The following clients are the most widely used. Captain FTP has the ability to split files, download each segmentindividually, and then reassemble the pieces. Particularly whenconnecting to servers that limit the bandwidth for eachconnection, this can greatly improve download speeds.
It is shareware available from:. Fetch has a long history and enjoys tremendous popularityin the Mac OS community. Though it was not updated for several years,in version 4 it re-emerged as a modern, OS X-native FTP client,supporting server-to-server transfers, resumable downloads, and sitemirroring. Developed by Jim Matthews, formerly of Dartmouth, it is available freeof charge to users affiliated with academic institutions.
For others,Fetch is available as shareware. Visit the Fetch web site at:. Although Hefty FTP does not have a particularly intuitive interface, itdoes have a few unique features, such as the ability to scheduledownloads and play MP3 files. It also has a separate windowthat you can use to queue file transfers, pause and restart downloads,and adjust the priority of queued items.
Developed by Stairways Software, itis now a commercial product. Visit the Interarchy web site at:. NetFinder offers an interface that looks and behaves more like theFinder than any of the other programs. It is very customizable andhas a strong feature set.
Perhaps its most useful feature is itsability to move files between directories and servers without usingthe hard drive as an intermediary. Visit the NetFinder web site at:. It is shareware available from JomoSoft. You maydownload it from:. It is a commercial product developed by RobertVasvari. You may download a demo from the RBrowser web site:. Although its interfacedoesn't attempt to mimic the Finder, it is straightforward anduncluttered.
Youmay download it from:. This arrangement also helps make the software stack easy to understand - your view of the modules will not be cluttered with a bunch of conflicting packages. Your default module view on Bell will include a set of compilers and the set of basic software that has no dependencies such as Matlab and Fluent.
To make software available that depends on a compiler, you must first load the compiler, and then software which depends on it becomes available to you. In this way, all software you see when doing "module avail" is completely compatible with each other. Your default module view on Bell will include a set of compilers, and the set of basic software that has no dependencies such as Matlab and Fluent. To continue further into the hierarchy of modules, you will need to choose a compiler.
As an example, if you are planning on using the Intel compiler you will first want to load the Intel compiler:. With intel loaded, you can repeat the avail command and at the bottom of the output you will see the a section of additional software that the intel module provides:.
Several of these new packages also provide additional software packages, such as MPI libraries. You can repeat the last two steps with one of the MPI packages such as openmpi and you will have a few more software packages available to you. If you are looking for a specific software package and do not see it in your default view, the module command provides a search function for searching the entire hierarchy tree of modules without need for you to manually load and avail on every module.
This will search for the openmpi software package. If you do not specify a specific version of the package, you will be given a list of versions available on the system. Select the version you wish to use and spider that to see how to access the module:. The output of this command will instruct you that you can load the this module directly, or in case of the above example, that you will need to first load a module or two.
With the information provide with this command, you can now construct a load command to load a version of OpenMPI into your environment:. Some user communities may maintain copies of their domain software for others to use.
For example, the Purdue Bioinformatics Core provides a wide set of bioinformatcs software for use by any user of ITaP clusters via the bioinfo module. The spider command will also search this repository of modules. If it finds a software package available in the bioinfo module repository, the spider command will instruct you to load the bioinfo module first.
All modules consist of both a name and a version number. When loading a module, you may use only the name to load the default version, or you may specify which version you wish to load. For each cluster, ITaP makes a recommendation regarding the set of compiler, math library, and MPI library for parallel code.
To load the recommended set:. When running a job, you must use the job submission file to load on the compute node s any relevant modules. Loading modules on the front end before submitting your job makes the software available to your session on the front-end, but not to your job submission script environment.
You must load the necessary modules in your job submission script. To learn more about what a module does to your environment, you may use the module show command. Here is an example showing what loading the default Matlab does to the processing environment:. A serial program is a single process which executes as a sequential stream of instructions on one processor core. The Intel and GNU compilers will not output anything for a successful compilation. Also, the Intel compiler does not recognize the suffix ".
