.. _nextflow_3-page: ******************* 3 Nextflow ******************* Decoupling resources, parameters and Nextflow script ======================= When making complex pipelines it is convenient to keep the definition of resources needed, the default parameters, and the main script separately from each other. This can be achieved using two additional files: - nextflow.config - params.config The **nextflow.config** file allows to indicate resources needed for each class of processes. This is achieved by labeling processes in the nextflow.config file: .. literalinclude:: ../nextflow/test2/nextflow.config :language: groovy :lines: 3-17 The first part defines the "default" resources for a process: .. literalinclude:: ../nextflow/test2/nextflow.config :language: groovy :lines: 3-17 :emphasize-lines: 1-4 Then are specified the resources needed for a class of processes labeled **bigmem**. In brief, the default options will be overridden for the processes labeled **bigmem** and **onecpu**: .. literalinclude:: ../nextflow/test2/nextflow.config :language: groovy :lines: 3-17 :emphasize-lines: 6-14 .. tip:: You can add the default configuration for shell executions within to the nextflow.config file: .. code-block:: groovy process { shell = ['/bin/bash', '-euo', 'pipefail'] ... In the script **/test2/test2.nf file**, there are two processes to run two programs: - `fastQC `__ - a tool that calculates a number of quality control metrics on single fastq files; - `multiQC `__ - an aggregator of results from bioinformatics tools and samples for generating a single html report. .. literalinclude:: ../nextflow/test2/test2.nf :language: groovy :emphasize-lines: 66 You can see that the process **fastQC** is labeled 'bigmem'. The last two rows of the config file indicate which containers to use. In this example, -- and by default, if the repository is not specified, -- a container is pulled from the DockerHub. In the case of using a singularity container, you can indicate where to store the local image using the **singularity.cacheDir** option: .. code-block:: groovy process.container = 'biocorecrg/c4lwg-2018:latest' singularity.cacheDir = "$baseDir/singularity" Let's now launch the script **test2.nf**. .. code-block:: console :emphasize-lines: 42,43 cd test2; nextflow run test2.nf N E X T F L O W ~ version 20.07.1 Launching `test2.nf` [distracted_edison] - revision: e3a80b15a2 BIOCORE@CRG - N F TESTPIPE ~ version 1.0 ============================================= reads : /home/ec2-user/git/CRG_Nextflow_Jun_2022/nextflow/nextflow/test2/../testdata/*.fastq.gz executor > local (2) [df/2c45f2] process > fastQC (B7_input_s_chr19.fastq.gz) [ 0%] 0 of 2 [- ] process > multiQC - Error executing process > 'fastQC (B7_H3K4me1_s_chr19.fastq.gz)' Caused by: Process `fastQC (B7_H3K4me1_s_chr19.fastq.gz)` terminated with an error exit status (127) Command executed: fastqc B7_H3K4me1_s_chr19.fastq.gz Command exit status: 127 executor > local (2) [df/2c45f2] process > fastQC (B7_input_s_chr19.fastq.gz) [100%] 2 of 2, failed: 2 ✘ [- ] process > multiQC - Error executing process > 'fastQC (B7_H3K4me1_s_chr19.fastq.gz)' Caused by: Process `fastQC (B7_H3K4me1_s_chr19.fastq.gz)` terminated with an error exit status (127) Command executed: fastqc B7_H3K4me1_s_chr19.fastq.gz Command exit status: 127 Command output: (empty) Command error: .command.sh: line 2: fastqc: command not found Work dir: /home/ec2-user/git/CRG_Nextflow_Jun_2022/nextflow/nextflow/test2/work/c5/18e76b2e6ffd64aac2b52e69bedef3 Tip: when you have fixed the problem you can continue the execution adding the option `-resume` to the run command line We will get a number of errors since no executable is found in our environment/path. This is because the executables are stored in our docker image and we have to tell Nextflow to use the docker image, using the `-with-docker` parameter. .. code-block:: console :emphasize-lines: 1 nextflow run test2.nf -with-docker N E X T F L O W ~ version 20.07.1 Launching `test2.nf` [boring_hamilton] - revision: e3a80b15a2 BIOCORE@CRG - N F TESTPIPE ~ version 1.0 ============================================= reads : /home/ec2-user/git/CRG_Nextflow_Jun_2022/nextflow/nextflow/test2/../testdata/*.fastq.gz executor > local (3) [22/b437be] process > fastQC (B7_H3K4me1_s_chr19.fastq.gz) [100%] 2 of 2 ✔ [1a/cfe63b] process > multiQC [ 0%] 0 of 1 executor > local (3) [22/b437be] process > fastQC (B7_H3K4me1_s_chr19.fastq.gz) [100%] 2 of 2 ✔ [1a/cfe63b] process > multiQC [100%] 1 of 1 ✔ This time it worked because Nextflow used the image specified in the **nextflow.config** file and containing the executables. Now let's take a look at the **params.config** file: .. literalinclude:: ../nextflow/test2/params.config :language: groovy As you can see, we indicated two pipeline parameters, `reads` and `email`; when running the pipeline, they can be overridden using `\-\-reads` and `\-\-email`. This file is included thanks again to the **nextflow.config** file, here shown entirely .. literalinclude:: ../nextflow/test2/nextflow.config :language: groovy :emphasize-lines: 1 Now, let's examine the folders generated by the pipeline. .. code-block:: console ls work/2a/22e3df887b1b5ac8af4f9cd0d88ac5/ total 0 drwxrwxr-x 3 ec2-user ec2-user 26 Apr 23 13:52 . drwxr-xr-x 2 root root 136 Apr 23 13:51 multiqc_data drwxrwxr-x 3 ec2-user ec2-user 44 Apr 23 13:51 .. We observe that Docker runs as "root". This can be problematic and generates security issues. To avoid this we can add this line of code within the process section of the config file: .. code-block:: groovy containerOptions = { workflow.containerEngine == "docker" ? '-u $(id -u):$(id -g)': null} This will tell Nextflow that if it is run with Docker, it has to produce files that belong to a user rather than the root. Publishing final results ------------------------- The script **test2.nf** generates two new folders, **output_fastqc** and **output_multiQC**, that contain the result of the pipeline output. We can indicate which process and output can be considered the final output of the pipeline using the **publishDir** directive that has to be specified at the beginning of a process. In our pipeline, we define these folders here: .. literalinclude:: ../nextflow/test2/test2.nf :language: groovy :emphasize-lines: 42-46,64,84 You can see that the default mode to publish the results in Nextflow is `soft linking`. You can change this behavior by specifying the mode as indicated in the **multiQC** process. .. note:: IMPORTANT: You can also "move" the results but this is not suggested for files that will be needed for other processes. This will likely disrupt your pipeline Adding help section to a pipeline ============================================= Here we describe another good practice: the use of the `\-\-help` parameter. At the beginning of the pipeline, we can write: .. literalinclude:: ../nextflow/test2/test2.nf :language: groovy :emphasize-lines: 25-40 so that launching the pipeline with `\-\-help` will show you just the parameters and the help. .. code-block:: console nextflow run test2.nf --help N E X T F L O W ~ version 20.07.1 Launching `test2.nf` [mad_elion] - revision: e3a80b15a2 BIOCORE@CRG - N F TESTPIPE ~ version 1.0 ============================================= reads : /home/ec2-user/git/CRG_Nextflow_Jun_2022/nextflow/nextflow/test2/../testdata/*.fastq.gz This is the Biocore's NF test pipeline Enjoy! EXERCISE =============== - Look at the very last EXERCISE of the day before. Change the script and the config file using the label for handling failing processes. .. raw:: html
Solution The process should become: .. literalinclude:: ../nextflow/test1/sol/sol_lab.nf :language: groovy :emphasize-lines: 38 and the nextflow.config file would become: .. literalinclude:: ../nextflow/test1/sol/nextflow.config :language: groovy .. raw:: html
| | - Now look at **test2.nf**. Change this script and the config file using the label for handling failing processes by retrying 3 times and incrementing time. You can specify a very low time (5, 10 or 15 seconds) for the fastqc process so it would fail at the beginning. .. raw:: html
Solution The code should become: .. literalinclude:: ../nextflow/test2/retry/retry.nf :language: groovy :emphasize-lines: 66 while the nextflow.config file would be: .. literalinclude:: ../nextflow/test2/retry/nextflow.config :language: groovy .. raw:: html
