Headlands PoC Guide
One stop shop for everything you need to test out during the W&B Pilot.
Created on February 10|Last edited on February 24
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Weights & Biases has been hosted internally in the Headlands Environment.
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For Any Questions, please reach out via Email.
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Weights and Biases (W&B) 💫Quick Documentations LinksW&B Installation & AuthenticationUse Cases / Test CasesTest Case 1: Logging model metrics and comparing themTest Case 2: Building plots for time series, feature importance, etc. Test Case 3: Tracking system utilizationTest Case 4: Versioning datasets and modelsTest Case 5: CollaborationTrack any python process or experiment with W&B's Experiment Tracking 🍽Visualize and query dataframes via W&B TablesTrack and version any serialized data via W&B Artifacts Tracking and VersioningHouse staged/candidate models via W&B's RegistryTune Hyperparameters via W&B SweepsOrganize visualizations and share your findings with collaborators via W&B ReportsOther Useful ResourcesImport/Export APISlack AlertsFAQs
Weights and Biases (W&B) 💫
Weights and Biases is a ML Ops platform built to facilitate collaboration and reproducibility across the machine learning development lifecycle. Machine learning projects can quickly become a mess without some best practices in place to aid developers and scientists as they iterate on models and move them to production.
W&B is lightweight enough to work with whatever framework or platform teams are currently using, but enables teams to quickly start logging their important results to a central system of record. On top of this system of record, W&B has built visualization, automation, and documentation capabilities for better debugging, model tuning, and project management.
Quick Documentations Links
- Track and version datasets, models, and other artifacts: https://docs.wandb.ai/guides/artifacts (Code)
- Visualize your data with interactive tables: https://docs.wandb.ai/guides/data-vis/tables-quickstart (Code)
W&B Installation & Authentication
To start using W&B, you first need to install the Python package (if it's not already there)
pip install wandb
Once it's installed, authenticate your user account by logging in through the CLI or SDK. You should have receive an email to sign up to the platform, after which you can obtain your API token (The API token is in your "Settings" section under your profile)
wandb login --host <YOUR W&B HOST URL> <YOUR API TOKEN>
OR through Python:
Once you are logged in, you are ready to track your workflows!
Use Cases / Test Cases
| S No | Capability & Success Criteria | W&B Product Area |
|---|---|---|
| 1 | Logging model metrics and comparing them | W&B Experiments, W&B Workspaces |
| 2 | Building plots for time series, feature importance, etc. | W&B Workspaces |
| 3 | Tracking system utilization | W&B Workspaces |
| 4 | Versioning datasets and models | W&B Artifacts and Registry |
| 5 | Collaboration | W&B Reports |
Test Case 1: Logging model metrics and comparing them
Test Case 2: Building plots for time series, feature importance, etc.
Test Case 3: Tracking system utilization
Test Case 4: Versioning datasets and models
Test Case 5: Collaboration
Track any python process or experiment with W&B's Experiment Tracking 🍽
Visualize and query dataframes via W&B Tables
Track and version any serialized data via W&B Artifacts Tracking and Versioning
House staged/candidate models via W&B's Registry
Tune Hyperparameters via W&B Sweeps
Organize visualizations and share your findings with collaborators via W&B Reports
Other Useful Resources
Import/Export API
All data logged to W&B can be accessed programmatically through the import/export API (also called the public API). This enables you to pull down run and artifact data, filter and manipulate it how you please in Python.
Slack Alerts
You can set slack alerts within a run to trigger when things happen in your training / evaluation scripts. For example, you may want to notify you when training is done or when a metric exceeds a certain value.
FAQs
1. I didn't name my run. Where is the run name coming from?
If you do not explicitly name your run, a random run name will be assigned to the run to help identify the run in the UI. For instance, random run names will look like "pleasant-flower-4" or "misunderstood-glade-2".
2. How can I configure the name of the run in my training code?
At the top of your training script when you call wandb.init, pass in an experiment name, like this:
wandb.init(name="my_awesome_run")
3. If wandb crashes, will it possibly crash my training run?
It is extremely important to us that we never interfere with your training runs. We run wandb in a separate process to make sure that if wandb somehow crashes, your training will nevertheless continue to run.
4. Why is a run marked crashed in W&B when it’s training fine locally?
This is likely a connection problem — if your server loses internet access and data stops syncing to W&B, we mark the run as crashed after a short period of retrying.
5. Does W&B support Distributed training?
Yes, W&B supports distributed training, here's the detailed guide on how to log distributed training experiments.
6. Can I use PyTorch profiler with W&B?
Here's a detailed report that walks through using the PyTorch profiler with W&B along with this associated Colab notebook.
7. How do I stop wandb from writing to my terminal or my jupyter notebook output?
Set the environment variable WANDB_SILENT to true.
In Python
os.environ["WANDB_SILENT"] = "true"
Within Jupyter Notebook
%env WANDB_SILENT=true
With Command Line
WANDB_SILENT=true
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