An OpenMP program is a single process that takes advantage of a multi-core processor and its shared memory to achieve a form of parallel computing called multithreading. It distributes the work of a process over processor cores in a single compute node without the need for MPI communications. A hybrid program combines both MPI and shared-memory to take advantage of compute clusters with multi-core compute nodes.
Also, the Intel compiler does not recognize the suffix. By using module load to load an Intel compiler your environment will have several variables set up to help link applications with MKL. Here are some example combinations of simplified linking options:. ITaP recommends you use the provided variables to define MKL linking options in your compiling procedures. ITaP recommends that you use dynamic linking of libguide. If you use static linking of libguide, then:.
Compiler sets from Intel and GNU are installed. To discover which versions are available:. An older version of the GNU compiler will be in your path by default. Do NOT use this version. Instead, load a newer version using the command module load gcc. More information on compiler options appear in the official man pages, which are accessible with the man command after loading the appropriate compiler module.
See the detailed hardware overview for the specifics on the GPUs in Bell. The above example illustrates only how to copy an array between a CPU and its GPU but does not perform a serious computation. For best performance, the input array or matrix must be sufficiently large to overcome the overhead in copying the input and output data to and from the GPU.
There is one method for submitting jobs to Bell. SLURM performs job scheduling. Jobs may be any type of program. You may use either the batch or interactive mode to run your jobs. Use the batch mode for finished programs; use the interactive mode only for debugging. In this section, you'll find a few pages describing the basics of creating and submitting SLURM jobs. The system will then take jobs from queues, allocate the necessary nodes, and execute them. All users share the front-end hosts, and running anything but the smallest test job will negatively impact everyone's ability to use Bell.
Follow the links below for information on these steps, and other basic information about jobs. Dependencies are an automated way of holding and releasing jobs. Jobs with a dependency are held until the condition is satisfied.
Once the condition is satisfied jobs only then become eligible to run and must still queue as normal. Job dependencies may be configured to ensure jobs start in a specified order. Jobs can be configured to run after other job state changes, such as when the job starts or the job ends. These examples illustrate setting dependencies in several ways. Typically dependencies are set by capturing and using the job ID from the last job submitted.
Sometimes you may want to submit a job but not have it run just yet. You may be wanting to allow labmates to cut in front of you in the queue - so hold the job until their jobs have started, and then release yours. Once a job is submitted , and has started, it will write its standard output and standard error to files that you can read. SLURM catches output written to standard output and standard error - what would be printed to your screen if you ran your program interactively.
Unless you specfied otherwise, SLURM will put the output in the directory where you submitted the job in a file named slurm- followed by the job id , with the extension out. For example slurm Note that both stdout and stderr will be written into the same file, unless you specify otherwise. If your program writes its own output files, those files will be created as defined by the program.
This may be in the directory where the program was run, or may be defined in a configuration or input file. You will need to check the documentation for your program for more details. It is possible to redirect job output to somewhere other than the default location with the --error and --output directives:. Bell, as a community cluster, has one or more queues dedicated to and named after each partner who has purchased access to the cluster. These queues provide partners and their researchers with priority access to their portion of the cluster.
Jobs in these queues are typically limited to hours. The expectation is that any jobs submitted to named partner queues will start within 4 hours, assuming the queue currently has enough capacity for the job that is, your labmates aren't using all of the cores currently.
Link to section 'Standby Queue' of 'Queues' Standby Queue Additionally, community clusters provide a "standby" queue which is available to all cluster users. This "standby" queue allows users to utilize portions of the cluster that would otherwise be idle, but at a lower priority than partner-queue jobs, and with a relatively short time limit, to ensure "standby" jobs will not be able to tie up resources and prevent partner-queue jobs from running quickly.
Jobs in standby are limited to 4 hours. There is no expectation of job start time. If the cluster is very busy with partner queue jobs, or you are requesting a very large job, jobs in standby may take hours or days to start. Link to section 'Debug Queue' of 'Queues' Debug Queue The debug queue allows you to quickly start small, short, interactive jobs in order to debug code, test programs, or test configurations.
