ClearML/clearml
1
0
Fork 0
mirror of https://github.com/clearml/clearml synced 2025-05-03 12:31:00 +00:00
Code Issues Actions Packages Projects Releases Wiki Activity

Remove old model upload examples

Browse Source
This commit is contained in:
Erez 2021-05-16 14:37:04 +03:00 committed by Allegro AI
parent 350a39b886
commit 652a29650a
3 changed files with 0 additions and 144 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

55
examples/frameworks/keras/manual_model_upload.py
View File

@ -1,55 +0,0 @@
# ClearML - Example of manual model configuration and uploading
#
import os
from tempfile import gettempdir
from tensorflow.keras import Input, layers, Model
from clearml import Task
# Connecting ClearML with the current process,
# from here on everything is logged automatically
task = Task.init(project_name='examples', task_name='Model configuration and upload')
def get_model():
# Create a simple model.
inputs = Input(shape=(32,))
outputs = layers.Dense(1)(inputs)
keras_model = Model(inputs, outputs)
keras_model.compile(optimizer='adam', loss='mean_squared_error')
return keras_model
# create a model
model = get_model()
# Connect a local configuration file
config_file = os.path.join('..', '..', 'reporting', 'data_samples', 'sample.json')
config_file = task.connect_configuration(config_file)
# then read configuration as usual, the backend will contain a copy of it.
# later when executing remotely, the returned `config_file` will be a temporary file
# containing a new copy of the configuration retrieved form the backend
# # model_config_dict = json.load(open(config_file, 'rt'))
# Or Store dictionary of definition for a specific network design
model_config_dict = {
'value': 13.37,
'dict': {'sub_value': 'string', 'sub_integer': 11},
'list_of_ints': [1, 2, 3, 4],
}
model_config_dict = task.connect_configuration(model_config_dict)
# We now update the dictionary after connecting it, and the changes will be tracked as well.
model_config_dict['new value'] = 10
model_config_dict['value'] *= model_config_dict['new value']
# store the label enumeration of the training model
labels = {'background': 0, 'cat': 1, 'dog': 2}
task.connect_label_enumeration(labels)
# storing the model, it will have the task network configuration and label enumeration
print('Any model stored from this point onwards, will contain both model_config and label_enumeration')
model.save(os.path.join(gettempdir(), "model"))
print('Model saved')

44
examples/frameworks/pytorch/manual_model_upload.py
View File

@ -1,44 +0,0 @@
# ClearML - Example of manual model configuration and uploading
#
import os
from tempfile import gettempdir
import torch
from clearml import Task
# Connecting ClearML with the current process,
# from here on everything is logged automatically
task = Task.init(project_name='examples', task_name='Model configuration and upload')
# create a model
model = torch.nn.Module
# Connect a local configuration file
config_file = os.path.join('..', '..', 'reporting', 'data_samples', 'sample.json')
config_file = task.connect_configuration(config_file)
# then read configuration as usual, the backend will contain a copy of it.
# later when executing remotely, the returned `config_file` will be a temporary file
# containing a new copy of the configuration retrieved form the backend
# # model_config_dict = json.load(open(config_file, 'rt'))
# Or Store dictionary of definition for a specific network design
model_config_dict = {
'value': 13.37,
'dict': {'sub_value': 'string', 'sub_integer': 11},
'list_of_ints': [1, 2, 3, 4],
}
model_config_dict = task.connect_configuration(model_config_dict)
# We now update the dictionary after connecting it, and the changes will be tracked as well.
model_config_dict['new value'] = 10
model_config_dict['value'] *= model_config_dict['new value']
# store the label enumeration of the training model
labels = {'background': 0, 'cat': 1, 'dog': 2}
task.connect_label_enumeration(labels)
# storing the model, it will have the task network configuration and label enumeration
print('Any model stored from this point onwards, will contain both model_config and label_enumeration')
torch.save(model, os.path.join(gettempdir(), "model.pt"))
print('Model saved')

45
examples/frameworks/tensorflow/manual_model_upload.py
View File

@ -1,45 +0,0 @@
# ClearML - Example of manual model configuration and uploading
#
import os
import tempfile
import tensorflow as tf
from clearml import Task
# Connecting ClearML with the current process,
# from here on everything is logged automatically
task = Task.init(project_name='examples', task_name='Model configuration and upload')
model = tf.Module()
# Connect a local configuration file
config_file = os.path.join('..', '..', 'reporting', 'data_samples', 'sample.json')
config_file = task.connect_configuration(config_file)
# then read configuration as usual, the backend will contain a copy of it.
# later when executing remotely, the returned `config_file` will be a temporary file
# containing a new copy of the configuration retrieved form the backend
# # model_config_dict = json.load(open(config_file, 'rt'))
# Or Store dictionary of definition for a specific network design
model_config_dict = {
'value': 13.37,
'dict': {'sub_value': 'string', 'sub_integer': 11},
'list_of_ints': [1, 2, 3, 4],
}
model_config_dict = task.connect_configuration(model_config_dict)
# We now update the dictionary after connecting it, and the changes will be tracked as well.
model_config_dict['new value'] = 10
model_config_dict['value'] *= model_config_dict['new value']
# store the label enumeration of the training model
labels = {'background': 0, 'cat': 1, 'dog': 2}
task.connect_label_enumeration(labels)
# storing the model, it will have the task network configuration and label enumeration
print('Any model stored from this point onwards, will contain both model_config and label_enumeration')
tempdir = tempfile.mkdtemp()
tf.saved_model.save(model, os.path.join(tempdir, "model"))
print('Model saved')