricultural Product ($B):Q", stack=None), color="Region:N", ) ) st.altair\_chart(chart, use\_container\_width=True) except URLError as e: st.error( """ \*\*This demo requires internet access.\*\* Connection error: %s """ % e.reason ) page\_names\_to\_funcs = { "—": intro, "Plotting Demo": plotting\_demo, "Mapping Demo": mapping\_demo, "DataFrame Demo": data\_frame\_demo } demo\_name = st.sidebar.selectbox("Choose a demo", page\_names\_to\_funcs.keys()) page\_names\_to\_funcs[demo\_name]() ``` Notice how large the file is! Each app “page" is written as a function, and the selectbox is used to pick which page to display. As our app grows, maintaining the code requires a lot of additional overhead. Moreover, we’re limited by the `st.selectbox` UI to choose which “page" to run, we cannot customize individual page titles with `st.set\_page\_config`, and we’re unable to navigate between pages using URLs. ## Convert an existing app into a multipage app Now that we've identified the limitations of a single-page app, what can we do about it? Armed with our knowledge from the previous section, we can convert the existing app to be a multipage app, of course! At a high level, we need to perform the following steps: 1. Create a new `pages` folder in the same folder where the “entrypoint file" (`hello.py`) lives 2. Rename our entrypoint file to `Hello.py` , so that the title in the sidebar is capitalized 3. Create three new files inside of `pages`: - `pages/1\_📈\_Plotting\_Demo.py` - `pages/2\_🌍\_Mapping\_Demo.py` - `pages/3\_📊\_DataFrame\_Demo.py` 4. Move the contents of the `plotting\_demo`, `mapping\_demo`, and `data\_frame\_demo` functions into their corresponding new files from Step 3 5. Run `streamlit run Hello.py` to view your newly converted multipage app! Now, let’s walk through each step of the process and view the corresponding changes in code. ## Create the entrypoint file `Hello.py` ```python import streamlit as st st.set\_page\_config( page\_title="Hello", page\_icon="👋", ) st.write("# Welcome to Streamlit! 👋") st.sidebar.success("Select a demo above.") st.markdown( """ Streamlit is an open-source app framework built specifically for Machine Learning and Data Science projects. \*\*👈 Select a demo from the sidebar\*\* to see some examples of what Streamlit can do! ### Want to learn more? - Check out [streamlit.io](https://streamlit.io) - Jump into our [documentation](https://docs.streamlit.io) - Ask a question in our [community forums](https://discuss.streamlit.io) ### See more complex demos - Use a neural net to [analyze the Udacity Self-driving Car Image Dataset](https://github.com/streamlit/demo-self-driving) - Explore a [New York City rideshare dataset](https://github.com/streamlit/demo-uber-nyc-pickups) """ ) ``` We rename our entrypoint file to `Hello.py` , so that the title in the sidebar is capitalized and only the code for the intro page is included. Additionally, we’re able to customize the page title and favicon — as it appears in the browser tab with `st.set\_page\_config`. We can do so for each of our pages too! Notice how the sidebar does not contain page labels as we haven’t created any pages yet. ## Create multiple pages A few things to remember here: 1. We can change the ordering of pages in our MPA by adding numbers to the beginning of each Python file. If we add a 1 to the front of our file name, Streamlit will put that file first in the list. 2. The name
| null |
./vendor/streamlit/content/get-started/fundamentals/tutorials/create-a-multi-page-app.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/fundamentals/tutorials/create-a-multi-page-app.md
|
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few things to remember here: 1. We can change the ordering of pages in our MPA by adding numbers to the beginning of each Python file. If we add a 1 to the front of our file name, Streamlit will put that file first in the list. 2. The name of each Streamlit app is determined by the file name, so to change the app name you need to change the file name! 3. We can add some fun to our app by adding emojis to our file names that will render in our Streamlit app. 4. Each page will have its own URL, defined by the name of the file. Check out how we do all this below! For each new page, we create a new file inside the pages folder, and add the appropriate demo code into it. `pages/1_📈_Plotting_Demo.py` ```python import streamlit as st import time import numpy as np st.set\_page\_config(page\_title="Plotting Demo", page\_icon="📈") st.markdown("# Plotting Demo") st.sidebar.header("Plotting Demo") st.write( """This demo illustrates a combination of plotting and animation with Streamlit. We're generating a bunch of random numbers in a loop for around 5 seconds. Enjoy!""" ) progress\_bar = st.sidebar.progress(0) status\_text = st.sidebar.empty() last\_rows = np.random.randn(1, 1) chart = st.line\_chart(last\_rows) for i in range(1, 101): new\_rows = last\_rows[-1, :] + np.random.randn(5, 1).cumsum(axis=0) status\_text.text("%i%% Complete" % i) chart.add\_rows(new\_rows) progress\_bar.progress(i) last\_rows = new\_rows time.sleep(0.05) progress\_bar.empty() # Streamlit widgets automatically run the script from top to bottom. Since # this button is not connected to any other logic, it just causes a plain # rerun. st.button("Re-run") ``` `pages/2_🌍_Mapping_Demo.py` ```python import streamlit as st import pandas as pd import pydeck as pdk from urllib.error import URLError st.set\_page\_config(page\_title="Mapping Demo", page\_icon="🌍") st.markdown("# Mapping Demo") st.sidebar.header("Mapping Demo") st.write( """This demo shows how to use [`st.pydeck\_chart`](https://docs.streamlit.io/develop/api-reference/charts/st.pydeck\_chart) to display geospatial data.""" ) @st.cache\_data def from\_data\_file(filename): url = ( "http://raw.githubusercontent.com/streamlit/" "example-data/master/hello/v1/%s" % filename ) return pd.read\_json(url) try: ALL\_LAYERS = { "Bike Rentals": pdk.Layer( "HexagonLayer", data=from\_data\_file("bike\_rental\_stats.json"), get\_position=["lon", "lat"], radius=200, elevation\_scale=4, elevation\_range=[0, 1000], extruded=True, ), "Bart Stop Exits": pdk.Layer( "ScatterplotLayer", data=from\_data\_file("bart\_stop\_stats.json"), get\_position=["lon", "lat"], get\_color=[200, 30, 0, 160], get\_radius="[exits]", radius\_scale=0.05, ), "Bart Stop Names": pdk.Layer( "TextLayer", data=from\_data\_file("bart\_stop\_stats.json"), get\_position=["lon", "lat"], get\_text="name", get\_color=[0, 0, 0, 200], get\_size=15, get\_alignment\_baseline="'bottom'", ), "Outbound Flow": pdk.Layer( "ArcLayer", data=from\_data\_file("bart\_path\_stats.json"), get\_source\_position=["lon", "lat"], get\_target\_position=["lon2", "lat2"], get\_source\_color=[200, 30, 0, 160], get\_target\_color=[200, 30, 0, 160], auto\_highlight=True, width\_scale=0.0001, get\_width="outbound", width\_min\_pixels=3, width\_max\_pixels=30, ), } st.sidebar.markdown("### Map Layers") selected\_layers = [ layer for layer\_name, layer in ALL\_LAYERS.items() if st.sidebar.checkbox(layer\_name, True) ] if selected\_layers: st.pydeck\_chart( pdk.Deck( map\_style="mapbox://styles/mapbox/light-v9", initial\_view\_state={ "latitude": 37.76, "longitude": -122.4, "zoom": 11, "pitch": 50, }, layers=selected\_layers, ) ) else: st.error("Please choose at least one layer above.") except URLError as e: st.error( """ \*\*This demo requires internet access.\*\* Connection error: %s """ % e.reason ) ``` `pages/3_📊_DataFrame_Demo.py` ```python import streamlit as st import pandas as pd import altair as alt from urllib.error import URLError st.set\_page\_config(page\_title="DataFrame Demo", page\_icon="📊") st.markdown("# DataFrame Demo") st.sidebar.header("DataFrame Demo") st.write( """This demo shows how to use `st.write` to visualize Pandas DataFrames. (Data courtesy of the [UN Data Explorer](http://data.un.org/Explorer.aspx).)""" ) @st.cache\_data def get\_UN\_data(): AWS\_BUCKET\_URL = "http://streamlit-demo-data.s3-us-west-2.amazonaws.com" df = pd.read\_csv(AWS\_BUCKET\_URL + "/agri.csv.gz") return df.set\_index("Region") try: df = get\_UN\_data() countries = st.multiselect( "Choose countries", list(df.index), ["China", "United States of America"] ) if not countries: st.error("Please select at least one country.") else: data = df.loc[countries] data /= 1000000.0 st.write("### Gross Agricultural Production ($B)", data.sort\_index()) data = data.T.reset\_index() data = pd.melt(data, id\_vars=["index"]).rename( columns={"index": "year", "value": "Gross Agricultural Product ($B)"} ) chart = ( alt.Chart(data) .mark\_area(opacity=0.3) .encode( x="year:T", y=alt.Y("Gross Agricultural Product ($B):Q", stack=None), color="Region:N", ) ) st.altair\_chart(chart, use\_container\_width=True) except URLError as e: st.error( """ \*\*This demo requires internet access.\*\* Connection error: %s """ % e.reason ) ``` With our additional pages created, we can now put it all together
| null |
./vendor/streamlit/content/get-started/fundamentals/tutorials/create-a-multi-page-app.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/fundamentals/tutorials/create-a-multi-page-app.md
|
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|
