malet 0.2.1

Malet: a tool for machine learning experiment

Malet: a tool for machine learning experiment

🔨 Malet (Machine Learning Experiment Tool) is a tool for efficient machine learning experiment execution, logging, analysis, and plot making.

The following features are provided:

• Simple YAML-based hyperparameter configuration w/ grid search syntax
• Experiment logging and resuming system
• User-friendly command-line tool for flexible graphing and easy data extraction from experiment logs
• Efficient parallelization by splitting a sequence of experiments over GPU jobs

Installation

You can install Malet using pip,

pip install malet

or from this repository.

pip install git+https://github.com/edong6768/Malet.git

Dependencies

• absl-py 1.0.0
• gitpython 3.1.40
• matplotlib 3.7.0
• ml-collections 0.1.0
• numpy 1.22.0
• pandas 2.0.3
• rich 13.6.0
• seaborn 0.11.2

Documentation (🚨 Will be migrated to Sphinx-based Read-the-docs in near future)

Contents

Quick start

1. Prerequisite
2. Running experiments
3. Plot making

Advanced topics

1. Advanced gridding in yaml
2. Advanced plot making
3. Parallel friendly grid splitting
4. Saving logs in intermediate epochs
5. Merging multiple log files

Quick start

1. Prerequisite

Experiment Folder

Using Malet starts with making a folder with a single yaml config file. Various files resulting from some experiment is saved in this single folder. We advise to create a folder for each experiment under experiments folder.

experiments/
└── {experiment folder}/
    ├── exp_config.yaml : experiment config yaml file            (User created)
    ├── log.tsv         : log file for saving experiment results (generated by malet.experiment)
    ├── (log_splits)    : folder for splitted logs               (generated by malet.experiment)
    └── figure          : folder for figures                     (generated by malet.plot)

Pre-existing training pipeline

Say you have some training pipeline that takes in a configuration (any object w/ dictionary-like interface). We require you to return the result of the training so it gets logged.

def train(config, ...):
    ...
    # training happens here
    ...
    metric_dict = {
        'train_accuracies': train_accuracies,
        'val_accuracies': val_accuracies,
        'train_losses': train_losses,
        'val_losses': val_losses,
    }
    return metric_dict

2. Running experiments

Experiment config yaml

You can configure as you would do in the yaml file. But we provide useful special keyword grid, used as follows:

# static configs
model: LeNet5
dataset: mnist

num_epochs: 100
batch_size: 128
optimizer: adam

# grided fields
grid:
    seed: [1, 2, 3]
    lr: [0.0001, 0.001, 0.01, 0.1]
    weight_decay: [0.0, 0.00005, 0.0001]

Specifying list of config values under grid lets you run all possible combination (i.e. grid) of your configurations, with field least frequently changing in the order of declaration in grid.

Running experiments

The following will run the train_fn on grid of configs based on {exp_folder_path} and train_fn.

from functools import partial
from malet.experiment import Experiment

train_fn = partial(train, ...{other arguments besides config}..)
metric_fields =  ['train_accuracies', 'val_accuracies', 'train_losses', 'val_losses']
experiment = Experiment({exp_folder_path}, train_fn, metric_fields)
experiment.run()

Note that you need to partially apply your original function so that you pass in a function with only config as its argument.

Experiment logs

The experiment log will be automatically saved in the {exp_folder_path} as log.tsv, where the static configs and the experiment log are eached saved in yaml and tsv like structure respectively. You can retrieve these data in python using ExperimentLog in malet.experiment as follows:

from malet.experiment import ExperimentLog

log = ExperimentLog.from_tsv({tsv_file})

static_configs = log.static_configs
df = log.df

Experiment logs also enables resuming to the most recently run config when a job is suddenly killed. Note that this only enable you to resume from the begining of the training. For resuming from intermediate log checkpoints, check out Saving logs in intermediate epochs.

3. Plot making

Running malet.plot lets you make plots based on log.tsv in the experiment folder.

malet-plot \
-exp_folder ../experiments/{exp_folder} \
-mode curve-epoch-train_accuracy

The key intuition for using this is to leave only two field in the dataframe for the x-axis and the y-axis by

1. specifying a specific value (e.g. model, dataset, optimizer, etc.),
2. averaging over (seed),
3. or choose value with best metric (other hyperparameters),

which will leave only one value for each field. This can be done using the following arguments.

