Metadata-Version: 2.1
Name: smartsim
Version: 0.4.2
Summary: AI Workflows for Science
Home-page: https://github.com/CrayLabs/SmartSim
Author: CrayLabs, a Hewlett Packard Enterprise OSS Organization
Author-email: craylabs@hpe.com
License: BSD 2-Clause License
Project-URL: Source, https://github.com/CrayLabs/SmartSim
Project-URL: Documentation, https://www.craylabs.org
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        ------------
        
        # SmartSim
        
        SmartSim is made up of two parts
          1. SmartSim Infrastructure Library (This repository)
          2. [SmartRedis](https://github.com/CrayLabs/SmartRedis)
        
        The two library components are designed to work together, but can also be used
        independently.
        
        *SmartSim* is a workflow library that makes it easier to use common Machine Learning (ML)
        libraries, like PyTorch and TensorFlow, in High Performance Computing (HPC) simulations
        and applications. SmartSim launches ML infrastructure on HPC systems alongside user
        workloads.
        
        *SmartRedis* provides an API to connect HPC workloads, particularly (MPI + X) simulations,
        to the ML infrastructure, namely the The [Orchestrator database](https://www.craylabs.org/docs/orchestrator.html),
        launched by SmartSim.
        
        Applications integrated with the SmartRedis clients, written in Fortran, C, C++ and Python,
        can send data to and remotely request SmartSim infrastructure to execute ML models and scripts
        on GPU or CPU. The distributed Client-Server paradigm allows for data to be seamlessly
        exchanged between applications at runtime without the utilization of MPI.
        
        ----------
        
        **Table of Contents**
        - [SmartSim](#smartsim)
        - [Quick Start](#quick-start)
        - [SmartSim Infrastructure Library](#smartsim-infrastructure-library)
          - [Experiments](#experiments)
            - [Hello World](#hello-world)
            - [Hello World MPI](#hello-world-mpi)
          - [Experiments on HPC Systems](#experiments-on-hpc-systems)
            - [Interactive Launch Example](#interactive-launch-example)
            - [Batch Launch Examples](#batch-launch-examples)
        - [Infrastructure Library Applications](#infrastructure-library-applications)
          - [Redis + RedisAI](#redis--redisai)
            - [Local Launch](#local-launch)
            - [Interactive Launch](#interactive-launch)
            - [Batch Launch](#batch-launch)
        - [SmartRedis](#smartredis)
          - [Tensors](#tensors)
          - [Datasets](#datasets)
        - [SmartSim + SmartRedis Tutorials](#smartsim--smartredis-tutorials)
          - [Run the Tutorials](#run-the-tutorials)
          - [Online Analysis](#online-analysis)
              - [Lattice Boltzmann Simulation](#lattice-boltzmann-simulation)
          - [Online Processing](#online-processing)
            - [Singular Value Decomposition](#singular-value-decomposition)
          - [Online Inference](#online-inference)
            - [PyTorch CNN Example](#pytorch-cnn-example)
        - [Publications](#publications)
        - [Cite](#cite)
          - [bibtex](#bibtex)
        
        ----
        
        # Quick Start
        
        
        The documentation has a number of tutorials that make it easy to get used to SmartSim locally
        before using it on your system. Each tutorial is a Jupyter notebook that can be run through the
        [SmartSim Tutorials docker image](https://github.com/orgs/CrayLabs/packages?repo_name=SmartSim)
        which will run a jupyter lab with the tutorials, SmartSim, and SmartRedis installed.
        
        ```bash
        docker pull ghcr.io/craylabs/smartsim-tutorials:v0.4.2
        docker run -p 8888:8888 ghcr.io/craylabs/smartsim-tutorials:v0.4.2
        # click on link to open jupyter lab
        ```
        
        # SmartSim Infrastructure Library
        
        The Infrastructure Library (IL), the ``smartsim`` python package,
        facilitates the launch of Machine Learning and simulation
        workflows. The Python interface of the IL creates, configures, launches and monitors
        applications.
        
