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‎docs/source/reference/collectors.rst‎

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@@ -233,7 +233,6 @@ across multiple inference workers:
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from torchrl.weight_update import (
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MultiProcessWeightSyncScheme,
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SharedMemWeightSyncScheme,
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NoWeightSyncScheme,
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)
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# Create environment and policy
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collector.shutdown()
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# Example 3: Multiple models (policy + value network)
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# ---------------------------------------------------
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value_net = TensorDictModule(
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nn.Linear(env.observation_spec["observation"].shape[-1], 1),
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in_keys=["observation"],
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out_keys=["value"],
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)
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weight_sync_schemes = {
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"policy": MultiProcessWeightSyncScheme(strategy="state_dict"),
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"value": MultiProcessWeightSyncScheme(strategy="state_dict"),
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}
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collector = SyncDataCollector(
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create_env_fn=lambda: GymEnv("CartPole-v1"),
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policy=policy,
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frames_per_batch=64,
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total_frames=1000,
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weight_sync_schemes=weight_sync_schemes,
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)
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# Update multiple models independently
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collector.update_policy_weights_(
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{"policy": policy.state_dict(), "value": value_net.state_dict()}
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)
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collector.shutdown()
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# Example 4: Disable weight synchronization
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# ------------------------------------------
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# Useful for debugging or when using a shared policy reference
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no_sync_scheme = NoWeightSyncScheme()
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collector = SyncDataCollector(
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create_env_fn=lambda: GymEnv("CartPole-v1"),
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policy=policy,
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frames_per_batch=64,
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total_frames=1000,
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weight_sync_schemes={"policy": no_sync_scheme},
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)
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.. note::
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When using ``SharedMemWeightSyncScheme``, weight updates are zero-copy and extremely fast since all
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processes share the same memory buffers. This is ideal for frequent weight updates but requires all

