Runs

`module` `Runs`

`class` `MlFoundryRun`

MlFoundryRun.

`property` auto_end

Tells whether automatic end for run is True or False

`property` dashboard_link

Get Mlfoundry dashboard link for a run

`property` fqn

Get fqn for the current run

`property` ml_repo

Get ml_repo name of which the current run is part of

`property` run_id

Get run_id for the current run

`property` run_name

Get run_name for the current run

`property` status

Get status for the current run

`function` `auto_log_metrics`

auto_log_metrics.

Args:

model_type (enums.ModelType): model_type
data_slice (enums.DataSlice): data_slice
predictions (Collection[Any]): predictions
actuals (Optional[Collection[Any]]): actuals
class_names (Optional[List[str]]): class_names prediction_probabilities:

Returns:
ComputedMetrics:

`function` `delete`

This function permanently delete the run

Example:

import truefoundry.ml as tfm

client = tfm.get_client()
client.create_ml_repo('iris-learning')
run = client.create_run(ml_repo="iris-learning", run_name="svm-model1")
run.log_params({"learning_rate": 0.001})
run.log_metrics({"accuracy": 0.7, "loss": 0.6})

run.delete()

In case we try to call or acess any other function of that run after deleting then it will through MlfoundryException

Example:

import truefoundry.ml as tfm

client = tfm.get_client()
client.create_ml_repo('iris-learning')
run = client.create_run(ml_repo="iris-learning", run_name="svm-model1")
run.log_params({"learning_rate": 0.001})
run.log_metrics({"accuracy": 0.7, "loss": 0.6})

run.delete()
run.log_params({"learning_rate": 0.001})

`function` `end`

End a run.

This function marks the run as FINISHED.

Example:

import truefoundry.ml as tfm

client = tfm.get_client()
run = client.create_run(
     ml_repo="my-classification-project", run_name="svm-with-rbf-kernel"
)
# ...
# Model training code
# ...
run.end()

In case the run was created using the context manager approach, We do not need to call this function.

import truefoundry.ml as tfm

client = tfm.get_client()
with client.create_run(
     ml_repo="my-classification-project", run_name="svm-with-rbf-kernel"
) as run:
     # ...
     # Model training code
     ...
# `run` will be automatically marked as `FINISHED` or `FAILED`.

`function` `get_metrics`

Get metrics logged for the current run grouped by metric name.

Args:

metric_names (Optional[Iterable[str]], optional): A list of metric names For which the logged metrics will be fetched. If not passed, then all metrics logged under the run is returned.

Returns:

Dict[str, List[Metric]]: A dictionary containing metric name to list of metrics map.

Examples:

import truefoundry.ml as tfm

client = tfm.get_client()
run = client.create_run(
    ml_repo="my-classification-project", run_name="svm-with-rbf-kernel"
)
run.log_metrics(metric_dict={"accuracy": 0.7, "loss": 0.6}, step=0)
run.log_metrics(metric_dict={"accuracy": 0.8, "loss": 0.4}, step=1)

metrics = run.get_metrics()
for metric_name, metric_history in metrics.items():
    print(f"logged metrics for metric {metric_name}:")
    for metric in metric_history:
         print(f"value: {metric.value}")
         print(f"step: {metric.step}")
         print(f"timestamp_ms: {metric.timestamp}")
         print("--")

run.end()

`function` `get_params`

Get all the params logged for the current run.

Returns:

Dict[str, str]: A dictionary containing the parameters. The keys in the dictionary are parameter names and the values are corresponding parameter values.

Examples:

import truefoundry.ml as tfm

client = tfm.get_client()
run = client.create_run(
    ml_repo="my-classification-project"
)
run.log_params({"learning_rate": 0.01, "epochs": 10})
print(run.get_params())

run.end()

`function` `get_tags`

Returns all the tags set for the current run.

Returns:

Dict[str, str]: A dictionary containing tags. The keys in the dictionary are tag names and the values are corresponding tag values.

Examples:

import truefoundry.ml as tfm

client = tfm.get_client()
run = client.create_run(
    ml_repo="my-classification-project"
)
run.set_tags({"nlp.framework": "Spark NLP"})
print(run.get_tags())

run.end()

`function` `list_artifact_versions`

Get all the version of a artifact from a particular run to download contents or load them in memory

Args:

artifact_type: Type of the artifact you want

Returns:

Iterator[ArtifactVersion]: An iterator that yields non deleted artifact-versions of a artifact under a given run sorted reverse by the version number

Examples:

import truefoundry.ml as tfm

client = tfm.get_client()
run = client.create_run(ml_repo="iris-learning", run_name="svm-model1")
artifact_versions = run.list_artifact_versions()

for artifact_version in artifact_versions:
     print(artifact_version)

run.end()

`function` `list_model_versions`

Get all the version of a models from a particular run to download contents or load them in memory

Returns:

Iterator[ModelVersion]: An iterator that yields non deleted model-versions under a given run sorted reverse by the version number

Examples:

import truefoundry.ml as tfm

client = tfm.get_client()
run = client.get_run(run_id="<your-run-id>")
model_versions = run.list_model_versions()

for model_version in model_versions:
     print(model_version)

run.end()

`function` `log_artifact`

Logs an artifact for the current ML Repo.