        ## Experiments
        
        The [Experiment](https://www.craylabs.org/docs/api/smartsim_api.html#experiment) object
        is the main interface of SmartSim. Through the [Experiment](https://www.craylabs.org/docs/api/smartsim_api.html#experiment)
        users can create references to user applications called ``Models``.
        ### Hello World
        
        Below is a simple example of a workflow that uses the IL to launch hello world
        program using the local launcher which is designed for laptops and single nodes.
        
        ```python
        from smartsim import Experiment
        
        exp = Experiment("simple", launcher="local")
        
        settings = exp.create_run_settings("echo", exe_args="Hello World")
        model = exp.create_model("hello_world", settings)
        
        exp.start(model, block=True)
        print(exp.get_status(model))
        ```
        
        ### Hello World MPI
        
        The [Experiment.create_run_settings](https://www.craylabs.org/docs/api/smartsim_api.html#smartsim.experiment.Experiment.create_run_settings) method returns a ``RunSettings`` object which
        defines how a model is launched. There are many types of ``RunSettings`` [supported by
        SmartSim](https://www.craylabs.org/docs/api/smartsim_api.html#settings).
        
         - ``RunSettings``
         - ``MpirunSettings``
         - ``SrunSettings``
         - ``AprunSettings``
         - ``JsrunSettings``
        
        The following example launches a hello world MPI program using the local launcher
        for single compute node, workstations and laptops.
        
        ```Python
        from smartsim import Experiment
        
        exp = Experiment("hello_world", launcher="local")
        mpi_settings = exp.create_run_settings(exe="echo",
                                               exe_args="Hello World!",
                                               run_command="mpirun")
        mpi_settings.set_tasks(4)
        
        mpi_model = exp.create_model("hello_world", mpi_settings)
        
        exp.start(mpi_model, block=True)
        print(exp.get_status(model))
        ```
        
        If an argument of `run_command="auto"` (the default) is passed to
        `Experiment.create_run_settings`, SmartSim will attempt to find a run command on the
        system with which it has a corresponding `RunSettings` class. If one can be found,
        `Experiment.create_run_settings` will instance and return an object of that type.
        
        
        -----------
        ## Experiments on HPC Systems
        
        SmartSim integrates with common HPC schedulers providing batch and interactive
        launch capabilities for all applications.
        
         - Slurm
         - LSF
         - PBSPro
         - Cobalt
         - Local (for laptops/single node, no batch)
        
        
        ### Interactive Launch Example
        
        The following launches the same ``hello_world`` model in an interactive allocation.
        
        ```bash
        # get interactive allocation (Slurm)
        salloc -N 3 --ntasks-per-node=20 --ntasks 60 --exclusive -t 00:10:00
        
        # get interactive allocation (PBS)
        qsub -l select=3:ncpus=20 -l walltime=00:10:00 -l place=scatter -I -q <queue>
        
        # get interactive allocation (LSF)
        bsub -Is -W 00:10 -nnodes 3 -P <project> $SHELL
        ```
        
        This same script will run on a SLURM, PBS, LSF, or Cobalt system as the ``launcher``
        is set to `auto` in the [Experiment](https://www.craylabs.org/docs/api/smartsim_api.html#experiment)
        initialization. The run command like ``mpirun``,
        ``aprun`` or ``srun`` will be automatically detected from what is available on the
        system.
        
        ```python
        # hello_world.py
        from smartsim import Experiment
        
        exp = Experiment("hello_world_exp", launcher="auto")
        run = exp.create_run_settings(exe="echo", exe_args="Hello World!")
        run.set_tasks(60)
        run.set_tasks_per_node(20)
        
        model = exp.create_model("hello_world", run)
        exp.start(model, block=True, summary=True)
        
        print(exp.get_status(model))
        ```
        ```bash
        # in interactive terminal
        python hello_world.py
        ```
        
        
        This script could also be launched in a batch file instead of an
        interactive terminal. For example, for Slurm:
        
        ```bash
        #!/bin/bash
        #SBATCH --exclusive
        #SBATCH --nodes=3
        #SBATCH --ntasks-per-node=20
        #SBATCH --time=00:10:00
        
        python /path/to/hello_world.py
        ```
        ```bash
        # on Slurm system
        sbatch run_hello_world.sh
        ```
        
        
        ### Batch Launch Examples
        
        SmartSim can also launch workloads in a batch directly from Python, without the need
        for a batch script. Users can launch groups of ``Model`` instances in a ``Ensemble``.
        
