feat(eda): migrate to Dask distributed with adaptive scaling and memory limits

pyproject.toml

@@ -19,4 +19,5 @@ dependencies = [
     "joblib>=1.3.0",
     "pyarrow>=23.0.0",
     "fastparquet>=2025.12.0",
+    "dask[distributed]>=2026.1.2",
 ]

scripts/distributed_eda.py

@@ -22,6 +22,7 @@ import yaml
 from concurrent.futures.process import BrokenProcessPool
 from scipy import sparse
 from tqdm import tqdm
+from dask.distributed import Client, LocalCluster, as_completed
 
 
 @dataclass
@@ -518,9 +519,10 @@ def process_failed_datasets(
         
         # Reduce parallelism on retries to avoid resource contention
         retry_workers = max(1, config["resources"]["max_workers"] // (2 ** retry_attempt))
+        memory_per_worker_gib = config["resources"]["max_memory_gib"] / retry_workers
         
         print(f"\nRetry attempt {retry_attempt + 1}/{max_retries}: {len(remaining)} datasets")
-        print(f"Using {retry_workers} workers (reduced to avoid resource issues)")
+        print(f"Using {retry_workers} workers, {memory_per_worker_gib:.1f} GiB per worker")
         
         wait_time = 2 ** retry_attempt  # 1s, 2s, 4s exponential backoff
         if wait_time > 0:
@@ -529,20 +531,31 @@ def process_failed_datasets(
         
         next_remaining = []
         
-        with tqdm(total=len(remaining), desc=f"Retry {retry_attempt + 1}", unit="dataset") as pbar:
-            executor = concurrent.futures.ProcessPoolExecutor(max_workers=retry_workers)
-            futures = {}
-            
-            try:
+        # Use Dask for retries with conservative settings
+        cluster = LocalCluster(
+            n_workers=retry_workers,
+            threads_per_worker=1,
+            processes=True,
+            memory_limit=f"{memory_per_worker_gib}GiB",
+            silence_logs=True,
+            dashboard_address=None,
+        )
+        client = Client(cluster)
+        
+        try:
+            with tqdm(total=len(remaining), desc=f"Retry {retry_attempt + 1}", unit="dataset") as pbar:
+                futures = {}
+                
                 for path, size_cat, strategy in remaining:
                     try:
-                        fut = executor.submit(
+                        fut = client.submit(
                             run_with_strategy_single_attempt,
                             path,
                             size_cat,
                             strategy,
                             config,
                             per_dataset_dir,
+                            pure=False,
                         )
                         futures[fut] = (path, size_cat, strategy)
                     except Exception as exc:
@@ -550,7 +563,7 @@ def process_failed_datasets(
                         tqdm.write(f"[retry failed to submit] {path.name}: {exc}")
                         pbar.update(1)
                 
-                for fut in concurrent.futures.as_completed(futures):
+                for fut in as_completed(futures):
                     path, size_cat, strategy = futures[fut]
                     try:
                         row = fut.result(timeout=3600)
@@ -562,16 +575,14 @@ def process_failed_datasets(
                             row["retry_attempt"] = retry_attempt + 1
                             successes.append(row)
                             tqdm.write(f"[retry ok] {path.name} (attempt {retry_attempt + 1})")
-                    except concurrent.futures.TimeoutError:
-                        next_remaining.append((path, size_cat, strategy))
-                        tqdm.write(f"[retry timeout] {path.name}")
                     except Exception as exc:
                         next_remaining.append((path, size_cat, strategy))
                         tqdm.write(f"[retry error] {path.name}: {exc}")
                     finally:
                         pbar.update(1)
-            finally:
-                executor.shutdown(wait=True)
+        finally:
+            client.close()
+            cluster.close()
         
         remaining = next_remaining
     
@@ -596,9 +607,9 @@ def process_stage(
     per_dataset_dir: Path,
     max_workers: int,
 ) -> tuple[list[dict], list[tuple[Path, str, dict]]]:
-    """Process datasets in a stage sequentially with batched executor recycling.
+    """Process datasets using Dask LocalCluster with adaptive scaling.
     