An artifact is a list of local files and directories. This function packs the mentioned files and directories in artifact_paths and uploads them to remote storage linked to the experiment

Args:

name (str): Name of the Artifact. If an artifact with this name already exists under the current ML Repo, the logged artifact will be added as a new version under that name. If no artifact exist with the given name, the given artifact will be logged as version 1.
artifact_paths (List[truefoundry.ml.ArtifactPath], optional): A list of pairs of (source path, destination path) to add files and folders to the artifact version contents. The first member of the pair should be a file or directory path and the second member should be the path inside the artifact contents to upload to.

E.g. >>> run.log_artifact(
              ...     name="xyz",
              ...     artifact_paths=[
                         tfm.ArtifactPath("foo.txt", "foo/bar/foo.txt"),
                         tfm.ArtifactPath("tokenizer/", "foo/tokenizer/"),
                         tfm.ArtifactPath('bar.text'),
                         ('bar.txt', ),
                         ('foo.txt', 'a/foo.txt')
                      ]
              ... )
         would result in
         .
         └── foo/
             ├── bar/
             │   └── foo.txt
             └── tokenizer/
                 └── # contents of tokenizer/ directory will be uploaded here

description (Optional[str], optional): arbitrary text upto 1024 characters to store as description. This field can be updated at any time after logging. Defaults to None
metadata (Optional[Dict[str, Any]], optional): arbitrary json serializable dictionary to store metadata. For example, you can use this to store metrics, params, notes. This field can be updated at any time after logging. Defaults to None
step (int): step/iteration at which the vesion is being logged, defaults to 0.

Returns:

truefoundry.ml.ArtifactVersion: an instance of ArtifactVersion that can be used to download the files, or update attributes like description, metadata.

Examples:

import os
import truefoundry.ml as tfm

with open("artifact.txt", "w") as f:
    f.write("hello-world")

client = tfm.get_client()
run = client.create_run(
    ml_repo="my-classification-project", run_name="svm-with-rbf-kernel"
)

run.log_artifact(
    name="hello-world-file",
    artifact_paths=[tfm.ArtifactPath('artifact.txt', 'a/b/')]
)

run.end()

`function` `log_images`

Log images under the current run at the given step.

Use this function to log images for a run. PIL package is needed to log images. To install the PIL package, run pip install pillow.

Args:

images (Dict[str, “truefoundry.ml.Image”]): A map of string image key to instance of truefoundry.ml.Image class. The image key should only contain alphanumeric, hyphens(-) or underscores(_). For a single key and step pair, we can log only one image.
step (int, optional): Training step/iteration for which the images should be logged. Default is 0.

Examples:

Logging images from different sources

import truefoundry.ml as tfm
import numpy as np
import PIL.Image

client = tfm.get_client()
run = client.create_run(
    ml_repo="my-classification-project",
)

imarray = np.random.randint(low=0, high=256, size=(100, 100, 3))
im = PIL.Image.fromarray(imarray.astype("uint8")).convert("RGB")
im.save("result_image.jpeg")

images_to_log = {
    "logged-image-array": tfm.Image(data_or_path=imarray),
    "logged-pil-image": tfm.Image(data_or_path=im),
    "logged-image-from-path": tfm.Image(data_or_path="result_image.jpeg"),
}

run.log_images(images_to_log, step=1)
run.end()

`function` `log_metrics`

Log metrics for the current run.

A metric is defined by a metric name (such as “training-loss”) and a floating point or integral value (such as 1.2). A metric is associated with a step which is the training iteration at which the metric was calculated.

Args:

metric_dict (Dict[str, Union[int, float]]): A metric name to metric value map. metric value should be either float or int. This should be a non-empty dictionary.
step (int, optional): Training step/iteration at which the metrics present in metric_dict were calculated. If not passed, 0 is set as the step.

Examples:

import truefoundry.ml as tfm

client = tfm.get_client()
run = client.create_run(
    ml_repo="my-classification-project"
)
run.log_metrics(metric_dict={"accuracy": 0.7, "loss": 0.6}, step=0)
run.log_metrics(metric_dict={"accuracy": 0.8, "loss": 0.4}, step=1)

run.end()

`function` `log_model`

Serialize and log a versioned model under the current ML Repo. Each logged model generates a new version associated with the given name and linked to the current run. Multiple versions of the model can be logged as separate versions under the same name.