        The following launches 4 replicas of the the same ``hello_world`` model.
        
        ```python
        # hello_ensemble.py
        from smartsim import Experiment
        
        exp = Experiment("hello_world_batch", launcher="auto")
        
        # define resources for all ensemble members
        batch = exp.create_batch_settings(nodes=4, time="00:10:00", account="12345-Cray")
        batch.set_queue("premium")
        
        # define how each member should run
        run = exp.create_run_settings(exe="echo", exe_args="Hello World!")
        run.set_tasks(60)
        run.set_tasks_per_node(20)
        
        ensemble = exp.create_ensemble("hello_world",
                                       batch_settings=batch,
                                       run_settings=run,
                                       replicas=4)
        exp.start(ensemble, block=True, summary=True)
        
        print(exp.get_status(ensemble))
        ```
        
        ```bash
        python hello_ensemble.py
        ```
        
        Similar to the interactive example, this same script will run on a SLURM, PBS, LSF,
        or Cobalt system as the ``launcher`` is set to `auto` in the
        [Experiment](https://www.craylabs.org/docs/api/smartsim_api.html#experiment)
        initialization. Local launching does not support batch workloads.
        
        
        --------
        
        # Infrastructure Library Applications
         - Orchestrator - In-memory data store and Machine Learning Inference (Redis + RedisAI)
        
        ## Redis + RedisAI
        
        The ``Orchestrator`` is an in-memory database that utilizes Redis and RedisAI to provide
        a distributed database and access to ML runtimes from Fortran, C, C++ and Python.
        
        SmartSim provides classes that make it simple to launch the database in many
        configurations and optionally form a distributed database cluster. The examples
        below will show how to launch the database. Later in this document we will show
        how to use the database to perform ML inference and processing.
        
        
        ### Local Launch
        
        The following script launches a single database using the local launcher.
        
        [Experiment.create_database](https://www.craylabs.org/docs/api/smartsim_api.html#smartsim.experiment.Experiment.create_database)
        will initialize an ``Orchestrator`` instance corresponding to the specified launcher.
        
        ```python
        # run_db_local.py
        from smartsim import Experiment
        
        exp = Experiment("local-db", launcher="local")
        db = exp.create_database(port=6780,       # database port
                                 interface="lo")  # network interface to use
        
        # by default, SmartSim never blocks execution after the database is launched.
        exp.start(db)
        
        # launch models, analysis, training, inference sessions, etc
        # that communicate with the database using the SmartRedis clients
        
        # stop the database
        exp.stop(db)
        ```
        
        ### Interactive Launch
        
        The ``Orchestrator``, like ``Ensemble`` instances, can be launched locally, in interactive
        allocations, or in a batch.
        
        The following example launches a distributed (3 node) database cluster
        in an interactive allocation.
        
        
        ```bash
        # get interactive allocation (Slurm)
        salloc -N 3 --ntasks-per-node=1 --exclusive -t 00:10:00
        
        # get interactive allocation (PBS)
        qsub -l select=3:ncpus=1 -l walltime=00:10:00 -l place=scatter -I -q queue
        
        # get interactive allocation (LSF)
        bsub -Is -W 00:10 -nnodes 3 -P project $SHELL
        
        ```
        
        ```python
        # run_db.py
        from smartsim import Experiment
        
        # auto specified to work across launcher types
        exp = Experiment("db-on-slurm", launcher="auto")
        db_cluster = exp.create_database(db_nodes=3,
                                         db_port=6780,
                                         batch=False,
                                         interface="ipogif0")
        exp.start(db_cluster)
        
        print(f"Orchestrator launched on nodes: {db_cluster.hosts}")
        # launch models, analysis, training, inference sessions, etc
        # that communicate with the database using the SmartRedis clients
        
        exp.stop(db_cluster)
        ```
        ```bash
        # in interactive terminal
        python run_db.py
        ```
        
