boost local bootstrap latency by 20% (#2176)

* boost local bootstrap latency by 20%

* more optimizations

* wire up logger

* wire up logger

metaflow/plugins/pypi/conda_environment.py

@@ -5,10 +5,11 @@
 import io
 import json
 import os
-import sys
 import tarfile
+import threading
 import time
-from concurrent.futures import ThreadPoolExecutor
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from functools import wraps
 from hashlib import sha256
 from io import BufferedIOBase, BytesIO
 from itertools import chain
@@ -50,7 +51,6 @@ def decospecs(self):
 
     def validate_environment(self, logger, datastore_type):
         self.datastore_type = datastore_type
-        self.logger = logger
 
         # Avoiding circular imports.
         from metaflow.plugins import DATASTORES
@@ -62,8 +62,21 @@ def validate_environment(self, logger, datastore_type):
         from .micromamba import Micromamba
         from .pip import Pip
 
-        micromamba = Micromamba()
-        self.solvers = {"conda": micromamba, "pypi": Pip(micromamba)}
+        print_lock = threading.Lock()
+
+        def make_thread_safe(func):
+            @wraps(func)
+            def wrapper(*args, **kwargs):
+                with print_lock:
+                    return func(*args, **kwargs)
+
+            return wrapper
+
+        self.logger = make_thread_safe(logger)
+
+        # TODO: Wire up logging
+        micromamba = Micromamba(self.logger)
+        self.solvers = {"conda": micromamba, "pypi": Pip(micromamba, self.logger)}
 
     def init_environment(self, echo, only_steps=None):
         # The implementation optimizes for latency to ensure as many operations can
@@ -150,6 +163,9 @@ def _path(url, local_path):
                 (
                     package["path"],
                     # Lazily fetch package from the interweb if needed.
+                    # TODO: Depending on the len_hint, the package might be downloaded from
+                    #       the interweb prematurely. save_bytes needs to be adjusted to handle
+                    #       this scenario.
                     LazyOpen(
                         package["local_path"],
                         "rb",
@@ -166,22 +182,60 @@ def _path(url, local_path):
                 if id_ in dirty:
                     self.write_to_environment_manifest([id_, platform, type_], packages)
 
-        # First resolve environments through Conda, before PyPI.
+        storage = None
+        if self.datastore_type not in ["local"]:
+            # Initialize storage for caching if using a remote datastore
+            storage = self.datastore(_datastore_packageroot(self.datastore, echo))
+
         self.logger("Bootstrapping virtual environment(s) ...")
-        for solver in ["conda", "pypi"]:
-            with ThreadPoolExecutor() as executor:
-                results = list(
-                    executor.map(lambda x: solve(*x, solver), environments(solver))
-                )
-            _ = list(map(lambda x: self.solvers[solver].download(*x), results))
-            with ThreadPoolExecutor() as executor:
-                _ = list(
-                    executor.map(lambda x: self.solvers[solver].create(*x), results)
-                )
-            if self.datastore_type not in ["local"]:
-                # Cache packages only when a remote datastore is in play.
-                storage = self.datastore(_datastore_packageroot(self.datastore, echo))
-                cache(storage, results, solver)
+        # Sequence of operations:
+        #  1. Start all conda solves in parallel
+        #  2. Download conda packages sequentially
+        #  3. Create and cache conda environments in parallel
+        #  4. Start PyPI solves in parallel after each conda environment is created
+        #  5. Download PyPI packages sequentially
+        #  6. Create and cache PyPI environments in parallel
+
+        with ThreadPoolExecutor() as executor:
+            # Start all conda solves in parallel
+            conda_futures = [
+                executor.submit(lambda x: solve(*x, "conda"), env)
+                for env in environments("conda")
+            ]
+
+            pypi_envs = {env[0]: env for env in environments("pypi")}
+            pypi_futures = []
+
+            # Process conda results sequentially for downloads
+            for future in as_completed(conda_futures):
+                result = future.result()
+                # Sequential conda download
+                self.solvers["conda"].download(*result)
+                # Parallel conda create and cache
+                create_future = executor.submit(self.solvers["conda"].create, *result)
+                if storage:
+                    executor.submit(cache, storage, [result], "conda")
+
+                # Queue PyPI solve to start after conda create
+                if result[0] in pypi_envs:
+
+                    def pypi_solve(env):
+                        create_future.result()  # Wait for conda create
+                        return solve(*env, "pypi")
+
+                    pypi_futures.append(
+                        executor.submit(pypi_solve, pypi_envs[result[0]])
+                    )
+
+            # Process PyPI results sequentially for downloads
+            for solve_future in pypi_futures:
+                result = solve_future.result()
+                # Sequential PyPI download
+                self.solvers["pypi"].download(*result)
+                # Parallel PyPI create and cache
+                executor.submit(self.solvers["pypi"].create, *result)
+                if storage:
+                    executor.submit(cache, storage, [result], "pypi")
         self.logger("Virtual environment(s) bootstrapped!")
 
     def executable(self, step_name, default=None):
@@ -382,7 +436,8 @@ def bootstrap_commands(self, step_name, datastore_type):
                 'DISABLE_TRACING=True python -m metaflow.plugins.pypi.bootstrap "%s" %s "%s" linux-64'
                 % (self.flow.name, id_, self.datastore_type),
                 "echo 'Environment bootstrapped.'",
-                "export PATH=$PATH:$(pwd)/micromamba",
+                # To avoid having to install micromamba in the PATH in micromamba.py, we add it to the PATH here.
+                "export PATH=$PATH:$(pwd)/micromamba/bin",
             ]
         else:
             # for @conda/@pypi(disabled=True).

metaflow/plugins/pypi/micromamba.py

@@ -1,7 +1,9 @@
+import functools
 import json
 import os
 import subprocess
 import tempfile
+import time
 
 from metaflow.exception import MetaflowException
 from metaflow.util import which
@@ -20,7 +22,7 @@ def __init__(self, error):
 
 
 class Micromamba(object):
-    def __init__(self):
+    def __init__(self, logger=None):
         # micromamba is a tiny version of the mamba package manager and comes with
         # metaflow specific performance enhancements.
 
