Initial commit

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+.idea/

.gradient/workflows/workflow.yaml

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+on:
+  github:
+    branches:
+      only: main
+
+jobs:
+  CloneRepo:
+    resources:
+      instance-type: C3
+    uses: git-checkout@v1
+    with:
+      # url: https://github.com/gradient-ai/mnist-sample.git
+      url: context.event.github.url
+      ref: context.event.github.ref
+    outputs:
+      mnist-sample:
+        type: volume
+  TrainModel:
+    resources:
+      instance-type: C3
+    env:
+      PS_MODEL_PATH: /my-trained-model
+    uses: container@v1
+    with:
+      args:
+        - bash
+        - -c
+        - >-
+          cd /inputs/mnist-sample && python mnist.py
+      image: tensorflow/tensorflow:1.15.5-py3
+    needs:
+      - CloneRepo
+    inputs:
+      mnist-sample: CloneRepo.outputs.mnist-sample

Dockerfile.cpu

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+# Docker image for running examples in Tensorflow models.
+# base_image depends on whether we are running on GPUs or non-GPUs
+FROM ubuntu:latest
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    ca-certificates \
+    build-essential \
+    git \
+    python \
+    python-pip \
+    python-setuptools
+
+RUN pip install tf-nightly
+
+# Checkout tensorflow/models at HEAD
+RUN git clone https://github.com/tensorflow/models.git /tensorflow_models
+

Dockerfile.gpu

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+# Docker image for running examples in Tensorflow models.
+# base_image depends on whether we are running on GPUs or non-GPUs
+FROM nvidia/cuda:9.0-cudnn7-runtime-ubuntu16.04
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    ca-certificates \
+    build-essential \
+    git \
+    python \
+    python-pip \
+    python-setuptools
+
+RUN pip install tf-nightly-gpu
+
+# Checkout tensorflow/models at HEAD
+RUN git clone https://github.com/tensorflow/models.git /tensorflow_models
+
+