Args:

name (str): Name of the model. If a model with this name already exists under the current ML Repo, the logged model will be added as a new version under that name. If no models exist with the given name, the given model will be logged as version 1.
model_file_or_folder (str): Path to either a single file or a folder containing model files. This folder is usually created using serialization methods of libraries or frameworks e.g. joblib.dump, model.save_pretrained(...), torch.save(...), model.save(...)
framework (Union[enums.ModelFramework, str]): Model Framework. Ex:- pytorch, sklearn, tensorflow etc. The full list of supported frameworks can be found in truefoundry.ml.enums.ModelFramework. Can also be None when model is None.
description (Optional[str], optional): arbitrary text upto 1024 characters to store as description. This field can be updated at any time after logging. Defaults to None
metadata (Optional[Dict[str, Any]], optional): arbitrary json serializable dictionary to store metadata. For example, you can use this to store metrics, params, notes. This field can be updated at any time after logging. Defaults to None
step (int): step/iteration at which the model is being logged, defaults to 0.

Returns:

truefoundry.ml.ModelVersion: an instance of ModelVersion that can be used to download the files, load the model, or update attributes like description, metadata, schema.

Examples:

Sklearn

import truefoundry.ml as tfm
from truefoundry.ml import ModelFramework

import joblib
import numpy as np
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
y = np.array([1, 1, 2, 2])
clf = make_pipeline(StandardScaler(), SVC(gamma='auto'))
clf.fit(X, y)
joblib.dump(clf, "sklearn-pipeline.joblib")

client = tfm.get_client()
client.create_ml_repo(  # This is only required once
     ml_repo="my-classification-project",
     # This controls which bucket is used.
     # You can get this from Integrations > Blob Storage. `None` picks the default
     storage_integration_fqn=None
)
run = client.create_run(
     ml_repo="my-classification-project"
)
model_version = run.log_model(
     name="my-sklearn-model",
     model_file_or_folder="sklearn-pipeline.joblib",
     framework=ModelFramework.SKLEARN,
     metadata={"accuracy": 0.99, "f1": 0.80},
     step=1,  # step number, useful when using iterative algorithms like SGD
)
print(model_version.fqn)

Huggingface Transformers

import truefoundry.ml as tfm
from truefoundry.ml import ModelFramework

import torch
from transformers import AutoTokenizer, AutoConfig, pipeline, AutoModelForCausalLM
pln = pipeline(
     "text-generation",
     model_file_or_folder="EleutherAI/pythia-70m",
     tokenizer="EleutherAI/pythia-70m",
     torch_dtype=torch.float16
)
pln.model.save_pretrained("my-transformers-model")
pln.tokenizer.save_pretrained("my-transformers-model")

client = tfm.get_client()
client.create_ml_repo(  # This is only required once
     ml_repo="my-llm-project",
     # This controls which bucket is used.
     # You can get this from Integrations > Blob Storage. `None` picks the default
     storage_integration_fqn=None
)
run = client.create_run(
     ml_repo="my-llm-project"
)
model_version = run.log_model(
     name="my-transformers-model",
     model_file_or_folder="my-transformers-model/",
     framework=ModelFramework.TRANSFORMERS
)
print(model_version.fqn)

`function` `log_params`

Logs parameters for the run.

Parameters or Hyperparameters can be thought of as configurations for a run. For example, the type of kernel used in a SVM model is a parameter. A Parameter is defined by a name and a string value. Parameters are also immutable, we cannot overwrite parameter value for a parameter name.

Args:

param_dict (ParamsType): A parameter name to parameter value map. Parameter values are converted to str.
flatten_params (bool): Flatten hierarchical dict, e.g. {'a': {'b': 'c'}} -> {'a.b': 'c'}. All the keys will be converted to str. Defaults to False

Examples:

Logging parameters using a dict.

import truefoundry.ml as tfm

client = tfm.get_client()
run = client.create_run(
    ml_repo="my-classification-project"
)
run.log_params({"learning_rate": 0.01, "epochs": 10})

run.end()

Logging parameters using argparse Namespace object

import argparse
import truefoundry.ml as tfm

parser = argparse.ArgumentParser()
parser.add_argument("-batch_size", type=int, required=True)
args = parser.parse_args()

client = tfm.get_client()
run = client.create_run(
    ml_repo="my-classification-project"
)
run.log_params(args)

`function` `log_plots`

Log custom plots under the current run at the given step.

Use this function to log custom matplotlib, plotly plots.