You are limited to one running job at a time in the queue, and you may run up to two compute nodes for 30 minutes. The expectation is that debug jobs should start within a couple of minutes, assuming all of its dedicated nodes are not taken by others.
This lists each queue you can submit to, the number of nodes allocated to the queue, how many are available to run jobs, and the maximum walltime you may request. Options to the command will give more detailed information. This command can be used to get a general idea of how busy an individual queue is and how long you may have to wait for your job to start. This job submission file is essentially a simple shell script. It will set any required environment variables, load any necessary modules, create or modify files and directories, and run any applications that you need:.
Once your script is prepared, you are ready to submit your job. Once a job is submitted there are several commands you can use to monitor the progress of the job. To retrieve useful information about your queued or running job, use the scontrol show job command with your job's ID number.
The output should look similar to the following:. SLURM will find, or wait for, available resources matching your request and run your job there. Slurm uses the word 'Account' and the option '-A' to specify different batch queues. To submit your job to a specific queue:.
By default, each job receives 30 minutes of wall time , or clock time. If you know that your job will not need more than a certain amount of time to run, request less than the maximum wall time, as this may allow your job to run sooner. To request the 1 hour and 30 minutes of wall time:. In some cases, you may want to request multiple nodes. To utilize multiple nodes, you will need to have a program or code that is specifically programmed to use multiple nodes such as with MPI.
Simply requesting more nodes will not make your work go faster. Your code must support this ability. If more convenient, you may also specify any command line options to sbatch from within your job submission file, using a special form of comment:. If an option is present in both your job submission file and on the command line, the option on the command line will take precedence. How long it takes for a job to start depends on the specific queue, the resources and time requested, and other jobs already waiting in that queue requested as well.
It is impossible to say for sure when any given job will start. For best results, request no more resources than your job requires. Once your job is submitted, you can monitor the job status , wait for the job to complete, and check the job output. This is a reference for the most common command, environment variables, and job specification options used by the workload management systems and their equivalents.
Unlike PBS, in Slurm interactive jobs and batch jobs are launched with completely distinct commands. Use sbatch [allocation request options] script to submit a job to the batch scheduler, and sinteractive [allocation request options] to launch an interactive job.
Therefore in Slurm you can examine the output and error files from your job during its execution. See the official Slurm Documentation for further details. A number of example jobs are available for you to look over and adapt to your own needs. The first few are generic examples, and latter ones go into specifics for particular software packages.
The following examples demonstrate the basics of SLURM jobs, and are designed to cover common job request scenarios. These example jobs will need to be modified to run your application or code. A job submission file contains a list of commands that run your program and a set of resource nodes, walltime, queue requests.
The resource requests can appear in the job submission file or can be specified at submit-time as shown below. This simple example submits the job submission file hello. For a real job you would replace echo "Hello World" with a command, or sequence of commands, that run your program.
After your job finishes running, the ls command will show a new file in your directory, the. The file slurm You should see the hostname of the compute node your job was executed on. Following should be the "Hello World" statement. This example shows a request for multiple compute nodes.
The job submission file contains a single command to show the names of the compute nodes allocated:. So far these examples have shown submitting jobs with the resource requests on the sbatch command line such as:. The resource requests can also be put into job submission file itself. Documenting the resource requests in the job submission is desirable because the job can be easily reproduced later.
Details left in your command history are quickly lost. SLURM will stop parsing directives as soon as it encounters a line that does not start with ' '. If you insert a directive in the middle of your script, it will be ignored. SLURM allows running a job on specific types of compute nodes to accommodate special hardware requirements e.
Cluster nodes have a set of descriptive features assigned to them, and users can specify which of these features are required by their job by using the constraint option at submission time. Only nodes having features matching the job constraints will be used to satisfy the request.