($B)"} ) chart = ( alt.Chart(data) .mark\_area(opacity=0.3) .encode( x="year:T", y=alt.Y("Gross Agricultural Product ($B):Q", stack=None), color="Region:N", ) ) st.altair\_chart(chart, use\_container\_width=True) except URLError as e: st.error( """ \*\*This demo requires internet access.\*\* Connection error: %s """ % e.reason ) ``` With our additional pages created, we can now put it all together in the final step below. ## Run the multipage app To run your newly converted multipage app, run: ```bash streamlit run Hello.py ``` That’s it! The `Hello.py` script now corresponds to the main page of your app, and other scripts that Streamlit finds in the pages folder will also be present in the new page selector that appears in the sidebar. ## Next steps Congratulations! 🎉 If you've read this far, chances are you've learned to create both single-page and multipage apps. Where you go from here is entirely up to your creativity! We’re excited to see what you’ll build now that adding additional pages to your apps is easier than ever. Try adding more pages to the app we've just built as an exercise. Also, stop by the forum to show off your multipage apps with the Streamlit community! 🎈 Here are a few resources to help you get started: - Deploy your app for free on Streamlit's [Community Cloud](/deploy/streamlit-community-cloud). - Post a question or share your multipage app on our [community forum](https://discuss.streamlit.io/c/streamlit-examples/9). - Check out our documentation on [Multipage apps](/develop/concepts/multipage-apps). - Read through [Concepts](/develop/concepts) for things like caching, theming, and adding statefulness to apps. - Browse our [API reference](/develop/api-reference/) for examples of every Streamlit command.
| null |
./vendor/streamlit/content/get-started/fundamentals/tutorials/create-a-multi-page-app.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/fundamentals/tutorials/create-a-multi-page-app.md
|
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|
# Install Streamlit using Anaconda Distribution This page walks you through installing Streamlit locally using Anaconda Distribution. At the end, you'll build a simple "Hello world" app and run it. You can read more about [Getting started with Anaconda Distribution](https://docs.anaconda.com/free/anaconda/getting-started/) in Anaconda's docs. If you prefer to manage your Python environments via command line, check out how to [Install Streamlit using command line](/get-started/installation/command-line). ## Prerequisites 1. \*\*A code editor\*\* Anaconda Distribution includes Python and basically everything you need to get started. The only thing left for you to choose is a code editor. Our favorite editor is [VS Code](https://code.visualstudio.com/download), which is also what we use in all our tutorials. 1. \*\*Knowledge about environment managers\*\* Environment managers create virtual environments to isolate Python package installations between projects. For a detailed introduction to Python environments, check out [Python Virtual Environments: A Primer](https://realpython.com/python-virtual-environments-a-primer/). But don't worry! In this guide we'll teach you how to install and use an environment manager (Anaconda). ## Install Anaconda Distribution 1. Go to [anaconda.com/download](https://www.anaconda.com/download). 2. Install Anaconda Distribution for your OS. ## Create an environment using Anaconda Navigator 3. Open Anaconda Navigator (the graphical interface included with Anaconda Distribution). 4. You can decline signing in to Anaconda if prompted. 5. In the left menu, click "\*\*Environments\*\*."  6. At the bottom of your environments list, click "\*\*Create\*\*."  7. Enter "streamlitenv" for the name of your environment. 8. Click "\*\*Create\*\*."## Activate your environment 9. Click the green play icon (*play\_circle*) next to your environment. 10. Click "\*\*Open Terminal\*\*."  11. A terminal will open with your environment activated. Your environment's name will appear in parentheses at the beginning of your terminal's prompt to show that it's activated. ## Install Streamlit in your environment 12. In your terminal, type: ```bash pip install streamlit ``` 13. To validate your installation, enter: ```bash streamlit hello ``` If this doesn't work, use the long-form command: ```bash python -m streamlit hello ``` 14. The Streamlit Hello example app will automatically open in your browser. If it doesn't, open your browser and go to the localhost address indicated in your terminal, typically `http://localhost:8501`. Play around with the app! 15. Close your terminal. ## Create a `Hello World` app and run it 17. Open VS Code with a new project. 18. Create a Python file named `app.py` in your project folder.  19. Copy the following code into `app.py` and save it. ```python import streamlit as st st.write("Hello World") ``` 20. Click your Python interpreter in the lower-right corner, then choose your `streamlitenv` environment from the drop-down.  21. Right-click `app.py` in your file navigation and click "\*\*Open in integrated terminal\*\*."  