Args:
plots (Dict[str, “matplotlib.pyplot”, “matplotlib.figure.Figure”, “plotly.graph_objects.Figure”, Plot][str, “matplotlib.pyplot”, “matplotlib.figure.Figure”, “plotly.graph_objects.Figure”, Plot]): A map of string plot key to the plot or figure object. The plot key should only contain alphanumeric, hyphens(-) or underscores(_). For a single key and step pair, we can log only one image.

step (int, optional): Training step/iteration for which the plots should be logged. Default is 0.

Examples:

Logging a plotly figure

import truefoundry.ml as tfm
import plotly.express as px

client = tfm.get_client()
run = client.create_run(
    ml_repo="my-classification-project",
)

df = px.data.tips()
fig = px.histogram(
    df,
    x="total_bill",
    y="tip",
    color="sex",
    marginal="rug",
    hover_data=df.columns,
)

plots_to_log = {
    "distribution-plot": fig,
}

run.log_plots(plots_to_log, step=1)
run.end()

Logging a matplotlib plt or figure

import truefoundry.ml as tfm
from matplotlib import pyplot as plt
import numpy as np

client = tfm.get_client()
run = client.create_run(
    ml_repo="my-classification-project",
)

t = np.arange(0.0, 5.0, 0.01)
s = np.cos(2 * np.pi * t)
(line,) = plt.plot(t, s, lw=2)

plt.annotate(
    "local max",
    xy=(2, 1),
    xytext=(3, 1.5),
    arrowprops=dict(facecolor="black", shrink=0.05),
)

plt.ylim(-2, 2)

plots_to_log = {"cos-plot": plt, "cos-plot-using-figure": plt.gcf()}

run.log_plots(plots_to_log, step=1)
run.end()

`function` `set_tags`

Set tags for the current run.

Tags are “labels” for a run. A tag is represented by a tag name and value.

Args:

tags (Dict[str, str]): A tag name to value map. Tag name cannot start with mlf., mlf. prefix is reserved for truefoundry. Tag values will be converted to str.

Examples:

import truefoundry.ml as tfm

client = tfm.get_client()
run = client.create_run(
    ml_repo="my-classification-project"
)
run.set_tags({"nlp.framework": "Spark NLP"})

run.end()

On this page

module Runs
class MlFoundryRun
property auto_end
property dashboard_link
property fqn
property ml_repo
property run_id
property run_name
property status
function auto_log_metrics
function delete
function end
function get_metrics
function get_params
function get_tags
function list_artifact_versions
function list_model_versions
function log_artifact
function log_images
Logging images from different sources
function log_metrics
function log_model
function log_params
function log_plots
function set_tags

Getting Started

Train and Deploy Models

Service Deployment

Job Deployment

Workflow Deployment

Async Service Deployment

Volumes

ML Repository

Secret Management

Access Control

Platform

Deploying On Your Own Cloud

HOW TOs

`module` `Runs`

`class` `MlFoundryRun`

`property` auto_end

`property` dashboard_link

`property` fqn

`property` ml_repo

`property` run_id

`property` run_name

`property` status

`function` `auto_log_metrics`

`function` `delete`

`function` `end`

`function` `get_metrics`

`function` `get_params`

`function` `get_tags`

`function` `list_artifact_versions`

`function` `list_model_versions`

`function` `log_artifact`

`function` `log_images`

Logging images from different sources

`function` `log_metrics`

`function` `log_model`

`function` `log_params`

`function` `log_plots`

`function` `set_tags`

Getting Started

Train and Deploy Models

Service Deployment

Job Deployment

Workflow Deployment

Async Service Deployment

Volumes

ML Repository

Secret Management

Access Control

Platform

Deploying On Your Own Cloud

HOW TOs

​module Runs

​class MlFoundryRun

​property auto_end

​property dashboard_link

​property fqn

​property ml_repo

​property run_id

​property run_name

​property status

​function auto_log_metrics

​function delete

​function end

​function get_metrics

​function get_params

​function get_tags

​function list_artifact_versions

​function list_model_versions

​function log_artifact

​function log_images

​Logging images from different sources

​function log_metrics

​function log_model

​function log_params

​function log_plots

​function set_tags

`module` `Runs`

`class` `MlFoundryRun`

`property` auto_end

`property` dashboard_link

`property` fqn

`property` ml_repo

`property` run_id

`property` run_name

`property` status

`function` `auto_log_metrics`

`function` `delete`

`function` `end`

`function` `get_metrics`

`function` `get_params`

`function` `get_tags`

`function` `list_artifact_versions`

`function` `list_model_versions`

`function` `log_artifact`

`function` `log_images`

Logging images from different sources

`function` `log_metrics`

`function` `log_model`

`function` `log_params`

`function` `log_plots`

`function` `set_tags`