diff --git a/var/ramble/repos/builtin/applications/py-cosmoflow/application.py b/var/ramble/repos/builtin/applications/py-cosmoflow/application.py
new file mode 100644
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+++ b/var/ramble/repos/builtin/applications/py-cosmoflow/application.py
@@ -0,0 +1,337 @@
+# Copyright 2022-2025 The Ramble Authors
+#
+# Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+# https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+# <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+# option. This file may not be copied, modified, or distributed
+# except according to those terms.
+
+from ramble.appkit import *
+import os
+
+import ruamel.yaml as yaml
+import spack.util.spack_yaml as syaml
+import ramble.util.yaml_generation
+
+from spack.util.path import canonicalize_path
+
+
+class PyCosmoflow(ExecutableApplication):
+    """This is a an implementation of the CosmoFlow 3D convolutional neural
+    network for benchmarking. It is written in TensorFlow with the Keras API
+    and uses Horovod for distributed training.
+    """
+
+    name = "py-cosmoflow"
+
+    tags("mlperf-hpc")
+
+    default_config_string = "{default_config_value}"
+
+    input_file(
+        "cosmoUniverse_mini",
+        url="https://portal.nersc.gov/project/dasrepo/cosmoflow-benchmark/cosmoUniverse_2019_05_4parE_tf_v2_mini.tar",
+        description="Cosmoflow Universe - Mini input",
+    )
+
+    input_file(
+        "cosmoUniverse",
+        url="https://portal.nersc.gov/project/dasrepo/cosmoflow-benchmark/cosmoUniverse_2019_05_4parE_tf_v2.tar",
+        description="Cosmoflow Universe - Main input",
+    )
+
+    input_file(
+        "mlperf-hpc",
+        url="https://github.com/mlcommons/hpc/archive/refs/heads/main.tar.gz",
+        description="MLPerf HPC Repo",
+    )
+
+    executable(
+        "execute",
+        "python {mlperf-hpc}/cosmoflow/train.py -d --rank-gpu {cosmoflow_config}",
+        use_mpi=True,
+    )
+
+    workload(
+        "cosmoUniverse_mini",
+        executables=["execute"],
+        inputs=["cosmoUniverse_mini", "mlperf-hpc"],
+    )
+
+    workload(
+        "cosmoUniverse",
+        executables=["execute"],
+        inputs=["cosmoUniverse", "mlperf-hpc"],
+    )
+
+    workload_group(
+        "all_workloads", workloads=["cosmoUniverse_mini", "cosmoUniverse"]
+    )
+
+    workload_variable(
+        "dockerfile_path",
+        default="{mlperf-hpc}/cosmoflow/builds/Dockerfile",
+        description="Dockerfile for cosmoflow from the MLPerf-HPC repo",
+        workload_group="all_workloads",
+    )
+
+    workload_variable(
+        "docker_tag_name",
+        default="cosmoflow",
+        description="Name of docker image tag",
+        workload_group="all_workloads",
+    )
+
+    workload_variable(
+        "docker_tag_version",
+        default="1.0",
+        description="Version of docker image tag",
+        workload_group="all_workloads",
+    )
+
+    workload_variable(
+        "cosmoflow_config",
+        default=os.path.join("{experiment_run_dir}", "cosmo.yaml"),
+        description="Name of generated input for cosmoflow",
+        workload_group="all_workloads",
+    )
+
+    workload_variable(
+        "data.data_dir",
+        default="{cosmoUniverse_mini}",
+        description="Cosmoflow Data Directory",
+        workload_group="all_workloads",
+    )
+
+    workload_variable(
+        "data.n_train",
+        default="1024",
+        description="Number of training data sets",
+        workload="cosmoUniverse_mini",
+    )
+
+    workload_variable(
+        "data.n_valid",
+        default="1024",
+        description="Number of valid data sets",
+        workload="cosmoUniverse_mini",
+    )
+
+    workload_variable(
+        "data.n_train",
+        default="524288",
+        description="Number of training data sets",
+        workload="cosmoUniverse",
+    )
+
+    workload_variable(
+        "data.n_valid",
+        default="65536",
+        description="Number of valid data sets",
+        workload="cosmoUniverse",
+    )
+
+    workload_variable(
+        "mlperf.org",
+        default="ramble",
+        description="Organization for reporting MLPerf results",
+        workload_group="all_workloads",
+    )
+
+    workload_variable(
+        "mlperf.division",
+        default="experiments",
+        description="Division for reporting MLPerf results",
+        workload_group="all_workloads",
+    )
+
+    workload_variable(
+        "mlperf.status",
+        default="unknown",
+        description="Cluster status for reporting MLPerf results",
+        workload_group="all_workloads",
+    )
+
+    workload_variable(
+        "mlperf.platform",
+        default="unknown",
+        description="Platform name for reporting MLPerf results",
+        workload_group="all_workloads",
+    )
+
+    workload_variable(
+        "output_dir",
+        default="{experiment_run_dir}",
+        description="Experiment output directory",
+        workload_group="all_workloads",
+    )
+
+    workload_variable(
+        "cosmoflow_base_config",
+        default=os.path.join(
