diff --git a/test/pytorch_mnist_image/Dockerfile b/test/pytorch_mnist_image/Dockerfile
deleted file mode 100644
index adbbd7cbc..000000000
--- a/test/pytorch_mnist_image/Dockerfile
+++ /dev/null
@@ -1,18 +0,0 @@
-# Build the manager binary
-FROM pytorch/pytorch:1.11.0-cuda11.3-cudnn8-runtime
-
-WORKDIR /test
-COPY entrypoint.sh entrypoint.sh
-
-# Install MNIST requirements
-COPY mnist_pip_requirements.txt requirements.txt
-RUN pip install --requirement requirements.txt
-
-# Prepare MNIST script
-COPY mnist.py mnist.py
-COPY download_dataset.py download_dataset.py
-RUN torchrun download_dataset.py
-
-USER 65532:65532
-WORKDIR /workdir
-ENTRYPOINT ["/test/entrypoint.sh"]
diff --git a/test/pytorch_mnist_image/download_dataset.py b/test/pytorch_mnist_image/download_dataset.py
deleted file mode 100644
index 698f68f67..000000000
--- a/test/pytorch_mnist_image/download_dataset.py
+++ /dev/null
@@ -1,21 +0,0 @@
-# Copyright 2022 IBM, Red Hat
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-
-from torchvision.datasets import MNIST
-
-PATH_DATASETS = os.environ.get("PATH_DATASETS", ".")
-MNIST(PATH_DATASETS, train=True, download=True)
-MNIST(PATH_DATASETS, train=False, download=True)
diff --git a/test/pytorch_mnist_image/entrypoint.sh b/test/pytorch_mnist_image/entrypoint.sh
deleted file mode 100755
index 7487b377a..000000000
--- a/test/pytorch_mnist_image/entrypoint.sh
+++ /dev/null
@@ -1,3 +0,0 @@
-#!/bin/sh
-
-torchrun /test/mnist.py
diff --git a/test/pytorch_mnist_image/mnist.py b/test/pytorch_mnist_image/mnist.py
deleted file mode 100644
index 134d56188..000000000
--- a/test/pytorch_mnist_image/mnist.py
+++ /dev/null
@@ -1,159 +0,0 @@
-# Copyright 2022 IBM, Red Hat
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-
-import torch
-import requests
-from pytorch_lightning import LightningModule, Trainer
-from pytorch_lightning.callbacks.progress import TQDMProgressBar
-from torch import nn
-from torch.nn import functional as F
-from torch.utils.data import DataLoader, random_split
-from torchmetrics import Accuracy
-from torchvision import transforms
-from torchvision.datasets import MNIST
-
-PATH_WORKDIR = os.environ.get("PATH_WORKDIR", ".")
-PATH_DATASETS = os.environ.get("PATH_DATASETS", "/test")
-BATCH_SIZE = 256 if torch.cuda.is_available() else 64
-# %%
-
-print("prior to running the trainer")
-print("MASTER_ADDR: is ", os.getenv("MASTER_ADDR"))
-print("MASTER_PORT: is ", os.getenv("MASTER_PORT"))
-
-class LitMNIST(LightningModule):
-    def __init__(self, data_dir=PATH_WORKDIR, hidden_size=64, learning_rate=2e-4):
-
-        super().__init__()
-
-        # Set our init args as class attributes
-        self.data_dir = data_dir
-        self.hidden_size = hidden_size
-        self.learning_rate = learning_rate
-
-        # Hardcode some dataset specific attributes
-        self.num_classes = 10
-        self.dims = (1, 28, 28)
-        channels, width, height = self.dims
-        self.transform = transforms.Compose(
-            [
-                transforms.ToTensor(),
-                transforms.Normalize((0.1307,), (0.3081,)),
-            ]
-        )
-
-        # Define PyTorch model
-        self.model = nn.Sequential(
-            nn.Flatten(),
-            nn.Linear(channels * width * height, hidden_size),
-            nn.ReLU(),
-            nn.Dropout(0.1),
-            nn.Linear(hidden_size, hidden_size),
-            nn.ReLU(),
