Radek Osmulski radekosmulski

## ppo_stablebaselines3.ipynb

      
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                radekosmulski
                / ppo_stablebaselines3.ipynb
            
            
              Created
              June 13, 2024 07:22
            
          
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## vpg_cartpole.ipynb

      
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                radekosmulski
                / vpg_cartpole.ipynb
            
            
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              June 11, 2024 05:40
            
          
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## Diffusion_Policy.ipynb

      
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                / Diffusion_Policy.ipynb
            
            
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              June 1, 2024 02:32
            
          
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## dqn_breakout.ipynb

      
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                / dqn_breakout.ipynb
            
            
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              May 26, 2024 05:35
            
          
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## Alpaca_with_LoRA_bf16.ipynb

      
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                radekosmulski
                / Alpaca_with_LoRA_bf16.ipynb
            
            
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              August 25, 2023 21:30
            
          
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## Alpaca_with_LoRA.ipynb

      
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                radekosmulski
                / Alpaca_with_LoRA.ipynb
            
            
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              August 24, 2023 23:34
            
          
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## evaluate_LLM.ipynb

      
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                / evaluate_LLM.ipynb
            
            
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              July 15, 2023 12:40
            
          
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## rmm_mem_mgmt.py
# Initialize RMM pool on ALL workers

def _rmm_pool():
    rmm.reinitialize(
        pool_allocator=True,
        initial_pool_size=None,  # Use default size
    )

client.run(_rmm_pool)

## nvtabular_gpu_cluster.py
# Deploy a Single-Machine Multi-GPU Cluster

protocol = "tcp"  # "tcp" or "ucx"
visible_devices = "0,1,2,3"  # Delect devices to place workers
device_spill_frac = 0.9  # Spill GPU-Worker memory to host at this limit.
# Reduce if spilling fails to prevent
# device memory errors.
cluster = None  # (Optional) Specify existing scheduler port
if cluster is None:
    cluster = LocalCUDACluster(

## Train_a_Two_Tower_model_and_perform_offline_batch_inference.ipynb

      
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                radekosmulski
                / Train_a_Two_Tower_model_and_perform_offline_batch_inference.ipynb
            
            
              Last active
              September 8, 2022 17:49
            
          
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	# Initialize RMM pool on ALL workers

	def _rmm_pool():
	rmm.reinitialize(
	pool_allocator=True,
	initial_pool_size=None, # Use default size
	)

	client.run(_rmm_pool)
	# Deploy a Single-Machine Multi-GPU Cluster

	protocol = "tcp" # "tcp" or "ucx"
	visible_devices = "0,1,2,3" # Delect devices to place workers
	device_spill_frac = 0.9 # Spill GPU-Worker memory to host at this limit.
	# Reduce if spilling fails to prevent
	# device memory errors.
	cluster = None # (Optional) Specify existing scheduler port
	if cluster is None:
	cluster = LocalCUDACluster(