A high-level Python framework to automate the project lifecycle of Machine and Deep Learning Projects
Project description
♾️ OneSpace ♾️
A high-level Python framework to automate the project lifecycle of Machine and Deep Learning Projects
Overview
OneSpace enables you to train high performace production ready Machine Learning And Deep Learning Models effortlessly with less than five lines of code. OneSpace provides you a unified workspace so you can work on all kinds of Machine Learning and Deep Learning problem statements without having to leave your workspace environment. It don't just run everything as a black box to present you results, instead it makes the complete model training process easily explainable using artifacts, logs and plots. OneSpace also provides you the optional parameters to pass database credentials which will create a table with the project name in the database of your choice and will log all the training activities and the parameters in the database.
Following are the major contents to follow, you can jump to any section:
- Installation
- Usage
- Getting Started with OneSpace (Tutorials)
A ) - Tabular
i ) - Training a Regression Model
ii) - Training a Classification Model
B ) - Computer Vision
i ) - Training an Image Classification Model with Tensorflow
ii) - Training an Image Classification Model with PyTorch- Contributing
- Conclusion
🔗 Project Link
OneSpace is being distributed through PyPI. Check out the PyPI Package here
1. Installation
To avoid any dependency conflict, make sure to create a new Python virtual environment and then install via Pip!
pip install onespace
2. Usage
Get the config.py and training.py files ready. You can get it from this repo or from the following tutorials section.
- Prepare
training.py
import config # In case, you renamed config.py to something else, make sure to use the same name here
from src.onespace.tabular.regression import Experiment # Importing Experiment class to train a regression model
def training(config):
exp = Experiment(config)
exp.run_experiment()
if __name__ == "__main__":
training(config)
- Now run the following command on your terminal to start the training job:
python training.py
Please following along with these quick tutorials👇 to understand the complete setup and training process.
3. Getting Started with OneSpace
-
Ensure you have Python 3.7+ installed.
-
Create a new Python conda environment for the OneSpace:
$ conda create -n venv # create venv
$ conda activate venv # activate venv
$ pip install onespace # install onespace
- Create a new Python virtual environment with pip for the OneSpace:
$ python3 -m venv venv # create venv
$ . venv/bin/activate # activate venv
$ pip install onespace # install onespace
A) - Tabular
In the section, you will learn to train a Machine Learning model with OneSpace:
i) - Training a Regression Model :
First step is to setup the initial directory. This should present a following tree structure at the beginning of the training:
│ .env
│ tabularConfig.py
│ training.py
│
└───data_folder
insurance.csv
Now let's discuss these files one by one
.env
This file should only be used when the database integration is required. You will be required to hold the database credentials in.envas shown below
MONGO_CONN_STR="Connection String for MongoDB"
MYSQL_HOST="data_base_host(could be an ip or domain)"
MYSQL_USER="database_user_name"
MYSQL_PASS="database_password"
MYSQL_DB="database_name"
tabularConfig.py
This file is used to setup the configuration for Experiment. It has standard format as shown below. Parameters could be updated according to the training job requirements
#--------------------------------------------------------------------------------------------------------------------------------
# Parameters
#--------------------------------------------------------------------------------------------------------------------------------
data_dir = "data_folder"
csv_file_name = "insurance.csv"
target_column ="charges"
autopilot = False # True if you want to automatically configure everything for the training job and run the job without user interaction else, False.
eda = False # Exploratory Data Analysis (EDA)
metrics = 'r2_score' # selection_for_classificaton = ['accuracy', 'f1_score', 'recall', 'precision']
# selection_for_regression = ['r2_score', 'mean_absolute_error','mean_squared_error', 'mean_absolute_percentage_error',
# 'median_absolute_error', 'explained_variance_score']
validation_split = 0.20
scaler = "RobustScaler" # available_selections = ['MinMaxScaler', 'StandardScaler', 'MaxAbsScaler', 'RobustScaler']
imputer = "SimpleImputer" # available_selections = ['KNNImputer', ''SimpleImputer']
PloynomialFeatures = False
remove_outliers = False
handle_imbalance = False # Only applicable to the classification problems.
