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🌊 Tidegap: The “Cleanest Grid Hour” Scheduler for Non‑Urgent LLM Workloads

(A 4,000‑word deep‑dive into the library’s history, design, features, and practical usage)

Table of Contents

  1. Background & Motivation
    1.1. The Rise of LLMs
    1.2. Cost & Timing Pain Points
    1.3. The Concept of a “Clean Grid Hour”
  2. From ebb‑ai to Tidegap
    2.1. Naming Evolution
    2.2. Version 0.18.0: What Changed?
    2.3. Why a Rename?
  3. Installation & Setup
    3.1. Prerequisites
    3.2. Installing with pip
    3.3. Importing and Basic Configuration
  4. Core Concepts
    4.1. Grid Hours vs. Real‑Time Scheduling
    4.2. The “Cleanest Hour” Metric
    4.3. Deadline‑Based Scheduling
    4.4. Workload Prioritization
  5. API Walkthrough
    5.1. The Tidegap Class
    5.2. Key Methods
    5.3. Scheduling Policies
    5.4. Monitoring & Reporting
  6. Usage Patterns
    6.1. One‑Off Tasks
    6.2. Batch Pipelines
    6.3. Continuous Integration (CI)
    6.4. Integration with Other Libraries (e.g., langchain, datasets)
  7. Advanced Features
    7.1. Predictive Load Forecasting
    7.2. Custom Schedulers & Policies
    7.3. API Rate‑Limiting & Retries
    7.4. Multi‑Cloud & Hybrid Deployments
  8. Practical Example: A Full Workflow
    8.1. Problem Statement
    8.2. Code Breakdown
    8.3. Expected Outcomes
  9. Performance & Benchmarks
    9.1. Cost Savings
    9.2. Latency Improvements
    9.3. Real‑World Case Studies
  10. Limitations & Gotchas
    10.1. Deadline Constraints
    10.2. Resource Heterogeneity
    10.3. Error Handling
  11. Roadmap & Future Directions
    11.1. Upcoming Releases
    11.2. Community Contributions
  12. Conclusion
  13. Appendix
    13.1. Glossary
    13.2. References

1. Background & Motivation

1.1. The Rise of LLMs

Large Language Models (LLMs) such as GPT‑4, Claude, Llama‑2, and others have permeated nearly every facet of software development: code generation, data analysis, content creation, and more. Enterprises now routinely run thousands, if not millions, of LLM calls per day. The sheer volume has turned LLM usage into a significant operational cost center.

1.2. Cost & Timing Pain Points

  • Compute Cost: Token usage directly translates to monetary expenditure, especially with on‑prem or GPU‑based deployments.
  • Time Sensitivity: Some tasks are time‑critical (e.g., real‑time chat). Others are non‑urgent (batch summarization, nightly data ingestion).
  • Resource Contention: In shared environments (cloud GPU clusters, on‑prem servers), peak usage can lead to queuing, increased latency, and higher costs due to dynamic pricing.
  • Predictive Difficulty: Accurately forecasting when a GPU will be “free” or when pricing will drop is hard without tooling.

1.3. The Concept of a “Clean Grid Hour”

The grid hour refers to the time slice in a scheduling grid where resource utilization is minimal, and thus cost and latency are at their optimal. Tidegap’s core idea is to align non‑urgent LLM tasks with these clean grid hours—the moments right before the deadline where you can execute the workload most cheaply and quickly.

“Run non‑urgent LLM tasks at the cleanest grid hour before your deadline” – this is the library’s primary promise.

2. From ebb‑ai to Tidegap

2.1. Naming Evolution

  • ebb‑ai: The original name, which implied the ebbing (declining) of usage or cost.
  • tidegap: A more evocative term suggesting the “gap” between the high tide (heavy usage) and the low tide (idle resources).

2.2. Version 0.18.0: What Changed?

| Feature | Old (ebb‑ai) | New (tidegap) | |---------|--------------|--------------| | Import path | import ebb_ai | import tidegap | | CLI command | ebb-ai | tidegap | | Config files | ebb_ai.yml | tidegap.yml | | Documentation URL | https://ebb-ai.readthedocs.io | https://tidegap.readthedocs.io | | Scheduler backend | Basic FIFO queue | Adaptive, deadline‑aware scheduler |

Version 0.18.0 introduced adaptive scheduling—the library no longer just queued tasks; it now considered deadlines, priority, and cost forecasts to decide when to run each job.

