Use the tarball with exercises
Goal
You can run the example files needed for the exercises
A tarball is a file that contains multiple files, similar to a zip file. To use the files it contains, it needs to be untarred/unzipped/uncompressed first.
Procedure
Prefer a video?
See this YouTube video.
The procedure has these steps:
Get the tarbal
Uncompress the tarbal
Step 1: get the tarbal
In a terminal, use the following command to download the file to your current folder:
wget https://github.com/UPPMAX/R-python-julia-matlab-HPC/raw/refs/heads/main/exercises/exercises.tar.gz
How does that look like?
Your output will look similar to this:
[sven@rackham3 ~]$ wget https://github.com/UPPMAX/R-python-julia-matlab-HPC/raw/refs/heads/main/exercises/exercises.tar.gz --2024-10-23 11:49:30-- https://github.com/UPPMAX/R-python-julia-matlab-HPC/raw/refs/heads/main/exercises/exercises.tar.gz Resolving github.com (github.com)... 4.225.11.194 Connecting to github.com (github.com)|4.225.11.194|:443... connected. HTTP request sent, awaiting response... 302 Found Location: https://raw.githubusercontent.com/UPPMAX/R-python-julia-matlab-HPC/refs/heads/main/exercises/exercises.tar.gz [following] --2024-10-23 11:49:30-- https://raw.githubusercontent.com/UPPMAX/R-python-julia-matlab-HPC/refs/heads/main/exercises/exercises.tar.gz Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ... Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 56007 (55K) [application/octet-stream] Saving to: ‘exercises.tar.gz’ 100%[======================================>] 56,007 --.-K/s in 0.002s 2024-10-23 11:49:30 (31.4 MB/s) - ‘exercises.tar.gz’ saved [56007/56007]
Step 2: Uncompress the tarbal
In a terminal, use the following command to uncompress the file:
tar -xvzf exercises.tar.gz
How does that look like?
Your output will look similar to this:
[sven@rackham3 ~]$ tar -xvzf exercises.tar.gz exercises/ exercises/julia/ exercises/julia/script-df-rackham.sh exercises/julia/parallelJulia/ exercises/julia/parallelJulia/solution/ exercises/julia/parallelJulia/solution/script-df-sol.jl exercises/julia/parallelJulia/script-df.jl exercises/julia/parallelJulia/runHPC2N.sh exercises/julia/parallelJulia/runUPPMAX.sh exercises/julia/parallelJulia/1.md exercises/julia/script-df.jl exercises/julia/batchJulia/ exercises/julia/batchJulia/3.md exercises/julia/batchJulia/2.md exercises/julia/batchJulia/3.uppmax-batch-script.sh exercises/julia/batchJulia/serial-sum.jl exercises/julia/batchJulia/3.kebnekaise-batch-script.sh exercises/julia/batchJulia/script-gpu.jl exercises/julia/batchJulia/Solutions/ exercises/julia/batchJulia/Solutions/2/ exercises/julia/batchJulia/Solutions/2/Kebnekaise.md exercises/julia/batchJulia/Solutions/2/Rackham.sh exercises/julia/batchJulia/Solutions/3/ exercises/julia/batchJulia/Solutions/3/3.kebnekaise-batch-script.sh exercises/julia/batchJulia/Solutions/3/Solution.md exercises/julia/batchJulia/Solutions/1/ exercises/julia/batchJulia/Solutions/1/Rackham.md exercises/julia/batchJulia/Solutions/1/Kebnekaise.md exercises/julia/batchJulia/1.md exercises/julia/README.md exercises/julia/sleep-threads.jl exercises/julia/script-df-kebnekaise.sh exercises/julia/script-df-fixme.jl exercises/julia/isolatedJulia/ exercises/julia/isolatedJulia/2.md exercises/julia/isolatedJulia/Solutions/ exercises/julia/isolatedJulia/Solutions/2/ exercises/julia/isolatedJulia/Solutions/2/Solution.md exercises/julia/isolatedJulia/Solutions/1/ exercises/julia/isolatedJulia/Solutions/1/Solution.md exercises/julia/isolatedJulia/1.md exercises/julia/loadRun/ exercises/julia/loadRun/2.md exercises/julia/loadRun/serial-sum.jl exercises/julia/loadRun/Solutions/ exercises/julia/loadRun/Solutions/2/ exercises/julia/loadRun/Solutions/2/Rackham.md exercises/julia/loadRun/Solutions/2/Kebnekaise.md exercises/julia/loadRun/Solutions/1/ exercises/julia/loadRun/Solutions/1/Solution.md exercises/julia/loadRun/1.md exercises/exercises.tar.gz exercises/python/ exercises/python/scikit-learn-kebnekaise.sh