clearml initial version 0.17.0

examples/frameworks/autokeras/autokeras_imdb_example.py

@@ -3,7 +3,7 @@ import numpy as np
 import tensorflow as tf
 from tensorflow import keras
 
-from trains import Task
+from clearml import Task
 
 task = Task.init(project_name="autokeras", task_name="autokeras imdb example with scalars")
 

examples/frameworks/autokeras/requirements.txt

@@ -1,5 +1,5 @@
 # Plese read this https://github.com/keras-team/autokeras#installation before doing changes
 autokeras
 tensorflow>=2.3.0
-trains
+clearml
 git+https://github.com/keras-team/keras-tuner.git@1.0.2rc2

examples/frameworks/fastai/fastai_with_tensorboard.py

@@ -1,10 +1,10 @@
-# TRAINS - Fastai with Tensorboard example code, automatic logging the model and Tensorboard outputs
+# ClearML - Fastai with Tensorboard example code, automatic logging the model and Tensorboard outputs
 #
 
 from fastai.callbacks.tensorboard import LearnerTensorboardWriter
 from fastai.vision import *  # Quick access to computer vision functionality
 
-from trains import Task
+from clearml import Task
 
 task = Task.init(project_name="example", task_name="fastai with tensorboard callback")
 

examples/frameworks/fastai/requirements.txt

@@ -1,4 +1,4 @@
 fastai
 tensorboard
 tensorboardX
-trains
+clearml

examples/frameworks/ignite/cifar_ignite.py

@@ -15,12 +15,12 @@ from ignite.utils import setup_logger
 from torch.utils.tensorboard import SummaryWriter
 from tqdm import tqdm
 
-from trains import Task, StorageManager
+from clearml import Task, StorageManager
 
-# Trains Initializations
+# ClearML Initializations
 task = Task.init(project_name='Image Example', task_name='image classification CIFAR10')
 params = {'number_of_epochs': 20, 'batch_size': 64, 'dropout': 0.25, 'base_lr': 0.001, 'momentum': 0.9, 'loss_report': 100}
-params = task.connect(params)  # enabling configuration override by trains
+params = task.connect(params)  # enabling configuration override by clearml
 print(params)  # printing actual configuration (after override in remote mode)
 
 manager = StorageManager()

examples/frameworks/keras/keras_tensorboard.py

@@ -1,4 +1,4 @@
-# TRAINS - Keras with Tensorboard example code, automatic logging model and Tensorboard outputs
+# ClearML - Keras with Tensorboard example code, automatic logging model and Tensorboard outputs
 #
 # Train a simple deep NN on the MNIST dataset.
 # Gets to 98.40% test accuracy after 20 epochs
@@ -19,7 +19,7 @@ from tensorflow.keras.layers import Activation, Dense
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.optimizers import RMSprop
 
-from trains import Task
+from clearml import Task
 
 
 class TensorBoardImage(TensorBoard):
@@ -89,7 +89,7 @@ model.compile(loss='categorical_crossentropy',
               optimizer=RMSprop(),
               metrics=['accuracy'])
 
-# Connecting TRAINS
+# Connecting ClearML
 task = Task.init(project_name='examples', task_name='Keras with TensorBoard example')
 
 # To set your own configuration:

examples/frameworks/keras/legacy/keras_tensorboard.py

@@ -1,4 +1,4 @@
-# TRAINS - Keras with Tensorboard example code, automatic logging model and Tensorboard outputs
+# ClearML - Keras with Tensorboard example code, automatic logging model and Tensorboard outputs
 #
 # Train a simple deep NN on the MNIST dataset.
 # Gets to 98.40% test accuracy after 20 epochs
@@ -18,7 +18,7 @@ from keras.layers.core import Dense, Activation
 from keras.optimizers import RMSprop
 from keras.utils import np_utils
 import tensorflow as tf
-from trains import Task
+from clearml import Task
 
 
 class TensorBoardImage(TensorBoard):
@@ -88,7 +88,7 @@ model.compile(loss='categorical_crossentropy',
               optimizer=RMSprop(),
               metrics=['accuracy'])
 