@@ -33,6 +35,12 @@ def __init__(self):
             os.path.expanduser(_home),
             "micromamba",
         )
+
+        if logger:
+            self.logger = logger
+        else:
+            self.logger = lambda *args, **kwargs: None  # No-op logger if not provided
+
         self.bin = (
             which(os.environ.get("METAFLOW_PATH_TO_MICROMAMBA") or "micromamba")
             or which("./micromamba")  # to support remote execution
@@ -78,6 +86,7 @@ def solve(self, id_, packages, python, platform):
                 "--dry-run",
                 "--no-extra-safety-checks",
                 "--repodata-ttl=86400",
+                "--safety-checks=disabled",
                 "--retry-clean-cache",
                 "--prefix=%s/prefix" % tmp_dir,
             ]
@@ -91,10 +100,11 @@ def solve(self, id_, packages, python, platform):
                 cmd.append("python==%s" % python)
             # TODO: Ensure a human readable message is returned when the environment
             #       can't be resolved for any and all reasons.
-            return [
+            solved_packages = [
                 {k: v for k, v in item.items() if k in ["url"]}
                 for item in self._call(cmd, env)["actions"]["LINK"]
             ]
+            return solved_packages
 
     def download(self, id_, packages, python, platform):
         # Unfortunately all the packages need to be catalogued in package cache
@@ -103,8 +113,6 @@ def download(self, id_, packages, python, platform):
         # Micromamba is painfully slow in determining if many packages are infact
         # already cached. As a perf heuristic, we check if the environment already
         # exists to short circuit package downloads.
-        if self.path_to_environment(id_, platform):
-            return
 
         prefix = "{env_dirs}/{keyword}/{platform}/{id}".format(
             env_dirs=self.info()["envs_dirs"][0],
@@ -113,10 +121,14 @@ def download(self, id_, packages, python, platform):
             id=id_,
         )
 
-        # Another forced perf heuristic to skip cross-platform downloads.
+        # cheap check
         if os.path.exists(f"{prefix}/fake.done"):
             return
 
+        # somewhat expensive check
+        if self.path_to_environment(id_, platform):
+            return
+
         with tempfile.TemporaryDirectory() as tmp_dir:
             env = {
                 "CONDA_SUBDIR": platform,
@@ -174,6 +186,7 @@ def create(self, id_, packages, python, platform):
             cmd.append("{url}".format(**package))
         self._call(cmd, env)
 
+    @functools.lru_cache(maxsize=None)
     def info(self):
         return self._call(["config", "list", "-a"])
 
@@ -198,18 +211,24 @@ def metadata(self, id_, packages, python, platform):
         }
         directories = self.info()["pkgs_dirs"]
         # search all package caches for packages
-        metadata = {
-            url: os.path.join(d, file)
+
+        file_to_path = {}
+        for d in directories:
+            if os.path.isdir(d):
+                try:
+                    with os.scandir(d) as entries:
+                        for entry in entries:
+                            if entry.is_file():
+                                # Prefer the first occurrence if the file exists in multiple directories
+                                file_to_path.setdefault(entry.name, entry.path)
+                except OSError:
+                    continue
+        ret = {
+            # set package tarball local paths to None if package tarballs are missing
+            url: file_to_path.get(file)
             for url, file in packages_to_filenames.items()
-            for d in directories
-            if os.path.isdir(d)
-            and file in os.listdir(d)
-            and os.path.isfile(os.path.join(d, file))
         }
-        # set package tarball local paths to None if package tarballs are missing
-        for url in packages_to_filenames:
-            metadata.setdefault(url, None)
-        return metadata
+        return ret
 
     def interpreter(self, id_):
         return os.path.join(self.path_to_environment(id_), "bin/python")

metaflow/plugins/pypi/pip.py

@@ -4,6 +4,7 @@
 import shutil
 import subprocess
 import tempfile
+import time
 from concurrent.futures import ThreadPoolExecutor
 from itertools import chain, product
 from urllib.parse import unquote
@@ -50,10 +51,14 @@ def __init__(self, error):
 
 
 class Pip(object):
-    def __init__(self, micromamba=None):
+    def __init__(self, micromamba=None, logger=None):
         # pip is assumed to be installed inside a conda environment managed by
         # micromamba. pip commands are executed using `micromamba run --prefix`
-        self.micromamba = micromamba or Micromamba()
+        self.micromamba = micromamba or Micromamba(logger)
+        if logger:
+            self.logger = logger
+        else:
+            self.logger = lambda *args, **kwargs: None  # No-op logger if not provided
 
     def solve(self, id_, packages, python, platform):
         prefix = self.micromamba.path_to_environment(id_)
@@ -123,7 +128,7 @@ def _format(dl_info):
                         **res,
                         subdir_str=(
                             "#subdirectory=%s" % subdirectory if subdirectory else ""
-                        )
+                        ),
                     )
                     # used to deduplicate the storage location in case wheel does not
                     # build with enough unique identifiers.