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Emmi AI Core documentation

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Tutorials

Prerequisites
Getting Started: Install & Verify
Training First Model (with Configs)
Training First Model (with Code)
Running First Inference

How-to Guides

Working with the CLI
How to Load a Custom Dataset
How to Use Private Data Source
How to Write Your Own Data Preprocessors
How to Write Your Own Data Collators
How to Configure All Training Options
How to Use Custom Model
How to Write Custom Loss or Metrics
How to Run Inference via the CLI
How to Run Inference via the Code

Explanation

Overview
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Disk Caching

Reference

Emmi Data Management
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- emmi_data_management
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  - emmi_data_management.checkpoint
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    - emmi_data_management.checkpoint.iohash
    - emmi_data_management.checkpoint.providers
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      - emmi_data_management.checkpoint.providers.file_provider
      - emmi_data_management.checkpoint.providers.huggingface_provider
    - emmi_data_management.checkpoint.resolver
    - emmi_data_management.checkpoint.types
  - emmi_data_management.cli
  - emmi_data_management.cli_aws
  - emmi_data_management.cli_checkpoint
  - emmi_data_management.cli_huggingface
  - emmi_data_management.cli_utils
  - emmi_data_management.cli_verification
  - emmi_data_management.credentials
  - emmi_data_management.diskcache
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    - emmi_data_management.diskcache.cache_benchmark
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  - emmi_data_management.interfaces
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  - emmi_data_management.logging_config
  - emmi_data_management.logging_progress
  - emmi_data_management.providers
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Emmi Inference Module
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- emmi_inference
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  - emmi_inference.api
  - emmi_inference.cli
  - emmi_inference.cli_utils
  - emmi_inference.collator_registry
  - emmi_inference.common
  - emmi_inference.dataset_registry
  - emmi_inference.datasets
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    - emmi_inference.datasets.shapenet
  - emmi_inference.device
  - emmi_inference.examples
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  - emmi_inference.logging_config
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    - emmi_inference.models.abupt
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        emmi_inference.models.modules.attention.anchor_attention
        
        emmi_inference.models.modules.attention.dot_product_attention
        
        emmi_inference.models.modules.attention.perceiver_attention
        
        emmi_inference.models.modules.attention.sharedweights_crossattn_attention
        
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      - emmi_inference.models.modules.mlp
      - emmi_inference.models.modules.rope
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    - emmi_inference.models.registry
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  - emmi_inference.preprocess
  - emmi_inference.runner
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High level API (emmi)
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- emmi
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  - emmi.callbacks
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  - emmi.datasets
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  - emmi.functional
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  - emmi.models
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    - emmi.modules.activations
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    - emmi.modules.layers
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        emmi.modules.preprocessors.normalizers.mean_std_normalization
        
        emmi.modules.preprocessors.normalizers.position_normalizer
        
        emmi.modules.preprocessors.normalizers.shift_and_scale_normalizer
        
        emmi.modules.preprocessors.normalizers.types
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  - emmi.optim
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  - emmi.preprocessing
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  - emmi.schemas
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Low level API (ksuit)
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- ksuit
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  - ksuit.cli
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  - ksuit.configs
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        ksuit.data.wrappers.dataset_wrappers.repeat_wrapper
        
        ksuit.data.wrappers.dataset_wrappers.shuffle_wrapper
        
        ksuit.data.wrappers.dataset_wrappers.subset_wrapper
      - ksuit.data.wrappers.property_subset_wrapper
  - ksuit.distributed
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  - ksuit.extractors
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  - ksuit.factory
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      - ksuit.factory.base.factory_base
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  - ksuit.initializers
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      - ksuit.initializers.base.checkpoint_initializer
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    - ksuit.initializers.previous_run_initializer
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  - ksuit.models
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        ksuit.optimizer.param_group_modifiers.base.param_group_modifier_base
      - ksuit.optimizer.param_group_modifiers.lr_scale_by_name_modifier
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  - ksuit.providers
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  - ksuit.runners
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  - ksuit.schedules
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  - ksuit.scripts
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  - ksuit.utils
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    - ksuit.utils.model
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      - ksuit.utils.model.helpers
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    - ksuit.utils.platform
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  - ksuit.writers
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How to Write Custom Loss or Metrics¶

Warning

The information below is incomplete and requires extra work. Use it at your own risk!

How to Run Inference via the CLI

How to Use Custom Model

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