Feature constraints can be used for both batch and interactive jobs, as well as for individual job steps inside a job. Multiple constraints can be specified with a predefined syntax to achieve complex request logic see detailed description of the '--constraint' option in man sbatch or online Slurm documentation. Refer to Detailed Hardware Specification section for list of available sub-cluster labels, their respective per-node memory sizes and other hardware details.
You could also use sfeatures command to list available constraint feature names for different node types. Interactive jobs are run on compute nodes, while giving you a shell to interact with. They give you the ability to type commands or use a graphical interface as if you were on a front-end. To submit an interactive job, use sinteractive to run a login shell on allocated resources.
This shows how to submit one of the serial programs compiled in the section Compiling Serial Programs. A shared-memory job is a single process that takes advantage of a multi-core processor and its shared memory to achieve parallelization. When running OpenMP programs, all threads must be on the same compute node to take advantage of shared memory. The threads cannot communicate between nodes. This should almost always be equal to the number of cores on a compute node.
You may want to set to another appropriate value if you are running several processes in parallel in a single job or node. If an OpenMP program uses a lot of memory and threads use all of the memory of the compute node, use fewer processor cores OpenMP threads on that compute node.
An MPI job is a set of processes that take advantage of multiple compute nodes by communicating with each other. Use module load to set up the paths to access these libraries. Use module avail to see all MPI packages installed on Bell. The number of processes is requested with the -n option. If you do not specify the -n option, it will default to the total number of processor cores you request from SLURM. If the code is built with OpenMPI, it can be run with a simple srun -n command.
If an MPI job uses a lot of memory and MPI ranks per compute node use all of the memory of the compute nodes, request more compute nodes, while keeping the total number of MPI ranks unchanged. Submit the job with double the number of compute nodes and modify the resource request to halve the number of MPI ranks per compute node. Requesting a GPU from the scheduler is required. You can request total number of gpus, or gpus-per-node, or even gpus-per-task:.
To use multiple GPUs in your job, simply specify a larger value to the gpu-specification parameter. However, be aware of the number of GPUs installed on the node s you may be requesting. The scheduler can not allocate more GPUs than exist. See detailed hardware overview and output of sfeatures command for the specifics on the GPUs in Bell.
Knowing the precise resource utilization an application had during a job, such as CPU load or memory, can be incredibly useful. This is especially the case when the application isn't performing as expected. One approach is to run a program like htop during an interactive job and keep an eye on system resources. You can get precise time-series data from nodes associated with your job using XDmod as well, online. But these methods don't gather telemetry in an automated fashion, nor do they give you control over the resolution or format of the data.
As a matter of course, a robust implementation of some HPC workload would include resource utilization data as a diagnostic tool in the event of some failure. The monitor utility is a simple command line system resource monitoring tool for gathering such telemetry and is available as a module. Complete documentation is available online at resource-monitor. A full manual page is also available for reference, man monitor. In the context of a SLURM job you will need to put this monitoring task in the background to allow the rest of your job script to proceed.
Be sure to interrupt these tasks at the end of your job. A particularly elegant solution would be to include such tools in your prologue script and have the tear down in your epilogue script. For large distributed jobs spread across multiple nodes, mpiexec can be used to gather telemetry from all nodes in the job. The hostname is included in each line of output so that data can be grouped as such. A concise way of constructing the needed list of hostnames in SLURM is to simply use srun hostname sort -u.
To get resource data in a more readily computable format, the monitor program can be told to output in CSV format with the --csv flag. For a distributed job you will need to suppress the header lines otherwise one will be created by each host. The following examples demonstrate job submission files for some common real-world applications. Gaussian is a computational chemistry software package which works on electronic structure. This section illustrates how to submit a small Gaussian job to a PBS queue.
This Gaussian example runs the Fletcher-Powell multivariable optimization. Prepare a Gaussian input file with an appropriate filename, here named myjob. The final blank line is necessary:. To submit this job, load Gaussian then run the provided script, named subg This job uses one compute node with processor cores:. View results in the file for Gaussian output, here named myjob.
