Computer Vision handson
Created on March 9|Last edited on March 20
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In this report, we'll cover a process of computer vision with W&B. There is a free training course of llm finetuning "Effective MLOps: Model Development". Please check it out too.
Asset to learn about Wandb
You can leverage many assets except for wandb official documentation. Visit to "For those who started to use W&B " to see learning resources provided by wandb.
- Training video
- Youtube
- Use case
- Chatbot
- Sample code
Wandb setup
1. Wandb login
For wandb setup, please refer this document. If you are using W&B dedicated cloud, VPC, or on-prem, please refer this and the following figure.
2. 101 of experiment tracking and W&B dashboard
You can catch up a basics of W&B experiment tracking in this course (W&B 101) for 15 minutes. Sample code is here. Colaboratory is here.
Please note that if you register the key as an environment variable, you can log in without entering the API key each time.
You can find you API key in your user setting page or in https://wandb.ai/authorize (this is multi-tenant SaaS only).
3. Team collaboration
W&B experiments are mainly managed in the order of entity => project => Run. An entity is a team unit. By default, it is set as a personal entity, but you can create a team entity and manage the same project within a team. However, for personal or academic use, you can only participate in one entity other than your personal one. The unit under the entity is the project. As the name suggests, please use it for one ML or DL project. You'll need to run many experiments within the project, and Runs are managed under the project. Note that entities and projects are created manually, while Runs are automatically created with each execution.
Segmentation
Please use V100 or A100!!!
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1. Data visualization (Table) and data versioning (Artifacts)
Table
With wandb.Table, you can visualize Images on wandb dashboard.
Run set
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If you want to learn more about Table, please check "Beyond experiment tracking and best practice of wandb" and Table section of wandb official documentation.
Artifacts
Beyond visualization, let's log the data into wandb with Artifacts. With Artifacts, you can manage data version.
You can use reference artifacts to track files saved outside the W&B server. In this mode an artifact only stores metadata about the files, such as URLs, size, and checksums. The underlying data never leaves your system. See the Quick start for information on how to save files and directories to W&B servers instead.
If you want to learn more about Artifacts, please check "Beyond experiment tracking and best practice of wandb" and Artifacts section of wandb official documentation.
2. Training (Experiment tracking)
Let's train model and track the process with wandb.
Run: lucky-star-42
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3. Hyper-parameter tuning (Sweep)
Please use V100 or A100!!!
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You can use W&B Sweeps to automate hyperparameter search and visualize rich, interactive experiment tracking. Pick from popular search methods such as Bayesian, grid search, and random to search the hyperparameter space. Scale and parallelize sweep across one or more machines.
Sweep: fiupujmj 1
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Sweep: fiupujmj 2
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4. Share results (Reports)
Let's share your insights with a nice reporting tool, Reports! Actually, this blog's platform is Reports!
5. Manage models in the team and create automated process (Model registry / Automations / Launch)
You can learn about Model registry with a nice video and a training.
Reference
Please check the following reports too.
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