TraceML

Engine for ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.

Install

pip install traceml

If you would like to use the tracking features, you need to install polyaxon as well:

pip install polyaxon traceml

[WIP] Local sandbox

Coming soon

Offline usage

You can enable the offline mode to track runs without an API:

export POLYAXON_OFFLINE="true"

Or passing the offline flag

from traceml import tracking
tracking.init(..., is_offline=True, ...)

Simple usage in a Python script

import random
import traceml as tracking
tracking.init(
    is_offline=True,
    project='quick-start',
    name="my-new-run",
    description="trying TraceML",
    tags=["examples"],
    artifacts_path="path/to/artifacts/repo"
)
# Tracking some data refs
tracking.log_data_ref(content=X_train, name='x_train')
tracking.log_data_ref(content=y_train, name='y_train')
# Tracking inputs
tracking.log_inputs(
    batch_size=64,
    dropout=0.2,
    learning_rate=0.001,
    optimizer="Adam"
)
def get_loss(step):
    result = 10 / (step + 1)
    noise = (random.random() - 0.5) * 0.5 * result
    return result + noise
# Track metrics
for step in range(100):
    loss = get_loss(step)
    tracking.log_metrics(
    loss=loss,
    accuracy=(100 - loss) / 100.0,
)
# Track some one time results
tracking.log_outputs(validation_score=0.66)
# Optionally manually stop the tracking process
tracking.stop()

Integration with deep learning and machine learning libraries and frameworks

Keras

You can use TraceML’s callback to automatically save all metrics and collect outputs and models, you can also track additional information using the logging methods:

from traceml import tracking
from traceml.integrations.keras import Callback
tracking.init(
    is_offline=True,
    project='tracking-project',
    name="keras-run",
    description="trying TraceML & Keras",
    tags=["examples"],
    artifacts_path="path/to/artifacts/repo"
)
tracking.log_inputs(
    batch_size=64,
    dropout=0.2,
    learning_rate=0.001,
    optimizer="Adam"
)
tracking.log_data_ref(content=x_train, name='x_train')
tracking.log_data_ref(content=y_train, name='y_train')
tracking.log_data_ref(content=x_test, name='x_test')
tracking.log_data_ref(content=y_test, name='y_test')
# ...
model.fit(
    x_train,
    y_train,
    validation_data=(X_test, y_test),
    epochs=epochs,
    batch_size=100,
    callbacks=[Callback()],
)

PyTorch

You can log metrics, inputs, and outputs of Pytorch experiments using the tracking module:

from traceml import tracking
tracking.init(
    is_offline=True,
    project='tracking-project',
    name="pytorch-run",
    description="trying TraceML & PyTorch",
    tags=["examples"],
    artifacts_path="path/to/artifacts/repo"
)
tracking.log_inputs(
    batch_size=64,
    dropout=0.2,
    learning_rate=0.001,
    optimizer="Adam"
)
# Metrics
for batch_idx, (data, target) in enumerate(train_loader):
    output = model(data)
    loss = F.nll_loss(output, target)
    loss.backward()
    optimizer.step()
    tracking.log_metrics(loss=loss)
asset_path = tracking.get_outputs_path('model.ckpt')
torch.save(model.state_dict(), asset_path)
# log model
tracking.log_artifact_ref(asset_path, framework="pytorch", ...)

Tensorflow

You can log metrics, outputs, and models of Tensorflow experiments and distributed Tensorflow experiments using the tracking module:

from traceml import tracking
from traceml.integrations.tensorflow import Callback
tracking.init(
    is_offline=True,
    project='tracking-project',
    name="tf-run",
    description="trying TraceML & Tensorflow",
    tags=["examples"],
    artifacts_path="path/to/artifacts/repo"
)
tracking.log_inputs(
    batch_size=64,
    dropout=0.2,
    learning_rate=0.001,
    optimizer="Adam"
)
# log model
estimator.train(hooks=[Callback(log_image=True, log_histo=True, log_tensor=True)])

Fastai

You can log metrics, outputs, and models of Fastai experiments using the tracking module:

from traceml import tracking
from traceml.integrations.fastai import Callback
tracking.init(
    is_offline=True,
    project='tracking-project',
    name="fastai-run",
    description="trying TraceML & Fastai",
    tags=["examples"],
    artifacts_path="path/to/artifacts/repo"
)
# Log model metrics
learn.fit(..., cbs=[Callback()])

Pytorch Lightning

You can log metrics, outputs, and models of Pytorch Lightning experiments using the tracking module:

from traceml import tracking
from traceml.integrations.pytorch_lightning import Callback
tracking.init(
    is_offline=True,
    project='tracking-project',
    name="pytorch-lightning-run",
    description="trying TraceML & Lightning",
    tags=["examples"],
    artifacts_path="path/to/artifacts/repo"
)
...
trainer = pl.Trainer(
    gpus=0,
    progress_bar_refresh_rate=20,
    max_epochs=2,
    logger=Callback(),
)

HuggingFace

You can log metrics, outputs, and models of HuggingFace experiments using the tracking module:

from traceml import tracking
from traceml.integrations.hugging_face import Callback
tracking.init(
    is_offline=True,
    project='tracking-project',
    name="hg-run",
    description="trying TraceML & HuggingFace",
    tags=["examples"],
    artifacts_path="path/to/artifacts/repo"
)
...
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset if training_args.do_train else None,
    eval_dataset=eval_dataset if training_args.do_eval else None,
    callbacks=[Callback],
    # ...
)