| | Using Singularity ======================= We recommend using Singularity instead of Docker in HPC environments. This can be done using the Nextflow parameter `-with-singularity` without changing the code. Nextflow will take care of **pulling, converting, and storing the image** for you. This will be done only once and then Nextflow will use the stored image for further executions. Within an AWS main node, both Docker and Singularity are available. While within the AWS batch system, only Docker is available. .. code-block:: console nextflow run test2.nf -with-singularity -bg > log tail -f log N E X T F L O W ~ version 20.10.0 Launching `test2.nf` [soggy_miescher] - revision: 5a0a513d38 BIOCORE@CRG - N F TESTPIPE ~ version 1.0 ============================================= reads : /home/ec2-user/git/CoursesCRG_Containers_Nextflow_May_2021/nextflow/test2/../../testdata/*.fastq.gz Pulling Singularity image docker://biocorecrg/c4lwg-2018:latest [cache /home/ec2-user/git/CoursesCRG_Containers_Nextflow_May_2021/nextflow/test2/singularity/biocorecrg-c4lwg-2018-latest.img] [da/eb7564] Submitted process > fastQC (B7_H3K4me1_s_chr19.fastq.gz) [f6/32dc41] Submitted process > fastQC (B7_input_s_chr19.fastq.gz) ... Let's inspect the folder `singularity`: .. code-block:: console ls singularity/ biocorecrg-c4lwg-2018-latest.img This singularity image can be used to execute the code outside the pipeline **exactly the same way** as inside the pipeline. Sometimes we can be interested in launching only a specific job, because it might fail or for making a test. For that, we can go to the corresponding temporary folder; for example, one of the fastQC temporary folders: .. code-block:: console cd work/da/eb7564*/ Inspecting the `.command.run` file shows us this piece of code: .. code-block:: groovy ... nxf_launch() { set +u; env - PATH="$PATH" SINGULARITYENV_TMP="$TMP" SINGULARITYENV_TMPDIR="$TMPDIR" singularity exec /home/ec2-user/git/CoursesCRG_Containers_Nextflow_May_2021/nextflow/test2/singularity/biocorecrg-c4lwg-2018-latest.img /bin/bash -c "cd $PWD; /bin/bash -ue /home/ec2-user/git/CoursesCRG_Containers_Nextflow_May_2021/nextflow/test2/work/da/eb756433aa0881d25b20afb5b1366e/.command.sh" } ... This means that Nextflow is running the code by using the **singularity exec** command. Thus we can launch this command outside the pipeline (locally): .. code-block:: console bash .command.run Started analysis of B7_H3K4me1_s_chr19.fastq.gz Approx 5% complete for B7_H3K4me1_s_chr19.fastq.gz Approx 10% complete for B7_H3K4me1_s_chr19.fastq.gz Approx 15% complete for B7_H3K4me1_s_chr19.fastq.gz Approx 20% complete for B7_H3K4me1_s_chr19.fastq.gz Approx 25% complete for B7_H3K4me1_s_chr19.fastq.gz Approx 30% complete for B7_H3K4me1_s_chr19.fastq.gz Approx 35% complete for B7_H3K4me1_s_chr19.fastq.gz Approx 40% complete for B7_H3K4me1_s_chr19.fastq.gz Approx 45% complete for B7_H3K4me1_s_chr19.fastq.gz Approx 50% complete for B7_H3K4me1_s_chr19.fastq.gz Approx 55% complete for B7_H3K4me1_s_chr19.fastq.gz Approx 60% complete for B7_H3K4me1_s_chr19.fastq.gz ... If you have to submit a job to a HPC you need to use the corresponding program, such as **qsub** if you have a Sun Grid Engine or **sbatch** if you have Slurm. Here an example using Slurm: .. code-block:: console sbatch .command.run Profiles ================= For deploying a pipeline in a cluster or Cloud, in the **nextflow.config** file, we need to indicate what kind of the `executor `__ to use. In the Nextflow framework architecture, the executor indicates which **batch-queue system** to use to submit jobs to an HPC or to Cloud. The executor is completely abstracted, so you can switch from SGE to SLURM just by changing this parameter in the configuration file. You can group different classes of configuration or **profiles** within a single **nextflow.config** file. Let's inspect the **nextflow.config** file in **test3** folder. We can see three different profiles: - standard - cluster - cloud The first profile indicates the resources needed for running the pipeline locally. They are quite small since we have little power and CPU on the test node. .. literalinclude:: ../nextflow/test3/nextflow.config :language: groovy :lines: 1-21 As you can see, we explicitly indicated the **local** executor. By definition, the local executor is a default executor if the pipeline is run without specifying a profile. The second profile is for running the pipeline on the **cluster**; here in particular for the cluster supporting the SLURM Workload Manager queuing system: .. literalinclude:: ../nextflow/test3/nextflow.config :language: groovy :lines: 22-38 This profile indicates that the system uses **SLURM** as a job scheduler and that we have different queues for small jobs and more intensive ones. .. note:: IMPORTANT: You need to have either Docker or Singularity installed and running in your computing nodes to run the pipeline. In some HPC you might need to "load" the programs using the modules. For this we advice to add this command inside your **.bash_profile** file. .. code-block:: console vi $HOME/.bash_profile #ADD THIS module load apptainer Deployment in the AWS cloud ============================= The final profile is for running the pipeline in the **Amazon Cloud**, known as Amazon Web Services or AWS. In particular, we will use **AWS Batch** that allows the execution of containerized workloads in the Amazon cloud infrastructure (where NNNN is the number of your bucket which you can see in the mounted folder `/mnt` by typing the command **df**). .. literalinclude:: ../nextflow/test3/nextflow.config :language: groovy :emphasize-lines: 40-57 We indicate the **AWS specific parameters** (**region** and **cliPath**) and the executor **awsbatch**. Then we indicate the working directory, which should be mounted as `S3 volume `__. This is mandatory when running Nextflow on the cloud. We can now launch the pipeline indicating `-profile cloud`: .. code-block:: console nextflow run test3.nf -bg -with-docker -profile cloud > log Note that there is no longer a **work** folder in the directory where test3.nf is located, because, in the AWS cloud, the output is copied locally in the folder **/mnt/nf-class-bucket-NNN/work** (you can see the mounted folder - and the corresponding number - typing **df**). The multiqc report can be seen on the AWS webpage at https://nf-class-bucket-NNN.s3.eu-central-1.amazonaws.com/results/ouptut_multiQC/multiqc_report.html But you need before to change permissions for that file (where NNNN is the number of your bucket): .. code-block:: console chmod 775 /mnt/nf-class-bucket-NNNN/results/ouptut_multiQCmultiqc_report.html Sometimes you can find that the Nextflow process itself is very memory intensive and the main node can run out of memory. To avoid this, you can reduce the memory needed by setting an environmental variable: .. code-block:: console export NXF_OPTS="-Xms50m -Xmx500m" Again we can copy the output file to the bucket. We can also tell Nextflow to directly copy the output file to the S3 bucket: to do so, change the parameter **outdir** in the params file (use the bucket corresponding to your AWS instance): .. code-block:: groovy outdir = "s3://nf-class-bucket-NNNN/results" Making a Nextflow pipeline for image processing ====================== We can build a pipeline incrementally adding more and more processes. Nextflow will handle the dependencies between the input/output and the parallelization. Let's see the content of the folder **nextflow/ex_alba** .. code-block:: console ls etl_peem.py importUview_v3.py step1_denoise.py step2_normalize.py step3_aggregate_and_save.py We have some Python scripts made by Fernán and Nicolas for making those steps: - **step1_denoise.py**: it will read an image file (".dat"), turn it into a NumPy array ("_den.npy"), and denoise it. .. code-block:: console # command line ./step1_denoise.py my_image.dat # output my_image_den.npy - **step2_normalize.py**: it will read a denoised NumPy array ("_den.npy"), that is output by the previous step and produces a nomalized / denoised NumPy array ("_den_norm.npy"). It needs the original image file in the current directory for working even if not specified in the command line. .. code-block:: console ls ./ step2_normalize.py my_image_den.npy my_image.dat # command line ./step2_normalize.py my_image_den.npy # output my_image_den_norm.npy - **step3_aggregate_and_save.py**: it will read all the normalized/denoised NumPy arrays together and it will produce a NeXus/HDF5 file called **example.nxs** EXERCISE ============ Make the pipeline considering the use of the docker/singularity image **biocorecrg/alba:0.1** hosted at dockerhub. The images are at **../../testdata/test_images/** and the executor must be specified for working with our infrastructure. .. raw:: html
Solution .. literalinclude:: ../nextflow/test_alba/test_alba.nf :language: groovy .. raw:: html
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