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docs: Add longer NXF_SYNTAX_PARSER descriptions (#6637)
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docs/amazons3.md

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@@ -47,9 +47,9 @@ If the access credentials are not found in the above file, Nextflow looks for AW
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2. The environment variables `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY`
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3. The environment variables `AWS_ACCESS_KEY` and `AWS_SECRET_KEY`
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4. The profile in the AWS credentials file located at `~/.aws/credentials`
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- Uses the `default `profile or the environment variable `AWS_PROFILE` if set
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- Uses the `default` profile or the environment variable `AWS_PROFILE` if set
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5. The profile in the AWS client configuration file located at `~/.aws/config`
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- Uses the `default `profile or the environment variable `AWS_PROFILE` if set
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- Uses the `default` profile or the environment variable `AWS_PROFILE` if set
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6. The temporary AWS credentials provided by an IAM instance role. See [IAM Roles](http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/iam-roles-for-amazon-ec2.html) documentation for details.
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More information regarding [AWS Security Credentials](http://docs.aws.amazon.com/general/latest/gr/aws-security-credentials.html) are available in the AWS documentation.

docs/kubernetes.md

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Kubernetes abstracts also the storage provisioning through the definition of one more persistent volumes that allow containers to access to the underlying storage systems in a transparent and portable manner.
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When using the `k8s` executor Nextflow deploys the workflow execution as a Kubernetes pod. This pod orchestrates the workflow execution and submits a separate pod execution for each job that need to be carried out by the workflow application.
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When using the `k8s` executor Nextflow deploys the workflow execution as a Kubernetes pod. This pod orchestrates the workflow execution and submits a separate pod execution for each job that needs to be carried out by the workflow application.
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```{image} _static/nextflow-k8s-min.png
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```

docs/migrations/25-04.md

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The *strict syntax* is a strict parser for Nextflow DSL2 that implements the {ref}`Nextflow language specification <syntax-page>`. Originally introduced by the [Nextflow language server](https://github.com/nextflow-io/language-server) alongside Nextflow 24.10, the strict syntax is now available in the Nextflow CLI.
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The strict syntax is disabled by default. It can be enabled by setting the environment variable `NXF_SYNTAX_PARSER=v2`. See {ref}`strict-syntax-page` for details.
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The strict syntax is disabled by default. Enable it by setting the environment variable `NXF_SYNTAX_PARSER=v2`. See {ref}`strict-syntax-page` for details.
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<h3>Linting and formatting</h3>
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docs/migrations/25-10.md

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<h3>Workflow params</h3>
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:::note
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To use workflow params, set the `NXF_SYNTAX_PARSER` environment variable to `v2`:
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```bash
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export NXF_SYNTAX_PARSER=v2
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```
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:::
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The `params` block is a new way to declare pipeline parameters in a Nextflow script:
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```nextflow

docs/module.md

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include { hello } from './some/module'
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```
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Module directories allow the use of module scoped binaries scripts. See [Module binaries] for details.
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Module directories allow the use of module scoped binary scripts. See [Module binaries] for details.
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## Multiple inclusions
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nextflow.enable.moduleBinaries = true
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```
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The binary scripts must be placed in the module directory names `<module-dir>/resources/usr/bin`:
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The binary scripts must be placed in the module directory named `<module-dir>/resources/usr/bin`:
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```
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<module-dir>

docs/process-typed.md

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}
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```
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In order to use this feature, you'll need to:
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To use this feature:
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1. Set the `NXF_SYNTAX_PARSER` environment variable to `v2`:
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```bash
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export NXF_SYNTAX_PARSER=v2
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```
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1. Enable {ref}`strict syntax <strict-syntax-page>` by setting `NXF_SYNTAX_PARSER=v2`.
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2. Set `nextflow.preview.types = true` in every script that uses typed processes.
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See {ref}`syntax-process-typed` for the complete syntax reference and {ref}`migrating-static-types` to migrate existing code to static types.