-            nn.Dropout(0.1),
-            nn.Linear(hidden_size, self.num_classes),
-        )
-
-        self.val_accuracy = Accuracy()
-        self.test_accuracy = Accuracy()
-
-    def forward(self, x):
-        x = self.model(x)
-        return F.log_softmax(x, dim=1)
-
-    def training_step(self, batch, batch_idx):
-        x, y = batch
-        logits = self(x)
-        loss = F.nll_loss(logits, y)
-        return loss
-
-    def validation_step(self, batch, batch_idx):
-        x, y = batch
-        logits = self(x)
-        loss = F.nll_loss(logits, y)
-        preds = torch.argmax(logits, dim=1)
-        self.val_accuracy.update(preds, y)
-
-        # Calling self.log will surface up scalars for you in TensorBoard
-        self.log("val_loss", loss, prog_bar=True)
-        self.log("val_acc", self.val_accuracy, prog_bar=True)
-
-    def test_step(self, batch, batch_idx):
-        x, y = batch
-        logits = self(x)
-        loss = F.nll_loss(logits, y)
-        preds = torch.argmax(logits, dim=1)
-        self.test_accuracy.update(preds, y)
-
-        # Calling self.log will surface up scalars for you in TensorBoard
-        self.log("test_loss", loss, prog_bar=True)
-        self.log("test_acc", self.test_accuracy, prog_bar=True)
-
-    def configure_optimizers(self):
-        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)
-        return optimizer
-
-    ####################
-    # DATA RELATED HOOKS
-    ####################
-
-    def prepare_data(self):
-        MNIST(PATH_DATASETS, train=True, download=True)
-        MNIST(PATH_DATASETS, train=False, download=True)
-
-    def setup(self, stage=None):
-
-        # Assign train/val datasets for use in dataloaders
-        if stage == "fit" or stage is None:
-            mnist_full = MNIST(PATH_DATASETS, train=True, transform=self.transform)
-            self.mnist_train, self.mnist_val = random_split(mnist_full, [55000, 5000])
-
-        # Assign test dataset for use in dataloader(s)
-        if stage == "test" or stage is None:
-            self.mnist_test = MNIST(
-                PATH_DATASETS, train=False, transform=self.transform
-            )
-
-    def train_dataloader(self):
-        return DataLoader(self.mnist_train, batch_size=BATCH_SIZE)
-
-    def val_dataloader(self):
-        return DataLoader(self.mnist_val, batch_size=BATCH_SIZE)
-
-    def test_dataloader(self):
-        return DataLoader(self.mnist_test, batch_size=BATCH_SIZE)
-
-
-# Init DataLoader from MNIST Dataset
-
-model = LitMNIST()
-
-print("GROUP: ", int(os.environ.get("GROUP_WORLD_SIZE", 1)))
-print("LOCAL: ", int(os.environ.get("LOCAL_WORLD_SIZE", 1)))
-
-# Initialize a trainer
-trainer = Trainer(
-    accelerator="auto",
-    # devices=1 if torch.cuda.is_available() else None,  # limiting got iPython runs
-    max_epochs=5,
-    callbacks=[TQDMProgressBar(refresh_rate=20)],
-    num_nodes=int(os.environ.get("GROUP_WORLD_SIZE", 1)),
-    devices=int(os.environ.get("LOCAL_WORLD_SIZE", 1)),
-    strategy="ddp",
-)
-
-# Train the model ⚡
-trainer.fit(model)
diff --git a/test/pytorch_mnist_image/mnist_pip_requirements.txt b/test/pytorch_mnist_image/mnist_pip_requirements.txt
deleted file mode 100644
index 87edeef27..000000000
--- a/test/pytorch_mnist_image/mnist_pip_requirements.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-pytorch_lightning==1.5.10
-torchmetrics==0.9.1
-torchvision==0.12.0