Data related arguments

1. -mode: Mode consists of mode of the plot (currently only has 'curve' and 'bar'), the field for x-axis, and the metric to use for y-axis.

   -mode {plot_mode}-{x_field}-{metric}
   

   Any other field except x_field and seed (always averaged over) is automatically chosen value with best metric. To specify a value of a field, you can use the following -filter argument.

2. -filter: Use to explicitly choose only certain subset values of some field.

   -filter '{field1} {v1} {v2} / {field2} {v3} {v4} ...'
   

   Here, two special fields are automatically generated:

   • step - from explodeing list-type metric, with special value 'best' and 'last' for selecting best performing step and last step respectively and with slicing syntax (e.g., 50:100),
   • metric - from melting different metrics column name into a new column.

   are automatically generated.

3. -multi_line_fields: Specify the fields to plot multiple lines over.

   -multi_line_field '{field1} {field2} ...'
   

4. -multi_plot_fields: Specify the fields to plot multiple plot (column/row) over.

   -multi_plot_field '{column field}'
   -multi_plot_field '{column field} {row field}'
   

5. -animate_field: Specify the fields to animate over. Saves gif intead of pdf.

   -animate_field '{field}'
   

6. -best_at_max (Default: False): Specify whether chosen metric is best when largest (e.g. accuracy).

   -best_at_max
   -nobest_at_max
   

Styling arguments

1. -colors: Name or list of names of matplotlib colormaps.

   -colors 'default'
   

2. -annotate: Option to add annotation based on field specified in annotate_fields.

   -annotate
   

3. -annotate_fields: Field to annotate.

   -annotate_fields '{field1} {field2} ...'
   

4. -fig_size: Figure size.

   • Square figure

     -fig_size 7
     

   • Rectangular figure (x, y)

     -fig_size 10 8
     

5. -style: Matplotlib style.

   -style 'ggplot'
   

6. -plot_config: The path for a yaml file to configure all aspects the plot.

   -plot_config {plot_config_path}
   

   In this yaml, you can specify the line_style and ax_style under each mode as follows:

   'curve-epoch-train_accuracy':
     annotate: false
     std_plot: fill
     line_style: 
       linewidth: 4
       marker: 'D'
       markersize: 10
   
     ax_style:
       frame_width: 2.5
       fig_size: 7
       legend: [{'fontsize': 20}]
       grid: [true, {'linestyle': '--'}]
       tick_params:
         - axis: both
           which: major
           labelsize: 25
           direction: in
           length: 5
   

   • line_style: Style of the plotted line (linewidth, marker, markersize, markevery)

   • ax_style: Style of the figure. Most attribute of matplotlib.axes.Axes object can be set as follows:

     yscale: [parg1, parg2, {'kwarg1': v1, 'kwarg2': v2}]
     

     is equivalent to running

     ax.set_yscale(parg1, parg2, kwarg1=v1, kwarg2=v2)
     

For more details, go to Advanced plot making section.

Advanced topics

Advanced gridding in yaml

1. List comprehension

This serves similar functionality as list comprehensions in python, and used as follows:

lr: [10**{-i};1:1:5]

Syntax:

[{expression};{start}:{step}:{end}]

where expression should be any python-interpretable using symbols i, +, -, *, /, [], () and numbers. This is equivalent to python expression

[{expression} for i in range({start}, {end}, {step})]

2. Grid sequences

We can execute sequence of grids by passing in a list of dictionary instead of a dictionary under grid keyword as follows

grid:
    - optimizer: sgd
      lr: [0.001, 0.01]
      seed: [1, 2, 3]

    - optimizer: adam
      lr: [0.005]
      seed: [1, 2, 3]

3. Grouping

Grouping lets you group two different fields so it gets treated as a single field in the grid.

grid:
    group:
        optimizer: [[sgd], [adam]]
        lr: [[0.001, 0.01], [0.005]]
    seed: [1, 2, 3]

Syntax:

grid:
    group:
        cfg1: [A1, B1]
        cfg2: [A2, B2]
    cfg3: [1, 2, 3]

is syntactically equivalent to

grid:
    - cfg1: A1
      cfg2: A2
      cfg3: [1, 2, 3]

    - cfg1: B1
      cfg2: B2
      cfg3: [1, 2, 3]

Here the two config fields cfg1 and cfg2 has grouped values (A1, A2) and (B1, B2) that acts like a single config field and arn't gridded seperately (A1-2, B1-2 are lists of values.)

