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Copy file name to clipboardExpand all lines: docs/envsetup/index.md
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For more detailed instructions for GitHub Codespaces, see the [GitHub Codespaces env-setup docs](01_setup.md).
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If you cannot use GitHub Codespaces and wish to use a local development environment, see the [documentation for local installation](02_local.md).
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!!! info "Deprecation of GitPod"
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!!! info "Deprecation of Gitpod"
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Nextflow Training used to use [Gitpod](https://gitpod.io) until February 2025.
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However, the makers of Gitpod have decided to retire the free functionality in favor of their new [Gitpod Flex](https://www.gitpod.io/blog/introducing-gitpod-flex) system.
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For that reason, we have switched to using GitHub Codespaces, which also offer a one-click developer environment with no prior setup.
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Depending on when you signed up to GitPod and when exactly they retire the service, you may still be able to launch the training in their old cloud IDE, though we cannot guarantee reliable access going forward:
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Depending on when you signed up to Gitpod and when exactly they retire the service, you may still be able to launch the training in their old cloud IDE, though we cannot guarantee reliable access going forward:
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[Open in Gitpod](https://gitpod.io/#https://github.com/nextflow-io/training).
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# Orientation
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The GitHub Codespaces environment contains all the software, code and data necessary to work through this training course, so you don't need to install anything yourself.
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However, you do need a (free) account to log in, and you should take a few minutes to familiarize yourself with the interface.
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However, you do need a (free) GitHub account to log in, and you should take a few minutes to familiarize yourself with the interface.
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If you have not yet done so, please follow [this link](../../envsetup/) before going any further.
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If you have not yet done so, please go through the [Environment Setup](../../envsetup/) mini-course before going any further.
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## Materials provided
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## Working directory
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Throughout this training course, we'll be working in the `hello-nextflow/` directory.
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Change directory now by running this command in the terminal:
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```bash
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cd hello-nextflow/
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```
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Throughout this training course, we'll be working in the `hello-nextflow/` directory, which loads by default when you open the training workspace.
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This directory contains all the code files, test data and accessory files you will need.
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!!!tip
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If for whatever reason you move out of this directory, you can always use the full path to return to it, assuming you're running this within the Github Codespaces training environment:
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```bash
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cd /workspaces/training/hello-nextflow
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```
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Feel free to explore the contents of this directory; the easiest way to do so is to use the file explorer on the left-hand side of the training workspace.
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Now let's have a look at the contents of this directory.
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## Materials provided
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You can explore the contents of this directory by using the file explorer on the left-hand side of the training workspace.
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Alternatively, you can use the `tree` command.
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Throughout the course, we use the output of `tree` to represent directory structure and contents in a readable form, sometimes with minor modifications for clarity.
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Here we generate a table of contents to the second level down:
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The name and number in the filename correspond to the step of the relevant part of the course.
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For example, the file `hello-world-4.nf` is the expected result of completing steps 1 through 4 of Part 1: Hello World.
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!!!tip
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If for whatever reason you move out of this directory, you can always run this command to return to it (within the training environment):
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```bash
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cd /workspaces/training/hello-nextflow
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```
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Now, to begin the course, click on the arrow in the bottom right corner of this page.
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**Now, to begin the course, click on the arrow in the bottom right corner of this page.**
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# Orientação
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O ambiente Github Codespaces contém todo o software, código e dados necessários para este curso. Você não precisa instalar nada por conta própria. No entanto, é necessária uma conta (gratuita) para logar - e recomendamos que você reserve alguns minutos para se familiarizar com a interface.
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O ambiente GitHub Codespaces contém todo o software, código e dados necessários para este curso. Você não precisa instalar nada por conta própria. No entanto, é necessária uma conta (gratuita) para logar - e recomendamos que você reserve alguns minutos para se familiarizar com a interface.
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Caso ainda não tenha feito isso, siga [este link](../../envsetup/) antes de prosseguir.
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We're going to add a step to convert the greeting to uppercase.
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To that end, we need to do three things:
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- Define the command we'lre going to use to do the uppercase conversion.
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- Define the command we're going to use to do the uppercase conversion.
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- Write a new process that wraps the uppercasing command.
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-Add the new process to the workflow and set it up to take the output of the `sayHello()` process as input.
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-Call the new process in the workflow block and set it up to take the output of the `sayHello()` process as input.
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### 1.1. Define the uppercasing command and test it in the terminal
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To do the conversion of the greetings to uppercase, we're going to a classic UNIX tool called `tr` for 'text replacement', with the following syntax:
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To do the conversion of the greetings to uppercase, we're going to use a classic UNIX tool called `tr` for 'text replacement', with the following syntax:
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```bash title="Syntax"
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tr '[a-z]''[A-Z]'
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This is where things could get a little tricky, because we need to be able to handle an arbitrary number of input files.
