remotemount

monarch_remotemount/examples/run_apptainer.sh

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+#!/bin/bash
+
+rm -f /tmp/overlay.img
+apptainer overlay create --size 2048 /tmp/overlay.img
+
+rm -rf /tmp/apptainer-work
+mkdir /tmp/apptainer-work
+
+apptainer exec --containall --network none --workdir /tmp/apptainer-work --overlay /tmp/overlay.img /tmp/myapp/img.sif uv pip install requests numpy pandas
+apptainer exec --containall --network none --workdir /tmp/apptainer-work --overlay /tmp/overlay.img /tmp/myapp/img.sif python -c "import pandas; print(pandas.__version__)"

monarch_remotemount/examples/run_cached_pip.sh

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+#!/bin/bash
+
+set -ex
+
+python -m venv /tmp/myvenv
+source /tmp/myvenv/bin/activate
+pip install --no-index --find-links /tmp/flat_wheels torch transformers sentencepiece

monarch_remotemount/examples/run_disk_large_file.sh

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+#!/bin/bash
+
+( time cat /scratch/cpuhrsch/myfiledir/myfile.img > /dev/null ) 2> /tmp/total_time
+cat /tmp/total_time
+( time cat /scratch/cpuhrsch/myfiledir/myfile.img > /dev/null ) 2> /tmp/total_time
+cat /tmp/total_time

monarch_remotemount/examples/run_disk_time.sh

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+#!/bin/bash
+
+find /scratch/cpuhrsch/venv -type f > /tmp/all_files
+python -c "import random; random.seed(123); import sys; lines = sys.stdin.read().split('\n')[:-1]; random.shuffle(lines); print('\n'.join(lines))" < /tmp/all_files > /tmp/all_files_shuf
+
+( time xargs -d '\n' cat < /tmp/all_files_shuf > /tmp/bigfile  ) 2> /tmp/total_time
+
+cat /tmp/total_time

monarch_remotemount/examples/run_hf_example.sh

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+#!/bin/bash
+
+set -ex
+
+source /scratch/cpuhrsch/venv/bin/activate
+cd /scratch/cpuhrsch/venv
+HF_HOME=/scratch/cpuhrsch/venv/hf_cache python hf_example.py

monarch_remotemount/examples/run_import.sh

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+#!/bin/bash
+
+set -ex
+
+source /scratch/cpuhrsch/venv_torch/bin/activate
+cd /scratch/cpuhrsch/venv
+python -c "import torch; print(torch.randn(123).cuda().mean())"

monarch_remotemount/examples/run_import_torch.sh

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+#!/bin/bash
+
+source /scratch/cpuhrsch/venv/bin/activate
+( time python -c "import torch" ) 2>&1
+( time python -c "import torch" ) 2>&1

monarch_remotemount/hf_example.py

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+import torch
+from transformers import pipeline
+
+# 1. Select a sub-10B model
+# Microsoft Phi-3 Mini is ~3.8 Billion parameters
+model_id = "microsoft/Phi-3-mini-4k-instruct"
+
+print(f"Downloading and loading {model_id}...")
+
+# 2. Initialize the pipeline
+# device_map="auto" will automatically use your GPU if available, otherwise CPU
+# torch_dtype=torch.float16 reduces memory usage by half (requires GPU usually)
+pipe = pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={
+        "dtype": torch.float16 if torch.cuda.is_available() else torch.float32,
+        "low_cpu_mem_usage": True,
+    },
+    device_map="auto", 
+)
+
+# 3. Define your prompt
+messages = [
+    {"role": "user", "content": "Explain the concept of recursion to a 5-year-old."},
+]
+
+# 4. Run the generation
+print("\nGenerating response...")
+outputs = pipe(
+    messages,
+    max_new_tokens=256,
+    do_sample=True,
+    temperature=0.7,
+)
+
+# 5. Print the result
+generated_text = outputs[0]["generated_text"][-1]["content"]
+print("-" * 50)
+print(f"Prompt: {messages[0]['content']}")
+print("-" * 50)
+print(f"Response:\n{generated_text}")
+print("-" * 50)

monarch_remotemount/remotemount/__init__.py

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+from .remotemount import remotemount