https://github.com/valaydave/mnist-experiments-with-metaflow
Experimentation Of different deep learning models for classification of digits on a MNIST dataset using Metaflow
https://github.com/valaydave/mnist-experiments-with-metaflow
deeplearning keras metaflow tensorflow
Last synced: about 2 months ago
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Experimentation Of different deep learning models for classification of digits on a MNIST dataset using Metaflow
- Host: GitHub
- URL: https://github.com/valaydave/mnist-experiments-with-metaflow
- Owner: valayDave
- Created: 2020-02-07T23:18:21.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-03-07T19:35:03.000Z (over 5 years ago)
- Last Synced: 2025-04-11T21:12:13.124Z (about 2 months ago)
- Topics: deeplearning, keras, metaflow, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 445 KB
- Stars: 5
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
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README
# Experiments With MNIST Using Metaflow
Used Metaflow to test and experiment with different deep learning models for classification of digits on a MNIST dataset
## Setting it Up
- ``pip install -r requirements.txt``
- Conda is required to Run this.
1. Download Miniconda at https://docs.conda.io/en/latest/miniconda.html
2. ```conda config --add channels conda-forge```
- export PATH="/Users/valaydave/miniconda3/bin:$PATH" --> Change this to where u install miniconda. U need to run this before executing the Experiments.
## Why Metaflow ?
- Metaflow is this neat library that helps datascientists focus on thier core responsiblities of managing insights extraction by abstracting out computation and data warehousing, versioning and deployments.
- The below is how a `Flow` is defined on metaflow. Every function marked with the `@step` decorator which helps define a compuational Node in a DAG. Metaflow works on the **dataflow paradigm** so nodes later in the flow can have access to properties set during the pervious steps in the flow. The below flow is an example. see [hello_mnist.py](hello_mnist.py) for actual code.
- The below flow show cases how 3 different types of models can be trained efficiently in parallel with easy access to results of the entire data pipeline.
- Please note when run locally each `@step` is a process. But with AWS, one can use `@batch(cpu=3,memory=4000,image=python:3.7)` to run the same step of the flow on a python container in AWS's Batch Service.
```python
from metaflow import FlowSpec, step
class MNISTFlowExplain(FlowSpec):
@step
def start(self):
"""
Parse the MNIST Dataset into Flattened and None Flattened Data artifacts.
"""
self.data_flattened,self.unflattened = process_data()
@step
def train_sequential(self):
"""
Train sequential Neural Network with with the Set of parameters.
"""
self.history = train_sequential(self.data_flattened)
self.next(self.join)
@step
def train_convolution(self):
"""
Train a Convolutional Neural Network with the Set of parameters.
"""
self.history = train_convolution(self.unflattened)
self.next(self.join)
@step
def train_convolution_batch_norm(self):
"""
Train a Convolutional Neural Network with Batch Norm and Dropout with the Set of parameters.
"""
self.history = train_batch_norm(self.unflattened)
self.next(self.join)
@step
def join(self,inputs):
"""
Join our parallel branches and merge results,
"""
self.history = {
'convolution' : inputs.train_convolution.history,
'sequential' : inputs.train_sequential.history,
'convolution_batch_norm' : inputs.train_convolution_batch_norm.history
}
self.next(self.end)
@step
def end(self):
print("Done Computation")
```
- The above flow can be presented in the below state diagram.
- 
- Experiments can be defined with multiple `Parameters` which may be set at runtime. These are stored as a part of every `Run` of the Flow.
- Once the flow is completed. It can be accessed via
```python
from metaflow import Metaflow, Flow, get_metadata,Run
print(Metaflow().flows)
```
- See *Experiment Analysis* for more details.
## Experimentation Strategy :
- Run individual Experiments Using ``python hello_mnist.py --environment=conda run --num_training_examples 10000``
## Metaflow Help
- ``python hello_mnist.py --environment=conda run --help`` : This will show all the parameters which are currently avaliable to perform experiments on.
- ``python hello_mnist.py --environment=conda show`` : This will show the steps of the DAG used for this Experiment. And each step.
## Experiment Analysis
- After running a couple of Experiments successfully running the [experiments_analytics.ipynb](experiments_analytics.ipynb) will help create charts that will help analyse different parameters of the Experiment.