W&B: Clear Space & Optimize Ml Project Storage

Effective management of machine learning projects requires careful attention to resource utilization. Weights & Biases (W&B), a popular platform for experiment tracking and collaboration, can accumulate significant amounts of data over time. This data includes experiment artifacts, model checkpoints, and log files. These files consume storage space, potentially leading to issues with W&B cloud storage limits or local disk capacity. Therefore, the process of clearing space in W&B, by deleting or archiving old runs and associated data, becomes essential for maintaining an efficient and cost-effective workflow.

Alright, let’s dive into the world of Weights & Biases (W&B) – your trusty sidekick for all things machine learning! Think of W&B as your ML experiment command center, diligently tracking every tweak, turn, and triumph in your models. It’s like having a super-organized lab notebook, only way cooler and digital!

Now, imagine this: you’re in the zone, your model’s training, and suddenly…bam! The dreaded “Storage Quota Exceeded” error pops up. Ugh, talk about a buzzkill! This is where storage management becomes your secret weapon. Think of it like this: you wouldn’t let your desk get buried under piles of old papers, right? Same goes for your W&B workspace! Keeping things tidy ensures a smooth, uninterrupted workflow, letting you focus on what really matters: building awesome models.

Effective storage management is essential for a seamless workflow. It allows your data to be organized, and well managed. You can imagine that W&B is like a digital workplace. Effective storage management is also important to avoid getting the “Storage Quota Exceeded” error. It will disrupt your experimentations, waste your time and ruin your productivity. So, are you ready to unlock the secrets of keeping your W&B storage shipshape? We’ll explore practical strategies to clear space, optimize usage, and prevent those pesky storage errors from derailing your ML adventures.

Understanding W&B Storage Dynamics: Where Does All My Space Go?

Alright, let’s dive into the nitty-gritty of where your precious W&B storage is actually being used. Think of your W&B account like a digital lab notebook and filing cabinet. Every experiment, every dataset, every model checkpoint…it all takes up space. Understanding how different entities contribute to this usage is the first step toward becoming a true storage master.

The Usual Suspects: Breaking Down Storage Consumption

• Projects: These are your high-level containers, holding all the runs, artifacts, and associated data for a specific project. They don’t directly consume much storage themselves, but they house everything that does. Think of them as the overall folder on your computer.

• Runs: These are the bread and butter of your experiments – individual executions of your training scripts. Every logged metric, parameter, and system statistic gets stored here, so runs can quickly become storage hogs. It’s like keeping detailed notes for every single experiment you run!

• Artifacts: This is where things get interesting! Artifacts are versioned datasets, models, and any other files you want to track. Storing your datasets and model checkpoints as artifacts ensures reproducibility, but it also contributes to your storage footprint. Think of artifacts as the carefully organized files in your filing cabinet, complete with version numbers.

  • Model Checkpoints: Those saved states of your model during training? Yep, they’re artifacts, and the more frequently you save them, the more space they’ll consume. Consider logging a checkpoint when the model hits a specific milestone.
  • Dataset Artifacts: Versioned datasets can be large, especially when dealing with images or videos.
  • Media Files: Images, videos, and audio files logged during runs can be surprisingly large! A seemingly innocent image can take up megabytes, and those bytes add up fast.

• Run History: This is the log of all the metrics, parameters, and system statistics captured during your runs. While individual entries might be small, the sheer volume of data over many runs can significantly impact storage.

• Large Files: These are the storage monsters we need to be aware of! A few large files can quickly eat up your entire quota. Keep an eye on those datasets, model checkpoints, and media files!

• Sweeps: Ah, sweeps! Automated hyperparameter optimization runs are great for finding the best model settings, but they can also generate a ton of runs. Each sweep run contributes to storage, so managing your sweeps is essential.

Keeping an Eye on the Meter: Monitoring Storage Usage

So, how do you know how much space you’re actually using? Thankfully, W&B provides an easy way to monitor your storage usage and quota limits right within the interface.

• Dashboard: The main W&B dashboard usually provides an overview of your current storage usage and quota.
• Settings: Dig into your account settings for a more detailed breakdown of storage consumption by project, run, and artifact. This is where you can really get a handle on what’s taking up the most space.

By understanding these storage dynamics and keeping a close eye on your usage, you can avoid that dreaded “Storage Quota Exceeded” error and keep your machine learning experiments running smoothly.

Identifying Candidates for Removal: A Strategic Approach

Okay, so you’re staring at your W&B dashboard, and it’s telling you that your storage is about to hit the roof. Don’t panic! It’s time for a little digital decluttering. But before you go all Marie Kondo on your experiments, let’s talk strategy. We need to figure out what to get rid of without accidentally tossing out that groundbreaking model you’ll need next week.