pca = False # Principal Component Analysis (PCA).
feature_selection = False # This will use recursive feature elimination (RFE)
#--------------------------------------------------------------------------------------------------------------------------------
# Artifacts (Directory names to store the results & resources, can be customized according to the user requirements)
#--------------------------------------------------------------------------------------------------------------------------------
project_name = 'Insurance Project'
artifacts_dir = "Artifacts"
pipelines_dir = "Pipelines"
plots_dir = "Plots"
model_name = "my_test_model"
experiment_name = "light model testing"
#--------------------------------------------------------------------------------------------------------------------------------
# Logs (Directory names to record logs, can be customized according to the user requirements)
#--------------------------------------------------------------------------------------------------------------------------------
logs_dir = "Logs"
csv_logs_dir = "CSV Logs"
csv_logs_file = "csv_logs_file"
comments = "making comparision for optimizers"
executed_by = 'hasanain'
#--------------------------------------------------------------------------------------------------------------------------------
# Database Integration
#--------------------------------------------------------------------------------------------------------------------------------
# Please Note that before making any change in this section, create a .env file and store the mongo db connection string or MySQL credentials in the environment variables
# Guideline for creating .env is available on project description main page
db_integration_mysql = False
db_integration_mongodb = False
training.py
import tabularConfig
from onespace.tabular.classification import Experiment
def training(config):
exp = Experiment(config)
exp.run_experiment()
if __name__ == "__main__":
training(tabularConfig)
Run the following command on the terminal to start the training job
python training.py
Now let the OneSpace take care of your end-to-end model training and evaluation process.
After the training job is completed, the directories in the workspace should look like as follows:
│ .env
│ tabularConfig.py
│ training.py
│
├───cachedir
│ └───joblib
│ └───sklearn
│ └───pipeline
│ └───_fit_transform_one
├───data_folder
│ insurance.csv
│
├───Tabular
│ └───Regression
│ └───Insurance Project
│ ├───Artifacts
│ │ └───Pipelines_on_20221012_at_004113
│ │ GradientBoostingRegressor_on_20221012_at_004124.pkl
│ │ LGBMRegressor_on_20221012_at_004126.pkl
│ │ StackingRegressor_on_20221012_at_004128.pkl
│ │ VotingRegressor_on_20221012_at_004128.pkl
│ │
│ ├───Logs
│ │ ├───Final
│ │ │ csv_logs_file.csv
│ │ │
│ │ ├───Model Comparision
│ │ │ modelsComparisionAfterTuning_on_20221012_at_004128..csv
│ │ │ modelsComparisionBeforTuning_on_20221012_at_004116..csv
│ │ │
│ │ └───Preprocessing
│ │ preprocessed_on_20221012_at_004113..csv
│ │
│ └───Plots
│ ├───EDA
│ │ └───EDA_on_20221012_at_004111
│ │ barplot.png
│ │ boxplot.png
│ │ corr_heatmap.png
│ │ countplot.png
│ │ histogram.png
│ │
│ └───Evaluation
│ └───Evaluation_on_20221012_at_004120
│ GradientBoostingRegressor_feature_importance.png
│ GradientBoostingRegressor_residplot.png
│ LGBMRegressor_feature_importance.png
│ LGBMRegressor_residplot.png
│
└───__pycache__
tabularConfig.cpython-310.pyc
ii) - Training a Classification Model :
First step is to setup the initial directory. This should present a following tree structure at the beginning of the training:
│ .env
│ tabularConfig.py
│ training.py
│
└───data_folder
titanic.csv
Now let's discuss these files one by one
.env
This file should only be used when the database integration is required. You will be required to hold the database credentials in.envas shown below
MONGO_CONN_STR="Connection String for MongoDB"
MYSQL_HOST="data_base_host(could be an ip or domain)"
MYSQL_USER="database_user_name"
MYSQL_PASS="database_password"
MYSQL_DB="database_name"
tabularConfig.py
This file is used to setup the configuration for Experiment. It has standard format as shown below. Parameters could be updated according to the training job requirements
#--------------------------------------------------------------------------------------------------------------------------------
# Parameters
#--------------------------------------------------------------------------------------------------------------------------------
data_dir = "data_folder"
csv_file_name = "titanic.csv"
target_column ="Survived"
autopilot = False # True if you want to automatically configure everything for the training job and run the job without user interaction else, False.