You can also create several groupings with list of dictionary under group keyword as follows.

grid:
    group:
        - cfg1: [A1, B1]
          cfg2: [A2, B2]
        - cfg3: [C1, D1]
          cfg4: [C2, D2]
    cfg5: [1, 2, 3]

Advanced Plot making

1. Other arguments for malet.plot

• -best_ref_x_fields: On defualt, each point in x_field get its own optimal hyperparameter set, which is sometimes undesirable. This argument lets you specify on which value of x_field to choose the best hyperparamter.

  -best_ref_x_field {x_field_value}
  

• -best_ref_ml_fields: Likewise, we might want to use the same hyperparameter for all lines in multi_line_field with best hyperparameter chosen from a single value in multi_line_field.

  -best_ref_ml_field {ml_field_value}
  

• -best_ref_metric_field: To plot one metric with the hyperparameter set chosen based on another, pass the name of the metric of reference in metric_field_value.

  -best_ref_metric_field {metric_field_value}
  

2. Advanced yaml plot config

More details on ax_style keyword

Unlike other fields, frame_width, fig_size, tick_params, legend, grid are not attributes of Axes but are enabled for convinience. From these, frame_width and fig_size should be set as a number, while others can be similarly used like the rest of the attributes in Axes.

Default style

You can change the default plot style by adding the default_style keyword in the yaml file.

'default_style':
  annotate: false
  std_plot: fill
  line_style: 
    linewidth: 4
    marker: 'D'
    markersize: 10

  ax_style:
    frame_width: 2.5
    fig_size: 7
    legend: [{'fontsize': 20}]
    grid: [true, {'linestyle': '--'}]
    tick_params:
      - axis: both
        which: major
        labelsize: 25
        direction: in
        length: 5

Mode aliases

You can specify a set of arguments for malet.plot in the yaml file and give it an alias you can pass in to mode argument.

'sam_rho':
  mode: curve-rho-val-accuracy
  multi_line_field: optimizer
  filter: 'optimizer sgd sam'
  annotate: True
  colors: ''
  
  std_plot: bar

  ax_style:
    title: ['SGD vs SAM', {'size': 27}]
    xlabel: ['$\rho$', {'size': 30}]
    ylabel: ['Val Accuracy (%)', {'size': 30}]

malet-plot \
-exp_folder ../experiments/{exp_folder} \
-plot_config {plot_config_path} \
-mode sam_rho

When using mode aliases, the conflicting argument passed within the shell will be ignored.

Style hierarchy

If conflicting style is passed in, we use the specifications given in the highest priority, given as the following:

default_style < {custom style} < {mode alias}

3. Custom dataframe processing

The legend and the tick are automatically determined based on the processed dataframe within draw_metric function. You can pass in a function to the preprcs_df keyword argument in draw_metric with the following arguments and return values:

def preprcs_df(df, legend):
    ...
    # Process df and legend
    ...
    return processed_df, processed_legend

We advise to assign a new mode for each preprcs_df.

4. Custom plotting using avgbest_df and ax_draw in plot_utils.metric_drawer

Much of what malet.plot does comes from avgbest_df and ax_draw.

avgbest_df(df, metric_field, avg_over=None, best_over=tuple(), best_of=dict(), best_at_max=True)

