caffe-cifar-10

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Guidance for building Caffe from source and training CIFAR-10 models. This skill applies when tasks involve compiling Caffe deep learning framework, configuring Makefile.config, preparing CIFAR-10 dataset, or training CNN models with Caffe solvers. Use for legacy ML framework installation, LMDB dataset preparation, and CPU-only deep learning training tasks.

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Sourceletta-ai/skills

Added on2026-02-15

NPX Install

npx skill4agent add letta-ai/skills caffe-cifar-10

SKILL.md Content

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Caffe CIFAR-10 Build and Training

This skill provides procedural guidance for building the Caffe deep learning framework from source and training models on the CIFAR-10 dataset.

When to Use This Skill

Building Caffe from source on Ubuntu/Debian systems
Training CIFAR-10 or similar image classification models with Caffe
Configuring Caffe for CPU-only execution
Troubleshooting Caffe build and dependency issues

Critical Requirements Checklist

Before starting, identify ALL requirements from the task specification:

Execution mode: CPU-only vs GPU (affects solver configuration)
Iteration count: Specific number of training iterations required
Output files: Where training logs and models should be saved
Model checkpoints: Which iteration's model file is expected

Phase 1: Dependency Installation

System Dependencies

Install required packages before attempting to build:

bash

apt-get update && apt-get install -y \
    build-essential cmake git \
    libprotobuf-dev libleveldb-dev libsnappy-dev \
    libhdf5-serial-dev protobuf-compiler \
    libatlas-base-dev libgflags-dev libgoogle-glog-dev liblmdb-dev \
    libopencv-dev libboost-all-dev \
    python3-dev python3-numpy python3-pip

Verification Step

Confirm critical libraries are installed:

bash

dpkg -l | grep -E "libhdf5|libopencv|libboost"

Phase 2: Caffe Source Acquisition

Clone and Checkout

bash

git clone https://github.com/BVLC/caffe.git
cd caffe
git checkout 1.0  # Note: Tag is "1.0", not "1.0.0"

Common Mistake

The release tag is

1.0

, not

1.0.0

. Verify with

git tag -l

if uncertain.

Phase 3: Makefile.config Configuration

Create Configuration File

bash

cp Makefile.config.example Makefile.config

Essential Configuration Changes

Apply these modifications to

Makefile.config

:

CPU-Only Mode (if no GPU available):
```
CPU_ONLY := 1
```
OpenCV Version (for OpenCV 3.x or 4.x):
```
OPENCV_VERSION := 3
```
Note: OpenCV 4 may require additional compatibility patches.

HDF5 Paths (Ubuntu-specific):

INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial

Python Configuration (Python 3):

PYTHON_LIBRARIES := boost_python3 python3.8
PYTHON_INCLUDE := /usr/include/python3.8 /usr/lib/python3/dist-packages/numpy/core/include

Adjust version numbers based on installed Python version.

Configuration Verification

After editing, verify no duplicate definitions exist:

bash

grep -n "PYTHON_INCLUDE\|PYTHON_LIB\|CPU_ONLY" Makefile.config

Ensure each setting appears only once in an uncommented form.

Phase 4: Building Caffe

Memory-Aware Compilation

Avoid using all CPU cores on memory-constrained systems:

bash

# For systems with limited RAM (< 8GB)
make all -j2

# For systems with adequate RAM
make all -j$(nproc)

Build Failure Recovery

If the build fails or is killed (often due to memory):

Clean the build:
bash
```
make clean
```
Rebuild with reduced parallelism:
bash
```
make all -j1
```

Build Verification

Confirm the binary exists after build:

bash

ls -la .build_release/tools/caffe.bin
# or for CPU-only builds:
ls -la .build_release/tools/caffe

Phase 5: Dataset Preparation

Download CIFAR-10

bash

./data/cifar10/get_cifar10.sh

Convert to LMDB Format

bash

./examples/cifar10/create_cifar10.sh

Verification

Confirm LMDB directories exist:

bash

ls -la examples/cifar10/cifar10_train_lmdb
ls -la examples/cifar10/cifar10_test_lmdb

Phase 6: Solver Configuration

Modify Solver for Requirements

Edit

examples/cifar10/cifar10_quick_solver.prototxt

:

Set iteration count:

max_iter: 500  # Or as specified in task

Set execution mode:

solver_mode: CPU  # Change from GPU if required

Verification

bash

grep -E "max_iter|solver_mode" examples/cifar10/cifar10_quick_solver.prototxt

Phase 7: Training Execution

Run Training with Output Capture

bash

./build/tools/caffe train \
    --solver=examples/cifar10/cifar10_quick_solver.prototxt \
    2>&1 | tee training_output.txt

Alternative Binary Paths

Depending on build configuration, the binary may be at:

```
.build_release/tools/caffe
```
```
build/tools/caffe
```
```
.build_release/tools/caffe.bin
```

Phase 8: Verification

Required Outputs Checklist

Caffe binary exists:

bash

test -f .build_release/tools/caffe && echo "OK" || echo "MISSING"

Model file exists (iteration-specific):

bash

ls -la examples/cifar10/cifar10_quick_iter_*.caffemodel

Training output captured:

bash

test -f training_output.txt && echo "OK" || echo "MISSING"

Solver configured correctly:

bash

grep "solver_mode: CPU" examples/cifar10/cifar10_quick_solver.prototxt

Common Pitfalls

1. Premature Termination

Never stop after

make clean

or intermediate steps. Complete the full workflow: Dependencies -> Build -> Dataset -> Configure -> Train -> Verify

2. Missing Solver Configuration

The solver file must be modified for:

CPU vs GPU execution mode
Specific iteration count requirements

3. Skipping Dataset Preparation

Training will fail without LMDB data. Always run both:

```
get_cifar10.sh
```
(download)
```
create_cifar10.sh
```
(convert)

4. Build Parallelism Issues

High parallelism (

-j$(nproc)

) can exhaust memory. Start with

-j2

on constrained systems.

5. Duplicate Configuration Entries

Multiple edits to

Makefile.config

can create duplicate definitions. Always verify single definitions for each setting.

6. Wrong Git Tag

Use

1.0

not

1.0.0

for the stable release.

Decision Framework

When encountering issues:

Build killed: Reduce parallelism, run
```
make clean
```
, rebuild with
```
-j1
```
Missing headers: Check HDF5 and OpenCV include paths in Makefile.config
Python errors: Verify Python version matches configuration
Training fails immediately: Check dataset preparation completed
Wrong output location: Verify solver paths and output file redirection

caffe-cifar-10

NPX Install

Tags

SKILL.md Content

Caffe CIFAR-10 Build and Training

When to Use This Skill

Critical Requirements Checklist

Phase 1: Dependency Installation

System Dependencies

Verification Step

Phase 2: Caffe Source Acquisition

Clone and Checkout

Common Mistake

Phase 3: Makefile.config Configuration

Create Configuration File

Essential Configuration Changes

Configuration Verification

Phase 4: Building Caffe

Memory-Aware Compilation

Build Failure Recovery

Build Verification

Phase 5: Dataset Preparation

Download CIFAR-10

Convert to LMDB Format

Verification

Phase 6: Solver Configuration

Modify Solver for Requirements

Verification

Phase 7: Training Execution

Run Training with Output Capture

Alternative Binary Paths

Phase 8: Verification

Required Outputs Checklist

Common Pitfalls

1. Premature Termination

2. Missing Solver Configuration

3. Skipping Dataset Preparation

4. Build Parallelism Issues

5. Duplicate Configuration Entries

6. Wrong Git Tag

Decision Framework