Think of your W&B runs and artifacts like that pile of clothes in your closet. Some are your favorite, go-to pieces, others are those questionable impulse buys you’ve worn maybe once (or never). Our goal? To identify the forgettable ones.

Filtering Runs: The Art of the Cull

W&B gives you some seriously handy tools to sift through your runs like a pro. Let’s break it down:

• Age: Let’s be honest, that experiment you ran six months ago that didn’t go anywhere? Probably safe to say goodbye. Filter by date and time.
• Status: Anything that crashed spectacularly or got interrupted early is likely a safe bet for deletion. No need to hoard those error logs.
• Metrics: Remember that model with the abysmal accuracy? Yeah, wave farewell! Filter those low-performers out.
• Tags and Notes: This is where good documentation comes in handy. Use tags like “FailedExperiment,” or notes like “DoNotUse”. They make identifying candidates for deletion so much easier.

Selective Logging: Less is More (Storage-Wise)

Here’s a pro tip: You don’t need to log every single detail of every single run. It’s like filming your entire life when only a few highlight reels are worth watching. This is where selective logging comes into play.

Use wandb.define_metric to control how frequently you log certain metrics. Maybe you only need to track validation loss every 100 steps, not every single step. It might seem like a small change, but those bytes add up!

Sweeps: Taming the Hyperparameter Beast

Sweeps are amazing for finding the perfect hyperparameters, but they can generate tons of runs. A single sweep can quickly eat into your storage quota. Here’s how to manage them:

• Prune ruthlessly: Use the sweep UI to kill off runs that are clearly underperforming.
• Define clear goals upfront: Don’t let a sweep run indefinitely. Set a maximum number of runs or a target metric to achieve.
• Consider smaller sweeps: Do you really need to try every single hyperparameter combination? Be strategic!
• Review Regularly: After a sweep is done, take the time to archive or delete the less promising runs.

Clearing Space: Step-by-Step Methods

Alright, let’s get down to brass tacks and talk about the fun part: actually zapping those storage-hogging runs and artifacts. Think of this as your digital decluttering spree, Marie Kondo-style, but for your machine learning experiments. “Does this run spark joy?” If not, it’s gotta go! We’ve all been there and accidently run out of storage!

Deleting Runs and Artifacts

• Via the W&B UI: Ready for some point-and-click action?

  1. Navigate to Your Project: Open up your Weights & Biases workspace and head to the project containing the runs you want to delete.
  2. Select Runs: In the project view, you’ll see a list of your runs. Use the checkboxes to select the ones you’ve deemed unworthy.
  3. Initiate Deletion: Once you’ve made your selection, look for the “Delete” button (it might be hidden under a “More” or “Actions” menu). Click it!
  4. Confirm Deletion: A confirmation dialog will pop up, making sure you really want to delete these runs. Double-check, then confirm. Poof! They’re gone.
  5. Deleting Artifacts: Head over to the “Artifacts” tab in your project. Find the artifact you want to eliminate and hover the artifact name, and then simply click the “Delete” button.

• Via the W&B API (Python): For those who prefer the command line, here’s how to wield the power of Python.

  import wandb
  
  #Authenticate and connect
  wandb.login()
  api = wandb.Api()
  
  # Replace with your project name
  project_name = "your-wandb-username/your-project-name"
  
  # Replace with the run IDs you want to delete. You can get these from UI
  run_ids_to_delete = ["run_id_1", "run_id_2", "run_id_3"]
  
  for run_id in run_ids_to_delete:
      try:
          # Get the run object
          run = api.run(f"{project_name}/{run_id}")
          if run:
              # Delete the run
              run.delete()
              print(f"Run {run_id} deleted successfully.")
          else:
              print(f"Run {run_id} not found.")
      except Exception as e:
          print(f"Error deleting run {run_id}: {e}")
  

  Please note: Replace placeholder variables accordingly.

• Via the W&B CLI: Bulk deletion? The CLI’s got your back.
  bash
# Delete runs by ID
wandb delete <entity>/<project>/<run_id>
#Delete artifacts by name and version
wandb artifacts delete <artifact_name>:<artifact_version>

Archiving Runs

Sometimes, you’re not quite ready to say goodbye forever. That’s where archiving comes in. Archiving removes the run from your active workspace (freeing up storage quota), but keeps the data accessible for later. Like stashing old yearbooks in the attic.

• Via the W&B UI: It’s as easy as deleting, but with a slightly different button!

  1. Navigate to Your Project: Same as before, head to your W&B project.
  2. Select Runs: Pick the runs you want to archive.
  3. Initiate Archiving: Look for the “Archive” button (might also be under “More” or “Actions”).
  4. Confirm Archiving: Confirm your choice, and those runs will be tucked away in the archives.