Only the first and last few lines appear here:. The collection of these packages are referred to as ML-Toolkit throughout this documentation. Currently, following 9 applications are included in ML-Toolkit. Note that managing Python dependencies of ML applications is non-trivial, therefore, we recommend that you read the documentations carefully before embarking on a journey to build intelligent machines.
Currently, applications are supported for two major Python versions 2. Detailed instructions for searching and using the installed ML applications are presented below. Important: You must load one of the learning modules described below before loading the ML applications. Make sure your Python environment is clean. Python is very sensitive about packages installed in your local pip folder or in your Conda environments. It is always safer to start with a clean environment.
The steps below archive all your existing python packages to backup directories reducing chances of conflict. To search or load a machine learning application, you must first load one of the learning modules. The learning module loads the prerequisites such as anaconda and cudnn and makes ML applications visible to the user. There are four learning modules available on bell, each corresponding to a specific Python version and whether the ML applications have GPU support or not.
In the example below, we want to use the learning module for Python 3. You can now use the module spider command to find installed applications. The following example searches for available PyTorch installations. Note that the ML packages are installed under the common application name ml-toolkit-X , where X can be cpu or gpu.
To list all GPU versions of machine learning packages installed on bell, run the command:. Step 4. After loading a preferred learning module in Step 1, you can now load the desired ML applications in your environment. Step 5. You can list which ML applications are loaded in your environment using the command. Step 6. The next step is to check that you can actually use the desired ML application.
You can do this by running the import command in Python. If the import operation succeeded, then you can run your own ML codes. Few ML applications such as tensorflow print diagnostic warnings while loading--this is the expected behavior. Step 7. To load a different set of applications, unload the previously loaded applications and load the new applications. ML applications depend on a wide range of Python packages and mixing multiple versions of these packages can lead to error.
The following guidelines will assist you in identifying the cause of the problem. More examples showing how to use ml-toolkit modules in a batch job are presented in this guide. If the ML application you are trying to use is not in the list of supported applications or if you need a newer version of an installed application, you can install it in your home directory.
We recommend using anaconda environments to install and manage Python packages. Please follow the steps carefully, otherwise you may end up with a faulty installation. The example below shows how to install PyTorch. Step 3: Create a custom anaconda environment. Make sure the python version matches the Python version in the anaconda module. Step 4: Activate the anaconda environment by loading the modules displayed at the end of step 3. Step 5: Now install the desired ML application. You can install multiple Python packages at this step using either conda or pip.
After running source activate , you may not be able to access any Python packages in anaconda or ml-toolkit modules. Therefore, using conda-env-mod is the preferred way of using your custom installations. In most situations, dependencies among Python modules lead to error. If you cannot use a Python package after installing it, please follow the steps below to find a workaround.
Batch jobs allow us to automate model training without human intervention. They are also useful when you need to run a large number of simulations on the clusters. We consider two situations: in the first example, we use the ML-Toolkit modules to run tensorflow, while in the second example, we use our custom installation of tensorflow. Once the job finishes, you will find an output slurm-xxxxx.
If tensorflow ran successfully, then the output file will contain the message shown below. ITaP provides a set of stable tensorflow builds on Bell. At present, tensorflow is part of the ML-Toolkit packages. You must load one of the learning modules before you can load the tensorflow module. We recommend getting an interactive job for running Tensorflow. ITaP recommends downloading and installing Tensorflow in user's home directory using anaconda environments.
Installing Tensorflow in your home directory has the advantage that it can be upgraded to newer versions easily. Therefore, researchers will have access to the latest libraries when needed. For this we shall use the matrix multiplication example from Tensorflow documentation. For more details, please refer to Tensorflow User Guide. The MATLAB client can be run in the front-end for application development, however, computationally intensive jobs must be run on compute nodes.