-# Connecting TRAINS
+# Connecting ClearML
 task = Task.init(project_name='examples', task_name='Keras with TensorBoard example')
 task.connect_configuration({'test': 1337, 'nested': {'key': 'value', 'number': 1}})
 

examples/frameworks/keras/legacy/requirements.txt

@@ -1,2 +1,2 @@
-trains
+clearml
 Keras>=2.2.4

examples/frameworks/keras/manual_model_upload.py

@@ -1,11 +1,11 @@
-# TRAINS - Example of manual model configuration and uploading
+# ClearML - Example of manual model configuration and uploading
 #
 import os
 from tempfile import gettempdir
 
 from keras import Input, layers, Model
 
-from trains import Task
+from clearml import Task
 
 
 task = Task.init(project_name='examples', task_name='Model configuration and upload')

examples/frameworks/keras/requirements.txt

@@ -1,3 +1,3 @@
 Keras
 tensorflow>=2.0
-trains
+clearml

examples/frameworks/kerastuner/keras_tuner_cifar.py

@@ -3,9 +3,9 @@
 import kerastuner as kt
 import tensorflow as tf
 import tensorflow_datasets as tfds
-from trains.external.kerastuner import TrainsTunerLogger
+from clearml.external.kerastuner import TrainsTunerLogger
 
-from trains import Task
+from clearml import Task
 
 physical_devices = tf.config.list_physical_devices('GPU')
 if physical_devices:

examples/frameworks/kerastuner/requirements.txt

@@ -1,4 +1,4 @@
 keras-tuner
 tensorflow>=2.0
 tensorflow-datasets
-trains
+clearml

examples/frameworks/lightgbm/requirements.txt

@@ -1,4 +1,4 @@
 lightgbm
 scikit-learn
 pandas
-trains
+clearml

examples/frameworks/lightgbm/train_with_lightbgm.py

@@ -1,10 +1,10 @@
-# TRAINS - Example of LightGBM integration
+# ClearML - Example of LightGBM integration
 #
 import lightgbm as lgb
 import pandas as pd
 from sklearn.metrics import mean_squared_error
 
-from trains import Task
+from clearml import Task
 
 task = Task.init(project_name="examples", task_name="LIGHTgbm")
 

examples/frameworks/matplotlib/matplotlib_example.py

@@ -1,9 +1,9 @@
-# TRAINS - Example of Matplotlib and Seaborn integration and reporting
+# ClearML - Example of Matplotlib and Seaborn integration and reporting
 #
 import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
-from trains import Task
+from clearml import Task
 
 
 task = Task.init(project_name='examples', task_name='Matplotlib example')

examples/frameworks/matplotlib/requirements.txt

@@ -1,4 +1,4 @@
 matplotlib >= 3.1.1 ; python_version >= '3.6'
 matplotlib >= 2.2.4 ; python_version < '3.6'
 seaborn
-trains
+clearml

examples/frameworks/pytorch/manual_model_upload.py

@@ -1,10 +1,10 @@
-# TRAINS - Example of manual model configuration and uploading
+# ClearML - Example of manual model configuration and uploading
 #
 import os
 from tempfile import gettempdir
 
 import torch
-from trains import Task
+from clearml import Task
 
 
 task = Task.init(project_name='examples', task_name='Model configuration and upload')

examples/frameworks/pytorch/pytorch_distributed_example.py

@@ -1,4 +1,4 @@
-# TRAINS - example of TRAINS torch distributed support
+# ClearML - example of ClearML torch distributed support
 # notice all nodes will be reporting to the master Task (experiment)
 
 import os
@@ -15,7 +15,7 @@ import torch.nn.functional as F
 from torch import optim
 from torchvision import datasets, transforms
 
-from trains import Task
+from clearml import Task
 
 
 local_dataset_path = './MNIST_data'
@@ -150,7 +150,7 @@ if __name__ == "__main__":
 
     # We have to initialize the task in the master process,
     # it will make sure that any sub-process calling Task.init will get the master task object
-    # notice that we exclude the `rank` argument, so we can launch multiple sub-processes with trains-agent
+    # notice that we exclude the `rank` argument, so we can launch multiple sub-processes with clearml-agent
     # otherwise, the `rank` will always be set to the original value.
     task = Task.init("examples", "test torch distributed", auto_connect_arg_parser={'rank': False})
 

examples/frameworks/pytorch/pytorch_matplotlib.py

@@ -1,4 +1,4 @@
-# TRAINS - Example of Pytorch and matplotlib integration and reporting
+# ClearML - Example of Pytorch and matplotlib integration and reporting
 #
 """
 Neural Transfer Using PyTorch
@@ -60,7 +60,7 @@ import torchvision.transforms as transforms
 import torchvision.models as models
 