• Paramters:
  • df (pandas.DataFrame) : Base dataframe to operate over. All hyperparameters should be set as MultiIndex.
  • metric_field (str) : Column name of the metric. Used to evaluate best hyperparameter.
  • avg_over (str) : MultiIndex level name to average over.
  • best_over (List[str]) : List of MultiIndex level names to find value yielding best values of metric_field.
  • best_of (Dict[str, Any]) : Dictionary of pair {MultiIndex name}: {value in MultiIndex} to find best hyperparameter of. The other values in {MultiIndex name} will follow the best hyperparamter found for these values.
  • best_at_max (bool) : True when larger metric is better, and False otherwise.
• Returns: Processed DataFrame (pandas.DataFrame)

ax_draw(ax, df, label, annotate=True, std_plot='fill', unif_xticks=False, plot_config = {'linewidth':4, 'color':'orange', 'marker':'D', 'markersize':10, 'markevery':1})

• Paramters:
  • ax (matplotlib.axes.Axes) : Axes to plot in.
  • df (pandas.DataFrame) : Dataframe used for the plot. This dataframe should have one named index for the x-axis and one column for the y-axis.
  • label (str) : label for drawn line to be used in the legend.
  • std_plot (Literal['none','fill','bar']) : Style of standard error drawn in to the plot.
  • unif_xticks (bool) : When True, the xticks will be uniformly distanced regardless of its value.
  • plot_config (Dict[str, Any]) : Dictionary of configs to use when plotting the line (e.g. linewidth, color, marker, markersize, markevery).
• Returns: Axes (matplotlib.axes.Axes) with single line added based on df.

Parallel friendly grid splitting

When using GPU resource allocation programs such as Slurm, you might want to split multiple hyperparameter configurations over different GPU jobs in parallel. We provide two methods of spliting the grid as arguments of Experiment. We advise to use flags to pass these as argument of your train.py file.

from absl import app, flags
from malet.experiment import Experiment

...

FLAGS = flags.FLAGS
def main(argv):
  ...
  experiment = Experiment({exp_folder_path}, train_fn, metric_fields,
                          total_splits=FLAGS.total_splits,
                          curr_splits=FLAGS.curr_splits,
                          auto_update_tsv=FLAGS.auto_update_tsv,
                          configs_save=FLAGS.configs_save)
  experiment.run()

if __name__=='__main__':
  flags.DEFINE_string('total_splits', '1')
  flags.DEFINE_string('curr_splits', '0')
  flags.DEFINE_bool('auto_update_tsv', False)
  flags.DEFINE_bool('configs_save', False)
  app.run(main)

1. Partitioning

1. Uniform Partitioning (Pass in number)

   This method of splits the experiments uniformally given the following arguments

   1. number of total partition (total_splits),
   2. batch index to allocate to this script (curr_splits).

   Each sbatch script needs to be using different curr_splits numbers (=0~total-1).

   splits = 4
   echo "run sbatch slurm_train $n"
   for ((i=0;i<n;i++))
   do
     python train.py ./experiments/{exp_folder} \
       --workdir=./logdir \
       --total_splits=splits \
       --curr_splits=$i
   done
   

2. Field Partitioning (Pass in field name)

   This method of splits the experiments based on some field given in the following arguments

   1. name of the field to split over (total_splits),
   2. string of field values seperated by ' ' to allocate to this current split script (curr_splits).

   Each sbatch script needs different field values (whitespace seperated strings for multiple values) in curr_splits.

   python experiment_util.py ./experiments/{exp_folder} \
       --total_splits 'optimizer' \
       --curr_splits= 'sgd'
   
   python experiment_util.py ./experiments/{exp_folder} \
       --total_splits 'optimizer' \
       --curr_splits= 'rmsprop adam'
   

   Both of these split methods result in multiple .tsv files, which is saved in {exp_folder}/log_splits/split_{i}.tsv folder.

Comments on auto_update_tsv argument.

auto_update_tsv is used for 'Current run checking' stated in the next section, but using it in 'Partitioning' doesn't cause problems. However we advise to not use it by adding since additional read/writing adds unnessacery computation time, especially as the log.tsv file grows larger.

2. Queueing

With this method, each jobs, once finished running its config, runs the next config in the queue of the unrun configs. More precisly, it skips any configs that finished running or are currently running. The key to doing this is configs_save=True, which saves the configs to the {exp_folder}/log.tsv file before a config is run, enabling other jobs to know what config is currently running and skip it.

python experiment_util.py ./experiments/{exp_folder} --workdir=./logdir \
--auto_update_tsv \
--configs_save

This method requires the keyword auto_update_tsv=True in Experiment to automatically read/write tsv files after a job starts/finishes running a config.