-    Adaptively reduces workers when throughput drops, indicating heavier datasets.
+    Dask automatically manages memory per worker and scales workers based on load.
     """
     if not tasks:
         return [], []
@@ -606,124 +617,90 @@ def process_stage(
     successes = []
     failures = []
     
-    # Adaptive worker tracking
-    current_workers = max_workers
-    baseline_throughput = None  # Initial stable throughput
-    last_check_idx = 0
-    last_check_time = time.time()
-    
-    # Batch size: process in smaller batches to allow executor recreation
-    # This prevents memory buildup in the process pool
-    # Smaller batches with fewer workers process faster and recycle more frequently
-    batch_size = max(30, min(100, len(tasks) // 4))  # 30-100 datasets per batch
-    
-    # Adaptive tuning parameters
-    check_interval = 50  # Check throughput every N datasets
-    slowdown_threshold = 0.5  # Reduce workers if throughput drops to 50% of baseline
-    min_workers = max(1, max_workers // 4)  # Don't go below 25% of initial
+    # Calculate memory per worker from config
+    max_memory_gib = config["resources"]["max_memory_gib"]
+    memory_per_worker_gib = max_memory_gib / max_workers
+    min_workers = max(1, max_workers // 4)
     
     print(f"\n{'='*80}")
     print(f"Stage: {stage_name.upper()} ({len(tasks)} datasets)")
-    print(f"Workers: {current_workers} (adaptive: {min_workers}-{max_workers})")
-    print(f"Batch size: {batch_size} (executor recycled between batches)")
+    print(f"Workers: {min_workers}-{max_workers} (adaptive via Dask)")
+    print(f"Memory per worker: {memory_per_worker_gib:.1f} GiB")
     print(f"{'='*80}\n")
     
-    overall_pbar = tqdm(total=len(tasks), desc=f"{stage_name}", unit="dataset")
+    # Create Dask LocalCluster with memory limits
+    cluster = LocalCluster(
+        n_workers=min_workers,  # Start with minimum
+        threads_per_worker=1,   # Single-threaded for memory isolation
+        processes=True,
+        memory_limit=f"{memory_per_worker_gib}GiB",
+        silence_logs=True,
+        dashboard_address=None,  # Disable dashboard for HPC
+    )
+    
+    # Enable adaptive scaling
+    cluster.adapt(
+        minimum=min_workers,
+        maximum=max_workers,
+        target_duration='30s',  # Target 30s task duration
+        wait_count=3,           # Wait for 3 tasks before scaling up
+    )
+    
+    client = Client(cluster)
     
     try:
-        # Process in batches
-        for batch_start in range(0, len(tasks), batch_size):
-            batch_end = min(batch_start + batch_size, len(tasks))
-            batch_tasks = tasks[batch_start:batch_end]
-            batch_num = (batch_start // batch_size) + 1
-            total_batches = (len(tasks) + batch_size - 1) // batch_size
-            
-            # Check if we should reduce workers based on throughput
-            processed_count = len(successes) + len(failures)
-            if processed_count >= last_check_idx + check_interval and processed_count > check_interval:
-                current_time = time.time()
-                delta_count = processed_count - last_check_idx
-                delta_time = current_time - last_check_time
-                recent_throughput = delta_count / delta_time if delta_time > 0 else 0
-                
-                # Establish baseline from first measurement window
-                if baseline_throughput is None and processed_count >= check_interval * 2:
-                    baseline_throughput = recent_throughput
-                    tqdm.write(f"[{stage_name}] Baseline throughput: {baseline_throughput:.2f} datasets/sec")
-                
-                # Check for slowdown
-                if baseline_throughput and recent_throughput < baseline_throughput * slowdown_threshold:
-                    if current_workers > min_workers:
-                        old_workers = current_workers
-                        current_workers = max(min_workers, current_workers // 2)
-                        tqdm.write(f"[{stage_name}] ⚠️  Throughput dropped to {recent_throughput:.2f} datasets/sec (baseline: {baseline_throughput:.2f})")
-                        tqdm.write(f"[{stage_name}] 🔧 Reducing workers: {old_workers} → {current_workers}")
-                        baseline_throughput = None  # Reset baseline after adjustment
-                
-                last_check_idx = processed_count
-                last_check_time = current_time
-            
-            if len(tasks) > batch_size:
-                tqdm.write(f"\n[{stage_name}] Batch {batch_num}/{total_batches} ({len(batch_tasks)} datasets, {current_workers} workers)")
-            
-            # Create fresh executor for each batch with current worker count
-            executor = concurrent.futures.ProcessPoolExecutor(max_workers=current_workers)
+        tqdm.write(f"[{stage_name}] Dask cluster started: {client.dashboard_link if hasattr(client, 'dashboard_link') else 'N/A'}")
+        
+        overall_pbar = tqdm(total=len(tasks), desc=f"{stage_name}", unit="dataset")
+        
+        try:
+            # Submit all tasks to Dask
             futures = {}
+            for path, size_cat, strategy in tasks:
+                try:
+                    fut = client.submit(
+                        run_with_strategy_single_attempt,
+                        path,
+                        size_cat,
+                        strategy,
+                        config,
+                        per_dataset_dir,
+                        pure=False,  # Don't cache results (each task is unique)
+                    )
+                    futures[fut] = (path, size_cat, strategy)
+                except Exception as exc:
+                    failures.append((path, size_cat, strategy))
+                    tqdm.write(f"[{stage_name}] submit failed: {path.name}: {exc}")
+                    overall_pbar.update(1)
             
