feature/basic-model-setup #3

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s495727 merged 9 commits from feature/basic-model-setup into main 2024-05-11 20:00:07 +02:00
21 changed files with 257 additions and 168 deletions
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@ -34,8 +34,3 @@ RUN pip install -r requirements.txt
ENV CUDNN_PATH="/.pyenv/versions/3.10.12/lib/python3.10/site-packages/nvidia/cudnn/"
ENV LD_LIBRARY_PATH="$CUDNN_PATH/lib":"/usr/local/cuda-12.2/lib64"
ENV PATH="$PATH":"/usr/local/cuda-12.2/bin"
COPY . .
ARG api_key
RUN wandb login $api_key

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@ -1,5 +1,6 @@
.PHONY: download-dataset resize-dataset sobel-dataset
# Use inside docker container
download-dataset:
python3 ./file_manager/data_manager.py --download
@ -8,3 +9,16 @@ resize-dataset:
sobel-dataset:
python3 ./file_manager/data_manager.py --sobel --source "resized_dataset"
login:
wandb login $$(cat "$$API_KEY_SECRET")
# Outside docker
docker-run:
docker-compose run --entrypoint=/bin/bash gpu
docker-build:
docker-compose build
check-gpu:
python3 ./gpu_check.py

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@ -15,3 +15,23 @@
# Dokumentacja
[Link do dokumentacji](https://uam-my.sharepoint.com/personal/krzboj_st_amu_edu_pl/_layouts/15/doc.aspx?sourcedoc={dc695bbe-68d1-4947-8c29-1d008f252a3b}&action=edit)
# Setup
1. Install Docker on your local system.
2. To build docker image and run the shell, use Makefile
```bash
make docker-build
make docker-run
```
3. Get your API key from https://wandb.ai/settings#api, and add the key to **secrets.txt** file.
4. After running the container
```bash
make login # to login to WanDB.
make check-gpu # to verify if GPU works
```
5. If needed, to manually run containers, run:
```bash
docker build -t gpu api_key="<wandb_api_key>" .
docker run --rm -it --gpus all --entrypoint /bin/bash gpu
```

23
compose.yaml Normal file
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@ -0,0 +1,23 @@
services:
gpu:
image: gpu
volumes:
- .:/app
command: nvidia-smi
build:
context: .
dockerfile: Dockerfile
environment:
API_KEY_SECRET: /run/secrets/api_key_secret
secrets:
- api_key_secret
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
secrets:
api_key_secret:
file: ./secrets.txt

15
main.py Normal file
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@ -0,0 +1,15 @@
from model.test_model import TestModel
from pathlib import Path
from dataset.dataset import Dataset
if __name__ == "__main__":
# Loading dataset
train_dataset = Dataset(Path('data/resized_dataset/train'))
valid_dataset = Dataset(Path('data/resized_dataset/valid'))
for i in train_dataset.take(1):
print(i)
# Training model
model = TestModel()
history = model.fit()
model.save("./src/model/test_model_final.keras")

55
model/resnet_50_model.py Normal file
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@ -0,0 +1,55 @@
import tensorflow as tf
from wandb_utils.config import Config
from wandb.keras import WandbMetricsLogger
class Resnet50Model:
def __init__(self):
self.config = Config(epoch=8, batch_size=64).config()
self.config.learning_rate = 0.01
# Define specific configuration below, they will be visible in the W&B interface
# Start of config
self.config.optimizer = "sgd"
self.config.loss = "sparse_categorical_crossentropy"
self.config.metrics = ["accuracy"]
# End
self.model = self.__build_model()
self.__compile()
self.__load_dataset()
def __build_model(self):
return tf.keras.applications.ResNet50(
input_shape=(224, 224, 3), include_top=False, weights='imagenet'
# output - odblokować ostatnią warstwę freeze
# zobaczyc czy dziala to by default, czy wewn. warstwy są frozen, i czy ost. jest dynamiczna
)
def __compile(self):
self.model.compile(
optimizer=self.config.optimizer,
loss=self.config.loss,
metrics=self.config.metrics,
)
def __load_dataset(self):
(self.x_train, self.y_train), (self.x_test, self.y_test) = tf.keras.datasets.cifar10.load_data()
self.x_train = self.x_train.astype('float32') / 255.0
self.x_test = self.x_test.astype('float32') / 255.0
def fit(self):
wandb_callbacks = [
WandbMetricsLogger(log_freq=5),
# Not supported with Keras >= 3.0.0
# WandbModelCheckpoint(filepath="models"),
]
return self.model.fit(
x=self.x_train,
y=self.y_train,
epochs=self.config.epoch,
batch_size=self.config.batch_size,
callbacks=wandb_callbacks
)
def save(self, filepath):
self.model.save(filepath)

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@ -1,4 +1,7 @@
tensorflow==2.16.1
tensorflow[and-cuda]==2.16.1
tensorflow-io==0.37.0
numpy==1.26.4
opencv-python==4.9.0.80
numpy==1.26.4
wget==3.2
wandb==0.16.6

1
secrets.txt Normal file
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@ -0,0 +1 @@
FILL IN

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@ -1 +0,0 @@
3.10.12

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@ -1,12 +0,0 @@
# Setup
1. Install Docker on your local system
2. Build docker image and run the shell
3. Get your API key from https://wandb.ai/settings#api, docker will automatically connect to WanDB.
```bash
docker build -t gpu api_key="<wandb_api_key>" .
docker run --rm -it --gpus all --entrypoint /bin/bash gpu
```
4. To double check if tensorflow is configured properly run `python3 gpu_check.py`.

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@ -1,7 +0,0 @@
from model.test_model import TestModel
if __name__ == "__main__":
model = TestModel()
history = model.fit()
model.save("model/test_model_final.keras")

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@ -1,7 +0,0 @@
tensorflow[and-cuda]==2.16.1
tensorflow-io==0.37.0
numpy==1.26.4
opencv-python==4.9.0.80
numpy==1.26.4
wget==3.2
wandb==0.16.6

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10
test.py
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@ -1,10 +0,0 @@
from pathlib import Path
from dataset.dataset import Dataset
train_dataset = Dataset(Path('data/resized_dataset/train'))
valid_dataset = Dataset(Path('data/resized_dataset/valid'))
for i in train_dataset.take(1):
print(i)