Pruning Sweeps

Sweeps are fantastic for hyperparameter optimization, but they can quickly generate a ton of runs. Time to thin the herd!

• Identify Poor Performers: Sort your sweep runs by the metric you’re trying to optimize (e.g., validation loss). The runs at the bottom of the list are prime candidates for removal.
• Consider Early Stopping: If you’re using early stopping, runs that were stopped early are likely not worth keeping.
• Delete Strategically: Use the methods described above (UI, API, CLI) to delete the unpromising sweep runs. Don’t be afraid to be ruthless! Your storage quota will thank you.

Optimizing Storage Usage: Proactive Techniques

Okay, let’s talk about being proactive. Think of your W&B storage as a garden. You can either let it grow wild with weeds (unnecessary data!), or you can cultivate it to be a beautiful, efficient space. The latter is what we’re aiming for, and it starts with understanding how to minimize your storage footprint before you even start an experiment.

File Size Optimization: Think Small!

First up, let’s shrink those files! We’re not talking about magic shrinking rays, but clever techniques.

• Compress those media files: Images and videos can be storage hogs. Before you log them, think about compression. Are those perfect, uncompressed PNGs really necessary, or can you get away with a well-compressed JPEG?

• Resolution Revolution: Ask yourself, does that image need to be 4K? Probably not! Reduce the resolution before you log it. Your storage (and your viewers) will thank you.

• Codec Choices: For videos, choose your codecs wisely. Some codecs are far more efficient than others. Experiment to find the sweet spot between quality and size.

Data Serialization: The Art of Being Compact

Next, let’s talk about how you’re storing your data. Are you using a suitcase when a backpack would do?

• Lists vs. NumPy Arrays: Lists are great, but for numerical data, NumPy arrays are your friends. They’re way more efficient. Think of it as neatly stacking your data instead of throwing it in a drawer.

• Compression is Key: Don’t just store your NumPy arrays; compress them! .npz is your magic extension here. You get all the benefits of NumPy with the added bonus of compression. It’s like vacuum-sealing your data for freshness and compactness.

Selective Logging: Logging Less is More

Finally, let’s talk about being selective about what you log. It’s not about hoarding; it’s about quality over quantity.

• The Essentials Only: Do you really need to log every single metric every single epoch? Probably not. Focus on the metrics that truly matter. Think of it as decluttering your data closet.

• **wandb.log with Finesse:** Use `wandb.log` judiciously. Control the frequency of logging. Maybe log validation metrics every epoch, but training metrics less often.

• **wandb.watch Wisely:** `wandb.watch` is powerful, but it can also be a storage monster if you’re not careful. Think carefully about the frequency at which you’re logging model gradients and parameters. Do you really need them every batch? Experiment to find the right balance.

Think of these techniques as preventative medicine for your W&B storage. A little effort upfront can save you a lot of headaches down the road. Now go forth and optimize!

Automation and Best Practices: Maintaining a Clean Workspace

Okay, so you’re ready to turn your Weights & Biases workspace into a zen garden of perfectly organized experiments? Awesome! Let’s dive into automating the cleanup and setting up some rock-solid best practices. Think of it as teaching your W&B workspace to tidy up after itself.

Automated Cleanup Scripts: Your Secret Weapon

Imagine never having to manually delete old runs again. Sounds dreamy, right? With automated cleanup scripts, it’s totally achievable. The goal here is to create a script that periodically scans your W&B projects, identifies candidates for removal (based on age, status, metrics, etc.), and then bam, deletes them.

• How to use the W&B API to identify and delete old runs/artifacts: The W&B API is your best friend here. Using Python, you can write a script that authenticates with your W&B account, queries for runs that meet specific criteria (e.g., older than 30 days, status is “completed,” and a certain metric falls below a threshold), and then deletes those runs. You can do the same for artifacts.

  • Example Python snippet:

    import wandb
    import datetime
    
    # Authenticate with W&B
    api = wandb.Api()
    
    # Define your criteria for deletion
    days_old = 30
    metric_threshold = 0.5
    project_name = "your-project-name"
    
    # Calculate the date threshold
    date_threshold = datetime.datetime.now() - datetime.timedelta(days=days_old)
    
    # Query for runs that meet the criteria
    runs = api.runs(project_name, filters={
        "createdAt": {"$lt": date_threshold.isoformat()},
        "state": "finished",
        "summary_metrics.your_metric": {"$lt": metric_threshold}
    })
    
    # Delete the runs
    for run in runs:
        run.delete()
        print(f"Deleted run: {run.name}")
    

• Scheduling Cleanup Tasks with Cron Jobs or Task Scheduler: Once you have your cleanup script ready, you’ll need a way to run it automatically. That’s where Cron Jobs (on Linux/macOS) and Task Scheduler (on Windows) come in. These tools allow you to schedule tasks to run at specific intervals (e.g., daily, weekly, monthly).