22. A terminal will open with your environment activated. Confirm this by looking for "(streamlitenv)" at the beginning of your next prompt. If it is not there, manually activate your environment with the command: ```bash conda activate streamlitenv ``` 23. In your terminal, type: ```bash streamlit run app.py ``` If this doesn't work, use the long-form command: ```bash python -m streamlit run app.py ```  24. Your app will automatically open in your browser. If it doesn't for any reason, open your browser and go to the localhost address indicated in your terminal, typically `http://localhost:8501`. 25. Change `st.write` to `st.title` and save your file: ```python import streamlit as st st.title("Hello World") ``` 26. In your browser, click
| null |
./vendor/streamlit/content/get-started/installation/anaconda-distribution.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/installation/anaconda-distribution.md
|
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] | 0.110845
|
app will automatically open in your browser. If it doesn't for any reason, open your browser and go to the localhost address indicated in your terminal, typically `http://localhost:8501`. 25. Change `st.write` to `st.title` and save your file: ```python import streamlit as st st.title("Hello World") ``` 26. In your browser, click "\*\*Always rerun\*\*" to instantly rerun your app whenever you save a change to your file.  27. Your app will update! Keep making changes and you will see your changes as soon as you save your file.  28. When you're done, you can stop your app with `Ctrl+C` in your terminal or just by closing your terminal. ## What's next? Read about our [Basic concepts](/get-started/fundamentals/main-concepts) and try out more commands in your app.
| null |
./vendor/streamlit/content/get-started/installation/anaconda-distribution.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/installation/anaconda-distribution.md
|
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] | 0.024042
|
# Use Streamlit in Snowflake to code in a secure environment Snowflake is a single, global platform that powers the Data Cloud. If you want to use a secure platform with role-based access control, this is the option for you! This page walks you through creating a trial Snowflake account and building a "Hello world" app. Your trial account comes with an account credit so you can try out the service without entering any payment information. For more information, see [Limitations and unsupported features](https://docs.snowflake.com/en/developer-guide/streamlit/limitations) in the Snowflake documentation. ## Prerequisites All you need is an email address! Everything else happens in your 30-day trial account. ## Create an account 1. Go to . (This link will open in a new tab.) 1. Fill in your information, and click "\*\*CONTINUE\*\*." 1. Select "\*\*Standard\*\*" for your Snowflake edition and "\*\*Amazon Web Services\*\*" for your cloud provider. 1. Choose the region nearest you, accept the terms, and click "\*\*GET STARTED\*\*."1. Answer a few questions to let us know more about yourself, or skip them. 1. A message will display: "You're now signed up!" Go to your email, and click on the activation link. (Within your link, note the subdomain. This is your Snowflake account identifier. `https://.snowflakecomputing.com`) 1. Set your username and password. This will be an admin user account within your Snowflake account. Your Snowflake account can have multiple users within it. 1. If you are not signed in after setting your password, follow the instructions to enter your Snowflake account identifier, and then enter your username and password. If you've accidentally closed your browser, you can sign in at [app.snowflake.com](https://app.snowflake.com/). Congratulations! You have a trial Snowflake account. The displayed interface is called Snowsight. Snowsight provides a web-based, graphical user interface for your Snowflake account. The default page is "\*\*Home\*\*," which provides popular quick actions to get started. You can access your "\*\*Projects\*\*" in the left navigation or at the bottom of your "\*\*Home\*\*" page. "\*\*Projects\*\*" include worksheets, notebooks, Streamlit apps, and dashboards. Check out the Snowflake docs for a [quick tour](https://docs.snowflake.com/en/user-guide/ui-snowsight-quick-tour).)  ## Optional: Create a warehouse Warehouses provide compute resources for tasks and apps in your Snowflake account. Your trial account already has an XS warehouse which you can use. This is named "COMPUTE\_WH." However, if you want to use more compute resources, you can create another warehouse. 1. In the lower-left corner under your name, confirm that your current role is "ACCOUNTADMIN." If it isn't, click your name, hover over "\*\*Switch Role\*\*," and select "\*\*ACCOUNTADMIN\*\*." 