Integrate ClearML into code which uses autokeras. Initialize a ClearML Task in your code, and ClearML automatically logs scalars, plots, and images reported to TensorBoard, Matplotlib, Plotly, and Seaborn, as well as all other automatic logging, and explicit reporting you add to your code (see Logging).
ClearML allows you to:
Visualize experiment results in the ClearML Web UI.
Track and upload models.
Track model performance and create tracking leaderboards.
Rerun experiments, reproduce experiments on any target machine, and tune experiments.
See the AutoKeras example, which shows ClearML automatically logging of scalars, hyperparameters, the log, and models, including visualizations in the ClearML Web UI.
If you are not already using ClearML, see Getting Started.
Adding ClearML to code¶
Add two lines of code:
from clearml import Task task = Task.init(project_name="myProject", task_name="myExperiment")
When the code runs, it initializes a Task in ClearML Server. A hyperlink to the experiment’s log is output to console.
CLEARML Task: created new task id=c1f1dc6cf2ee4ec88cd1f6184344ca4e CLEARML results page: https://app.clearml-master.hosted.allegro.ai/projects/1c7a45633c554b8294fa6dcc3b1f2d4d/experiments/c1f1dc6cf2ee4ec88cd1f6184344ca4e/output/log
Later in the code, for example, define callbacks using TensorBoard, and ClearML logs TensorBoard scalars, histograms, and images.