Kindo AI PoV Guide Template

One stop shop for everything you need to test out during the W&B Pilot.
Created on May 22|Last edited on May 22
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Weights and Biases (W&B) 💫Quick Documentations LinksW&B Installation & AuthenticationUse Cases / Test Cases (Customize this section)Test Case 1: Use-Case 1 (Customize according your use-case)Test Case 2: Test Case 2 (Customize according your use-case)Test Case 3: Use-Case 3 (Customize according your use-case)Track 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 ReportsTrack and evaluate GenAI applications via W&B Weave Other Useful ResourcesImport/Export APISlack AlertsFAQsW&B ModelsW&B Weave
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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 LinksAccess the main documentation page: https://docs.wandb.ai/﻿
Learn how to integrate W&B with your stack: https://docs.wandb.ai/guides/integrations﻿
Track your experiment metrics: https://docs.wandb.ai/guides/track﻿
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)
Tune hyper-parameters via sweeps: https://docs.wandb.ai/guides/sweeps (Code)
Use reports to share your findings: https://docs.wandb.ai/guides/reports﻿
Trace and Evaluate GenAI applications: https://weave-docs.wandb.ai/﻿﻿﻿
W&B Installation & AuthenticationTo 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:
﻿﻿wandb.login(host=os.getenv("WANDB_BASE_URL"), key=os.getenv("WANDB_API_KEY"))
In headless environments, you can instead define the WANDB_API_KEY environment variable.﻿
Once you are logged in, you are ready to track your workflows!
Use Cases / Test Cases (Customize this section)
























S NoCapability & Success CriteriaW&B Product Area
1Test Case 1W&B Experiments
2Test Case 2W&B Artifacts
3Test Case 3W&B Reports
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Test Case 1: Use-Case 1 (Customize according your use-case)Add More relevant details related to the Test Case
Test Case 2: Test Case 2 (Customize according your use-case)Add More relevant details related to the Test Case
Test Case 3: Use-Case 3 (Customize according your use-case)W&B reports help contextualize and document the system of record built through logging diagnostics and results from different pieces of your pipeline. Reports are interactive and dynamic, reflecting filtered run sets logged in W&B. You can add all sorts of assets to a report; the one you are reading now includes plots, tables, images, code, and nested reports. 
More details in the Reports section below
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
Track and evaluate GenAI applications via W&B Weave 
Other Useful Resources
Import/Export APIAll 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 AlertsYou 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. 
Details on enabling these alerts on your dedicated deployments can be found here﻿
FAQs
W&B Models1. 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
W&B Weave1. How can I add a custom cost for my GenAI model?
You can add a custom cost by using the add_cost method. This guide walks you through the steps of adding a custom cost. Additionally we also have this cookbook on Setting up a custom cost model with associated notebook.
2. How can I create my own custom Scorers with W&B Weave?
W&B Weave has it's own predefined scorers that you use as well as create your own Scorers. This documentation walks through creating your own scorers with W&B Weave
3. Can I control/customize the data that is logged?
Yes, If you want to change the data that is logged to weave without modifying the original function (e.g. to hide sensitive data), you can pass postprocess_inputs and postprocess_output to the op decorator.
﻿Here's more details on how to do so
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S No	Capability & Success Criteria	W&B Product Area
1	Test Case 1	W&B Experiments
2	Test Case 2	W&B Artifacts
3	Test Case 3	W&B Reports
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