TraceML

Engine for ML/Data tracking, visualization, 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

Local sandbox

WIP: this command is in preview

Start a local sandbox to track and visualize the run

polyaxon sandbox -f path/to/artifacts/repo

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_mtrics(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)

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