 import copy
-from trains import Task
+from clearml import Task
 
 
 task = Task.init(project_name='examples', task_name='pytorch with matplotlib example', task_type=Task.TaskTypes.testing)

examples/frameworks/pytorch/pytorch_mnist.py

@@ -1,4 +1,4 @@
-# TRAINS - Example of Pytorch mnist training integration
+# ClearML - Example of Pytorch mnist training integration
 #
 from __future__ import print_function
 import argparse
@@ -11,7 +11,7 @@ import torch.nn.functional as F
 import torch.optim as optim
 from torchvision import datasets, transforms
 
-from trains import Task, Logger
+from clearml import Task, Logger
 
 
 class Net(nn.Module):

examples/frameworks/pytorch/pytorch_tensorboard.py

@@ -1,4 +1,4 @@
-# TRAINS - Example of pytorch with tensorboard>=v1.14
+# ClearML - Example of pytorch with tensorboard>=v1.14
 #
 from __future__ import print_function
 
@@ -14,7 +14,7 @@ from torchvision import datasets, transforms
 from torch.autograd import Variable
 from torch.utils.tensorboard import SummaryWriter
 
-from trains import Task
+from clearml import Task
 
 
 class Net(nn.Module):
@@ -99,7 +99,7 @@ def main():
     parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                         help='how many batches to wait before logging training status')
     args = parser.parse_args()
-    task = Task.init(project_name='examples', task_name='pytorch with tensorboard')  # noqa: F841
+    Task.init(project_name='examples', task_name='pytorch with tensorboard')
     writer = SummaryWriter('runs')
     writer.add_text('TEXT', 'This is some text', 0)
     args.cuda = not args.no_cuda and torch.cuda.is_available()

examples/frameworks/pytorch/requirements.txt

@@ -3,4 +3,4 @@ tensorboardX
 tensorboard>=1.14.0
 torch>=1.1.0
 torchvision>=0.3.0
-trains
+clearml

examples/frameworks/pytorch/tensorboard_toy_pytorch.py

@@ -5,7 +5,7 @@ import numpy as np
 from PIL import Image
 from torch.utils.tensorboard import SummaryWriter
 
-from trains import Task
+from clearml import Task
 task = Task.init(project_name='examples', task_name='pytorch tensorboard toy example')
 
 

examples/frameworks/scikit-learn/requirements.txt

@@ -2,4 +2,4 @@ joblib>=0.13.2
 matplotlib >= 3.1.1 ; python_version >= '3.6'
 matplotlib >= 2.2.4 ; python_version < '3.6'
 scikit-learn
-trains
+clearml

examples/frameworks/scikit-learn/sklearn_joblib_example.py

@@ -10,7 +10,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 
-from trains import Task
+from clearml import Task
 
 task = Task.init(project_name="examples", task_name="scikit-learn joblib example")
 

examples/frameworks/scikit-learn/sklearn_matplotlib_example.py

@@ -6,7 +6,7 @@ from sklearn.model_selection import learning_curve
 from sklearn.naive_bayes import GaussianNB
 from sklearn.svm import SVC
 
-from trains import Task
+from clearml import Task
 
 
 def plot_learning_curve(estimator, title, X, y, axes=None, ylim=None, cv=None, n_jobs=None,

examples/frameworks/tensorboardx/pytorch_tensorboardX.py

@@ -1,4 +1,4 @@
-# TRAINS - Example of pytorch with tensorboardX
+# ClearML - Example of pytorch with tensorboardX
 #
 from __future__ import print_function
 
@@ -14,7 +14,7 @@ from tensorboardX import SummaryWriter
 from torch.autograd import Variable
 from torchvision import datasets, transforms
 