+            "{mlperf-hpc}", "cosmoflow", "configs", "cosmo.yaml"
+        ),
+        description="Base configuration file to generate cosmoflow inputs from",
+        workload_group="all_workloads",
+    )
+
+    figure_of_merit(
+        "Best Epoch",
+        fom_regex=r".*INFO\s+epoch: (?P<idx>[0-9]+)",
+        group_name="idx",
+        units="",
+    )
+
+    figure_of_merit(
+        "Best Epoch Loss",
+        fom_regex=r".*INFO\s+loss: (?P<loss>[0-9\.]+)",
+        group_name="loss",
+        units="",
+    )
+
+    figure_of_merit(
+        "Best Epoch LR",
+        fom_regex=r".*INFO\s+lr: (?P<lr>[0-9\.]+)",
+        group_name="lr",
+        units="",
+    )
+
+    figure_of_merit(
+        "Best Epoch Mean Absolute Error",
+        fom_regex=r".*INFO\s+mean_absolute_error: (?P<abs_err>[0-9\.]+)",
+        group_name="abs_err",
+        units="",
+    )
+
+    figure_of_merit(
+        "Best Epoch Time",
+        fom_regex=r".*INFO\s+time: (?P<time>[0-9\.]+)",
+        group_name="time",
+        units="s",
+    )
+
+    figure_of_merit(
+        "Best Epoch Val Loss",
+        fom_regex=r".*INFO\s+val_loss: (?P<val_loss>[0-9\.]+)",
+        group_name="val_loss",
+        units="",
+    )
+
+    figure_of_merit(
+        "Best Epoch Val Mean Absolute Error",
+        fom_regex=r".*INFO\s+val_mean_absolute_error: (?P<val_error>[0-9\.]+)",
+        group_name="val_error",
+        units="",
+    )
+
+    figure_of_merit(
+        "Total epoch time",
+        fom_regex=r".*INFO\s+Total epoch time: (?P<total_time>[0-9\.]+)",
+        group_name="total_time",
+        units="s",
+    )
+
+    figure_of_merit(
+        "Mean epoch time",
+        fom_regex=r".*INFO\s+Mean epoch time: (?P<mean_time>[0-9\.]+)",
+        group_name="mean_time",
+        units="s",
+    )
+
+    register_phase(
+        "ingest_default_configs", pipeline="setup", run_after=["get_inputs"]
+    )
+
+    def _ingest_default_configs(self, workspace, app_inst):
+        """Read config options from nemo_base_config, and define any that were
+        not defined in the input ramble.yaml or workload definition."""
+
+        base_config = get_file_path(
+            canonicalize_path(
+                self.expander.expand_var_name("cosmoflow_base_config")
+            ),
+            workspace,
+        )
+
+        # Avoid problems with missing base config files
+        if not os.path.exists(base_config):
+            return
+
+        config_data = ramble.util.yaml_generation.read_config_file(base_config)
+
+        for option_name in ramble.util.yaml_generation.all_config_options(
+            config_data
+        ):
+            if option_name not in self.variables:
+                value = ramble.util.yaml_generation.get_config_value(
+                    config_data, option_name
+                )
+
+                self.define_variable(option_name, value)
+
+    register_phase(
+        "write_config", pipeline="setup", run_after=["make_experiments"]
+    )
+
+    def _write_config(self, workspace, app_inst):
+
+        base_config = get_file_path(
+            canonicalize_path(
+                self.expander.expand_var_name("cosmoflow_base_config")
+            ),
+            workspace,
+        )
+
+        if not os.path.exists(base_config):
+            return
+
+        config_data = ramble.util.yaml_generation.read_config_file(base_config)
+
+        ramble.util.yaml_generation.apply_default_config_values(
+            config_data, self, self.default_config_string
+        )
+
+        # Set config options in config_data
+        for var_name in self.variables:
+            if "." in var_name and len(var_name.split(".")) > 1:
+                var_val = self.expander.expand_var(
+                    self.expander.expansion_str(var_name), typed=True
+                )
+
+                # Convert any invalid tuples back to their default strings.
+                if isinstance(var_val, tuple):
+                    var_val = self.expander.expand_var(
+                        self.expander.expansion_str(var_name)
+                    )
+                elif isinstance(var_val, list):
+                    for i in range(0, len(var_val)):
+                        var_val[i] = self.expander.expand_var(
+                            var_val[i], typed=True
+                        )
+
+                ramble.util.yaml_generation.set_config_value(
+                    config_data, var_name, var_val, force=True
+                )
+
+        # Set output dir:
+        ramble.util.yaml_generation.set_config_value(
+            config_data,
+            "output_dir",
+            self.expander.expand_var_name("output_dir"),
+        )
+
+        # Create string of yaml data.
+        config_str = yaml.dump(
+            config_data,
+            default_flow_style=False,
+            width=syaml.maxint,
+            Dumper=syaml.OrderedLineDumper,
+        )
+
+        # Write out experiment config
+        config_path = canonicalize_path(
+            self.expander.expand_var("{cosmoflow_config}"),
+        )
+
+        with open(config_path, "w+") as f:
+            f.write(config_str)