├── README.md
├── sconce
    ├── __init__.py
    └── tflearn.py
└── setup.py


/README.md:
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 1 | # sconce
 2 | 
 3 | Sconce is a dashboard for monitoring and comparing data in real time. It was built to be an easy way to visualize the training progress of different machine learning models.
 4 | 
 5 | ![](https://i.imgur.com/cb5ExqZ.png)
 6 | 
 7 | ## Installation
 8 | 
 9 | Install with `pip`:
10 | 
11 | ```
12 | pip install sconce
13 | ```
14 | 
15 | ## Usage
16 | 
17 | To start, create a sconce Job with a name and (optionally) parameters. Jobs with the same name will be displayed together, so you can see how different parameters affect results.
18 | 
19 | ```python
20 | import sconce
21 | 
22 | # Create a sconce job with a name and parameters
23 | job = sconce.Job('my-neural-network', {
24 |     'n_layers': 6,
25 |     'hidden_size': 250,
26 |     'learning_rate': 0.001
27 | })
28 | ```
29 | 
30 | To plot data, call `job.record(x, y)`. Instead of posting every data point, Sconce will average the values and plot them every `plot_every` calls.
31 | 
32 | ```python
33 | # Record x, y values
34 | for x in range(1000):
35 |     y = train()
36 |     job.record(x, y)
37 | ```
38 | 
39 | Then visit http://sconce.prontotype.us/jobs/my-neural-network to view your results in real time.
40 | 
41 | ## Configuration
42 | 
43 | If you want more or less granularity, change `job.plot_every` and `job.print_every`:
44 | 
45 | ```python
46 | job.plot_every = 10
47 | job.print_every = 100
48 | ```
49 | 
50 | ## Private jobs
51 | 
52 | **TODO:** Register for an account and API key at http://sconce.prontotype.us/ to create private boards.
53 | 
54 | ```python
55 | sconce.login('spro', 'asdf1234uiop5768')
56 | ```
57 | 


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/sconce/__init__.py:
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 1 | import requests
 2 | import time
 3 | import math
 4 | import os
 5 | import sys
 6 | import socket
 7 | from multiprocessing import Process
 8 | 
 9 | HOST = 'api.sconce.prontotype.us'
10 | if 'SCONCE_HOST' in os.environ:
11 |     HOST = os.environ['SCONCE_HOST']
12 | 
13 | # Helpers
14 | 
15 | def time_since(since):
16 |     now = time.time()
17 |     s = now - since
18 |     m = math.floor(s / 60)
19 |     s -= m * 60
20 |     return '%dm %ds' % (m, s)
21 | 
22 | def req(method, path, json):
23 |     return requests.request(method, 'http://%s/%s.json' % (HOST, path), json=json)
24 | 
25 | # Start a job
26 | 
27 | class Job:
28 |     def __init__(self, name, params={}, hostname=None):
29 |         self.name = name
30 |         self.params = params
31 |         self.hostname = hostname or socket.gethostname()
32 | 
33 |         self.start()
34 | 
35 |     def start(self):
36 |         r = req('post', 'jobs', {'name': self.name, 'params': self.params, 'hostname': self.hostname})
37 |         body = r.json()
38 | 
39 |         if r.status_code != 200:
40 |             print("JOB CLAIMED", r.status_code, r.content)
41 |             sys.exit()
42 | 
43 |         self.job_id = body['id']
44 |         print("Starting job %s at %s" % (self.job_id, time.strftime('%Y-%m-%d %H:%M:%S', time.localtime())))
45 |         self.start_time = time.time()
46 | 
47 |         self.log_every = 50
48 |         self.plot_every = 50
49 |         self.loss_avg = 0
50 | 
51 |     def stop(self, ):
52 |         req('put', 'jobs/%s' % self.job_id, {'status': 'done'})
53 | 
54 |     def log(self, l):
55 |         print('[log] %s' % time_since(self.start_time), l)
56 |         Process(target=req, args=('post', 'jobs/%s/logs' % self.job_id, {'body': l})).start()
57 | 
58 |     def plot(self, x, y):
59 |         Process(target=req, args=('post', 'jobs/%s/points' % self.job_id, {'x': x, 'y': y})).start()
60 | 
61 |     def record(self, e, loss):
62 |         self.loss_avg += loss
63 | 
64 |         if e > 0 and e % self.log_every == 0:
65 |             self.log('(%s) %.4f' % (e, loss))
66 | 
67 |         if e > 0 and e % self.plot_every == 0:
68 |             self.plot(e, self.loss_avg / self.plot_every)
69 |             self.loss_avg = 0
70 | 
71 | 


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/sconce/tflearn.py:
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 1 | import sconce
 2 | import tflearn
 3 | 
 4 | class TflearnJob(tflearn.callbacks.Callback):
 5 |     def __init__(self, name):
 6 |         self.job = sconce.Job(name)
 7 | 
 8 |     def on_epoch_end(self, training_state):
 9 |         self.job.record(training_state.step, training_state.loss_value)
10 | 
11 | 


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/setup.py:
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 1 | from setuptools import setup
 2 | 
 3 | setup(name='sconce',
 4 |       version='0.0.2',
 5 |       description='Sconce graph helper',
 6 |       url='http://github.com/spro/sconce-python',
 7 |       author='Sean Robertson',
 8 |       author_email='sprobertson@gmail.com',
 9 |       license='MIT',
10 |       packages=['sconce'],
11 |       zip_safe=False)
12 | 
13 | 


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