[DecoupledLionW8Bit] [zeta.cli][zeta.main][zeta cloud]

docs/corporate/zeta_cloud.md

@@ -60,3 +60,106 @@ The estimated timeline for shipping Zeta Cloud is as follows:
 | API Access for Third-Party Integrations | Providing API access for integration with other tools and services. | Developers, businesses needing integrations. | Monthly/Annual subscription or pay-per-use. |
 
 
+
+
+# GTM - Go To Market
+
+### **Contents**
+
+1. Positioning Statement
+2. Early Adopter Segments
+3. Branding
+4. Channel Strategy
+5. Initial Marketing Methods
+6. Testing Plan
+7. LTV/CAC
+
+---
+
+### **1. Positioning Statement**
+
+*For AI engineers and data scientists who struggle with the complexities of model training and deployment, Zeta Cloud is a new cloud-based AI service that simplifies these processes. Unlike traditional cloud services, we offer an automated, user-friendly platform with a strong focus on accessibility and efficiency.*
+
+---
+
+### **2. Early Adopter Segments**
+
+**Segment Characteristics:**
+- Demographics: AI engineers and data scientists in mid-sized tech companies and startups.
+- Unmet Needs: Simplification of AI model deployment, efficient resource management, cost-effective scaling.
+- Behaviors: Active users of cloud computing services, frequent participants in tech forums and communities.
+- Psychographics: Value innovation, efficiency, and user-friendly interfaces.
+- Multi-party Decision Making: End users (engineers and scientists), economic buyers (company executives), and key influencers (tech thought leaders and industry experts).
+
+**Implications for Targeted Marketing:**
+- Focused engagement in tech forums and communities.
+- Tailored content marketing addressing specific needs and pain points.
+- Leveraging influencers and thought leaders to reach decision-makers.
+
+---
+
+### **3. Branding**
+
+**Strengths of Product Name:**
+- 'Zeta Cloud' conveys a sense of technological advancement and cloud-based efficiency.
+
+**Brand Association Words:**
+- Innovative, Efficient, User-Friendly, Accessible, Empowering, Reliable.
+
+**Aspirational Brand Similarities:**
+- Brands like AWS, Google Cloud, and Azure for their technological prowess and market presence.
+
+---
+
+### **4. Channel Strategy**
+
+**Channels:**
+- Own Website: Primary channel for direct sales and customer engagement.
+- Sales Force: Blend of inside sales for smaller accounts and field sales for larger, enterprise-level deals.
+- Channel Partners: Collaborations with tech marketplaces and value-added resellers.
+
+**Partner Responsibilities and Margins:**
+- Education and initial engagement by Zeta Cloud, with partners focusing on closing sales and after-sales service.
+- Attractive margins to incentivize partner engagement and commitment.
+
+---
+
+### **5. Initial Marketing Methods**
+
+**Hypothesized Effective Methods:**
+1. **Content Marketing:** Strength - establishes thought leadership; Weakness - time-intensive.
+2. **Social Media and Community Engagement:** Strength - builds brand awareness; Weakness - requires consistent, high-quality engagement.
+3. **Paid Digital Advertising (e.g., Google Ads, LinkedIn):** Strength - targets specific segments; Weakness - can be costly.
+
+**Performance Metrics:**
+- Engagement rates, conversion rates, customer acquisition costs.
+
+**Secondary Marketing Methods:**
+- Email marketing, PR activities, and webinars; secondary due to longer lead times and higher resource requirements.
+
+---
+
+### **6. Testing Plan**
+
+**Completed Tests:**
+- Initial A/B testing on website messaging and layout.
+
+**Upcoming Tests:**
+- Content marketing effectiveness: Measuring engagement and conversion rates from different content types.
+- Social media ad campaigns: Assessing customer acquisition costs and conversion rates.
+- Budget for tests: Approximately $20,000 over three months.
+
+---
+
+### **7. LTV/CAC**
+
+**LTV Targets:**
+- Average annual revenue per customer: $5,000.
+- Variable contribution margin: 70%.
+- Retention rate: 85% annually.
+
+**CAC Projections:**
+- Mix of free and paid methods: 40% free methods (referrals), 60% paid methods.
+- Viral coefficient: 0.5.
+- CAC for paid methods: $500 - $1,000, varying by channel.
+

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "zetascale"
-version = "0.9.9"
+version = "1.1.6"
 description = "Transformers at zeta scales"
 authors = ["Zeta Team <kye@apac.ai>"]
 license = "MIT"
@@ -40,6 +40,9 @@ beartype = "0.16.4"
 tiktoken = "0.5.2"
 tqdm = "4.66.1"
 rich = "13.7.0"
+argparse = "^1.4.0"
+skypilot = "0.4.1"
+
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]
@@ -73,6 +76,7 @@ preview = true
 
 
 
-
+[tool.poetry.scripts]
+zeta = 'zeta.cli.main:main'
 
