1stondagshub/src/copy_of_experiment_tracking_session.py

# -*- coding: utf-8 -*-
"""Copy of Experiment Tracking Session.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1Rw0_Y3JfbUwjbGnqQOzdQ3gAw8v0Pe63

# Change the Runtime to GPU

On the top nav-bar, choose `Runtme`, click on `change runtime type`, and under `Hardware accelerator` choose GPU.

# Create a Repository on DAGsHub 🏗

- We will start by [creating a new repository](https://dagshub.com/repo/create) on DAGsHub.

<center>
<img src="https://dagshub.com/nirbarazida/images/raw/22ffadf74508f2f8626a528ea55f0fbf3d43f941/colab-mlflow/create-a-repo.png" height="700"/> 
</center>

<center><b>Congratulations</b> - you created your first DAGsHub repository! 🥳 </center>

# Configure MLflow 🧐
"""

"""# Model 🪐"""
import tensorflow as tf
import IPython
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

import os
from getpass import getpass

!pip install mlflow --quiet


"""**Initialize MLflow**"""
import mlflow

DAGSHUB_TOKEN = getpass('Enter your DAGsHub access token or password: ')

os.environ['MLFLOW_TRACKING_USERNAME'] = USER_NAME
os.environ['MLFLOW_TRACKING_PASSWORD'] = DAGSHUB_TOKEN

mlflow.set_tracking_uri('https://dagshub.com/anupampatil44/1stondagshub.mlflow')

mlflow.tensorflow.autolog()
mlflow.log_param("task",2) #manual logging



import requests
from getpass import getpass
import datetime

"""**Set Environment Variables**

"""

#@title Enter the repository name for the project:

REPO_NAME= "1stondagshub" #@param {type:"string"}

#@title Enter the repository name for the project:

REPO_OWNER= "anupampatil44" #@param {type:"string"}

#@title Enter the username of your DAGsHub account:

USER_NAME = "anupampatil44" #@param {type:"string"}



"""## Import the Data Files

![](https://camo.githubusercontent.com/01c057a753e92a9bc70b8c45d62b295431851c09cffadf53106fc0aea7e2843f/687474703a2f2f692e7974696d672e636f6d2f76692f3051493378675875422d512f687164656661756c742e6a7067)
"""

mnist = tf.keras.datasets.mnist

(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train, X_test = X_train / 255.0, X_test / 255.0

image = np.reshape(X_train[1], [28, 28])
image_array = np.asarray(image)
fig, ax = plt.subplots(figsize=(10, 15))
img = ax.imshow(image_array, cmap='gray')

for x in range(28):
    for y in range(28):
        value = round(image[y][x], 2)
        color = 'black' if value > 0.5 else 'white'
        ax.annotate(s=value, xy=(x, y), ha='center', va='center', color=color)

"""## Configure a Tensorflow Model"""

batch_size = 32
validation_split=0.2
epochs=3
optimizer='adam'
metrics=['accuracy']

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10)
])

loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)

model.compile(optimizer=optimizer,
              loss=loss_fn,
              metrics=metrics)

"""## Train the Model"""

model.fit(X_train, y_train, batch_size= batch_size,
          validation_split=validation_split, epochs=epochs,verbose=0)

"""# Finish Line 🏁

**Congratulations**  - You made it to the finish line! 🥳

In this section, we covered how to track fast.ai models with MLflow to DAGsHub servers. If you want to start fast with DAGsHub, this [notebook](https://colab.research.google.com/drive/1JJIwAH0TBSY49um5s2FD0GEA6bw3SKrd#scrollTo=XcU2y1F_Hyub) is for you.  <br><br>

More resources that can interest you:
- [DAGsHub Docs](https://dagshub.com/docs/).
- [Get Started Tutorial](https://dagshub.com/docs/getting-started/overview/).
- [DAGsHub Blog](https://dagshub.com/blog/).
- [FAQ](https://dagshub.com/docs/faq/).

<br>

We hope that this Tutorial was helpful and made the onboarding process easier for you. If you found an issue in the notebook, please [let us know](https://dagshub.com/DAGsHub-Official/DAGsHub-Issues/issues/). If you have any questions feel free to join our [Discord channel](https://discord.com/invite/9gU36Y6) and ask there. We can't wait to see what remarkable project you will create and share with the Data Science community!
<br><br>
"""