  • Cron Job Example (Linux/macOS): Open your terminal and type crontab -e. This will open the crontab file in a text editor. Add a line like this:

    0 0 * * * /usr/bin/python3 /path/to/your/cleanup_script.py
    

    This will run your script every day at midnight.

  • Task Scheduler Example (Windows): Open Task Scheduler, create a new basic task, and follow the prompts to schedule your cleanup script. Specify the Python executable and the path to your script.

  • Example Configurations:

    • Daily Cleanup: Good for active projects with lots of runs.
    • Weekly Cleanup: Suitable for less active projects or when you want to retain data for a longer period.
    • Monthly Cleanup: A more relaxed approach for projects where storage isn’t a major concern.

Best Practices: Be a W&B Wizard!

Using Weights & Biases effectively isn’t just about tracking experiments; it’s about how you track them. Following these best practices will keep your workspace clean and organized from the get-go.

• wandb.init() and wandb.finish(): The Dynamic Duo: Always, always use wandb.init() at the beginning of your script to initialize a new run and wandb.finish() at the end to properly close it. Think of wandb.init() as opening the door to your experiment’s record book, and wandb.finish() as closing it and putting it away. If you don’t call wandb.finish(), W&B might not properly sync all your data, and your run could remain in a weird, limbo state.
• wandb.config: Configuration Chaos Coordinator: The wandb.config object is your best friend for managing experiment configurations. Store all your hyperparameters, data paths, and other settings in wandb.config. This makes it super easy to track which settings were used for each run, and it keeps your code clean and organized.
• Tags and Notes: The Ultimate Categorization Crew: Use tags and notes to categorize and document your runs effectively. Tags are like hashtags for your experiments. Notes are short descriptions that give context to your runs. For example, you could tag runs with the model architecture, the dataset used, or the type of experiment (e.g., “baseline,” “hyperparameter tuning”). Notes can be used to explain the purpose of the run or to record any observations or insights.

Troubleshooting Common Storage Issues

Okay, so you’ve been cruising along, running experiments, and W&B is your best friend. But then BAM! You hit a wall: the dreaded “Storage Quota Exceeded” error. Don’t panic! We’ve all been there. It’s like when your closet is so full you can’t find your favorite socks, but for your machine learning experiments. Let’s get this sorted out.

Addressing the “Storage Quota Exceeded” Error:

• Immediate Action: First, take a deep breath. You’re not alone. This is W&B’s way of saying, “Hey, remember me? You’re using a lot of space!” The most important thing is to stop any new runs from starting. This prevents you from potentially losing data or making the problem worse.

• Finding the Storage Hogs: Now, it’s time to put on your detective hat and find out what’s eating all your storage. Head over to your W&B dashboard. Look for sections like “Project Storage Usage” or similar. This will give you a breakdown of where all your data is hiding. Usually, it’s those massive artifact files or the countless sweep runs you forgot about.

• Quick Fix (Temporary Relief): If you’re in a real bind and need to keep experimenting right now, consider requesting a temporary quota increase from the W&B team. This isn’t a long-term solution, but it buys you time to clean things up properly. But always keep in mind to clean up!

Handling Issues with `wandb sync`:

`wandb sync` is a lifesaver when you’re running experiments offline and want to upload them later. But sometimes, things don’t go as planned.

• Offline Runs Aren’t Syncing? Double-check that the internet connection is working. It sounds obvious, but it’s the most common culprit. Also, make sure your W&B API key is correctly configured in your environment.

• Large Files or Corrupted Data: If the sync seems to be stuck, it could be due to a massive file that’s taking forever to upload or, worse, a corrupted data file. Check the logs for any error messages related to specific files. Try breaking up large files or regenerate the corrupted ones if possible.

Debugging Deletion and Archival Processes:

So, you’ve identified the culprits and hit the delete button. But are they really gone?

• Deletions Not Happening? First, refresh the W&B page. Sometimes it’s just a caching issue. If that doesn’t work, double-check your permissions. Do you have the rights to delete runs and artifacts in that project? If you are doing it from the code try re-authenticating and make sure there is no problem on the credentials.

• API or CLI Errors: Scrutinize the error messages. API calls and CLI commands can fail for various reasons, from incorrect syntax to authentication issues. Make sure you’re using the correct commands and that your API key is valid.

• Permissions and Authentication: Always ensure you are logged in correctly and have the necessary permissions to perform these actions. If you’re part of a team, verify that your role allows you to delete or archive runs and artifacts.

So, that’s pretty much it! Clearing up your W&B space doesn’t have to be a headache. Just a little housekeeping now and then, and you’ll be back to smooth sailing in no time. Happy experimenting!