eda = True # Exploratory Data Analysis (EDA)
metrics = 'f1_score' # selection_for_classificaton = ['accuracy', 'f1_score', 'recall', 'precision']
# selection_for_regression = ['r2_score', 'mean_absolute_error','mean_squared_error', 'mean_absolute_percentage_error',
# 'median_absolute_error', 'explained_variance_score']
validation_split = 0.20
scaler = "RobustScaler" # available_selections = ['MinMaxScaler', 'StandardScaler', 'MaxAbsScaler', 'RobustScaler']
imputer = "SimpleImputer" # available_selections = ['KNNImputer', ''SimpleImputer']
PloynomialFeatures = False
remove_outliers = False
handle_imbalance = False # Only applicable to the classification problems.
pca = False # Principal Component Analysis (PCA).
feature_selection = False # This will use recursive feature elimination (RFE)
#--------------------------------------------------------------------------------------------------------------------------------
# Artifacts (Directory names to store the results & resources, can be customized according to the user requirements)
#--------------------------------------------------------------------------------------------------------------------------------
project_name = 'Titanic Project1.0'
artifacts_dir = "Artifacts"
pipelines_dir = "Pipelines"
plots_dir = "Plots"
model_name = "my_test_model"
experiment_name = "light model testing"
#--------------------------------------------------------------------------------------------------------------------------------
# Logs (Directory names to record logs, can be customized according to the user requirements)
#--------------------------------------------------------------------------------------------------------------------------------
logs_dir = "Logs"
csv_logs_dir = "CSV Logs"
csv_logs_file = "csv_logs_file"
comments = "making comparision for optimizers"
executed_by = 'hasanain'
#--------------------------------------------------------------------------------------------------------------------------------
# Database Integration
#--------------------------------------------------------------------------------------------------------------------------------
# Please Note that before making any change in this section, create a .env file and store the mongo db connection string or MySQL credentials in the environment variables
# Guideline for creating .env is available on project description main page
db_integration_mysql = False
db_integration_mongodb = False
training.py
import tabularConfig
from onespace.tabular.regression import Experiment
def training(config):
exp = Experiment(config)
exp.run_experiment()
if __name__ == "__main__":
training(tabularConfig)
Run the following command on the terminal to start the training job
python training.py
Now let the OneSpace take care of your end-to-end model training and evaluation process.
After the training job is completed, the directories in the workspace should look like as follows:
│ .env
│ tabularConfig.py
│ training.py
│
├───cachedir
│ └───joblib
│ └───sklearn
│ └───pipeline
│ └───_fit_transform_one
├───data_folder
│ titanic.csv
│
├───Tabular
│ └───Classification
│ └───Titanic Project1.0
│ ├───Artifacts
│ │ └───Pipelines_on_20221012_at_003107
│ │ ExtraTreesClassifier_on_20221012_at_003128.pkl
│ │ RandomForestClassifier_on_20221012_at_003130.pkl
│ │ StackingClf_on_20221012_at_003136.pkl
│ │ VotingClf_on_20221012_at_003136.pkl
│ │
│ ├───Logs
│ │ ├───Final
│ │ │ csv_logs_file.csv
│ │ │
│ │ └───Preprocessing
│ │ preprocessed_on_20221012_at_003107..csv
│ │
│ └───Plots
│ ├───EDA
│ │ └───EDA_on_20221012_at_003048
│ │ boxplot.png
│ │ corr_heatmap.png
│ │ countplot.png
│ │ histogram.png
│ │ pairplot.png
│ │ pie_bar.png
│ │ violinplot.png
│ │
│ └───Evaluation
│ └───Evaluation_on_20221012_at_003124
│ auc_roc_plot.png
│ ExtraTreesClassifier_confusion_matrix_count.png
│ ExtraTreesClassifier_confusion_matrix_pct.png
│ ExtraTreesClassifier_feature_importance.png
│ RandomForestClassifier_confusion_matrix_count.png
│ RandomForestClassifier_confusion_matrix_pct.png
│ RandomForestClassifier_feature_importance.png
│ StackingClf_confusion_matrix_count.png
│ StackingClf_confusion_matrix_pct.png
│ VotingClf_confusion_matrix_count.png
│ VotingClf_confusion_matrix_pct.png
│
└───__pycache__
tabularConfig.cpython-310.pyc
B) - Computer Vision
This API enable users to create image classification models with Tensorflow or PyTorch. More services for computer vision API are being developed.