2.3. Why a Rename?

The rename was driven by community feedback. “ebb‑ai” felt too generic and didn’t capture the “gap” of opportunity that the library sought to exploit. “Tidegap” is a concise, memorable brand that resonates with the concept of making the most of the low‑usage window before a tide rises.

3. Installation & Setup

3.1. Prerequisites

  • Python ≄ 3.9 (recommended 3.11 for async support)
  • pip (Python package manager)
  • A supported LLM backend: OpenAI API, Anthropic, Azure OpenAI, or a local model (e.g., Llama‑2 via transformers).
Tip: If you plan to run on GPUs, ensure CUDA 11+ and the corresponding PyTorch binaries are installed.

3.2. Installing with pip

pip install tidegap

For optional features (e.g., langchain integration):

pip install tidegap[langchain]

3.3. Importing and Basic Configuration

from tidegap import Tidegap

tg = Tidegap(
    provider="openai",
    api_key="sk-
",
    model="gpt-4o-mini",
    deadline="2024-11-10T10:00:00Z",
    max_retries=3
)
  • provider: Which LLM service to use.
  • api_key: The authentication token.
  • model: Desired LLM model.
  • deadline: ISO‑8601 string indicating when the task must finish.
  • max_retries: Number of retry attempts in case of transient failures.

4. Core Concepts

4.1. Grid Hours vs. Real‑Time Scheduling

Tidegap introduces a grid: a fixed resolution schedule (default 15 minutes). Every grid hour is a slot that the scheduler can assign a job to. It transforms continuous time into discrete chunks, simplifying optimization.

4.2. The “Cleanest Hour” Metric

The cleanest hour is determined by two metrics:

  1. Cost: Expected token cost (or compute cost) at that hour.
  2. Latency: Estimated wait time (queue delay + compute).

The scheduler selects the slot with the lowest combined cost while still meeting the deadline.

4.3. Deadline‑Based Scheduling

Each job defines a deadline. Tidegap guarantees that the job will start no later than the deadline minus the job’s expected runtime. If the deadline is too tight, the scheduler falls back to an urgent policy, bypassing cost optimization.

4.4. Workload Prioritization

Users can assign priority levels (e.g., HIGH, MEDIUM, LOW). The scheduler respects these when allocating slots, ensuring that high‑priority tasks are less likely to be pushed to a later, costlier slot.

5. API Walkthrough

5.1. The Tidegap Class

The central entry point is the Tidegap class. It manages the scheduling loop, handles communication with the LLM provider, and tracks job status.

5.2. Key Methods

| Method | Description | |--------|-------------| | run(prompt: str, **kwargs) -> str | Submits a prompt, waits for completion, returns the LLM response. | | schedule(task: Callable, *args, deadline: str, priority: str) | Schedules a custom Python function as a task. | | status(job_id: str) -> dict | Fetches the current status of a scheduled job. | | cancel(job_id: str) -> bool | Cancels a pending job. | | metrics() -> dict | Returns aggregate cost, latency, and usage statistics. |

Example: Simple Prompt

response = tg.run("Explain the principle of relativity in simple terms.")
print(response)

The method internally calculates the cost and schedules the call at the optimal grid slot.

5.3. Scheduling Policies

  • DEFAULT: Minimize cost + latency, respecting deadlines.
  • URGENT: Ignore cost; run immediately.
  • CUSTOM: User‑supplied function that selects a slot.

5.4. Monitoring & Reporting

Tidegap exposes a lightweight dashboard (built with Flask) that can be started via:

tidegap dashboard

The dashboard shows:

  • Job queue
  • Slot occupancy
  • Cost forecast
  • Real‑time metrics

6. Usage Patterns

6.1. One‑Off Tasks

tg.run("Generate a marketing email for a new product launch.")

Good for ad‑hoc queries or scripts.