1. In the left navigation, select "\*\*Admin\*\*" → "\*\*Warehouses\*\*." 1. In the upper-right corner, click the blue "*add* \*\*Warehouse\*\*" button. 1. Enter "STREAMLIT\_WH" for the name and select a type and size. The default type and size are "Standard" and "X-Small," respectively. Click "\*\*Create Warehouse\*\*." ## Create a database Databases provide storage for data and apps in your Snowflake account. Your trial account comes with a shared database of sample data, but you must create a new database in your account to store your app files. 1. In the left navigation, select "\*\*Data\*\*" → "\*\*Databases\*\*." 1. In the upper-right corner, click the blue "*add* \*\*Database\*\*" button. 1. Enter "STREAMLIT\_DB" for the name, and click "\*\*Create\*\*." Yay! You now have a new database to hold all your Streamlit apps.  The databases in the above screenshot are as follows: - "SNOWFLAKE:" A built-in database that stores meta data for your account. - "SNOWFLAKE\_SAMPLE\_DATA:" A shared database with sample data. - "STREAMLIT\_DB:" Your new database where you'll put your Streamlit files. ## Create
| null |
./vendor/streamlit/content/get-started/installation/sis.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/installation/sis.md
|
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|
 The databases in the above screenshot are as follows: - "SNOWFLAKE:" A built-in database that stores meta data for your account. - "SNOWFLAKE\_SAMPLE\_DATA:" A shared database with sample data. - "STREAMLIT\_DB:" Your new database where you'll put your Streamlit files. ## Create a "Hello World" Streamlit app 1. In the left navigation, select "\*\*Projects\*\*" → "\*\*Streamlit\*\*." 1. In the upper-right corner, click the blue "*add* \*\*Streamlit App\*\*" button. 1. Enter your app details as follows: - App title: "Hello World" - App location: "STREAMLIT\_DB" and "PUBLIC" - App warehouse: "COMPUTE\_WH" (default) or "STREAMLIT\_WH" (if you created a new warehouse) If you can't select your database, refresh the page to ensure that the interface is displaying current information. 1. Click "\*\*Create\*\*." (Note that the app will run with the rights of "ACCOUNTADMIN" for simplicity. You can curate your roles and permissions to choose who can create and access apps.)  Your new app is prefilled with example code and opens in editing mode. The left panel shows your code. The right panel shows the resulting app. 1. Optional: Explore the example app. 1. In the left code editor, delete everything, and replace it with the following: ```python import streamlit as st st.write("Hello World") ``` If you want to return to the sample code later, you can always make another app to edit the same example again. 1. In the upper-right corner, click the blue "*play\_arrow* \*\*Run\*\*" button to make the running app reflect your changes. Hooray! You just wrote a Streamlit app.  In the lower-left corner of the editing interface, you can click the splitscreen icons to toggle the visibility of your file navigation, code editor, and app preview. 1. Change `st.write` to `st.title`: ```python import streamlit as st st.title("Hello World") ``` 1. In the upper-right corner, click the blue "*play\_arrow* \*\*Run\*\*" button to make the running app reflect your changes. Your app now displays the larger title text. 1. Optional: Make more edits to your app. You must interact with your app or click the blue "*play\_arrow* \*\*Run\*\*" button to display the changes. This is different from a local environment where you can save your changes to trigger a rerun. 1. To return to Snowsight, in the upper-left corner, click "*chevron\_left* \*\*Streamlit Apps\*\*." ## Return to your app When you want to view or edit your app again, you can return to it through Snowsight. If you are returning to the site, you can sign in at [app.snowflake.com](https://app.snowflake.com/). 1. In the left navigation, select "\*\*Projects\*\*" → "\*\*Streamlit\*\*." 1. In your list of apps, click "\*\*Hello World\*\*." If you don't see your app, check that your role is set to "ACCOUNTADMIN" as described in [Optional: Create a warehouse](/get-started/installation/streamlit-in-snowflake#optional-create-a-warehouse). 1. Your app will open in viewing mode. To edit your app, in the upper-right corner, click "\*\*Edit\*\*."  ## What's next? Read about our [Basic concepts](/get-started/fundamentals/main-concepts) and try out more commands in your app. Or, create more apps in Snowflake! You can reuse your warehouse and database, so you don't need to repeat those steps. For more information about creating and managing Streamlit in Snowflake apps, check out the [Snowflake docs](https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit).
