Every time I begin a machine learning (ML) project, I go through more or less the same steps. I start by quickly hacking a model prototype and a training script. After a few days, the codebase grows unruly and any modification is starting to take unreasonably long time due to badly-handled dependencies and the general lack of structure. At this point, I decide that some refactoring is needed: parts of the model are wrapped into separate, meaningful objects, and the training script gets somewhat general structure, with clearly delineated sections. Further down the line, I am often faced with the need of supporting multiple datasets and a variety of models, where the differences between model variants are much more than just hyperparameters - they often differ structurally and have different inputs or outputs. At this point, I start copying training scripts to support model variants. It is straightforward to set up, but maintenance becomes a nightmare: with copies of the code living in separate files, any modification has to be applied to all the files.

For me, it is often unclear how to handle this last bit cleanly. It can be project-dependent. It is often easy to come up with simple hacks, but they do not generalise and can make code very messy very quickly. Given that most experiments look similar among the projects I have worked on, there should exist a general solution. Let’s have a look at the structure of a typical experiment:

1. You specify the data and corresponding hyperparameters.
2. You define the model and its hyperparameters.
3. You run the training script and (hopefully) save model checkpoints and logs during training.
4. Once the training has converged, you might want to load a model checkpoint in another script or a notebook for thorough evaluation or deploy the model.

In most projects I have seen, 1. and 2. were split between the training script (dataset and model classes or functions) and external configs (hyperparameters as command-line arguments or config files). Logging and saving checkpoints should be a part of every training script, and yet it can be time-consuming to set up correctly. As far as I know, there is no general mechanism to do 4., and it is typically handled by retrieving hyperparameters used in a specific experiment and using the dataset/model classes/functions directly to instantiate them in a script or a notebook.

If this indeed is a general structure of an experiment, then there should exist tools to facilitate it. I am not familiar with any, however. Please let me know if such tools exist, or if the structure outlined above does not generally hold. Sacred and artemis are great for managing configuration files and experimental results; you can retrieve configuration of an experiment, but if you want to load a saved model in a notebook, for example, you need to know how to instantiate the model using the config. I prefer to automate this, too. When it comes to tensorflow, there is keras and the estimator api that simplify model building, fitting and evaluation. While generally useful, they are rather heavy and make access to low-level model features difficult. Their lack of flexibility is a no-go for me since I often work on non-standard models and require access to the most private of their parts.

All this suggests that we could benefit from a lightweight experimental framework for managing ML experiments. For me, it would be ideal if it satisfied the following requirements.

1. It should require minimal setup.
2. It has to be compatible with tensorflow (my primary tool for ML these days).
3. Ideally, it should be usable with non-tensorflow models - software evolves quickly, and my next project might be in pytorch. Who knows?
4. Datasets and models should be specified and configured separately so that they can be mixed and matched later on.
5. Hyerparameters and config files should be stored for every experiment, and it would be great if we could browse them quickly, without using non-standard apps to do so (so no databases).
6. Loading a trained model should be possible with minimum overhead, ideally without touching the original model-building code. Pointing at a specific experiment should be enough.

As far as I know, such a framework does not exist. So how do I go about it? Since I started my master thesis at BRML, I have been developing tools, including parts of an experimental framework, that meet some of the above requirements. However, for every new project I started, I would copy parts of the code responsible for running experiments from the previous project. After doing that for five different projects (from HART to SQAIR), I’ve had enough. When I was about to start a new project last week, I’ve taken all the experiment-running code, made it project-agnostic, and put it into a separate repo, wrote some docs, and gave it a name. Lo and behold: Forge.

# Forge

While it is very much work in progress, I would like to show you how to set up your project using forge. Who knows, maybe it can simplify your workflow, too?

### Configs

Configs are perhaps the most useful component in forge. The idea is that we can specify an arbitrarily complicated config file as a python function, and then we can load it using forge.load(config_file, *args, **kwargs), where config_file is a path on your filesystem. The convention is that the config file should define load function with the following signature: load(config, *args, **kwargs). The arguments and kw-args passed to forge.load are automatically forwarded to the load function in the config file. Why would you load a config by giving its file path? To make code maintenance easier! Once you write config-loading code in your training/experimentation scripts, it is best not to touch it anymore. But how do you swap config files? Without touching the training script: If we specify file paths as command-line arguments, then we can do it easily. Here’s an example. Suppose that our data config file data_config.py is the following:

from tensorflow.examples.tutorials.mnist import input_data

# The config argument is here unused, but you can treat it
# as a dict of keys and values accessible as attributes - it acts
# like an AttrDict

# to the current working dir and load it
return dataset


Our model file defines a simple one-layer fully-connected neural net, classification loss and some metrics in model_config.py. It can read as follows.

import sonnet as snt
import tensorflow as tf

from forge import flags

flags.DEFINE_integer('n_hidden', 128, 'Number of hidden units.')

def process_dataset(x):
pass
# this function should return a minibatch, somehow

imgs, labels = process_dataset(dataset)

imgs = snt.BatchFlatten()(imgs)
mlp = snt.nets.MLP([config.n_hidden, 10])
logits = mlp(imgs)
labels = tf.cast(labels, tf.int32)

