Metadata-Version: 2.1
Name: upstride-alchemy
Version: 1.0.0a2
Summary: UNKNOWN
Home-page: UNKNOWN
Author: Upstride
Author-email: pypi@upstride.io
License: UNKNOWN
Description: # Alchemy CLI
        
        this is the software development kit (SDK) and command line interface (CLI) for alchemy
        
        ## install
        
        to install this package run
        ```python
        pip install upstride-alchemy
        ```
        
        or clone this repository and run
        ```python
        pip install [-e] .
        ```
        or
        ```python
        python setup.py {install, develop}
        ```
        
        ## SDK
        To import the sdk in your project, do a simple
        
        ```python
        import alchemy
        ```
        
        you can now login to alchemy using your credentials and initialize the project :
        
        ```python
        alchemy.login('your@email', 'yourpassword')
        alchemy.init(project_name='project42', 
                     run_name='mobilenet on cifar10', 
                     dataset='cifar10', 
                     model='mobilenet_v3', 
                     tags=['pytorch'])
        ```
        
        now you can simply call the `log` function to send results to the platform 
        
        ```python
        alchemy.log(epochs=1, metrics={'accuracy': 0.8, 'loss': 3.1415, 'whatever metric you want': 0.9})
        ```
        
        ## Command line interface
        
        this repository provides a script to update results from a tensorboard checkpoint : `alchemy_cli`
        
        One parameter is mandatory : `log_file`, the path of the tensorboard log file to parse.
        
        The other parameter can be passed using the command line or will be asked by the script to the user
        
        These parameters are:
        - `--user`: email to connect to alchemy
        - `--password`: password to connect to alchemy
        - `--step`: step between two points to upload
        - `--project`: Alchemy project to update
        - `--run`: Alchemy run to update
        - `--tags`: tags associated with the run (only if new run)
        - `--dataset`: dataset used in run (only if new run)
        - `--model`: Neural Network model used in the run (only if new run)
        - `--scalar_plots`: scalar graph to upload
        
        Every parameter except ` log_file` can be stored in a yaml file, and provided with the `yaml_file`. An example yaml file can be:
        ```yaml
        user: your@email
        project: my_project
        run: my_run
        scalar_plots:
         - my_metric1
         - my_metric2
        tags:
         - tag1
         - tag2
        dataset: my_dataset
        model: my_model
        ```
        
        This script can be run with
        ```bash
        alchemy_cli <log_fie> <parameters>
        ```
        
        ### Upload_everything
        
        If your experiments are stored in a way that every event files comes with a corresponding yaml, you can use a dedicated script to upload runs based on a wildcard file pattern
        
        ```
        bash upload_everything.sh "*my_pattern*" "my_password"
        ```
Platform: UNKNOWN
Description-Content-Type: text/markdown