Tracking artifacts

import altair as alt
import matplotlib.pyplot as plt
import numpy as np
import plotly.express as px
from bokeh.plotting import figure
from vega_datasets import data
from traceml import tracking
def plot_mpl_figure(step):
    np.random.seed(19680801)
    data = np.random.randn(2, 100)
    figure, axs = plt.subplots(2, 2, figsize=(5, 5))
    axs[0, 0].hist(data[0])
    axs[1, 0].scatter(data[0], data[1])
    axs[0, 1].plot(data[0], data[1])
    axs[1, 1].hist2d(data[0], data[1])
    tracking.log_mpl_image(figure, 'mpl_image', step=step)
def log_bokeh(step):
    factors = ["a", "b", "c", "d", "e", "f", "g", "h"]
    x = [50, 40, 65, 10, 25, 37, 80, 60]
    dot = figure(title="Categorical Dot Plot", tools="", toolbar_location=None,
                 y_range=factors, x_range=[0, 100])
    dot.segment(0, factors, x, factors, line_width=2, line_color="green", )
    dot.circle(x, factors, size=15, fill_color="orange", line_color="green", line_width=3, )
    factors = ["foo 123", "bar:0.2", "baz-10"]
    x = ["foo 123", "foo 123", "foo 123", "bar:0.2", "bar:0.2", "bar:0.2", "baz-10", "baz-10",
         "baz-10"]
    y = ["foo 123", "bar:0.2", "baz-10", "foo 123", "bar:0.2", "baz-10", "foo 123", "bar:0.2",
         "baz-10"]
    colors = [
        "#0B486B", "#79BD9A", "#CFF09E",
        "#79BD9A", "#0B486B", "#79BD9A",
        "#CFF09E", "#79BD9A", "#0B486B"
    ]
    hm = figure(title="Categorical Heatmap", tools="hover", toolbar_location=None,
                x_range=factors, y_range=factors)
    hm.rect(x, y, color=colors, width=1, height=1)
    tracking.log_bokeh_chart(name='confusion-bokeh', figure=hm, step=step)
def log_altair(step):
    source = data.cars()
    brush = alt.selection(type='interval')
    points = alt.Chart(source).mark_point().encode(
        x='Horsepower:Q',
        y='Miles_per_Gallon:Q',
        color=alt.condition(brush, 'Origin:N', alt.value('lightgray'))
    ).add_selection(
        brush
    )
    bars = alt.Chart(source).mark_bar().encode(
        y='Origin:N',
        color='Origin:N',
        x='count(Origin):Q'
    ).transform_filter(
        brush
    )
    chart = points & bars
    tracking.log_altair_chart(name='altair_chart', figure=chart, step=step)
def log_plotly(step):
    df = px.data.tips()
    fig = px.density_heatmap(df, x="total_bill", y="tip", facet_row="sex", facet_col="smoker")
    tracking.log_plotly_chart(name="2d-hist", figure=fig, step=step)
plot_mpl_figure(100)
log_bokeh(100)
log_altair(100)
log_plotly(100)

Tracking DataFrames

Summary

An extension to pandas dataframes describe function.

The module contains DataFrameSummary object that extend describe() with:

• properties
  • dfs.columns_stats: counts, uniques, missing, missing_perc, and type per column
  • dsf.columns_types: a count of the types of columns
  • dfs[column]: more in depth summary of the column
• function
  • summary(): extends the describe() function with the values with columns_stats

The DataFrameSummary expect a pandas DataFrame to summarise.

from traceml.summary.df import DataFrameSummary
dfs = DataFrameSummary(df)

getting the columns types

dfs.columns_types
numeric     9
bool        3
categorical 2
unique      1
date        1
constant    1
dtype: int64

getting the columns stats

dfs.columns_stats
                      A            B        C              D              E
counts             5802         5794     5781           5781           4617
uniques            5802            3     5771            128            121
missing               0            8       21             21           1185
missing_perc         0%        0.14%    0.36%          0.36%         20.42%
types            unique  categorical  numeric        numeric        numeric

getting a single column summary, e.g. numerical column

# we can also access the column using numbers A[1]
dfs['A']
std                                                                 0.2827146
max                                                                  1.072792
min                                                                         0
variance                                                           0.07992753
mean                                                                0.5548516
5%                                                                  0.1603367
25%                                                                 0.3199776
50%                                                                 0.4968588
75%                                                                 0.8274732
95%                                                                  1.011255
iqr                                                                 0.5074956
kurtosis                                                            -1.208469
skewness                                                            0.2679559
sum                                                                  3207.597
mad                                                                 0.2459508
cv                                                                  0.5095319
zeros_num                                                                  11
zeros_perc                                                               0,1%
deviating_of_mean                                                          21
deviating_of_mean_perc                                                  0.36%
deviating_of_median                                                        21
deviating_of_median_perc                                                0.36%
top_correlations                         {u'D': 0.702240243124, u'E': -0.663}
counts                                                                   5781
uniques                                                                  5771
missing                                                                    21
missing_perc                                                            0.36%
types                                                                 numeric
Name: A, dtype: object

[WIP] Summaries

• Add summary analysis between columns, i.e. dfs[[1, 2]]

[WIP] Visualizations

• Add summary visualization with matplotlib.
• Add summary visualization with plotly.
• Add summary visualization with altair.
• Add predefined profiling.

[WIP] Catalog and Versions

• Add possibility to persist summary and link to a specific version.
• Integrate with quality libraries.