i) - Training an Image Classification Model with Tensorflow :
First step is to setup the initial directory. This should present a following tree structure at the beginning of the training:
│ .env
│ tensorflowConfig.py
│ training.py
│
└───data_folder
├───train
│ ├───ants
│ │ 0013035.jpg
│ │ 1030023514_aad5c608f9.jpg
│ │ 1095476100_3906d8afde.jpg
│ │ 1099452230_d1949d3250.jpg
│ │ 116570827_e9c126745d.jpg
│ │
│ └───bees
│ 1092977343_cb42b38d62.jpg
│ 1093831624_fb5fbe2308.jpg
│ 1097045929_1753d1c765.jpg
│ 1232245714_f862fbe385.jpg
│ 129236073_0985e91c7d.jpg
│
└───val
├───ants
│ 10308379_1b6c72e180.jpg
│ 1053149811_f62a3410d3.jpg
│ 1073564163_225a64f170.jpg
│
└───bees
1032546534_06907fe3b3.jpg
10870992_eebeeb3a12.jpg
1181173278_23c36fac71.jpg
Now let's discuss these files one by one
.env
This file should only be used when the database integration is required. You will be required to hold the database credentials in.envas shown below
MONGO_CONN_STR="Connection String for MongoDB"
MYSQL_HOST="data_base_host(could be an ip or domain)"
MYSQL_USER="database_user_name"
MYSQL_PASS="database_password"
MYSQL_DB="database_name"
tensorflowConfig.py
This file is used to setup the configuration for Experiment. It has standard format as shown below. Parameters could be updated according to the training job requirements
#--------------------------------------------------------------------------------------------------------------------------------
# Parameters
#--------------------------------------------------------------------------------------------------------------------------------
data_dir = "data_folder" # main directory for data
train_folder_name = "train" # name of the training folder. Pass "None" if there is no training and validation folder available
val_folder_name = "val" # name of the validation folder pass "None" if there is no training and validation folder available
transfer_learning = False # Pass "False" if you want to train the model from scratch
model_architecture = "MobileNetV3Small" # available_selections = ["VGG16", "MobileNetV3Large", "MobileNetV3Small", "DenseNet201", "EfficientNetV2L", "EfficientNetV2S", "ResNet50", "ResNet50V2", "ResNet152V2", "VGG19", "Xception"]
freeze_all = True # Will freeze the weights for all the layers except for last one (Dense Layer)
freeze_till = None # selection = [-1,-2,-3,-4,-5] => if you want to freeze weights until a specific layer, select any value from the list and observe the trainable prams
augmentation = False # Pass True if the augmentation should be applied on the images. It helps with regularization.