 

requirements.txt

@@ -28,4 +28,5 @@ torchaudio==2.1.2
 mkdocs
 mkdocs-material
 mkdocs-glightbox
-glightbox
+skypilot==0.4.1
+argparse

tests/cloud/main.py

@@ -0,0 +1,101 @@
+import pytest
+from unittest.mock import MagicMock, patch
+from zeta.cloud.main import zetacloud
+
+
+@patch("zeta.cloud.main.skyapi")
+@patch("zeta.cloud.main.logger")
+def test_zetacloud_basic(mock_logger, mock_skyapi):
+    # Arrange
+    mock_task = MagicMock()
+    mock_skyapi.create_task.return_value = mock_task
+
+    # Act
+    zetacloud(task_name="test_task")
+
+    # Assert
+    mock_skyapi.create_task.assert_called_once_with(
+        name="test_task",
+        setup="pip install requirements.txt",
+        run="python train.py",
+        workdir=".",
+    )
+    mock_logger.info.assert_called_with(
+        "Task: {} has been created".format(mock_task)
+    )
+    mock_task.set_resources.assert_called_once()
+    mock_skyapi.launch.assert_called_once_with(mock_task, "[ZetaTrainingRun]")
+
+
+# ... replicate this test with different arguments for thoroughness
+
+
+@patch("zeta.cloud.main.skyapi")
+@patch("zeta.cloud.main.logger")
+def test_zetacloud_with_stop(mock_logger, mock_skyapi):
+    # Arrange
+    mock_task = MagicMock()
+    mock_skyapi.create_task.return_value = mock_task
+
+    # Act
+    zetacloud(task_name="test_task", stop=True)
+
+    # Assert
+    mock_skyapi.stop.assert_called_once_with("[ZetaTrainingRun]")
+    mock_logger.info.assert_called_with(
+        "Cluster: [ZetaTrainingRun] has been stopped"
+    )
+
+
+@patch("zeta.cloud.main.skyapi")
+@patch("zeta.cloud.main.logger")
+def test_zetacloud_with_down(mock_logger, mock_skyapi):
+    # Arrange
+    mock_task = MagicMock()
+    mock_skyapi.create_task.return_value = mock_task
+
+    # Act
+    zetacloud(task_name="test_task", down=True)
+
+    # Assert
+    mock_skyapi.down.assert_called_once_with("[ZetaTrainingRun]")
+    mock_logger.info.assert_called_with(
+        "Cluster: [ZetaTrainingRun] has been deleted"
+    )
+
+
+@patch("zeta.cloud.main.skyapi")
+@patch("zeta.cloud.main.logger")
+def test_zetacloud_with_status_report(mock_logger, mock_skyapi):
+    # Arrange
+    mock_task = MagicMock()
+    mock_skyapi.create_task.return_value = mock_task
+
+    # Act
+    zetacloud(task_name="test_task", status_report=True)
+
+    # Assert
+    mock_skyapi.status.assert_called_once_with(
+        cluster_names=["[ZetaTrainingRun]"]
+    )
+    mock_logger.info.assert_called_with(
+        "Cluster: [ZetaTrainingRun] has been reported on"
+    )
+
+
+@patch("zeta.cloud.main.skyapi")
+@patch("zeta.cloud.main.logger")
+def test_zetacloud_with_exception(mock_logger, mock_skyapi):
+    # Arrange
+    mock_skyapi.create_task.side_effect = Exception("Test exception")
+
+    # Act
+    with pytest.raises(Exception):
+        zetacloud(task_name="test_task")
+
+    # Assert
+    mock_logger.error.assert_called_once()
+
+
+# ... replicate similar tests with minor changes for thoroughness
+# Examples: test different cloud providers, test other parameter combinations, etc.

tests/nn/modules/test_image_projector.py

@@ -249,17 +249,13 @@ def test_patch_projector_output_shape_consistency(sample_input_tensor):
 # Test case for edge case: invalid max_patch_size
 def test_patch_projector_invalid_max_patch_size():
     with pytest.raises(ValueError):
-        ImagePatchCreatorProjector(
-            max_patch_size=0, embedding_dim=768
-        )
+        ImagePatchCreatorProjector(max_patch_size=0, embedding_dim=768)
 
 
 # Test case for edge case: invalid embedding_dim
 def test_patch_projector_invalid_embedding_dim():
     with pytest.raises(ValueError):
-        ImagePatchCreatorProjector(
-            max_patch_size=16, embedding_dim=0
-        )
+        ImagePatchCreatorProjector(max_patch_size=16, embedding_dim=0)
 
 
 # Test case for edge case: invalid input tensor shape

tests/ops/test_einops_poly.py

@@ -148,9 +148,7 @@ def test_reduce_many_with_sum_reduction():
 # Additional tests for rearrange_with_anon_dims function
 def test_rearrange_with_anon_dims_invalid_dim_list():
     with pytest.raises(ValueError):
-        rearrange_with_anon_dims(
-            input_data, pattern="...a b c", a=(1,)
-        )
+        rearrange_with_anon_dims(input_data, pattern="...a b c", a=(1,))
 
 
 def test_rearrange_with_anon_dims_invalid_pattern():