LICENSE

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+MIT License
+
+Copyright (c) 2019 Paperspace
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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-# MNIST
+# MNIST in TensorFlow
+
+This repository demonstrates using Paperspace Gradient to train and deploy a deep learning model to recognize handwritten characters, which is a canonical sample problem in machine learning.
+
+We build a convolutional neural network to classify the [MNIST
+dataset](http://yann.lecun.com/exdb/mnist/) using the
+[tf.data](https://www.tensorflow.org/api_docs/python/tf/data),
+[tf.estimator.Estimator](https://www.tensorflow.org/api_docs/python/tf/estimator/Estimator),
+and
+[tf.layers](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf)
+APIs.
+
+# Gradient Setup
+
+## Single Node Training on Gradient
+
+### Install Gradient CLI
+
+```
+pip install -U gradient
+```
+
+[Please check our documentation on how to install Gradient CLI and obtain an API Key](https://docs.paperspace.com/gradient/get-started/install-the-cli)
+
+### Create project and get the project id
+
+[Please check our documentation on how to create a project and get the project id](https://docs.paperspace.com/gradient/get-started/managing-projects)
+Your project ID will look like `pr1234567`.
+
+### Create and start a workflow
+
+```
+gradient workflows create --name mnist-sample --projectId pr1234567
++--------------+--------------------------------------+
+| Name         | ID                                   |
++--------------+--------------------------------------+
+| mnist-sample | 12345678-1234-1234-1234-1234567890ab |
++--------------+--------------------------------------+
+
+```
+
+Clone this repo, and change directoru into it, or copy [mnist-sample.yaml](mnist-sample.yaml) to your local machine.
+
+Then run the workflow using the workflow ID from the create workflow command above.
+
+```
+gradient workflows run --id 12345678-1234-1234-1234-1234567890ab --path mnist-sample.yaml
+```
+
+That's it!
+
+### Exporting a Model for inference
+
+#### Export your Tensorflow model
+
+In order to serve a Tensorflow model, simply export a SavedModel from your Tensorflow program. [SavedModel](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md) is a language-neutral, recoverable, hermetic serialization format that enables higher-level systems and tools to produce, consume, and transform TensorFlow models.
+
+Please refer to [Tensorflow documentation](https://www.tensorflow.org/guide/saved_model#save_and_restore_models) for detailed instructions on how to export SavedModels.
+
+#### Example code showing how to export your model:
+
+```
+tf.estimator.train_and_evaluate(mnist_classifier, train_spec, eval_spec)
+
+#Starting to Export model
+image = tf.placeholder(tf.float32, [None, 28, 28])
+input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn({
+            'image': image,
+        })
+mnist_classifier.export_savedmodel(<export directory>,
+                                    input_fn,
+                                    strip_default_attrs=True)
+#Model Exported
+```
+
+We use TensorFlow's [SavedModelBuilder module](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/builder.py) to export the model. SavedModelBuilder saves a "snapshot" of the trained model to reliable storage so that it can be loaded later for inference.
+
+For details on the SavedModel format, please see the documentation at [SavedModel README.md](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md).
+
+For export directory, be sure to set it to `PS_MODEL_PATH` when running a model deployment on Gradient:
+
+```
+export_dir = os.path.abspath(os.environ.get('PS_MODEL_PATH'))
+```
+
+You can also use Gradient SDK to ensure you have the correct path:
+
+```
+from gradient_sdk.utils import data_dir, model_dir, export_dir
+```
+
+# (Optional) Local Setup using a Virtual Environment
+
+Users sometimes run into local machine environment issues when trying to use Python. A common solution for this is to create and use a Python virtual environment to run Python from within. To do so:
+
+1. Create and activate a Python virtual environment (we recommend using python3.7+):
+
+```
+cd mnist-sample
+
+python3 -m venv venv
+
+source venv/bin/activate
+```
+
+2. Install the required Python packages:
+
+```
+pip install -r requirements-local.txt
+```
+
+# Local Training
+
+To train a the mnist model locally:
+
+1. Make sure you have the latest version of TensorFlow installed.
+
+2. Also make sure you've [added the models folder to your Python path](https://github.com/mlcommons/training/blob/master/image_classification/tensorflow/official/README.md#running-the-models); otherwise you may encounter an error like `ImportError: No module named mnist`.
+
+3. Download the code from GitHub:
+
+```
+git clone [email protected]:Paperspace/mnist-sample.git
+```
+
+4. Start training the model:
+
+```
+python mnist.py
+```
+
+_Note: local training will take a long time, so be prepared to wait!_
+
+If you want to shorten model training time, you can change the max steps parameter:
+
+```
+python mnist.py --max_steps=1500
+```
+
+The mnist dataset is downloaded to the `./data` directory.
+
+Model results are stored in the `./models` directory.
+
+Both directories can be safely deleted if you would like to start the training over from the beginning.
+
+## Exporting the model to a specific directory
+
+You can export the model into a specific directory, in the Tensorflow [SavedModel](https://www.tensorflow.org/guide/saved_model) format, by using the argument `--export_dir`:
+
+```
+python mnist.py --export_dir /tmp/mnist_saved_model
+```
+
+If no export directory is specified, the model is saved to a timestamped directory under `./models` subdirectory (e.g. `mnist-sample/models/1513630966/`).
+
+## Testing a Tensorflow Serving-deployed model on your local machine using Docker
+
+Open another terminal window and run the following in the directory where you cloned this repo:
+
+```
+docker run -t --rm -p 8501:8501 -v "$PWD/models:/models/mnist" -e MODEL_NAME=mnist tensorflow/serving
+```
+
+Now you can test the local inference endpoint by running:
+
+```
+python serving_rest_client_test.py
+```
+
+Optionally you can provide a path to an image file to run a prediction on:
+
+```
+python serving_rest_client_test.py --path example3.png
+```
+
+Once you've completed local testing using the tensorflow/serving docker container, stop the running container as follows:
+
+```
+docker ps
+docker kill <container-id-or-name>
+```
+
+## Training the model on a node with a GPU for use with Tensorflow Serving on a node with only a CPU
+
+If you are training on Tensorflow using a GPU but would like to export the model for use in Tensorflow Serving on a CPU-only server, you can train and/or export the model using `--data_format=channels_last`:
+
+```
+python mnist.py --data_format=channels_last
+```
+
+The SavedModel will be saved in a timestamped directory under `models` subdirectory (e.g. `mnist-sample/models/1513630966/`).
+
+## Inspecting and getting predictions with the SavedModel file
+
+You can also use the [`saved_model_cli`](https://www.tensorflow.org/guide/saved_model#cli_to_inspect_and_execute_savedmodel) tool to inspect and execute the SavedModel.