One adventage of 'Queueing' over 'Partitioning' is that you can freely allocate/deallocate new GPUs while running an experiment.

3. Use Both (Partitioning + Queueing)

However as log.tsv grows larger, read/write time gets larger which cause various conflicts across different GPU jobs. One workaround is to use 'Partitioning' to split experiments to be saved in seperate log_splits/split_{i}.tsv to keep the .tsv files small, while using 'Queueing' in each splits to freely allocate GPU jobs to leverage the advantages of both methods.

splits = 4
echo "run sbatch slurm_train $n"
for ((i=0;i<n;i++))
do
  python experiment_util.py ./experiments/{exp_folder} \
    --workdir=./logdir \
    --total_splits=splits \
    --curr_splits=$i \
    --auto_update_tsv \
    --configs_save
done

Saving logs in intermediate epochs

We checkpoint training state so that we can resume training in the event of an unexpected termination. We can also checkpoint the experiment log so that we don't have to retrain a certain config to re-evaluate the metrics.

Training pipeline

For this, we need to add exp_log argument in train function for checkpointing the experiment log, where you can use it to add the following code for retrieveing/saving intermediate metric dictionary from/to the tsv file.

import os

def get_ckpt_dir(config):
    ...
    return ckpt_dir

def get_ckpt(ckpt_dir):
    ...
    return ckpt

def save_ckpt(new_ckpt, ckpt_dir):
    ...

def train(config, experiment, ...):

    ... # set up
    
    # retrieve model/trainstate checkpoint if there exists
    # these are just placeholders for the logic
    ckpt_epoch = 0
    ckpt_dir = get_ckpt_dir(config)
    if os.path.exists(ckpt_dir)
      ckpt = get_ckpt(ckpt_dir)
      ckpt_epoch = ckpt.epoch
    
    ############# retrieve log checkpoint if there exists #############
    metric_dict = {
        'train_accuracies': [],
        'val_accuracies': [],
        'train_losses': [],
        'val_losses': [],
    }
    if config in experiment.log:
      metric_dict = experiment.get_log_checkpoint(config)[0]
    ###################################################################
    ...
    # training happens here
    for epoch in range(config.ckpt_epoch, config.epochs):
      
      ... # train
      
      ... # update metric_dict

      if not (epoch+1) % config.ckpt_every:

        ... # train state, model checkpoint

        ####################### checkpoint log #######################
        save_ckpt(new_ckpt, ckpt_dir)
        experiment.update_log(config, **metric_dict) 
        ##############################################################
    ...

    return metric_dict

The ExperimentLog.get_log_checkpoint method retrieves the metric_dict based on the status field in the dataframe.

status	Description	Behavior when resumed
R	Currently running	Get skipped
C	Completed	Get skipped
F	Failed while running	Rerun and metric_dict is retrieved

Note that with some external halt (e.g. computer shut down, slurm job cancellation), malet won't be able to log the status as F (failed). In these cases, you need to manually find the row in the log.tsv file corresponding to the halted job and change the status from R (running) to F (falied).

Running experiment

from functools import partial
from malet.experiment import Experiment

train_fn = partial(train, ...{other arguments besides config & exp_log}..)
metric_fields =  ['train_accuracies', 'val_accuracies', 'train_losses', 'val_losses']
experiment = Experiment({exp_folder_path}, train_fn, metric_fields, 
                        checkpoint=True, auto_update_tsv=True) 
experiment.run()

You should add checkpoint=True, auto_update_tsv=True when instanciating Experiment.

Merging multiple log files

There are two methods for merging multiple log files.

1. Merge all logs in a folder

from malet.experiment import ExperimentLog

ExperimentLog.merge_folder({log_folder_path})

2. Merge a portion of the logs in a folder

from malet.experiment import ExperimentLog

names = ["log1", "log2", ..., "logn"]
ExperimentLog.merge_tsv(names, {log_folder_path})

Both methods automatically merges and saves as log_merged.tsv in the folder. These methods are helpful after running splitted experiments, where merging is required for using plot tools.