Also, prepare a job submission file, here named myjob. Run with the name of the script:. View results of the job. Output shows that a processor core on one compute node bell-a processed the job. Output also displays the three random numbers. MATLAB implements implicit parallelism which is automatic multithreading of many computations, such as matrix multiplication, linear algebra, and performing the same operation on a set of numbers.
This is different from the explicit parallelism of the Parallel Computing Toolbox. Since these processor cores, or threads, share a common memory, many MATLAB functions contain multithreading potential. Vector operations, the particular application or algorithm, and the amount of computation array size contribute to the determination of whether a function runs serially or with multithreading. When your job triggers implicit parallelism, it attempts to allocate its threads on all processor cores of the compute node on which the MATLAB client is running, including processor cores running other jobs.
This competition can degrade the performance of all jobs running on the node. When you know that you are coding a serial job but are unsure whether you are using thread-parallel enabled operations, run MATLAB with implicit parallelism turned off. When you are using implicit parallelism, make sure you request exclusive access to a compute node, as MATLAB has no facility for sharing nodes. MATLAB offers two kinds of profiles for parallel execution: the 'local' profile and user-defined cluster profiles.
The 'local' profile runs a MATLAB job on the processor core s of the same compute node, or front-end, that is running the client. To prepare a user-defined cluster profile, use the Cluster Profile Manager in the Parallel menu. This profile contains the scheduler details queue, nodes, processors, walltime, etc. For your convenience, ITaP provides a generic cluster profile that can be downloaded: myslurmprofile. Please note that modifications are very likely to be required to make myslurmprofile.
You may need to change values for number of nodes, number of workers, walltime, and submission queue specified in the file. As well, the generic profile itself depends on the particular job scheduler on the cluster, so you may need to download or create two or more generic profiles under different names. Each time you run a job using a Cluster Profile, make sure the specific profile you are using is appropriate for the job and the cluster. In the Cluster Profile Manager, select Import , navigate to the folder containing the profile, select myslurmprofile.
Remember that the profile will need to be customized for your specific needs. If you have any questions, please contact us. It offers a shared-memory computing environment running on the local cluster profile in addition to your MATLAB client. This section illustrates the fine-grained parallelism of a parallel for loop parfor in a pool job.
The following examples illustrate a method for submitting a small, parallel, MATLAB program with a parallel loop parfor statement as a job to a queue. This MATLAB program prints the name of the run host and shows the values of variables numlabs and labindex for each iteration of the parfor loop.
The execution of a pool job starts with a worker executing the statements of the first serial region up to the parfor block, when it pauses. A set of workers the pool executes the parfor block. When they finish, the first worker resumes by executing the second serial region. The code displays the names of the compute nodes running the batch session and the worker pool.
Use an appropriate filename, here named mylclbatch. Submit the job as a single compute node with one processor core. To scale up this method to handle a real application, increase the wall time in the submission command to accommodate a longer running job. Secondly, increase the wall time of myslurmprofile by using the Cluster Profile Manager in the Parallel menu to enter a new wall time in the property SubmitArguments. Four DCS licenses run the four copies of the spmd statement.
This job is completely off the front end. Prepare a job submission file with an appropriate filename, here named myjob. View results for the job. Output shows the name of one compute node a that processed the job submission file myjob. The job submission scattered four processor cores four MATLAB labs among four different compute nodes a,a,a,a that processed the four parallel regions.
The total elapsed time demonstrates that the jobs ran in parallel. The tasks of a parallel job are identical, run simultaneously on several MATLAB workers labs , and communicate with each other. This section illustrates an MPI-like program.
The MATLAB program broadcasts an integer to four workers and gathers the names of the compute nodes running the workers and the lab IDs of the workers. This example uses the job submission command to submit a Matlab script with a user-defined cluster profile which scatters the MATLAB workers onto different compute nodes. Four DCS licenses run the four copies of the parallel job. Also, prepare a job submission, here named myjob. Notice: Python 2. Please update your codes and your job scripts to use Python 3.
Python is a high-level, general-purpose, interpreted, dynamic programming language. We suggest using Anaconda which is a Python distribution made for large-scale data processing, predictive analytics, and scientific computing.