| null |
./vendor/streamlit/content/get-started/installation/sis.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/installation/sis.md
|
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|
# Install Streamlit using command line This page will walk you through creating an environment with `venv` and installing Streamlit with `pip`. These are our recommended tools, but if you are familiar with others you can use your favorite ones too. At the end, you'll build a simple "Hello world" app and run it. If you prefer to have a graphical interface to manage your Python environments, check out how to [Install Streamlit using Anaconda Distribution](/get-started/installation/anaconda-distribution). ## Prerequisites As with any programming tool, in order to install Streamlit you first need to make sure your computer is properly set up. More specifically, you’ll need: 1. \*\*Python\*\* We support [version 3.9 to 3.13](https://www.python.org/downloads/). 1. \*\*A Python environment manager\*\* (recommended) Environment managers create virtual environments to isolate Python package installations between projects. We recommend using virtual environments because installing or upgrading a Python package may cause unintentional effects on another package. For a detailed introduction to Python environments, check out [Python Virtual Environments: A Primer](https://realpython.com/python-virtual-environments-a-primer/). For this guide, we'll be using `venv`, which comes with Python. 1. \*\*A Python package manager\*\* Package managers handle installing each of your Python packages, including Streamlit. For this guide, we'll be using `pip`, which comes with Python. 1. \*\*Only on MacOS: Xcode command line tools\*\* Download Xcode command line tools using [these instructions](https://mac.install.guide/commandlinetools/4.html) in order to let the package manager install some of Streamlit's dependencies. 1. \*\*A code editor\*\* Our favorite editor is [VS Code](https://code.visualstudio.com/download), which is also what we use in all our tutorials. ## Create an environment using `venv` 1. Open a terminal and navigate to your project folder. ```bash cd myproject ``` 2. In your terminal, type: ```bash python -m venv .venv ``` 3. A folder named ".venv" will appear in your project. This directory is where your virtual environment and its dependencies are installed. ## Activate your environment 4. In your terminal, activate your environment with one of the following commands, depending on your operating system. ```bash # Windows command prompt .venv\Scripts\activate.bat # Windows PowerShell .venv\Scripts\Activate.ps1 # macOS and Linux source .venv/bin/activate ``` 5. Once activated, you will see your environment name in parentheses before your prompt. "(.venv)" ## Install Streamlit in your environment 6. In the terminal with your environment activated, type: ```bash pip install streamlit ``` 7. Test that the installation worked by launching the Streamlit Hello example app: ```bash streamlit hello ``` If this doesn't work, use the long-form command: ```bash python -m streamlit hello ``` 8. Streamlit's Hello app should appear in a new tab in your web browser! 9. Close your terminal when you are done. ## Create a "Hello World" app and run it 10. Create a file named `app.py` in your project folder. ```python import streamlit as st st.write("Hello world") ``` 11. Any time you want to use your new environment, you first need to go to your project folder (where the `.venv` directory lives) and run the command to activate it: ```bash # Windows command prompt .venv\Scripts\activate.bat # Windows PowerShell .venv\Scripts\Activate.ps1 # macOS and Linux source .venv/bin/activate ``` 12. Once activated, you will see your environment's name in parentheses at the beginning of your terminal prompt. "(.venv)" 13. Run your Streamlit app. ```bash streamlit run app.py ``` If this doesn't work, use the long-form command: ```bash python -m streamlit run app.py ``` 14. To stop the Streamlit server, press `Ctrl+C` in the terminal. 15. When you're done using this environment, return to your normal shell by typing: ```bash deactivate ``` ## What's next? Read about our [Basic concepts](/get-started/fundamentals/main-concepts) to understand Streamlit's dataflow model.
| null |
./vendor/streamlit/content/get-started/installation/command-line.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/installation/command-line.md
|
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|
stop the Streamlit server, press `Ctrl+C` in the terminal. 15. When you're done using this environment, return to your normal shell by typing: ```bash deactivate ``` ## What's next? Read about our [Basic concepts](/get-started/fundamentals/main-concepts) to understand Streamlit's dataflow model.