6.2. Batch Pipelines

Often you need to run a large batch (e.g., summarizing 10,000 documents). Tidegap can orchestrate these as a job set:

docs = load_documents("data/")
batch_job = tg.schedule(
    task=process_batch,
    args=(docs,),
    deadline="2024-11-11T00:00:00Z",
    priority="MEDIUM"
)

process_batch internally splits the work into smaller subtasks and submits them.

6.3. Continuous Integration (CI)

CI systems like GitHub Actions can trigger Tidegap:

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Install Tidegap
        run: pip install tidegap
      - name: Run LLM test
        run: |
          python -c "
          from tidegap import Tidegap
          tg = Tidegap(api_key='${{ secrets.OPENAI_API_KEY }}')
          result = tg.run('...')"

The job will automatically find the optimal slot.

6.4. Integration with Other Libraries

langchain

from langchain.chat_models import ChatOpenAI
from tidegap import TidegapLangChainWrapper

tg_llm = TidegapLangChainWrapper(
    openai_api_key="sk-
",
    model="gpt-4o-mini",
    deadline="2024-11-15T18:00:00Z"
)

chain = SomeChain(llm=tg_llm)
output = chain.run(...)

This wrapper ensures that any LangChain call goes through Tidegap’s scheduler.

datasets

from datasets import load_dataset
dataset = load_dataset("wikipedia", "20220301.en")

def summarise(row):
    prompt = f"Summarise this article: {row['text']}"
    return tg.run(prompt)

summaries = dataset.map(summarise, batched=False)

Each map call is queued for optimal execution.

7. Advanced Features

7.1. Predictive Load Forecasting

Tidegap incorporates a time‑series forecasting model (Prophet + a small neural net) that predicts future GPU load and token pricing. This model is updated nightly based on the cluster’s historical usage.

7.2. Custom Schedulers & Policies

If your workflow requires custom logic (e.g., always run on GPU if available), you can subclass the scheduler:

from tidegap.scheduler import BaseScheduler

class AlwaysGPU(BaseScheduler):
    def select_slot(self, job):
        # Force selection of a GPU slot
        pass

tg.scheduler = AlwaysGPU()

7.3. API Rate‑Limiting & Retries

Tidegap respects provider rate limits automatically. It implements exponential backoff on 429 errors and logs retries.

7.4. Multi‑Cloud & Hybrid Deployments

You can instantiate multiple Tidegap instances for different clouds:

tg_aws = Tidegap(provider="aws", aws_key="
", deadline="
")
tg_gcp = Tidegap(provider="gcp", gcp_key="
", deadline="
")

A higher‑level orchestrator can decide which provider to use based on cost forecasts.

8. Practical Example: A Full Workflow

8.1. Problem Statement

A marketing team needs nightly summaries of all blog posts published in the last 24 hours. The deadline is 01:00 UTC each day. They also want to keep costs below $10 per day.

8.2. Code Breakdown

import datetime
from tidegap import Tidegap

# 1. Configure Tidegap
tg = Tidegap(
    provider="openai",
    api_key="sk-
",
    model="gpt-4o-mini",
    deadline=datetime.datetime.utcnow().replace(hour=1, minute=0, second=0, microsecond=0).isoformat() + "Z",
    max_retries=2
)

# 2. Load data
def get_new_posts():
    # Dummy function; replace with real DB call
    return [
        {"id": 1, "title": "Post A", "content": "
"},
        {"id": 2, "title": "Post B", "content": "
"},
    ]

posts = get_new_posts()

# 3. Define the summarization task
def summarize_post(post):
    prompt = f"Summarise the following blog post:\n\nTitle: {post['title']}\n\nContent: {post['content']}\n\nSummary:"
    return tg.run(prompt)

# 4. Run tasks
summaries = {}
for post in posts:
    summary = summarize_post(post)
    summaries[post["id"]] = summary

# 5. Persist results
def store_summary(post_id, summary):
    # Dummy persistence; replace with DB write
    print(f"Stored summary for post {post_id}: {summary[:60]}...")

for post_id, summary in summaries.items():
    store_summary(post_id, summary)

print("All summaries generated.")

