antgo is a machine learning experiment manage platform, which has been integrated deeply with MLTalker. antgo provides some easy cli commands to help ML researchers to manage, analyze, and challenge all kinds of ML tasks.
Based on amounts of statistical evaluation methods, antgo could give a fruitful evaluation report, which help researchers analyze and trade-off their model.
Antgo tutorial is at MLTalker Blog.
install 3rd software or packages:
1. install rocksdb
sudo apt-get update
sudo apt-get install -y build-essential libgflags-dev libsnappy-dev zlib1g-dev libbz2-dev liblz4-dev
git clone https://github.com/facebook/rocksdb.git
cd rocksdb/
make shared_lib
export CPLUS_INCLUDE_PATH=${CPLUS_INCLUDE_PATH}:`pwd`/include
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:`pwd`
export LIBRARY_PATH=${LIBRARY_PATH}:`pwd`
cd ..
2. install ipfs (https://ipfs.io/)
wget -q https://raw.githubusercontent.com/ipfs/install-go-ipfs/master/install-ipfs.sh
chmod +x install-ipfs.sh
./install-ipfs.sh
3. install graphviz (http://www.graphviz.org)
sudo apt-get install graphviz
install antgo:
1. git clone https://github.com/jianzfb/antgo.git 2. cd antgo 3. pip install -r requirements.txt 4. python setup.py build_ext install
Register in MLTalker.
All user experiment records would be managed by MLTalker in user's personal page.
1.step Apply Task from mlalker.com:
/**********************************************************************************/ /******************** enter antgo cli *******************/ /**********************************************************************************/ antgo --token=<your token> /**********************************************************************************/ /*************** list all public and your created private tasks *****************/ /*** id name time dataset applicants ****/ /*** 3 *** 2018-03-21 19:34:48 *** 1 ****/ /*** ... ****/ /**********************************************************************************/ apply /**********************************************************************************/ /************* apply your interest task ***************/ /**********************************************************************************/ apply --id=... /**********************************************************************************/ /************************ list all your applied tasks **************************/ /*** id name time dataset experiments token ****/ /*** 3 *** 2018-03-21 19:08:08 *** 1 6ebb7***24743e1a ****/ /*** ... ****/ /**********************************************************************************/ task /**********************************************************************************/ /********************* list all experiments in your task *********************/ /*** id name time optimum report model ****/ /*** 7 20180322.115607.896376 2018-03-22 11:56:07 0 0 0 ****/ /*** ... ****/ /**********************************************************************************/ task --id=...
2.step Run Train Task:
(1) build running main file (eg. training_task.py)
from antgo.context import *
from antgo.dataflow.common import *
# 1.step ctx take control interaction with antgo
ctx = Context()
# 2.step build visualization channel
# curve channel
loss_channel = ctx.job.create_channel("loss","NUMERIC")
# histogram channel
histogram_channel = ctx.job.create_channel("Layer1-activation-histogram",'HISTOGRAM')
# build chart (bind multi-channels)
ctx.job.create_chart([loss_channel],"Loss Curve", "step", "value")
ctx.job.create_chart([histogram_channel], "Weight","value","frequence")
# 3.step custom training process
def training_callback(data_source,dump_dir):
# data_source: data generator
# dump_dir: save your training intermidiate data
# 3.1 step stack batch
stack_batch = BatchData(Node.inputs(data_source, batch_size=16)
# 3.2 step running some epochs
iter = 0
for epoch in range(ctx.params.max_epochs):
for data, label in stack_batch.iterator_value():
# run once iterator
loss, weight = your_training_model(data, label)
# send running information
# 1. loss value
loss_channel.send(x=iter, y=loss)
# 2. activation histogram
histogram_channel.send(x=iter, y=weight)
# 4.step custom infer process
def infer_callback(data_source, dump_dir):
# data_source: data generator
# dump_dir: your training intermidiate data folder
# 4.1 step load your custom model
...
# 4.2 step traverse data and do forward process
for data in data_source.iterator_value():
# forward process
...
# record result
ctx.recorder.record(result)
# 5.step bind training_callback and infer_callback
ctx.training_process = training_callback
ctx.infer_process = infer_callback
(2) call antgo cli at terminal
antgo run --main_file=challenge_task.py --main_param=challenge_task.yaml --token=<task token>
3.step Run Challenge Task:
(1) build running main file (eg. challenge_task.py)
from antgo.context import *
# 1.step ctx take control interaction with antgo
ctx = Context()
# 2.step custom infer process
def infer_callback(data_source, dump_dir):
# data_source: data generator
# dump_dir : your training intermidiate data folder
# 2.1 step load custom model
...
# 2.2 step traverse data and do forward process
for data in data_source.iterator_value():
# forward process
...
# record result
ctx.recorder.record(result)
# bind infer_callback
ctx.infer_process = infer_callback
(2) call antgo cli at terminal
antgo challenge --main_file=challenge_task.py --main_param=challenge_task.yaml --token=<task token>