-            try:
-                # Submit batch tasks
-                for path, size_cat, strategy in batch_tasks:
-                    try:
-                        fut = executor.submit(
-                            run_with_strategy_single_attempt,
-                            path,
-                            size_cat,
-                            strategy,
-                            config,
-                            per_dataset_dir,
-                        )
-                        futures[fut] = (path, size_cat, strategy)
-                    except Exception as exc:
+            # Collect results as they complete
+            for fut in as_completed(futures):
+                path, size_cat, strategy = futures[fut]
+                try:
+                    row = fut.result(timeout=7200)  # 2 hour timeout
+                    
+                    if row.get("status") in ("failed", "failed_after_retries"):
                         failures.append((path, size_cat, strategy))
-                        tqdm.write(f"[{stage_name}] submit failed: {path.name}: {exc}")
-                        overall_pbar.update(1)
-                
-                # Collect batch results
-                for fut in concurrent.futures.as_completed(futures):
-                    path, size_cat, strategy = futures[fut]
-                    try:
-                        row = fut.result(timeout=7200)  # 2 hour timeout
-                        
-                        if row.get("status") in ("failed", "failed_after_retries"):
-                            failures.append((path, size_cat, strategy))
-                            error_msg = row.get('error', 'Unknown')[:80]
-                            tqdm.write(f"[{stage_name}] failed: {path.name}: {error_msg}")
-                        else:
-                            row["stage"] = stage_name
-                            row["batch"] = batch_num
-                            successes.append(row)
-                            if config["behavior"]["log_each_dataset"]:
-                                elapsed = row.get("elapsed_sec", "?")
-                                tqdm.write(f"[{stage_name}] ok: {path.name} ({elapsed}s)")
-                    except concurrent.futures.TimeoutError:
-                        failures.append((path, size_cat, strategy))
-                        tqdm.write(f"[{stage_name}] timeout: {path.name}")
-                    except (BrokenProcessPool, Exception) as exc:
-                        failures.append((path, size_cat, strategy))
-                        tqdm.write(f"[{stage_name}] error: {path.name}: {exc}")
-                    finally:
-                        overall_pbar.update(1)
-            finally:
-                # Shutdown executor after each batch
-                executor.shutdown(wait=True)
-                # Force cleanup between batches
-                gc.collect()
-                time.sleep(1)  # Brief pause for cleanup
+                        error_msg = row.get('error', 'Unknown')[:80]
+                        tqdm.write(f"[{stage_name}] failed: {path.name}: {error_msg}")
+                    else:
+                        row["stage"] = stage_name
+                        successes.append(row)
+                        if config["behavior"]["log_each_dataset"]:
+                            elapsed = row.get("elapsed_sec", "?")
+                            tqdm.write(f"[{stage_name}] ok: {path.name} ({elapsed}s)")
+                except Exception as exc:
+                    failures.append((path, size_cat, strategy))
+                    tqdm.write(f"[{stage_name}] error: {path.name}: {exc}")
+                finally:
+                    overall_pbar.update(1)
+        finally:
+            overall_pbar.close()
     finally:
-        overall_pbar.close()
-    
-    # Final cleanup after stage
-    gc.collect()
+        # Cleanup Dask cluster
+        client.close()
+        cluster.close()
+        gc.collect()
     