| null |
./vendor/streamlit/content/get-started/installation/command-line.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/installation/command-line.md
|
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|
# Use Streamlit Playground in your browser The fastest way to try out Streamlit is to try out our Playground! Streamlit Playground runs in your browser. Just visit the Playground, and a \_limited\_ version of Streamlit loads as browser scripts. Enjoy the following conveniences: - Start playing with code right away, completely in your browser. - No configuration. - No command line. - No application installations in your OS. Although the Playground has everything you need to get started, it doesn't contain the full version of Streamlit. To access the full awesomeness of Streamlit, see [Install Streamlit using command line](/get-started/installation/command-line) or [Install Streamlit using Anaconda Distribution](/get-started/installation/anaconda-distribution). ## Prerequisites Because the Playground runs Streamlit \_locally\_ in your browser, you should visit the Playground from a personal computer, not a mobile device. ## Go to the Playground 1. Go to [streamlit.io/playground](https://streamlit.io/playground). 1. Wait for the playground to load. Behind the scenes, the site installs a browser-based version of Python and Streamlit. This can take as little as a few seconds. The setup time can vary depending on your machine and internet connection. When Streamlit is done loading, an example app is displayed in the right panel.  1. Optional: To view different examples, above the editor, select them from the examples list. ## Create a `Hello World` app 1. From the "EXAMPLES" list, select "\*\*Blank\*\*." 1. On the left, update the contents of the code editor to contain the following code: ```python import streamlit as st st.write("Hello World") ``` A second or two after typing or pasting the code into the editor, the right panel will display the updated app. The code editor saves your edits whenever you pause from typing. Therefore, if you pause between keystrokes as you type a new line of code, you may see an error on the right because Streamlit executed an incomplete line. If this happens, just keep typing to complete the line(s) you are writing. When you pause again at the end of the line, Streamlit reruns the app. 1. On the left, change `st.write` to `st.title` so the code editor has the following code: ```python import streamlit as st st.title("Hello World") ``` A second after you stop typing, Streamlit reruns the app and updates the display on the right. 1. Keep making changes! Watch as your edits are automatically saved and the new result is displayed on the right. ## What's next? - Option 1: If you're already intrigued and ready to install Streamlit on your computer, see one of the options to [Install Streamlit on your machine](/get-started/installation#install-on-your-machine). - Option 2: Otherwise, you can keep using the playground while you read about our [Basic concepts](/get-started/fundamentals/main-concepts) and try out more commands in your app. When you use the Streamlit Playground to work through the basic concepts, you can skip over any instructions to save your file or to select "\*\*Rerun on save\*\*." Streamlit Playground automatically saves your code when you pause from editing, as described above. Also, it is already configured to rerun on save.
| null |
./vendor/streamlit/content/get-started/installation/streamlit-playground.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/installation/streamlit-playground.md
|
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] | 0.026412
|
# Use Community Cloud to develop with GitHub Codespaces To use GitHub Codespaces for Streamlit development, you need a properly configured `devcontainer.json` file to set up the environment. Fortunately, Streamlit Community Cloud is here to help! Although Community Cloud is primarily used to deploy and share apps with the rest of the world, we've built in some handy features to make it easy to use GitHub Codespaces. This guide explains how to create a Community Cloud account and use an automated workflow to get you into a GitHub codespace and live-editing a Streamlit app. All this happens right in your browser, no installation required. If you already created a Community Cloud account and connected GitHub, jump ahead to [Create a new app from a template](/get-started/installation/community-cloud#create-a-new-app-from-a-template). ## Prerequisites - You must have a GitHub account. ## Sign up for Streamlit Community Cloud 1. Go to [share.streamlit.io](https://share.streamlit.io). 1. Click "\*\*Continue to sign-in\*\*." 1. Click "\*\*Continue with GitHub\*\*." 1. Enter your GitHub credentials and follow GitHub's authentication prompts. 1. Fill in your account information, and click "\*\*I accept\*\*" at the bottom. ## Add access to your public repositories 1. In the upper-left corner, click on "\*\*Workspaces *warning*\*\*."1. From the drop down, click "\*\*Connect GitHub account\*\*." 1. Enter your GitHub credentials and follow GitHub's authentication prompts. 1. Click "\*\*Authorize streamlit\*\*."## Optional: Add access to private repositories 1. In the upper-left corner, click on your GitHub username.1. From the drop down, click "\*\*Settings\*\*." 1. On the left side of the dialog, select "\*\*Linked accounts\*\*." 1. Under "Source control," click "\*\*Connect here *arrow\_forward*\*\*." 1. Click "\*\*Authorize streamlit\*\*."## Create a new app from a template 1. In the upper-right corner, click "\*\*Create app\*\*."1. When asked "Do you already have an app?" click "\*\*Nope, create one from a template\*\*." 