## Reliable training hack on the Google Colaboratory

Posted on November 19, 2018 by Noon van der Silk

It’s really quite handy, but by far the biggest downside is that the sessions time out. It makes sense; I’m sure even Google can’t give out an unlimited amount of compute-resources for free to every person.

### Background/Problem

On the weekend, I wanted to train a few sketch-rnn models on the quickdraw data.

Naively, I figured this would be really easy with Google colab. While it was straightforward to start training, what I noticed is that getting data on to and off of the instance was frustrating, and the timeouts blocked me from getting a good amount of training time.

### Solution

Happily, colab supports very nice integration with Google services, so my plan was:

2. Train, or continue training,
3. Push a checkpoint to Google Drive occasionally,
4. Repeat until happy.

Here’s how it looks, in code:

As I’m working with the quickdraw data, it’s already on the Google Cloud Platform, so this was very easy. In a cell, I simply ran the following to get the “eye” quickdraw data:

!gsutil cp gs://quickdraw_dataset/sketchrnn/eye.npz .

(Note that the gsutil command is already installed on the instance.)

Train, or continue training, and save to Drive

As I’m using the sketch_rnn model, I first simply install magenta (and I have to pick a Python 2 environment.)

!pip install magenta

Now, there’s some considerations. Recalling that I’m going to be pushing my checkpoints to Google Drive, I need to authenticate with Google Drive. This is how that looks:

from google.colab import auth
auth.authenticate_user()

Then you’ll be prompted to copy in a code. Once that’s done, you can connect to Google Drive like so:

from googleapiclient.discovery import build
drive_service = build('drive', 'v3')

Now, if I’m training from scratch, I’ll run something like this:

!sketch_rnn_train --log_root=logs --data_dir=./ --hparams="data_set=[eye.npz],num_steps=501"

This will run for however long, and utlimately produce checkpoints in the ./logs folder, supposing that eye.npz exists in the present directory.

Once that’s completed, I start my main training-pushing loop. Firstly, there’s a bit of busywork to zip files, get the latest checkpoint number, and upload it to Google Drive:

import os
import zipfile

def get_largest_num (dir="logs", prefix="vector"):

files = os.listdir(dir)

biggest = 0

for f in files:
if f.startswith(prefix):
k = int( f.split(".")[0].split("-")[1] )
if k > biggest:
biggest = k

return biggest

def zip_model (name, k):
sk     = str(k)
zipobj = zipfile.ZipFile(name + ".zip", "w", zipfile.ZIP_DEFLATED)

files = [ "checkpoint"
, "model_config.json"
, "vector-" + sk + ".meta"
, "vector-" + sk + ".index"
, "vector-" + sk + ".data-00000-of-00001"]

for f in files:
zipobj.write("logs/" + f, f)

"name":     name,
"mimeType": "binary/octet-stream" }

mimetype="binary/octet-stream",
resumable=True)

media_body=media,
fields="id").execute()
file_id = created.get("id")
return file_id

Then, the main loop:

iterations = 200
for k in range(iterations):
print("Iteration " + str(k))
cmd = 'sketch_rnn_train --log_root=logs --resume_training --data_dir=./ ' + \
' --hparams="data_set=[eye.npz],num_steps=1001"'
x = os.system(cmd)
zip_model("model", get_largest_num())
upload_to_drive()

So, all that does is run the main training command, to reload the model from the latest checkpoint and continue training, then zips and uploads!

Set the iterations to whatever you wish; chances are your instance will never run for that long anyway; the main point is to push up the checkpoints every-so-often (for me, every 1000 steps of the sketch_rnn model; which takes about 1 hour or so, depending on params.)

Brining down the most recent Drive checkpoint

Now, when your instance goes away, you’ll need to bring down the most recent checkpoint from Drive. I did this somewhat manually, but it works well enough:

# Mount Google Drive as a folder
drive.mount('/content/gdrive')
# Extract latest model zip file
!cp /content/gdrive/My\ Drive/model\ $$3$$.zip logs/model.zip && cd logs && unzip model.zip