docs/process.md

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### Dynamic retry with backoff
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There are cases in which the required execution resources may be temporary unavailable e.g. network congestion. In these cases immediately re-executing the task will likely result in the identical error. A retry with an exponential backoff delay can better recover these error conditions:
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There are cases in which the required execution resources may be temporarily unavailable e.g. network congestion. In these cases immediately re-executing the task will likely result in the identical error. A retry with an exponential backoff delay can better recover these error conditions:
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```nextflow
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process hello {

docs/sharing.md

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Once you have uploaded your pipeline project to GitHub other people can execute it simply using the project name or the repository URL.
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For if your GitHub account name is `acme` and you have uploaded a project into a repository named `hello` the repository URL will be `http://github.com/acme/hello` and people will able to download and run it by using either the command:
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For example, if your GitHub account name is `acme` and you have uploaded a project into a repository named `hello` the repository URL will be `http://github.com/acme/hello` and people will able to download and run it by using either the command:
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```bash
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nextflow run acme/hello
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Nextflow tackles this problem by integrating with existing tools for reproducible software, namely Git for source code and Docker for containers. These tools allow you to keep all the dependencies of your pipeline project in one place and track changes over time with version control.
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By making your pipeline project is self-contained, meaning all of its dependencies are fully defined in the project itself, you gain two major advantages:
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By making your pipeline project self-contained, meaning all of its dependencies are fully defined in the project itself, you gain two major advantages:
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- **Portability**: the pipeline can be run in virtually any environment with a Java VM and a container runtime
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- **Reproducibility**: any results produced by the pipelined can be easily reproduced, even across different environments
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- **Reproducibility**: any results produced by the pipeline can be easily reproduced, even across different environments
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One way to account for dependencies is to break them down into three categories: code, data, and environment. Here we will describe how to include each of these dependencies in your Nextflow pipeline:
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channel.of('ACGTTGCAATGCCGTA', 'GCGTACGGTACGTTAC')
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.map { seq -> new DNASequence(seq) }
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.view { dna ->
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"Found sequence '$dna' with melting temperaure ${dna.getMeltingTemperature()}°C"
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"Found sequence '$dna' with melting temperature ${dna.getMeltingTemperature()}°C"
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}
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}
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```
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It prints:
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```
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Found sequence 'DNA[ACGTTGCAATGCCGTA]' with melting temperaure 48.0°C
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Found sequence 'DNA[GCGTACGGTACGTTAC]' with melting temperaure 50.0°C
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Found sequence 'DNA[ACGTTGCAATGCCGTA]' with melting temperature 48.0°C
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Found sequence 'DNA[GCGTACGGTACGTTAC]' with melting temperature 50.0°C
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```
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:::{note}

docs/spack.md

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### Use Spack package names
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Spack package names can specified using the `spack` directive. Multiple package names can be specified by separating them with a blank space. For example:
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Spack package names can be specified using the `spack` directive. Multiple package names can be specified by separating them with a blank space. For example:
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```nextflow
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process hello {
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### Building Spack packages for Nextflow pipelines
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Spack builds most software package from their source codes, and it does this for a request package and for all its required dependencies. As a result, Spack builds can last for long, even several hours. This can represent an inconvenience, in that it can significantly lengthen the duration of Nextflow processes. Here we briefly discuss two strategies to mitigate this aspect, and render the usage of Spack more effective.
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Spack builds most software packages from their source codes, and it does this for a requested package and for all its required dependencies. As a result, Spack builds can last for long, even several hours. This can represent an inconvenience, in that it can significantly lengthen the duration of Nextflow processes. Here we briefly discuss two strategies to mitigate this aspect, and render the usage of Spack more effective.
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1. Use a Spack yaml file, and pre-build the environment outside of Nextflow, prior to running the pipeline.
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Building packages outside of the Nextflow pipeline will work since Spack always installs packages in its own directories,

docs/strict-syntax.md

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:::{versionadded} 25.04.0
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The strict syntax can be enabled in Nextflow by setting the environment variable `NXF_SYNTAX_PARSER=v2`.
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To enable strict syntax, set the `NXF_SYNTAX_PARSER` environment variable to `v2`:
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```bash
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export NXF_SYNTAX_PARSER=v2
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```
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Without this environment variable set, Nextflow will not enforce strict syntax rules. Add this to your shell profile (e.g., `~/.bashrc`, `~/.zshrc`) or set it before you run Nextflow commands.
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## Overview
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## Configuration syntax
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:::{versionadded} 25.04.0
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The strict config syntax can be enabled in Nextflow by setting the environment variable `NXF_SYNTAX_PARSER=v2`.
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To enable the {ref}`strict syntax <strict-syntax-page>`, set the `NXF_SYNTAX_PARSER` environment variable to `v2`:
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```bash
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export NXF_SYNTAX_PARSER=v2
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```
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See {ref}`Configuration <config-syntax>` for a comprehensive description of the configuration language.

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