-from trains import Task
+from clearml import Task
 
 
 class Net(nn.Module):

examples/frameworks/tensorboardx/requirements.txt

@@ -1,4 +1,4 @@
 tensorboardX>=1.8
 torch>=1.1.0
 torchvision>=0.3.0
-trains
+clearml

examples/frameworks/tensorflow/legacy/tensorboard_pr_curve.py

@@ -1,4 +1,4 @@
-# TRAINS - Example of new tensorboard pr_curves model
+# ClearML - Example of new tensorboard pr_curves model
 #
 # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
 #
@@ -37,7 +37,7 @@ from absl import flags
 from six.moves import xrange  # pylint: disable=redefined-builtin
 import tensorflow as tf
 from tensorboard.plugins.pr_curve import summary
-from trains import Task
+from clearml import Task
 
 task = Task.init(project_name='examples', task_name='tensorboard pr_curve')
 

examples/frameworks/tensorflow/legacy/tensorboard_toy.py

@@ -1,4 +1,4 @@
-# TRAINS - Example of tensorboard with tensorflow (without any actual training)
+# ClearML - Example of tensorboard with tensorflow (without any actual training)
 #
 import os
 from tempfile import gettempdir
@@ -7,7 +7,7 @@ import tensorflow as tf
 import numpy as np
 from PIL import Image
 
-from trains import Task
+from clearml import Task
 task = Task.init(project_name='examples', task_name='tensorboard toy example')
 
 

examples/frameworks/tensorflow/legacy/tensorflow_eager.py

@@ -1,4 +1,4 @@
-# TRAINS - Example of tensorflow eager mode, model logging and tensorboard
+# ClearML - Example of tensorflow eager mode, model logging and tensorboard
 #
 # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
 #
@@ -30,7 +30,7 @@ from tempfile import gettempdir
 import tensorflow as tf
 from tensorflow.examples.tutorials.mnist import input_data
 
-from trains import Task
+from clearml import Task
 
 tf.compat.v1.enable_eager_execution()
 

examples/frameworks/tensorflow/legacy/tensorflow_mnist_with_summaries.py

@@ -31,7 +31,7 @@ import tempfile
 import tensorflow as tf
 
 from tensorflow.examples.tutorials.mnist import input_data
-from trains import Task
+from clearml import Task
 
 FLAGS = None
 task = Task.init(project_name='examples', task_name='Tensorflow mnist with summaries example')

examples/frameworks/tensorflow/manual_model_upload.py

@@ -1,10 +1,10 @@
-# TRAINS - Example of manual model configuration and uploading
+# ClearML - Example of manual model configuration and uploading
 #
 import os
 import tempfile
 
 import tensorflow as tf
-from trains import Task
+from clearml import Task
 
 task = Task.init(project_name='examples', task_name='Model configuration and upload')
 

examples/frameworks/tensorflow/requirements.txt

@@ -1,3 +1,3 @@
 tensorboard>=2.0
 tensorflow>=2.0
-trains
+clearml

examples/frameworks/tensorflow/tensorboard_pr_curve.py

@@ -36,7 +36,7 @@ from six.moves import xrange  # pylint: disable=redefined-builtin
 import tensorflow as tf
 
 from tensorboard.plugins.pr_curve import summary
-from trains import Task
+from clearml import Task
 
 
 task = Task.init(project_name='examples', task_name='tensorboard pr_curve')

examples/frameworks/tensorflow/tensorboard_toy.py

@@ -1,4 +1,4 @@
-# TRAINS - Example of tensorboard with tensorflow (without any actual training)
+# ClearML - Example of tensorboard with tensorflow (without any actual training)
 #
 import os
 import tensorflow as tf
@@ -6,7 +6,7 @@ import numpy as np
 from tempfile import gettempdir
 from PIL import Image
 
-from trains import Task
+from clearml import Task
 
 
 def generate_summary(k, step):

examples/frameworks/tensorflow/tensorflow_mnist.py

@@ -8,7 +8,7 @@ import tensorflow as tf
 from tensorflow.keras.layers import Dense, Flatten, Conv2D
 from tensorflow.keras import Model
 
-from trains import Task
+from clearml import Task
 
 
 task = Task.init(project_name='examples',

examples/frameworks/xgboost/requirements.txt

@@ -1,7 +1,7 @@
 matplotlib >= 3.1.1 ; python_version >= '3.6'
 matplotlib >= 2.2.4 ; python_version < '3.6'
 sklearn
-trains
+clearml
 xgboost>=0.90 ; python_version >= '3'
 xgboost>=0.82 ; python_version < '3'
 # sudo apt-get install graphviz

examples/frameworks/xgboost/xgboost_sample.py

@@ -5,7 +5,7 @@ from sklearn.metrics import accuracy_score
 from sklearn.model_selection import train_test_split
 from xgboost import plot_tree
 
-from trains import Task
+from clearml import Task
 
 task = Task.init(project_name='examples', task_name='XGBoost simple example')
 iris = datasets.load_iris()