exercises/python/integration2d_gpu.sh exercises/python/integration2d_gpu-cosmos.sh exercises/python/serial-python-virt-cosmos.sh exercises/python/pytorch_fitting_gpu-snowy.sh exercises/python/multiproc-cosmos.sh exercises/python/sequential-python-snowy.sh exercises/python/script-df-rackham.sh exercises/python/multiproc-kebnekaise.sh exercises/python/integration2d_gpu.py exercises/python/sequential-python-cosmos.sh exercises/python/integration2d_gpu-snowy.sh exercises/python/mmmult-kebnekaise.sh exercises/python/sleep.py exercises/python/seaborn-code-kebnekaise.py exercises/python/add2.py exercises/python/seaborn-code-rackham.py exercises/python/script-df-fixme.py exercises/python/my_tf_program.py exercises/python/README.md exercises/python/pytorch_fitting_gpu.py exercises/python/sequential-python-kebnekaise.sh exercises/python/script-df.py exercises/python/integration2d_gpu_shared.py exercises/python/sum-2args-kebnekaise.sh exercises/python/add-list-cosmos.sh exercises/python/mmmult.py exercises/python/sum-2args.py exercises/python/integration2d_gpu-kebnekaise.sh exercises/python/gpu-python-snowy.sh exercises/python/script-df-kebnekaise.sh exercises/python/scikit-learn.py exercises/python/scikit-learn-snowy.sh exercises/python/scikit-learn-kebnekaise.py exercises/python/serial-python-scipybundle-kebnekaise.sh exercises/python/mmmult-cosmos.sh exercises/python/add-list-snowy.sh exercises/python/serial-python-scipybundle-cosmos.sh exercises/python/multiproc.py exercises/python/mmmult-rackham.sh exercises/python/sum-2args-rackham.sh exercises/python/scikit-learn-cosmos.sh exercises/python/serial-python-scipybundle-rackham.sh exercises/python/serial-python-virt-kebnekaise.sh exercises/python/multiproc-rackham.sh exercises/python/serial-python-virt-rackham.sh exercises/python/sum-2args-cosmos.sh exercises/python/example.py exercises/python/add-list-kebnekaise.sh exercises/python/seaborn-code-cosmos.py exercises/python/add-list.py exercises/python/pytorch_fitting_gpu-kebnekaise.sh exercises/python/gpu-python-cosmos.sh exercises/python/gpu-python-kebnekaise.sh exercises/python/tips.csv exercises/python/script-df-cosmos.sh exercises/python/pytorch_fitting_gpu-cosmos.sh exercises/README.md exercises/matlab/ exercises/matlab/parallel_example.m exercises/matlab/example-parallel-matlab.sh exercises/matlab/serial-monte-rackham.sh exercises/matlab/parallel_example-rackham.sh exercises/matlab/serial-monte-kebnekaise.sh exercises/matlab/parallel_example-kebnekaise.sh exercises/matlab/mmult.m exercises/matlab/parfeval_mean.m exercises/matlab/monte_carlo_pi.m exercises/matlab/parallel_example-cosmos.sh exercises/matlab/parfor-greet.m exercises/matlab/MorePractice.rst exercises/matlab/add2.m exercises/matlab/serial-monte-cosmos.sh exercises/matlab/dice_stats_par.m exercises/r/ exercises/r/iris_ml-rackham.sh exercises/r/Rscript_ML-kebnekaise.sh exercises/r/hello.R exercises/r/script-df.R exercises/r/add2-cosmos.sh exercises/r/Rscript_ML-cosmos.sh exercises/r/script-df-rackham.sh exercises/r/serial-rackham.sh exercises/r/iris.csv exercises/r/Rmpi-cosmos.sh exercises/r/validation-cosmos.sh exercises/r/validation-rackham.sh exercises/r/parallel_foreach.R exercises/r/serial_sum.R exercises/r/iris_ml.R exercises/r/serial.R exercises/r/serial-cosmos.sh exercises/r/parallel_foreach-cosmos.sh exercises/r/clusterapply.R exercises/r/parallel_foreach-kebnekaise.sh exercises/r/add2-kebnekaise.sh exercises/r/validation-kebnekaise.sh exercises/r/README.md exercises/r/Rmpi.R exercises/r/Rmpi-kebnekaise.sh exercises/r/serial-kebnekaise.sh exercises/r/Rscript_ML-rackham.sh exercises/r/parallel_foreach-rackham.sh exercises/r/script-df-kebnekaise.sh exercises/r/add2.R exercises/r/Rscript.R exercises/r/add2-rackham.sh exercises/r/sleep.R exercises/r/script-df-fixme.R exercises/r/Rmpi-rackham.sh exercises/r/iris_ml-kebnekaise.sh exercises/r/iris_ml-cosmos.sh exercises/r/validation.R exercises/r/script-df-cosmos.sh
After decompressing, there is a folder called exercises
that contains the exercises.