epochs = 2 # Number of Epochs
batch_size = 64
input_shape = (224, 224, 3)
activation = "relu" # available_selections = ["relu","selu", "swish", "elu", "gelu", "tanh"] => This param is used when there is no transferlearning involved and model is being trained from scratch
activation_output = "softmax" # available_selections = ["softmax","sigmoid", "hard_sigmoid"]
loss_function = "binary_crossentropy" # available_selection = ["binary_crossentropy","categorical_crossentropy" , "sparse_categorical_crossentropy"]
metrics = ["AUC","Recall" ] # available_selection = ["accuracy", "AUC", "Precision", "Recall" ]
lr_scheduler = 'InverseTimeDecay' #availabel_selection = ["InverseTimeDecay","CosineDecay", "ExponentialDecay", "CosineDecayRestarts", "PolynomialDecay"] # can be observed on tensorboard
optimizer = "Adam" # available_selection = ["SGD", "Adam", "RMSprop", "Adadelta", "Adagrad", "Adamax", "Nadam", "Ftrl"]
validation_split = 0.20 # The proportion of data should be used for validation. This param won't be used if a seperate Validation Data Folder is being passed
es_patience = 5 # Early Stopping Patience
#--------------------------------------------------------------------------------------------------------------------------------
# Artifacts (Directory names to store the results & resources, can be customized according to the user requirements)
#--------------------------------------------------------------------------------------------------------------------------------
project_name = 'AntsBeesProject_tf'
artifacts_dir = "Artifacts"
model_dir = "Models"
plots_dir = "Plots"
model_name = "my_test_model"
experiment_name = "Exp for Demo"
plot_name = "results_plot"
model_ckpt_dir = "Model Checkpoints"
callbacked_model_name = "model_ckpt"
#--------------------------------------------------------------------------------------------------------------------------------
# Logs (Directory names to record logs, can be customized according to the user requirements)
#--------------------------------------------------------------------------------------------------------------------------------
logs_dir = "Logs"
tensorboard_root_log_dir = "Tensorboard Logs"
csv_logs_dir = "CSV Logs"
csv_logs_file = "cv_test_logs.csv"
comments = "Running a demo with transfer learning"
executed_by = 'Hasanain'
#--------------------------------------------------------------------------------------------------------------------------------
# Database Integration
#--------------------------------------------------------------------------------------------------------------------------------
# Please Note that before making any change in this section, create a .env file and store the mongo db connection string or MySQL credentials in the environment variables
# Guideline for creating .env is available on project description main page
db_integration_mysql = False
db_integration_mongodb = False
training.py
import tensorflowConfig
from onespace.tensorflow.cv import Experiment
def training(config):
exp = Experiment(config)
exp.run_experiment()
if __name__ == "__main__":
training(tensorflowConfig)
Run the following command on the terminal to start the training job
python training.py
Now let the OneSpace take care of your end-to-end model training and evaluation process.
After the training job is completed, the directories in the workspace should look like as follows:
│ .env
│ tensorflowConfig.py
│ training.py
│
├───ComputerVision
│ └───TensorFlow
│ └───AntsBeesProject_tf
│ ├───Artifacts
│ │ ├───Images
│ │ │ ├───Training Images
│ │ │ │ img_grid__on_20221012_at_090752.png
│ │ │ │
│ │ │ └───Validation Images
│ │ │ img_grid__on_20221012_at_090752.png
│ │ │
│ │ ├───Models
│ │ │ ├───Model Checkpoints
│ │ │ │ model_ckpt__on_20221012_at_090755_.h5
│ │ │ │
│ │ │ └───TrainedModels
│ │ │ my_test_model__on_20221012_at_090800_.h5
│ │ │
│ │ └───Plots
│ │ ├───models
│ │ │ my_test_model__on_20221012_at_091731.png
│ │ │
│ │ └───results
│ │ results_plot__on_20221012_at_091731.png
│ │
│ └───Logs
│ ├───CSV Logs
│ │ cv_test_logs.csv
│ │
│ └───Tensorboard Logs
│ └───logs__on_20221012_at_090755
│ ├───train
│ │ events.out.tfevents.1665540475.MY-LAPTOP.143340.0.v2
│ │
│ └───validation
│ events.out.tfevents.1665540477.MY-LAPTOP.143430.1.v2
│
├───data_folder
│ ├───train
│ │ ├───ants
│ │ │ 0013035.jpg
│ │ │ 1030023514_aad5c608f9.jpg
│ │ │ 1095476100_3906d8afde.jpg
│ │ │ 1099452230_d1949d3250.jpg
│ │ │ 116570827_e9c126745d.jpg
│ │ └───bees
│ │ 1092977343_cb42b38d62.jpg
│ │ 1093831624_fb5fbe2308.jpg
│ │ 1097045929_1753d1c765.jpg
│ │ 1232245714_f862fbe385.jpg
│ │ 129236073_0985e91c7d.jpg
│ │
│ └───val
│ ├───ants
│ │ 10308379_1b6c72e180.jpg
│ │ 1053149811_f62a3410d3.jpg
│ │ 1073564163_225a64f170.jpg
│ │
│ └───bees
│ 1032546534_06907fe3b3.jpg
│ 10870992_eebeeb3a12.jpg
│ 1181173278_23c36fac71.jpg
│
└───__pycache__