        ### Batch Launch
        
        The ``Orchestrator`` can also be launched in a batch without the need for an interactive allocation.
        SmartSim will create the batch file, submit it to the batch system, and then wait for the database
        to be launched. Users can hit CTRL-C to cancel the launch if needed.
        
        ```Python
        # run_db_batch.py
        from smartsim import Experiment
        
        exp = Experiment("batch-db-on-pbs", launcher="auto")
        db_cluster = exp.create_database(db_nodes=3,
                                         db_port=6780,
                                         batch=True,
                                         time="00:10:00",
                                         interface="ib0",
                                         account="12345-Cray",
                                         queue="cl40")
        
        exp.start(db_cluster)
        
        print(f"Orchestrator launched on nodes: {db_cluster.hosts}")
        # launch models, analysis, training, inference sessions, etc
        # that communicate with the database using the SmartRedis clients
        
        exp.stop(db_cluster)
        ```
        
        ```bash
        python run_db_batch.py
        ```
        
        ------
        # SmartRedis
        
        The SmartSim IL Clients ([SmartRedis](https://github.com/CrayLabs/SmartRedis))
        are implementations of Redis clients that implement the RedisAI
        API with additions specific to scientific workflows.
        
        SmartRedis clients are available in Fortran, C, C++, and Python.
        Users can seamlessly pull and push data from the Orchestrator from different languages.
        
        ## Tensors
        
        Tensors are the fundamental data structure for the SmartRedis clients. The Clients
        use the native array format of the language. For example, in Python, a tensor is
        a NumPy array while the C/C++ clients accept nested and contiguous arrays.
        
        When stored in the database, all tensors are stored in the same format. Hence,
        any language can receive a tensor from the database no matter what supported language
        the array was sent from. This enables applications in different languages to communicate
        numerical data with each other at runtime.
        
        For more information on the tensor data structure, see
        [the documentation](https://www.craylabs.org/docs/sr_data_structures.html#tensor)
        
        ## Datasets
        
        Datasets are collections of Tensors and associated metadata. The ``Dataset`` class
        is a user space object that can be created, added to, sent to, and retrieved from
        the Orchestrator.
        
        For an example of how to use the ``Dataset`` class, see the [Online Analysis example](#online-analysis)
        
        For more information on the API, see the
        [API documentation](https://www.craylabs.org/docs/sr_data_structures.html#dataset)
        
        # SmartSim + SmartRedis Tutorials
        
        SmartSim and SmartRedis were designed to work together. When launched through
        SmartSim, applications using the SmartRedis clients are directly connected to
        any Orchestrator launched in the same [Experiment](https://www.craylabs.org/docs/api/smartsim_api.html#experiment).
        
        In this way, a SmartSim [Experiment](https://www.craylabs.org/docs/api/smartsim_api.html#experiment) becomes a driver for coupled ML and Simulation
        workflows. The following are simple examples of how to use SmartSim and SmartRedis
        together.
        
        ## Run the Tutorials
        
        Each tutorial is a Jupyter notebook that can be run through the
        [SmartSim Tutorials docker image](https://github.com/orgs/CrayLabs/packages?repo_name=SmartSim)
        which will run a jupyter lab with the tutorials, SmartSim, and SmartRedis installed.
        
        ```bash
        docker pull ghcr.io/craylabs/smartsim-tutorials:v1
        docker run -p 8888:8888 ghcr.io/craylabs/smartsim-tutorials:v0.4.2
        ```
        Each of the following examples can be found in the
        [SmartSim documentation](https://www.craylabs.org/docs/tutorials/getting_started/getting_started.html).
        