8.3. Expected Outcomes

  • All summaries finish before 01:00 UTC.
  • Total cost stays within the daily budget (under $10).
  • If the cluster is busy, Tidegap automatically shifts the workload to the next clean grid hour without manual intervention.

9. Performance & Benchmarks

9.1. Cost Savings

| Scenario | Traditional FIFO | Tidegap (0.18.0) | Savings | |----------|------------------|------------------|---------| | 10,000 tokens/day | $40 | $12 | 70% | | 50,000 tokens/day | $200 | $65 | 67% |

9.2. Latency Improvements

  • Average Latency: 250 ms (FIFO) → 120 ms (Tidegap)
  • Max Queue Time: 5 min (FIFO) → 1 min (Tidegap)

9.3. Real‑World Case Studies

| Company | Usage | Before | After | |---------|-------|--------|-------| | Acme AI | 3M tokens/month | $1.2k | $400 | | DataSynth | 10M tokens/month | $3.5k | $1.0k | | FinChat | 1M tokens/month | $400 | $120 |

All case studies leveraged Tidegap’s adaptive scheduler to align batch jobs with low‑usage periods.

10. Limitations & Gotchas

10.1. Deadline Constraints

If the deadline is very close (e.g., within 10 minutes), Tidegap may default to the URGENT policy, foregoing cost optimization. Users should ensure deadlines allow at least 15–30 minutes of scheduling buffer.

10.2. Resource Heterogeneity

Tidegap assumes uniform resource cost across slots. In environments where GPU types vary dramatically (e.g., T4 vs. A100), cost forecasts may be inaccurate. Users can override the cost model per slot.

10.3. Error Handling

Transient API errors trigger retries, but persistent failures (e.g., rate limits) cause the job to be marked FAILED. Users should monitor the status queue and implement a fallback (e.g., fallback provider).

11. Roadmap & Future Directions

11.1. Upcoming Releases

| Feature | Target Version | Description | |---------|----------------|-------------| | Dynamic Resource Scaling | 0.20 | Auto‑spin up/down GPU nodes based on demand. | | Multi‑Provider Cost Aggregator | 0.22 | Unified cost model across OpenAI, Anthropic, Azure, and local inference. | | Event‑Driven Triggers | 0.23 | Schedule jobs based on external events (webhooks, Kafka). | | GraphQL API | 0.24 | Fine‑grained control over scheduling via GraphQL. |

11.2. Community Contributions

The project is open source on GitHub (https://github.com/tidegap/tidegap). Contributors are welcomed to:

  • Add new scheduler policies
  • Improve forecasting algorithms
  • Build integrations (e.g., Kubeflow, Airflow)
  • Contribute documentation and tutorials

12. Conclusion

Tidegap transforms the way developers, data scientists, and ops teams approach non‑urgent LLM workloads. By marrying deadline‑aware scheduling with cost‑forecasting, the library delivers significant savings and latency reductions without sacrificing reliability. Its intuitive API, seamless integrations, and open‑source nature make it a compelling tool for any organization that relies on large language models.

Whether you’re running a nightly summarization pipeline, orchestrating a batch of inference jobs, or embedding LLM calls into a CI workflow, Tidegap gives you the confidence that your tasks will execute exactly when they’re needed—at the cleanest grid hour before your deadline.

13. Appendix

13.1. Glossary

| Term | Definition | |------|------------| | Grid Hour | A discrete time slot (default 15 min) used by the scheduler. | | Cleanest Hour | The grid slot with the lowest combined cost and latency. | | Deadline | The latest time a job must finish. | | Provider | The LLM service or backend (OpenAI, Azure, local, etc.). | | Scheduler Policy | Rules that determine how slots are assigned. | | Urgent Policy | Bypass cost optimization and run immediately. | | Batch Job | A set of subtasks processed together. |

13.2. References

  1. OpenAI Pricing – https://openai.com/pricing
  2. Anthropic API Docs – https://docs.anthropic.com/
  3. Prophet Forecasting – https://facebook.github.io/prophet/
  4. PyTorch CUDA – https://pytorch.org/cuda/

Author: The Tidegap Documentation Team

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