tensorflowConfig.cpython-310.pyc
ii) - Training an Image Classification Model with PyTorch :
First step is to setup the initial directory. This should present a following tree structure at the beginning of the training:
│ .env
│ pytorchConfig.py
│ training.py
│
└───data_folder
├───train
│ ├───ants
│ │ 0013035.jpg
│ │ 1030023514_aad5c608f9.jpg
│ │ 1095476100_3906d8afde.jpg
│ │ 1099452230_d1949d3250.jpg
│ │ 116570827_e9c126745d.jpg
│ │
│ └───bees
│ 1092977343_cb42b38d62.jpg
│ 1093831624_fb5fbe2308.jpg
│ 1097045929_1753d1c765.jpg
│ 1232245714_f862fbe385.jpg
│ 129236073_0985e91c7d.jpg
│
└───val
├───ants
│ 10308379_1b6c72e180.jpg
│ 1053149811_f62a3410d3.jpg
│ 1073564163_225a64f170.jpg
│
└───bees
1032546534_06907fe3b3.jpg
10870992_eebeeb3a12.jpg
1181173278_23c36fac71.jpg
Now let's discuss these files one by one
.env
This file should only be used when the database integration is required. You will be required to hold the database credentials in.envas shown below
MONGO_CONN_STR="Connection String for MongoDB"
MYSQL_HOST="data_base_host(could be an ip or domain)"
MYSQL_USER="database_user_name"
MYSQL_PASS="database_password"
MYSQL_DB="database_name"
pytorchConfig.py
This file is used to setup the configuration for Experiment. It has standard format as shown below. Parameters could be updated according to the training job requirements
#--------------------------------------------------------------------------------------------------------------------------------
# Parameters
#--------------------------------------------------------------------------------------------------------------------------------
data_dir = "data_folder"
train_folder_name = "train"
val_folder_name = "val"
transfer_learning = True
model_architecture = "MobileNet_v3_small" # available_selections = ["AlexNet", "ConvnNeXt", "DenseNet121", "DenseNet201", "EfficientNet_b7", "EfficientNet_v2_s", "EfficientNet_v2_m", "EfficientNet_v2_l", "Wide_Resnet50_2",
#"GoogleNet", "Inception_v3", "MnasNet0_5", "MnasNet1_3", "MobileNet_v2", "MobileNet_v3_large", "MobileNet_v3_small", "RegNet_y_32gf", "ResNet18",
#"ResNet34", "ResNet50", "ResNet152", "ResNext101_32x8d", "ShuffleNet_v2_x1_5", "SqueezeNet1_0", "VGG11", "VGG13", "VGG16", "VGG19", "VisionTransformer"]
augmentation = False
epochs = 1
batch_size = 32
input_shape = (224, 224, 3)
lr_scheduler = "ExponentialLR" # available_selections = ["OneCycleLR","StepLR", "LambdaLR", "ExponentialLR"]
optimizer = "RMSprop" # available_selections = ["SGD", "Adam", "Adadelta", "Adagrad", "RMSprop"]
validation_split = 0.20
grad_clip = 1 # Should be a value ranging from 0.5 to 1.0 OR None
weight_decay = 0.001 # Should be a value ranging from 0.5 to 1.0
#--------------------------------------------------------------------------------------------------------------------------------
# Artifacts (Directory names to store the results & resources, can be customized according to the user requirements)
#--------------------------------------------------------------------------------------------------------------------------------
project_name = 'AntsBeensProject_pytorch'
artifacts_dir = "Artifacts"
model_dir = "Models"
plots_dir = "Plots"
model_name = "antbees_model"
experiment_name = "Demo Experiment"
plot_name = "results_plot"
model_ckpt_dir = "Model Checkpoints"
callbacked_model_name = "model_ckpt"
#--------------------------------------------------------------------------------------------------------------------------------
# Logs (Directory names to record logs, can be customized according to the user requirements)
#--------------------------------------------------------------------------------------------------------------------------------
logs_dir = "Logs"
tensorboard_root_log_dir = "Tensorboard Logs"
csv_logs_dir = "CSV Logs"
csv_logs_file = "cv_test_logs.csv"
comments = "This is a Demo"
executed_by = 'Hasanain'
#--------------------------------------------------------------------------------------------------------------------------------
# Database Integration
#--------------------------------------------------------------------------------------------------------------------------------
# Please Note that before making any change in this section, create a .env file and store the mongo db connection string or MySQL credentials in the environment variables
# Guideline for creating .env is available on project description main page
db_integration_mysql = False
db_integration_mongodb = False
training.py
import pytorchConfig
from onespace.pytorch.cv import Experiment
def training(config):
exp = Experiment(config)
exp.run_experiment()
if __name__ == "__main__":
training(pytorchConfig)
Run the following command on the terminal to start the training job
python training.py
Now let the OneSpace take care of your end-to-end model training and evaluation process.