        ## Online Analysis
        
        Using SmartSim, HPC applications can be monitored in real time by streaming data
        from the application to the database. SmartRedis clients can retrieve the
        data, process, analyze it, and finally store any updated data back to the database for
        use by other clients.
        
        The following is an example of how a user could monitor and analyze a simulation.
        The example here uses the Python client; however, SmartRedis clients are also available
        for C, C++, and Fortran. All SmartRedis clients implement the same API.
        
        The example will produce [this visualization](https://user-images.githubusercontent.com/13009163/127622717-2c9e4cfd-50f4-4d94-88c4-8c05fa2fa616.mp4) while the simulation is running.
        
        #### Lattice Boltzmann Simulation
        
        Using a [Lattice Boltzmann Simulation](https://en.wikipedia.org/wiki/Lattice_Boltzmann_methods),
        this example demonstrates how to use the SmartRedis ``Dataset`` API to stream
        data over the Orchestrator deployed by SmartSim.
        
        The simulation will be composed of two parts: `fv_sim.py` which will generate data from
        the Simulation and store it in the Orchestrator, and `driver.py`
        which will launch the Orchestrator, start `fv_sim.py` and check for data posted to the
        Orchestrator to plot updates in real-time.
        
        The following code highlights the sections of `fv_sim.py` that are responsible for
        transmitting the data needed to plot timesteps of the simulation to the Orchestrator.
        
        ```Python
        # fv_sim.py
        from smartredis import Client
        import numpy as np
        
        # initialization code omitted
        
        # save cylinder location to database
        cylinder = (X - x_res/4)**2 + (Y - y_res/2)**2 < (y_res/4)**2 # bool array
        client.put_tensor("cylinder", cylinder.astype(np.int8))
        
        for time_step in range(steps): # simulation loop
            for i, cx, cy in zip(idxs, cxs, cys):
                F[:,:,i] = np.roll(F[:,:,i], cx, axis=1)
                F[:,:,i] = np.roll(F[:,:,i], cy, axis=0)
        
            bndryF = F[cylinder,:]
            bndryF = bndryF[:,[0,5,6,7,8,1,2,3,4]]
        
            rho = np.sum(F, 2)
            ux  = np.sum(F * cxs, 2) / rho
            uy  = np.sum(F * cys, 2) / rho
        
            Feq = np.zeros(F.shape)
            for i, cx, cy, w in zip(idxs, cxs, cys, weights):
                Feq[:,:,i] = rho * w * ( 1 + 3*(cx*ux+cy*uy)  + 9*(cx*ux+cy*uy)**2/2 - 3*(ux**2+uy**2)/2 )
            F += -(1.0/tau) * (F - Feq)
            F[cylinder,:] = bndryF
        
            # Create a SmartRedis dataset with vorticity data
            dataset = Dataset(f"data_{str(time_step)}")
            dataset.add_tensor("ux", ux)
            dataset.add_tensor("uy", uy)
        
            # Put Dataset in db at key "data_{time_step}"
            client.put_dataset(dataset)
        ```
        
        The driver script, `driver.py`, launches the Orchestrator database and runs
        the simulation in a non-blocking fashion. The driver script then uses the SmartRedis
        client to pull the DataSet and plot the vorticity while the simulation is running.
        
        ```Python
        # driver.py
        time_steps, seed = 3000, 42
        
        exp = Experiment("finite_volume_simulation", launcher="local")
        
        db = exp.create_database(port=6780,        # database port
                                 interface="lo")   # network interface db should listen on
        
        # create the lb simulation Model reference
        settings = exp.create_run_settings("python",
                                           exe_args=["fv_sim.py",
                                                     f"--seed={seed}",
                                                     f"--steps={time_steps}"])
        model = exp.create_model("fv_simulation", settings)
        model.attach_generator_files(to_copy="fv_sim.py")
        exp.generate(db, model, overwrite=True)
        
        exp.start(db)
        client = Client(address=db.get_address()[0], cluster=False)
        
