Monitor deep learning model training and hardware usage from mobile.

• Monitor running experiments from mobile phone or laptop
• Monitor hardware usage on any computer with a single command
• Integrate with just 2 lines of code (see examples below)
• Keeps track of experiments including infomation like git commit, configurations and hyper-parameters
• API for custom visualizations
• Pretty logs of training progress
• Open source!

To install MongoDB , refer to the official documentation here .

Install the package using pip:

pip install labml-app

#
 Start the server on the default port (5005)

labml app-server

#
 To start the server on a different port, use the following command

labml app-server --port PORT

Optional: to setup and configure Nginx in your server, please refer to this .

You can access the user interface either by visiting http://localhost:{port} or, if configured on a separate machine, by navigating to http://{server-ip}:{port} .

1. Install the package using pip.

pip install labml

2. Create a file named .labml.yaml at the top level of your project folder, and add the following line to the file:

app_url
: 
http://localhost:{port}/api/v1/default


#
 If you are setting up the project on a different machine, include the following line instead,

app_url
: 
http://{server-ip}:{port}/api/v1/default

from
 labml
 import
 tracker
, 
experiment


with
 experiment
.
record
(
name
=
'sample'
, 
exp_conf
=
conf
):
    
for
 i
 in
 range
(
50
):
        
loss
, 
accuracy
 =
 train
()
        
tracker
.
save
(
i
, {
'loss'
: 
loss
, 
'accuracy'
: 
accuracy
})

from
 labml
 import
 tracker
, 
experiment


uuid
 =
 experiment
.
generate_uuid
() 
# make sure to sync this in every machine

experiment
.
create
(
uuid
=
uuid
,
                  
name
=
'distributed training sample'
,
                  
distributed_rank
=
0
,
                  
distributed_world_size
=
8
,
                  )
with
 experiment
.
start
():
    
for
 i
 in
 range
(
50
):
        
loss
, 
accuracy
 =
 train
()
        
tracker
.
save
(
i
, {
'loss'
: 
loss
, 
'accuracy'
: 
accuracy
})

• Python API Reference
• Samples

• API to create experiments
• Track training metrics
• Monitored training loop and other iterators
• API for custom visualizations
• Configurations management API
• Logger for stylized logging

#
 Install packages and dependencies

pip install labml psutil py3nvml

#
 Start monitoring

labml monitor

If you use LabML for academic research, please cite the library using the following BibTeX entry.

@misc{labml,
 author = {Varuna Jayasiri, Nipun Wijerathne},
 title = {labml.ai: A library to organize machine learning experiments},
 year = {2020},
 url = {https://labml.ai/},
}