After the training job is completed, the directories in the workspace should look like as follows:
│ .env
│ pytorchConfig.py
│ training.py
│
├───ComputerVision
│ └───PyTorch
│ └───AntsBeensProject_pytorch
│ ├───Artifacts
│ │ ├───Images
│ │ │ ├───Training Images
│ │ │ │ img_grid__on_20221012_at_093114.png
│ │ │ │
│ │ │ └───Validation Images
│ │ │ img_grid__on_20221012_at_093114.png
│ │ │
│ │ ├───Models
│ │ │ ├───BaseModels
│ │ │ │ MobileNet_v3_small__on_20221012_at_093140_.pth
│ │ │ │
│ │ │ └───TrainedModels
│ │ │ antbees_model__on_20221012_at_093227_.pth
│ │ │
│ │ └───Plots
│ │ └───results
│ │ results_plot__on_20221012_at_093227.png
│ │
│ └───Logs
│ ├───CSV Logs
│ │ cv_test_logs.csv
│ │
│ └───Tensorboard Logs
│ └───logs__on_20221012_at_093052
│ │ events.out.tfevents.1665541852.My-Laptop.86003.0
│ │ projector_config.pbtxt
│ │
│ ├───00000
│ │ └───default
│ │ metadata.tsv
│ │ sprite.png
│ │ tensors.tsv
│ │
│ └───1665541924.1492205
│ events.out.tfevents.1665541924.My-Laptop.86003.1
│
├───data_folder
│ ├───train
│ │ ├───ants
│ │ │ 0013035.jpg
│ │ │ 1030023514_aad5c608f9.jpg
│ │ │ 1095476100_3906d8afde.jpg
│ │ │ 1099452230_d1949d3250.jpg
│ │ │ 116570827_e9c126745d.jpg
│ │ │
│ │ └───bees
│ │ 1092977343_cb42b38d62.jpg
│ │ 1093831624_fb5fbe2308.jpg
│ │ 1097045929_1753d1c765.jpg
│ │ 1232245714_f862fbe385.jpg
│ │ 129236073_0985e91c7d.jpg
│ │
│ └───val
│ ├───ants
│ │ 10308379_1b6c72e180.jpg
│ │ 1053149811_f62a3410d3.jpg
│ │ 1073564163_225a64f170.jpg
│ │
│ └───bees
│ 1032546534_06907fe3b3.jpg
│ 10870992_eebeeb3a12.jpg
│ 1181173278_23c36fac71.jpg
│
└───__pycache__
pytorchConfig.cpython-310.pyc
4. Contributing
Yes, Please! We believe that there is alot of oportunity to make Machine Learning more interesting and less complicated for the comunity, so let's make it more efficient, let's go with low-code!!
5. Conclusion
All the services which are being provided by OneSpace could be managed in a single directory without having to leave your workspace. A user only needs to take care of two things before running a training job:
- Setup the relavent configurations in
config.py. - Check if you are importing the right module in
training.py.
Now typepython training.pyon the terminal and hit enter.
Voila, you have a trained production ready model saved in the workspace, Great Work!
Please give this repository a star if you find our work useful, Thank you! 🙏
Copyright © 2022 OneSpace
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