     print(f"\n{stage_name.upper()} complete: {len(successes)} succeeded, {len(failures)} failed")
     

uv.lock

@@ -224,6 +224,7 @@ source = { virtual = "." }
 dependencies = [
     { name = "anndata", version = "0.11.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
     { name = "anndata", version = "0.12.10", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "dask", extra = ["distributed"] },
     { name = "fastparquet" },
     { name = "ipykernel" },
     { name = "joblib" },
@@ -245,6 +246,7 @@ dependencies = [
 [package.metadata]
 requires-dist = [
     { name = "anndata", specifier = ">=0.10.0" },
+    { name = "dask", extras = ["distributed"], specifier = ">=2026.1.2" },
     { name = "fastparquet", specifier = ">=2025.12.0" },
     { name = "ipykernel", specifier = ">=6.25.0" },
     { name = "joblib", specifier = ">=1.3.0" },
@@ -454,6 +456,27 @@ wheels = [
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 ]
 
+[[package]]
+name = "click"
+version = "8.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/3d/fa/656b739db8587d7b5dfa22e22ed02566950fbfbcdc20311993483657a5c0/click-8.3.1.tar.gz", hash = "sha256:12ff4785d337a1bb490bb7e9c2b1ee5da3112e94a8622f26a6c77f5d2fc6842a", size = 295065, upload-time = "2025-11-15T20:45:42.706Z" }
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+]
+
+[[package]]
+name = "cloudpickle"
+version = "3.1.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/27/fb/576f067976d320f5f0114a8d9fa1215425441bb35627b1993e5afd8111e5/cloudpickle-3.1.2.tar.gz", hash = "sha256:7fda9eb655c9c230dab534f1983763de5835249750e85fbcef43aaa30a9a2414", size = 22330, upload-time = "2025-11-03T09:25:26.604Z" }
+wheels = [
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+]
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -748,6 +771,30 @@ wheels = [
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 ]
 
+[[package]]
+name = "dask"
+version = "2026.1.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "click" },
+    { name = "cloudpickle" },
+    { name = "fsspec" },
+    { name = "importlib-metadata", marker = "python_full_version < '3.12'" },
+    { name = "packaging" },
+    { name = "partd" },
+    { name = "pyyaml" },
+    { name = "toolz" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/bd/52/b0f9172b22778def907db1ff173249e4eb41f054b46a9c83b1528aaf811f/dask-2026.1.2.tar.gz", hash = "sha256:1136683de2750d98ea792670f7434e6c1cfce90cab2cc2f2495a9e60fd25a4fc", size = 10997838, upload-time = "2026-01-30T21:04:20.54Z" }
+wheels = [
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+]
+
+[package.optional-dependencies]
+distributed = [
+    { name = "distributed" },
+]
+
 [[package]]
 name = "debugpy"
 version = "1.8.20"
@@ -795,6 +842,32 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/07/6c/aa3f2f849e01cb6a001cd8554a88d4c77c5c1a31c95bdf1cf9301e6d9ef4/defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61", size = 25604, upload-time = "2021-03-08T10:59:24.45Z" },
 ]
 
+[[package]]
+name = "distributed"
+version = "2026.1.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "click" },
+    { name = "cloudpickle" },
+    { name = "dask" },
+    { name = "jinja2" },
+    { name = "locket" },
+    { name = "msgpack" },
+    { name = "packaging" },
+    { name = "psutil" },
+    { name = "pyyaml" },
+    { name = "sortedcontainers" },
+    { name = "tblib" },
+    { name = "toolz" },
+    { name = "tornado" },
+    { name = "urllib3" },
+    { name = "zict" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/4e/75/b6e5b77229097ff03dd5ba6a07c77e2da87e7e991ccfef412549bba78746/distributed-2026.1.2.tar.gz", hash = "sha256:8333fa7a34151ed3b4cf1a03136fe1f1799eca706a5e47bdb63022c8795d853b", size = 2103721, upload-time = "2026-01-30T21:07:03.307Z" }
+wheels = [
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+]
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 [[package]]
 name = "donfig"
 version = "0.8.1.post1"
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 ]
 
+[[package]]
+name = "importlib-metadata"
+version = "8.7.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "zipp", marker = "python_full_version < '3.12'" },
+]
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+]
+
 [[package]]
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 version = "2.3.0"
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 ]
 
+[[package]]
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+]
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