For example, to use the default Anaconda distribution:. Change your job submission file to submit this script and the job will output a png file and blank standard output and error files. Conda is a package manager in Anaconda that allows you to create and manage multiple environments where you can pick and choose which packages you want to use. To use Conda you must load an Anaconda module:. The --name option specifies that the environment created will be named MyEnvName.
You can include as many packages as you require separated by a space. Including the -y option lets you skip the prompt to install the package. Installing packages when creating your environment, instead of one at a time, will help you avoid dependency issues. If you created your conda environment at a custom location using --prefix option, then you can activate or deactivate it using the full path. To use a custom environment inside a job you must load the module and activate the environment inside your job submission script.
Add the following lines to your submission script:. Pip is a Pythom package manager. Many Python package documentation provide pip instructions that result in permission errors because by default pip will install in a system-wide location and fail. If you encounter this error, it means that you cannot modify the global Python installation. We recommend installing Python packages in a conda environment.
Detailed instructions for installing packages with pip can be found in our Python package installation page. ITaP recommends installing Python packages in an Anaconda environment. One key advantage of Anaconda is that it allows users to install unrelated packages in separate self-contained environments. Individual packages can later be reinstalled or updated without impacting others. If you are unfamiliar with Conda environments, please check our Conda Guide. To facilitate the process of creating and using Conda environments, we support a script conda-env-mod that generates a module file for an environment, as well as an optional Jupyter kernel to use this environment in a JupyterHub notebook.
Users can use the conda-env-mod script to create an empty conda environment. This script needs either a name or a path for the desired environment. After the environment is created, it generates a module file for using it in future. Please note that conda-env-mod is different from the official conda-env script and supports a limited set of subcommands. Detailed instructions for using conda-env-mod can be found with the command conda-env-mod --help. Example 1: Create a conda environment named mypackages in user's home directory.
Example 2: Create a conda environment named mypackages at a custom location. Please follow the on-screen instructions while the environment is being created. After finishing, the script will print the instructions to use this environment. Note down the module names, as you will need to load these modules every time you want to use this environment.
You may also want to add the module load lines in your jobscript, if it depends on custom Python packages. If you used a custom module file location, you need to run the module use command as printed by the script. By default, only the environment and a module file are created no Jupyter kernel.
If you plan to use your environment in a JupyterHub notebook, you need to append a --jupyter flag to the above commands. The following instructions assume that you have used rcac-conda-env script to create an environment named mypackages Examples 1 or 2 above. If you used conda create instead, please use conda activate mypackages. Note that the conda-env module name includes the Python version that it supports Python 3.
This is same as the Python version in the anaconda module. If you used a custom module file location Example 3 above , please use module use to load the conda-env module. Now you can install custom packages in the environment using either conda install or pip install. Follow the on-screen instructions while the packages are being installed.
If installation is successful, please proceed to the next section to test the packages. Note: Do NOT run Pip with the --user argument, as that will install packages in a different location. To use the installed Python packages, you must load the module for your conda environment.
If you have not loaded the conda-env module, please do so following the instructions at the end of Step 1. If the commands finished without errors, then the installed packages can be used in your program. The conda-env-mod tool is intended to facilitate creation of a minimal Anaconda environment, matching module file and optionally a Jupyter kernel.
Once created, the environment can then be accessed via familiar module load command, tuned and expanded as necessary. Additionally, the script provides several auxiliary functions to help managing environments, module files and Jupyter kernels. Given a required name or prefix for an environment, the conda-env-mod script supports the following subcommands:. Using these subcommands, you can iteratively fine-tune your environments, module files and Jupyter kernels, as well as delete and re-create them with ease.
Below we cover several commonly occurring scenarios. If you already have an existing configured Anaconda environment and want to generate a module file for it, follow appropriate examples from Step 1 above, but use the module subcommand instead of the create one. With an optional --jupyter flag, a Jupyter kernel will also be generated. Note that if you intend to proceed with a Jupyter kernel generation via the --jupyter flag or a kernel subcommand later , you will have to ensure that your environment has ipython and ipykernel packages installed into it.
