Tf-notify

Latest version: v0.3.0

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0.3.0

A custom callback for email (via SMTP) integration is introduced, called `EmailCallback`:

python
import tensorflow as tf
from tf_notify import EmailCallback

model = tf.keras.Sequential(name="neural-network")
model.add(tf.keras.layers.Dense(1, input_dim=784))
model.compile(
optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.1),
loss="mean_squared_error",
metrics=["mean_absolute_error"],
)

model.fit(
x_train,
y_train,
batch_size=128,
epochs=2,
verbose=0,
validation_split=0.5,
callbacks=[
EmailCallback(
to="toaddressyahoo.com",
from_="fromaddressyahoo.com",
host="smtp.mail.yahoo.com",
port=465,
username="my-cool-username",
password="my-cool-password", one-time app password
ssl=True,
)
],
)


**Documentation**:
The public atrributes for all available callbacks are now documented

![image](https://user-images.githubusercontent.com/42189572/182725822-369689a2-9597-491f-abf8-b80818318e54.png)

0.2.0

Instead of the very strict `tensorflow = "~2.9.1"` required dependency, **we now support all Tensorflow versions between `v2.7.3` and `v2.9.1`.**

Additionally, CI matrix has been expanded:

yaml
matrix:
os: [ubuntu-latest, macos-latest, windows-latest]
python-version: ['3.7', '3.8', '3.9', '3.10']
tf-version: ['2.7.3', '2.8.2', '2.9.1']
exclude:
- python-version: '3.10'
tf-version: '2.7.3'


in order to test any new code against the allowed Tensorflow versions as well (in combination with `os` and `python-version`). Note that any matrix combination that consists of the sub-combination `(python-version, tf-version) = ('3.10', '2.7.3')` is excluded from the matrix, as support for python 3.10 has been introduced with tensorflow 2.8.

0.1.0

A custom callback for Telegram integration is introduced, called `TelegramCallback`:

python
import tensorflow as tf
from tf_notify import TelegramCallback


define the tf.keras model to add callbacks to
model = tf.keras.Sequential(name='neural-network')
model.add(tf.keras.layers.Dense(1, input_dim=784))
model.compile(
optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.1),
loss="mean_squared_error",
metrics=["mean_absolute_error"],
)

model.fit(
x_train,
y_train,
batch_size=128,
epochs=2,
verbose=0,
validation_split=0.5,
callbacks=[
TelegramCallback(token='XXXX:YYYY', chat_id=-1234)
], send a Telegram notification when training ends!
)

0.1.0.beta1

**Documentation:**

Project documentation is served in a continuous fashion over at [https://tf-notify.readthedocs.io](https://tf-notify.readthedocs.io).

The following packages are utilized:

- [mkdocs](https://github.com/mkdocs/mkdocs): Project documentation with Markdown
- [mkdocs-material](https://github.com/squidfunk/mkdocs-material): A Material Design theme for MkDocs
- [mkdocstrings](https://github.com/mkdocstrings/mkdocstrings): Automatic documentation from sources, for MkDocs.

To interact with the project's documentation locally, run:

shell
mkdocs serve


inside a virtual environment.

**CI/CD:**

Continuous Integration and Continuous Delivery for the package is enabled through Github Actions.

For more details, see:

- `.github/workflows/ci.yml`
- `.github/workflows/cd.yml`

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