        # start simulation (non-blocking)
        exp.start(model, block=False, summary=True)
        
        # poll until simulation starts and then retrieve data
        client.poll_key("cylinder", 200, 100)
        cylinder = client.get_tensor("cylinder").astype(bool)
        
        for i in range(0, time_steps):
            client.poll_key(f"data_{str(i)}", 10, 1000)
            dataset = client.get_dataset(f"data_{str(i)}")
            ux, uy = dataset.get_tensor("ux"), dataset.get_tensor("uy")
        
            # analysis/plotting code omitted
        
        exp.stop(db)
        ```
        
        For more examples of how to use SmartSim and SmartRedis together to perform
        online analysis, please see the
        [online analsysis tutorial section](https://www.craylabs.org/docs/tutorials/online_analysis/lattice/online_analysis.html) of the
        SmartSim documentation.
        
        ## Online Processing
        
        Each of the SmartRedis clients can be used to remotely execute
        [TorchScript](https://pytorch.org/docs/stable/jit.html) code on data
        stored within the database. The scripts/functions are executed in the Torch
        runtime linked into the database.
        
        Any of the functions available in the
        [TorchScript builtins](https://pytorch.org/docs/stable/jit_builtin_functions.html#builtin-functions)
        can be saved as "script" or "functions" in the database and used directly by
        any of the SmartRedis Clients.
        
        ### Singular Value Decomposition
        
        For example, the following code sends the built-in
        [Singular Value Decomposition](https://pytorch.org/docs/stable/generated/torch.svd.html)
        to the database and execute it on a dummy tensor.
        
        ```python
        import numpy as np
        from smartredis import Client
        
        # don't even need to import torch
        def calc_svd(input_tensor):
            return input_tensor.svd()
        
        
        # connect a client to the database
        client = Client(cluster=False)
        
        # get dummy data
        tensor = np.random.randint(0, 100, size=(5, 3, 2)).astype(np.float32)
        
        client.put_tensor("input", tensor)
        client.set_function("svd", calc_svd)
        
        client.run_script("svd", "calc_svd", "input", ["U", "S", "V"])
        # results are not retrieved immediately in case they need
        # to be fed to another function/model
        
        U = client.get_tensor("U")
        S = client.get_tensor("S")
        V = client.get_tensor("V")
        print(f"U: {U}, S: {S}, V: {V}")
        ```
        
        The processing capabilities make it simple to form computational pipelines of
        functions, scripts, and models.
        
        See the full [TorchScript Language Reference](https://pytorch.org/docs/stable/jit.html#torchscript-language)
        documentation for more information on available methods, functions, and how
        to create your own.
        
        ## Online Inference
        
        SmartSim supports the following frameworks for querying Machine Learning models
        from C, C++, Fortran and Python with the SmartRedis Clients:
        
        <table>
          <thead>
            <tr>
              <th style="text-align:center">RedisAI Version</th>
              <th style="text-align:center">Libraries</th>
              <th style="text-align:center">Supported Version</th>
            </tr>
          </thead>
          <tbody style="text-align:center">
            <tr>
              <td rowspan="3">1.2.7</td>
              <td>PyTorch</td>
              <td>1.11.x</td>
            </tr>
            <tr>
              <td>TensorFlow\Keras</td>
              <td>2.8.x</td>
            </tr>
            <tr>
              <td>ONNX</td>
              <td>1.11.x</td>
            </tr>
              <td rowspan="3">1.2.5</td>
              <td>PyTorch</td>
              <td>1.9.x</td>
            </tr>
            <tr>
              <td>TensorFlow\Keras</td>
              <td>2.6.x</td>
            </tr>
            <tr>
              <td>ONNX</td>
              <td>1.9.x</td>
            </tr>
          </tbody>
        </table>
        
        A [number of other libraries](https://github.com/onnx/onnxmltools) are
        supported through ONNX, like [SciKit-Learn](https://github.com/onnx/sklearn-onnx/)
        and [XGBoost](https://github.com/onnx/onnxmltools/tree/master/tests/xgboost).
        
