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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Engine for AI/ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.
| Date | Stars |
|---|---|
| 2026-07-24 | 534 |
| 2026-07-25 | 534 |
| 2026-07-28 | 534 |
| 2026-07-30 | 534 |
| 2026-08-06 | 534 |
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<a href="https://polyaxon.com"><img src="https://raw.githubusercontent.com/polyaxon/polyaxon/master/artifacts/packages/traceml.svg" width="125" height="125" align="right" /></a>
# TraceML
Engine for ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.
## Install
```bash
pip install traceml
```
If you would like to use the tracking features, you need to install `polyaxon` as well:
```bash
pip install polyaxon traceml
```
## [WIP] Local sandbox
> Coming soon
## Offline usage
You can enable the offline mode to track runs without an API:
```bash
export POLYAXON_OFFLINE="true"
```
Or passing the offline flag
```python
from traceml import tracking
tracking.init(..., is_offline=True, ...)
```
## Simple usage in a Python script
```python
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:
```python
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:
```python
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 = moExcerpt of 13,305 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:74dfbd50f283e550, topic:pytorch, topic:tensorflow
matched fp:74dfbd50f283e550, topic:mlops
matched fp:74dfbd50f283e550, topic:explainable-ai
