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https://github.com/andreaschandra/mnist
https://github.com/andreaschandra/mnist
Last synced: 15 days ago
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- Host: GitHub
- URL: https://github.com/andreaschandra/mnist
- Owner: andreaschandra
- License: mit
- Created: 2023-06-19T12:01:48.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-06-19T12:02:02.000Z (over 1 year ago)
- Last Synced: 2024-05-01T16:40:25.418Z (7 months ago)
- Language: Python
- Size: 427 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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(,
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
```
## 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.