1. From the list of templates on the left, select "\*\*Blank app\*\*." 1. At the bottom, select the option to "\*\*Open GitHub Codespaces...\*\*" 1. At the bottom, click "\*\*Deploy\*\*." ## Edit your app in GitHub Codespaces 1. Wait for GitHub to set up your codespace. It can take several minutes to fully initialize your codespace. After you see the Visual Studio Code editor in your codespace, it can take several minutes to install Python and start the Streamlit server. When complete, you will see a split screen view with a code editor on the left and a running app on the right. The code editor opens two tabs by default: the repository's readme file and the app's entrypoint file.1. Go to the app's entrypoint file (`streamlit\_app.py`) in the left pane, and change line 3 by adding "Streamlit" inside `st.title`. ```diff -st.title("🎈 My new app") +st.title("🎈 My new Streamlit app") ``` Files are automatically saved in your codespace with each edit. 1. A moment after typing a change, your app on the right side will display a rerun prompt. Click "\*\*Always rerun\*\*."If the rerun prompt disappears before you click it, you can hover over the overflow menu icon (*more\_vert*) to bring it back. 1. Optional: Continue to make edits and observe the changes within seconds. ## Publish your changes 1. In the left navigation bar, click the source control icon.1. In the source control sidebar on the left, enter a name for your commit. 1. Click "\*\**check* Commit\*\*."1. In the confirmation dialog, click "\*\*Yes\*\*" to stage and commit all your changes. Your changes are committed locally in your codespace. 1. In the source control sidebar on the left, click "\*\**cached* 1 *arrow\_upward*\*\*" to push your commit to GitHub. 1. In the confirmation dialog, click "\*\*OK\*\*" to push commits to "origin/main." Your changes are now saved to your GitHub repository. Community
| null |
./vendor/streamlit/content/get-started/installation/cloud-quickstart.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/installation/cloud-quickstart.md
|
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|
changes. Your changes are committed locally in your codespace. 1. In the source control sidebar on the left, click "\*\**cached* 1 *arrow\_upward*\*\*" to push your commit to GitHub. 1. In the confirmation dialog, click "\*\*OK\*\*" to push commits to "origin/main." Your changes are now saved to your GitHub repository. Community Cloud will immediately reflect the changes in your deployed app. 1. Optional: To see your updated, published app, return to the "\*\*My apps\*\*" section of your workspace at [share.streamlit.io](https://share.streamlit.io), and click on your app. ## Learn Streamlit fundamentals If you haven't learned Streamlit's basic concepts yet, this is a great time to go to [Fundamentals](/get-started/fundamentals). Use your codespace to walk through and try basic Streamlit commands. When finished, come back here to learn how to clean up your codespace. ## Stop or delete your codespace When you stop interacting with your codespace, GitHub will generally stop your codespace for you. However, the surest way to avoid undesired use of your capacity is to stop or delete your codespace when you are done. 1. Go to [github.com/codespaces](https://github.com/codespaces). At the bottom of the page, all your codespaces are listed. Click the overflow menu icon (*more\_horiz*) for your codespace.2. If you want to return to your work later, click "\*\*Stop codespace\*\*." Otherwise, click "\*\*Delete\*\*."3. Congratulations! You just deployed an app to Streamlit Community Cloud. 🎉 Return to your workspace at [share.streamlit.io/](https://share.streamlit.io/) and [deploy another Streamlit app](/deploy/streamlit-community-cloud/deploy-your-app).
| null |
./vendor/streamlit/content/get-started/installation/cloud-quickstart.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/installation/cloud-quickstart.md
|
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|
# Install Streamlit There are multiple ways to set up your development environment and install Streamlit. Developing locally with Python installed on your own computer is the most common scenario. Try a Streamlit Playground that runs in your browser — no installation required. (Note that this is not how Streamlit is meant to be used, because it has many downsides. That's why it's a \_playground\_!) ## Summary for experienced Python developers 1. To set up your Python environment and test your installation, execute the following terminal commands: ```bash pip install streamlit streamlit hello ``` 1. Jump to our [Basic concepts](/get-started/fundamentals/main-concepts). ## Install Streamlit on your machine ### Option 1: I like the command line Install Streamlit on your own machine using tools like `venv` and `pip`. ### Option 2: I prefer a graphical interface Install Streamlit using the Anaconda Distribution graphical user interface. This is also the best approach if you're on Windows or don't have Python set up. ## Create an app in the cloud ### Option 1: I want a free cloud environment Use Streamlit Community Cloud with GitHub Codespaces so you don't have to go through the trouble of installing Python and setting up an environment. ### Option 2: I need something secure, controlled, and in the cloud Use Streamlit in Snowflake to code your apps in the cloud, right alongside your data with role-based access controls.
| null |
./vendor/streamlit/content/get-started/installation/_index.md
|
https://github.com/streamlit/docs/blob/main//content/get-started/installation/_index.md
|
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|
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