To avoid this and other related complications, we highly recommend making a fresh environment using a suitable conda-env-mod create If you already have an existing configured Anaconda environment and want to generate a Jupyter kernel file for it, you can use the kernel subcommand. This will add a "Python My mypackages Kernel " item to the dropdown list of available kernels upon your next login to the JupyterHub.
Note that generated Jupiter kernels are always personal i. Note also that you or the creator of the shared environment will have to ensure that your environment has ipython and ipykernel packages installed into it. Here is a suggested workflow for a common group-shared Anaconda environment with Jupyter capabilities:. Lab members can start using the environment in their command line scripts or batch jobs simply by loading the corresponding module:.
This is because Jupyter kernels are private to individuals, even for shared environments. A similar process can be devised for instructor-provided or individually-managed class software, etc. We maintain several Anaconda installations. Anaconda maintains numerous popular scientific Python libraries in a single installation. If you need a Python library not included with normal Python we recommend first checking Anaconda. For a list of modules currently installed in the Anaconda Python distribution:.
If you see the library in the list, you can simply import it into your Python code after loading the Anaconda module. If you do not find the package you need, you should be able to install the library in your own Anaconda customization. First try to install it with Conda or Pip.
If the package is not available from either Conda or Pip, you may be able to install it from source. Use the following instructions as a guideline for installing packages from source. Make sure you have a download link to the software usually it will be a tar. You will substitute it on the wget line below. We also assume that you have already created an empty conda environment as described in our Python package installation guide. The "import app" line should return without any output if installed successfully.
You can then import the package in your python scripts. If you need further help or run into any issues installing a library contact us at rcac-help purdue. The --channel option specifies that it searches the anaconda channel for the biopython package. The -y argument is optional and allows you to skip the installation prompt. A list of packages will be displayed as they are installed. Remember to add the following lines to your job submission script to use the custom environment in your jobs:.
If you need further help or run into any issues with creating environments contact us at rcac-help purdue. The widely available Numpy package is the best way to handle numerical computation in Python. The numpy package provided by our anaconda modules is optimized using Intel's MKL library. It will automatically parallelize many operations to make use of all the cores available on a machine.
In many contexts that would be the ideal behavior. Having multiple processes contend for those resources will actually result in lesser performance. Our anaconda modules automatically set these variables to 1 if and only if you do not currently have that variable defined. When submitting batch jobs it is always a good idea to be explicit rather than implicit.
If you are submitting a job that you want to make use of the full resources available on the node, set one or both of these variables to the number of cores you want to allow numpy to make use of. If you are submitting multiple jobs that you intend to be scheduled together on the same node, it is probably best to restrict numpy to a single core. R, a GNU project, is a language and environment for data manipulation, statistics, and graphics. It is an open source version of the S programming language.
R is quickly becoming the language of choice for data science due to the ease with which it can produce high quality plots and data visualizations. It is a versatile platform with a large, growing community and collection of packages. The example job computes a Pythagorean triple. Step 0: Set up installation preferences. Rprofile preferences. This step needs to be done only once. Rprofile file previously on Bell, ignore this step. Step 1: Check if the package is already installed.
You can check if your package is alreday installed by opening an R terminal and entering the command installed. For example,. If the package you are trying to use is already installed, simply load the library, e. Otherwise, move to the next step to install the package. Step 2: Load required dependencies. However, some R packages depend on other libraries. For example, the sf package depends on gdal and geos libraries. So, you will need to load the corresponding modules before installing sf.
Read the documentation for the package to identify which modules should be loaded. Step 3: Install the package. Now install the desired package using the command install. R will automatically download the package and all its dependencies from CRAN and install each one. Your terminal will show the build progress and eventually show whether the package was installed successfully or not.
Once you have packages installed you can load them with the library function as shown below:.
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