# softmax cross-entropy
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels))

# predicted class and accuracy
pred_class = tf.argmax(logits, -1)
acc = tf.reduce_mean(tf.to_float(tf.equal(tf.to_int32(pred_class), labels)))

# put here everything that you might want to use later
# for example when you load the model in a jupyter notebook
artefacts = {
'mlp': mlp,
'logits': logits,
'loss': loss,
'pred_class': pred_class,
'accuracy': acc
}

# put here everything that you'd like to be reported every N training iterations
# as tensorboard logs AND on the command line
stats = {'crossentropy': loss, 'accuracy': acc}

# loss will be minimised with respect to the model parameters
return loss, stats, artefacts


Now we can write a simple script called experiment.py that loads some data and model config files and does useful things with them.


from os import path as osp

import tensorflow as tf

import forge
from forge import flags

# job config
flags.DEFINE_string('data_config', 'data_config.py', 'Path to a data config file.')
flags.DEFINE_string('model_config', 'model_config.py', 'Path to a model config file.')
flags.DEFINE_integer('batch_size', 32, 'Minibatch size used for training.')

config = forge.config()  # parse command-line flags
loss, stats, stuff = forge.load(config.model_config, config, dataset)

# ...
# do useful stuff


Here’s the best part. You can just run python experiment.py to run the script with the config files given above. But if you would like to run a different config, you can execute python experiment.py --data_config some/config/file/path.py without touching experimental code. All this is very lightweight, as config files can return anything and take any arguments you find necessary.

### Smart checkpoints

Given that we have very general and flexible config files, it should be possible to abstract away model loading. It would be great, for instance, if we could load a trained model snapshot without pointing to the config files (or model-building code, generally speaking) used to train the model. We can do it by storing config files with model snapshots. It can significantly simplify model evaluation and deployment and increase reproducibility of our experiments. How do we do it? This feature requires a bit more setup than just using config files, but bear with me - it might be even more useful.

The smart checkpoint framework depends on the following folder structure.

results_dir
|run_name
|1
|2
|...
|<integer>  # number of the current run


results_dir is the top-level directory containing potentially many experiment-specific folders, where every experiment has a separate folder denoted by run_name. We might want to re-run a specific experiment, and for this reason, every time we run it, forge creates a folder, whose name is an integral number - the number of this run. It starts at one and gets incremented every time we start a new run of the same experiment. Instead of starting a new run, we can also resume the last one by passing a flag. In this case, we do not create a new folder for it, but use the highest-numbered folder and load the latest model snapshot.

First, we need to import forge.experiment_tools and define the following flags.

from os import path as osp
from forge import experiment_tools as fet

flags.DEFINE_string('results_dir', '../checkpoints', 'Top directory for all experimental results.')
flags.DEFINE_string('run_name', 'test_run', 'Name of this job. Results will be stored in a corresponding folder.')
flags.DEFINE_boolean('resume', False, 'Tries to resume a job if True.')


We can then parse the flags and initialise our checkpoint.

config = forge.config()  # parse flags

# initialize smart checkpoint
logdir = osp.join(config.results_dir, config.run_name)
logdir, resume_checkpoint = fet.init_checkpoint(logdir, config.data_config, config.model_config, config.resume)


fet.init_checkpoint does a few useful things:

1. Creates the directory structure mentioned above.
2. Copies the data and model config files to the checkpoint folder.
3. Stores all configuration flags and the hash of the current git commit (if we’re in a git repo, very useful for reproducibility) in flags.json, or restores flags if restore was True.
4. Figures out whether there exists a model snapshot file that should be loaded.

logdir is the path to our checkpoint folder and evaluates to results_dir/run_name/<integer>. resume_checkpoint is a path to a checkpoint if resume was True, typically results_dir/run_name/<integer>/model.ckpt-<maximum global step>, or None otherwise.

Now we need to use logdir and resume_checkpoint to store any logs and model snapshots. For example:

...  # load data/model and do other setup

# Try to restore the model from a checkpoint
saver = tf.train.Saver(max_to_keep=10000)
if resume_checkpoint is not None:
print "Restoring checkpoint from '{}'".format(resume_checkpoint)
saver.restore(sess, resume_checkpoint)

...
# somewhere inside the train loop
saver.save(sess, checkpoint_name, global_step=train_itr)
...


If we want to load our model snapshot in another script, eval.py, say, we can do so in a very straightforward manner.

import tensorflow as tf

checkpoint_dir = '../checkpoints/mnist/1'
checkpoint_iter = int(1e4)

# data contains any outputs of the data config file
# model contains any outputs of the model config file
data, model, restore_func = load_from_checkpoint(checkpoint_dir, checkpoint_iter)

# Calling restore_func restores all model parameters
sess = tf.Session()
restore_func(sess)

...  # do exciting stuff with the model


### Working Example

Code for forge is available at github.com/akosiorek/forge and a working example is described in the README.

# Closing thoughts

Even though experimental code exhibits very similar structure among experiments, there seem to be no tools to streamline the experimentation process. This requires ML practitioners to write thousands of lines of boilerplate code, contributes to many errors and generally slows down research progress. forge is my attempt at introducing some good practices as well as simplifying the process. Hope you can take something from it for your own purposes.

#### Acknowledgements

I would like to thank Alex Bewley for inspiration, Adam Goliński for discussions about software engineering in ML and Martin Engelcke for his feedback on forge.