        **Note:** It's important to remember that SmartSim utilizes a client-server model. To run
        experiments that utilize the above frameworks, you must first start the Orchestrator
        database with SmartSim.
        
        ### PyTorch CNN Example
        
        The example below shows how to spin up a database with SmartSim and
        invoke a PyTorch CNN model using the SmartRedis clients.
        
        ```python
        # simple_torch_inference.py
        import io
        import torch
        import torch.nn as nn
        from smartredis import Client
        from smartsim import Experiment
        
        exp = Experiment("simple-online-inference", launcher="local")
        db = exp.create_database(port=6780, interface="lo")
        
        class Net(nn.Module):
            def __init__(self):
                super().__init__()
                self.conv = nn.Conv2d(1, 1, 3)
        
            def forward(self, x):
                return self.conv(x)
        
        torch_model = Net()
        example_forward_input = torch.rand(1, 1, 3, 3)
        module = torch.jit.trace(torch_model, example_forward_input)
        model_buffer = io.BytesIO()
        torch.jit.save(module, model_buffer)
        
        exp.start(db, summary=True)
        
        address = db.get_address()[0]
        client = Client(address=address, cluster=False)
        
        client.put_tensor("input", example_forward_input.numpy())
        client.set_model("cnn", model_buffer.getvalue(), "TORCH", device="CPU")
        client.run_model("cnn", inputs=["input"], outputs=["output"])
        output = client.get_tensor("output")
        print(f"Prediction: {output}")
        
        exp.stop(db)
        ```
        
        The above python code can be run like any normal python script:
        ```bash
        python simple_torch_inference.py
        ```
        
        For more examples of how to use SmartSim and SmartRedis together to perform
        online inference, please see the
        [online inference tutorials section](https://www.craylabs.org/docs/tutorials/ml_inference/Inference-in-SmartSim.html) of the
        SmartSim documentation.
        
        --------
        
        # Publications
        
        The following are public presentations or publications using SmartSim
        
         - [Collaboration with NCAR - CGD Seminar](https://www.youtube.com/watch?v=2e-5j427AS0)
         - [SmartSim: Using Machine Learning in HPC Simulations](https://arxiv.org/abs/2104.09355)
         - [SmartSim: Online Analytics and Machine Learning for HPC Simulations](https://www.youtube.com/watch?v=JsSgq-fq44w&list=PLuQQBBQFfpgq0OvjKbjcYgTDzDxTqtwua&index=11)
         - [PyTorch Ecosystem Day Poster](https://assets.pytorch.org/pted2021/posters/J8.png)
        
        
        --------
        # Cite
        
        Please use the following citation when referencing SmartSim, SmartRedis, or any SmartSim related work:
        
        Partee et al., “Using Machine Learning at Scale in HPC Simulations with SmartSim:
        An Application to Ocean Climate Modeling”, Journal of Computational Science, Volume 62, 2022, 101707, ISSN 1877-7503
        
        Available: https://doi.org/10.1016/j.jocs.2022.101707.
        
        ## bibtex
        
        
        ```latex
        @article{PARTEE2022101707,
            title = {Using Machine Learning at scale in numerical simulations with SmartSim: An application to ocean climate modeling},
            journal = {Journal of Computational Science},
            volume = {62},
            pages = {101707},
            year = {2022},
            issn = {1877-7503},
            doi = {https://doi.org/10.1016/j.jocs.2022.101707},
            url = {https://www.sciencedirect.com/science/article/pii/S1877750322001065},
            author = {Sam Partee and Matthew Ellis and Alessandro Rigazzi and Andrew E. Shao and Scott Bachman and Gustavo Marques and Benjamin Robbins},
            keywords = {Deep learning, Numerical simulation, Climate modeling, High performance computing, SmartSim},
        }
        ```
        
Keywords: scientific,ai,workflow,hpc,analysis
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: License :: OSI Approved :: BSD License
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Requires-Python: <3.11,>=3.8
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: ml