‎examples/collectors/weight_sync_collectors.py‎

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@@ -14,7 +14,6 @@
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single collectors, multiple collectors, multiple models, and no synchronization.
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"""
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import torch
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import torch.nn as nn
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from tensordict import TensorDict
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from tensordict.nn import TensorDictModule
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from torchrl.weight_update import (
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MultiProcessWeightSyncScheme,
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SharedMemWeightSyncScheme,
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NoWeightSyncScheme,
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)
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@@ -66,7 +64,7 @@ def example_single_collector_multiprocess():
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if i % 2 == 0:
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new_weights = policy.state_dict()
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collector.update_policy_weights_(new_weights)
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print(f" → Updated policy weights")
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print(" → Updated policy weights")
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if i >= 2: # Just run a few iterations for demo
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break
@@ -116,7 +114,7 @@ def example_multi_collector_shared_memory():
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# Update weights frequently (shared memory makes this very fast)
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collector.update_policy_weights_(TensorDict.from_module(policy))
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print(f" → Updated policy weights via shared memory")
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print(" → Updated policy weights via shared memory")
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if i >= 1: # Just run a couple iterations for demo
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break
@@ -125,115 +123,6 @@ def example_multi_collector_shared_memory():
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print("✓ Multi-collector with shared memory example completed!\n")
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def example_multiple_models():
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"""Example 3: Multiple models (policy + value network)."""
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print("\n" + "="*70)
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print("Example 3: Multiple Models (Policy + Value Network)")
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print("="*70)
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# Create environment
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env = GymEnv("CartPole-v1")
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# Create policy and value network
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policy = TensorDictModule(
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nn.Linear(
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env.observation_spec["observation"].shape[-1],
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env.action_spec.shape[-1]
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),
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in_keys=["observation"],
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out_keys=["action"],
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)
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value_net = TensorDictModule(
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nn.Linear(
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env.observation_spec["observation"].shape[-1],
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1
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),
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in_keys=["observation"],
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out_keys=["value"],
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)
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env.close()
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# Create separate schemes for each model
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weight_sync_schemes = {
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"policy": MultiProcessWeightSyncScheme(strategy="state_dict"),
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"value": MultiProcessWeightSyncScheme(strategy="state_dict"),
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}
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print("Creating collector with multiple models...")
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collector = SyncDataCollector(
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create_env_fn=lambda: GymEnv("CartPole-v1"),
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policy=policy,
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frames_per_batch=64,
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total_frames=200,
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weight_sync_schemes=weight_sync_schemes,
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)
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print("Collecting data...")
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for i, data in enumerate(collector):
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print(f"Iteration {i}: Collected {data.numel()} transitions")
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# Update both models independently
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collector.update_policy_weights_(
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{
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"policy": policy.state_dict(),
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"value": value_net.state_dict()
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}
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)
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print(f" → Updated both policy and value network weights")
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if i >= 1:
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break
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collector.shutdown()
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print("✓ Multiple models example completed!\n")
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def example_no_weight_sync():
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"""Example 4: Disable weight synchronization."""
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print("\n" + "="*70)
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print("Example 4: Disable Weight Synchronization")
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print("="*70)
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# Create environment and policy
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env = GymEnv("CartPole-v1")
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policy = TensorDictModule(
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nn.Linear(
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env.observation_spec["observation"].shape[-1],
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env.action_spec.shape[-1]
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),
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in_keys=["observation"],
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out_keys=["action"],
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)
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env.close()
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# Useful for debugging or when using a shared policy reference
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scheme = NoWeightSyncScheme()
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print("Creating collector with no weight synchronization...")
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collector = SyncDataCollector(
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create_env_fn=lambda: GymEnv("CartPole-v1"),
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policy=policy,
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frames_per_batch=64,
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total_frames=200,
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weight_sync_schemes={"policy": scheme},
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)
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print("Collecting data (no weight updates)...")
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for i, data in enumerate(collector):
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print(f"Iteration {i}: Collected {data.numel()} transitions")
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# Weight updates are no-ops with NoWeightSyncScheme
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collector.update_policy_weights_(policy.state_dict())
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print(f" → Weight update call was a no-op")
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if i >= 1:
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break
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collector.shutdown()
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print("✓ No weight sync example completed!\n")
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def main():
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"""Run all examples."""
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print("\n" + "="*70)
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# Run examples
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example_single_collector_multiprocess()
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example_multi_collector_shared_memory()
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example_multiple_models()
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example_no_weight_sync()
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print("\n" + "="*70)
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print("All examples completed successfully!")
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print("="*70)
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print("\nKey takeaways:")
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print(" • MultiProcessWeightSyncScheme: Good for general multiprocess scenarios")
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print(" • SharedMemWeightSyncScheme: Fast zero-copy updates for same-machine workers")
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print(" • Multiple models: Each model can have its own sync scheme")
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print(" • NoWeightSyncScheme: Useful for debugging or shared policy references")
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print("="*70 + "\n")
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‎versions.html‎

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<html>
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<head>
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<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<link rel="stylesheet" href="main/_static/css/theme.css" type="text/css" />
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<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Lato" type="text/css" />
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<link rel="stylesheet" href="main/_static/css/pytorch_theme.css" type="text/css" />
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<script src="main/_static/js/modernizr.min.js"></script>
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</head>
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<body>
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<div class="wy-nav-content">
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<div class="rst-content">
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<h1> PyTorch Documentation </h1>
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<div class="toctree-wrapper compound">
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<p class="caption"><span class="caption-text">Pick a version</span></p>
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<ul>
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<li class="toctree-l1">
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<a class="reference internal" href="main/">main (unstable)</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="0.10/">v0.10 (stable release)</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="0.9/">v0.9</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="0.8/">v0.8</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="0.7/">v0.7</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="0.6/">v0.6</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="0.5/">v0.5</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="0.4/">v0.4</a>
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</li>
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</ul>
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<p>You can view previous versions of the torchrl documentation
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<a href="https://pytorch.org/rl/versions.html">here</a>.
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</div>
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</div></div>
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</body>
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</html>

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