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Specifically, we can't write the command up front, so we need to tell Nextflow how to compose it at runtime based on what inputs flow into the process.
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In other words, if we have an input channel containing the item`[file1.txt, file2.txt, file3.txt]`, we need Nextflow to turn that into `cat file1.txt file2.txt file3.txt`.
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In other words, if we have an input channel containing the element`[file1.txt, file2.txt, file3.txt]`, we need Nextflow to turn that into `cat file1.txt file2.txt file3.txt`.
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Fortunately, Nextflow is quite happy to do that for us if we simply write `cat ${input_files}` in the script command.
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Some command-line tools require providing an argument (like `-input`) for each input file.
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In that case, we would have to do a little bit of extra work to compose the command.
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You can see an example of this in the 'Nextflow for Genomics' training course.
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You can see an example of this in the [Nextflow for Genomics](../../nf4_science/genomics/) training course.
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<!--[ADD LINK to note above] -->
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And have a look at the contents of the final output file too:
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```console title="COLLECTED-output.txt"
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```console title="results/COLLECTED-output.txt"
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Holà
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```
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Oh no. The collection step was run individually on each greeting, which is NOT what we wanted.
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We need to do something to tell Nextflow explicitly that we want that third step to run on all the items in the channel output by `convertToUpper()`.
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We need to do something to tell Nextflow explicitly that we want that third step to run on all the elements in the channel output by `convertToUpper()`.
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### 2.3. Use an operator to collect the greetings into a single input
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Looking at the output of the `view()` statements, we see the following:
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- Three `Before collect:` statements, one for each greeting: at that point the file paths are individual items in the channel.
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- A single `After collect:` statement: the three file paths are now packaged into a single item.
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- A single `After collect:` statement: the three file paths are now packaged into a single element.
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Have a look at the contents of the final output file too:
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```console title="COLLECTED-output.txt"
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```console title="results/COLLECTED-output.txt"
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BONJOUR
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HELLO
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HOLà
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```
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This time we have all three greetings in the final output file. Success!
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This time we have all three greetings in the final output file. Success! Remove the optional view calls to make the next outputs less verbose.
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!!! note
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If you run this several times without `-resume`, you will see that the order of the greetings changes from one run to the next.
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This shows you that the order in which items flow through the pipeline is not guaranteed to be consistent.
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This shows you that the order in which elements flow through process calls is not guaranteed to be consistent.
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### Takeaway
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That means we can use the built-in `size()` function to get the number of files in the `input_files` array.
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In the process block, make the following code change:
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In the `collectGreetings`process block, make the following code change:
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As an example, let's pull a container image that contains [cowpy](https://github.com/jeffbuttars/cowpy), a python implementation of a tool called `cowsay` that generates ASCII art to display arbitrary text inputs in a fun way.
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There are various repositories where you can find published containers.
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We used the [Seqera Containers](https://seqera.io/containers/) service to generate this Docker container from the `cowpy` Conda package: `'community.wave.seqera.io/library/cowpy:1.1.5--3db457ae1977a273'`.
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We used the [Seqera Containers](https://seqera.io/containers/) service to generate this Docker container image from the `cowpy` Conda package: `'community.wave.seqera.io/library/cowpy:1.1.5--3db457ae1977a273'`.
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Run the complete pull command:
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#### 1.3.1. Spin up the container
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To run interactively, we just add `-it` to the `docker pull` command.
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To run interactively, we just add `-it` to the `docker run` command.
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Optionally, we can specify the shell we want to use inside the container by appending _e.g._`/bin/bash` to the command.
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```bash
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cowpy "Hello Containers" -c tux
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```
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Now the output shows the Linux penguin, Tux, instead of the default cow, because we specified `-c tux` parameter.
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Now the output shows the Linux penguin, Tux, instead of the default cow, because we specified the `-c tux` parameter.
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```console title="Output"
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__________________
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-v <outside_path>:<inside_path>
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```
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In our case `<outside_path>` will be the current working directory, so we can just use a dot (`.`), and `<outside_path>` is just a name we make up; let's call it `/data`.
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In our case `<outside_path>` will be the current working directory, so we can just use a dot (`.`), and `<inside_path>` is just a name we make up; let's call it `/data`.
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To mount a volume, we replace the paths and add the volume mounting argument to the docker run command as follows:
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ls /data
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```
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Depending on what part of this training you've done before, the output below my look slightly different.
You see that the character is saying all the greetings, just as it did when we ran the `cowpy` command on the `greetings.csv` file from inside the container.
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### What's next?
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Celebrate and give yourself a big pat on the back! You have completed your very first Nextflow developer course.
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Then check out the training portal homepage for more training content that may be of interest.
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Next, we ask you to complete a very short survey about your experience with this training course, then we'll take you to a page with links to further training resources and helpful links.
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